From eb631d1b7c067bc3b07bffab20f39b351197ed62 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Tue, 11 Mar 2025 20:45:36 +0000 Subject: [PATCH 01/59] Update diff_model_train and make it compartitble with previous DDPM. Tsted with DDPM, not with rflow in this PR Signed-off-by: Can-Zhao --- .../maisi/configs/config_maisi-ddpm.json | 153 ++++++++++++++++++ .../maisi/configs/config_maisi-rflow.json | 149 +++++++++++++++++ generation/maisi/scripts/diff_model_infer.py | 39 +++-- generation/maisi/scripts/diff_model_train.py | 95 +++++++---- generation/maisi/scripts/utils.py | 15 +- 5 files changed, 412 insertions(+), 39 deletions(-) create mode 100644 generation/maisi/configs/config_maisi-ddpm.json create mode 100644 generation/maisi/configs/config_maisi-rflow.json diff --git a/generation/maisi/configs/config_maisi-ddpm.json b/generation/maisi/configs/config_maisi-ddpm.json new file mode 100644 index 000000000..8a781ca5b --- /dev/null +++ b/generation/maisi/configs/config_maisi-ddpm.json @@ -0,0 +1,153 @@ +{ + "spatial_dims": 3, + "image_channels": 1, + "latent_channels": 4, + "mask_generation_latent_shape": [ + 4, + 64, + 64, + 64 + ], + "autoencoder_def": { + "_target_": "monai.apps.generation.maisi.networks.autoencoderkl_maisi.AutoencoderKlMaisi", + "spatial_dims": "@spatial_dims", + "in_channels": "@image_channels", + "out_channels": "@image_channels", + "latent_channels": "@latent_channels", + "num_channels": [ + 64, + 128, + 256 + ], + "num_res_blocks": [2,2,2], + "norm_num_groups": 32, + "norm_eps": 1e-06, + "attention_levels": [ + false, + false, + false + ], + "with_encoder_nonlocal_attn": false, + "with_decoder_nonlocal_attn": false, + "use_checkpointing": false, + "use_convtranspose": false, + "norm_float16": true, + "num_splits": 8, + "dim_split": 1 + }, + "diffusion_unet_def": { + "_target_": "monai.apps.generation.maisi.networks.diffusion_model_unet_maisi.DiffusionModelUNetMaisi", + "spatial_dims": "@spatial_dims", + "in_channels": "@latent_channels", + "out_channels": "@latent_channels", + "num_channels": [ + 64, + 128, + 256, + 512 + ], + "attention_levels": [ + false, + false, + true, + true + ], + "num_head_channels": [ + 0, + 0, + 32, + 32 + ], + "num_res_blocks": 2, + "use_flash_attention": true, + "include_top_region_index_input": true, + "include_bottom_region_index_input": true, + "include_spacing_input": true + }, + "controlnet_def": { + "_target_": "monai.apps.generation.maisi.networks.controlnet_maisi.ControlNetMaisi", + "spatial_dims": "@spatial_dims", + "in_channels": "@latent_channels", + "num_channels": [ + 64, + 128, + 256, + 512 + ], + "attention_levels": [ + false, + false, + true, + true + ], + "num_head_channels": [ + 0, + 0, + 32, + 32 + ], + "num_res_blocks": 2, + "use_flash_attention": true, + "conditioning_embedding_in_channels": 8, + "conditioning_embedding_num_channels": [8, 32, 64] + }, + "mask_generation_autoencoder_def": { + "_target_": "monai.apps.generation.maisi.networks.autoencoderkl_maisi.AutoencoderKlMaisi", + "spatial_dims": "@spatial_dims", + "in_channels": 8, + "out_channels": 125, + "latent_channels": "@latent_channels", + "num_channels": [ + 32, + 64, + 128 + ], + "num_res_blocks": [1, 2, 2], + "norm_num_groups": 32, + "norm_eps": 1e-06, + "attention_levels": [ + false, + false, + false + ], + "with_encoder_nonlocal_attn": false, + "with_decoder_nonlocal_attn": false, + "use_flash_attention": false, + "use_checkpointing": true, + "use_convtranspose": true, + "norm_float16": true, + "num_splits": 8, + "dim_split": 1 + }, + "mask_generation_diffusion_def": { + "_target_": "monai.networks.nets.diffusion_model_unet.DiffusionModelUNet", + "spatial_dims": "@spatial_dims", + "in_channels": "@latent_channels", + "out_channels": "@latent_channels", + "channels":[64, 128, 256, 512], + "attention_levels":[false, false, true, true], + "num_head_channels":[0, 0, 32, 32], + "num_res_blocks": 2, + "use_flash_attention": true, + "with_conditioning": true, + "upcast_attention": true, + "cross_attention_dim": 10 + }, + "mask_generation_scale_factor": 1.0055984258651733, + "noise_scheduler": { + "_target_": "monai.networks.schedulers.ddpm.DDPMScheduler", + "num_train_timesteps": 1000, + "beta_start": 0.0015, + "beta_end": 0.0195, + "schedule": "scaled_linear_beta", + "clip_sample": false + }, + "mask_generation_noise_scheduler": { + "_target_": "monai.networks.schedulers.ddpm.DDPMScheduler", + "num_train_timesteps": 1000, + "beta_start": 0.0015, + "beta_end": 0.0195, + "schedule": "scaled_linear_beta", + "clip_sample": false + } +} diff --git a/generation/maisi/configs/config_maisi-rflow.json b/generation/maisi/configs/config_maisi-rflow.json new file mode 100644 index 000000000..56d769284 --- /dev/null +++ b/generation/maisi/configs/config_maisi-rflow.json @@ -0,0 +1,149 @@ +{ + "spatial_dims": 3, + "image_channels": 1, + "latent_channels": 4, + "mask_generation_latent_shape": [ + 4, + 64, + 64, + 64 + ], + "autoencoder_def": { + "_target_": "monai.apps.generation.maisi.networks.autoencoderkl_maisi.AutoencoderKlMaisi", + "spatial_dims": "@spatial_dims", + "in_channels": "@image_channels", + "out_channels": "@image_channels", + "latent_channels": "@latent_channels", + "num_channels": [ + 64, + 128, + 256 + ], + "num_res_blocks": [2,2,2], + "norm_num_groups": 32, + "norm_eps": 1e-06, + "attention_levels": [ + false, + false, + false + ], + "with_encoder_nonlocal_attn": false, + "with_decoder_nonlocal_attn": false, + "use_checkpointing": false, + "use_convtranspose": false, + "norm_float16": true, + "num_splits": 4, + "dim_split": 1 + }, + "diffusion_unet_def": { + "_target_": "monai.apps.generation.maisi.networks.diffusion_model_unet_maisi.DiffusionModelUNetMaisi", + "spatial_dims": "@spatial_dims", + "in_channels": "@latent_channels", + "out_channels": "@latent_channels", + "num_channels": [64, 128, 256, 512], + "attention_levels": [ + false, + false, + true, + true + ], + "num_head_channels": [ + 0, + 0, + 32, + 32 + ], + "num_res_blocks": 2, + "use_flash_attention": true, + "include_top_region_index_input": false, + "include_bottom_region_index_input": false, + "include_spacing_input": true, + "num_class_embeds": 128, + "resblock_updown": true, + "include_fc": true + }, + "controlnet_def": { + "_target_": "monai.apps.generation.maisi.networks.controlnet_maisi.ControlNetMaisi", + "spatial_dims": "@spatial_dims", + "in_channels": "@latent_channels", + "num_channels": [64, 128, 256, 512], + "attention_levels": [ + false, + false, + true, + true + ], + "num_head_channels": [ + 0, + 0, + 32, + 32 + ], + "num_res_blocks": 2, + "use_flash_attention": true, + "conditioning_embedding_in_channels": 8, + "conditioning_embedding_num_channels": [8, 32, 64], + "num_class_embeds": 128, + "resblock_updown": true, + "include_fc": true + }, + "mask_generation_autoencoder_def": { + "_target_": "monai.apps.generation.maisi.networks.autoencoderkl_maisi.AutoencoderKlMaisi", + "spatial_dims": "@spatial_dims", + "in_channels": 8, + "out_channels": 125, + "latent_channels": "@latent_channels", + "num_channels": [ + 32, + 64, + 128 + ], + "num_res_blocks": [1, 2, 2], + "norm_num_groups": 32, + "norm_eps": 1e-06, + "attention_levels": [ + false, + false, + false + ], + "with_encoder_nonlocal_attn": false, + "with_decoder_nonlocal_attn": false, + "use_flash_attention": false, + "use_checkpointing": true, + "use_convtranspose": true, + "norm_float16": true, + "num_splits": 8, + "dim_split": 1 + }, + "mask_generation_diffusion_def": { + "_target_": "monai.networks.nets.diffusion_model_unet.DiffusionModelUNet", + "spatial_dims": "@spatial_dims", + "in_channels": "@latent_channels", + "out_channels": "@latent_channels", + "channels":[64, 128, 256, 512], + "attention_levels":[false, false, true, true], + "num_head_channels":[0, 0, 32, 32], + "num_res_blocks": 2, + "use_flash_attention": true, + "with_conditioning": true, + "upcast_attention": true, + "cross_attention_dim": 10 + }, + "mask_generation_scale_factor": 1.0055984258651733, + "noise_scheduler": { + "_target_": "monai.networks.schedulers.rectified_flow.RFlowScheduler", + "num_train_timesteps": 1000, + "use_discrete_timesteps": false, + "use_timestep_transform": true, + "sample_method": "uniform", + "scale":1.4 + }, + "mask_generation_noise_scheduler": { + "_target_": "monai.networks.schedulers.ddpm.DDPMScheduler", + "num_train_timesteps": 1000, + "beta_start": 0.0015, + "beta_end": 0.0195, + "schedule": "scaled_linear_beta", + "clip_sample": false + } +} diff --git a/generation/maisi/scripts/diff_model_infer.py b/generation/maisi/scripts/diff_model_infer.py index 9ba837328..77aa0b229 100644 --- a/generation/maisi/scripts/diff_model_infer.py +++ b/generation/maisi/scripts/diff_model_infer.py @@ -25,10 +25,12 @@ from monai.inferers import sliding_window_inference from monai.utils import set_determinism +from monai.networks.schedulers import RFlowScheduler +from monai.inferers.inferer import SlidingWindowInferer from .diff_model_setting import initialize_distributed, load_config, setup_logging from .sample import ReconModel, check_input -from .utils import define_instance +from .utils import define_instance, dynamic_infer def set_random_seed(seed: int) -> int: @@ -144,14 +146,26 @@ def run_inference( image = noise noise_scheduler = define_instance(args, "noise_scheduler") - noise_scheduler.set_timesteps(num_inference_steps=args.diffusion_unet_inference["num_inference_steps"]) + if isinstance(noise_scheduler, RFlowScheduler): + noise_scheduler.set_timesteps( + num_inference_steps=args.diffusion_unet_inference["num_inference_steps"], + input_img_size_numel=torch.prod(torch.tensor(noise.shape[-3:])) + ) + else: + noise_scheduler.set_timesteps(num_inference_steps=args.diffusion_unet_inference["num_inference_steps"]) recon_model = ReconModel(autoencoder=autoencoder, scale_factor=scale_factor).to(device) autoencoder.eval() unet.eval() + all_timesteps = noise_scheduler.timesteps + all_next_timesteps = torch.cat((all_timesteps[1:], torch.tensor([0], dtype=all_timesteps.dtype))) + progress_bar = tqdm( + zip(all_timesteps, all_next_timesteps), + total=min(len(all_timesteps), len(all_next_timesteps)), + ) with torch.amp.autocast("cuda", enabled=True): - for t in tqdm(noise_scheduler.timesteps, ncols=110): + for t, next_t in progress_bar: model_output = unet( x=image, timesteps=torch.Tensor((t,)).to(device), @@ -159,23 +173,26 @@ def run_inference( bottom_region_index_tensor=bottom_region_index_tensor, spacing_tensor=spacing_tensor, ) - image, _ = noise_scheduler.step(model_output, t, image) + if not isinstance(noise_scheduler, RFlowScheduler): + image, _ = noise_scheduler.step(model_output, t, image) # type: ignore + else: + image, _ = noise_scheduler.step(model_output, t, image, next_t) # type: ignore - synthetic_images = sliding_window_inference( - inputs=image, + + inferer = SlidingWindowInferer( roi_size=( min(output_size[0] // divisor // 4 * 3, 96), min(output_size[1] // divisor // 4 * 3, 96), min(output_size[2] // divisor // 4 * 3, 96), ), sw_batch_size=1, - predictor=recon_model, + progress=True, mode="gaussian", overlap=2.0 / 3.0, sw_device=device, device=device, ) - + synthetic_images = dynamic_infer(inferer, recon_model, image) data = synthetic_images.squeeze().cpu().detach().numpy() a_min, a_max, b_min, b_max = -1000, 1000, 0, 1 data = (data - b_min) / (b_max - b_min) * (a_max - a_min) + a_min @@ -211,7 +228,7 @@ def save_image( @torch.inference_mode() -def diff_model_infer(env_config_path: str, model_config_path: str, model_def_path: str, num_gpus: int) -> None: +def diff_model_infer(env_config_path: str, model_config_path: str, model_def_path: str, num_gpus: int, include_body_region: bool = False ) -> None: """ Main function to run the diffusion model inference. @@ -219,6 +236,7 @@ def diff_model_infer(env_config_path: str, model_config_path: str, model_def_pat env_config_path (str): Path to the environment configuration file. model_config_path (str): Path to the model configuration file. model_def_path (str): Path to the model definition file. + include_body_region (bool): Whether to include body region in data """ args = load_config(env_config_path, model_config_path, model_def_path) local_rank, world_size, device = initialize_distributed(num_gpus) @@ -317,6 +335,7 @@ def diff_model_infer(env_config_path: str, model_config_path: str, model_def_pat default=1, help="Number of GPUs to use for distributed inference", ) + parser.add_argument("--include_body_region", dest="include_body_region", action="store_true", help="Whether to include body region in data") args = parser.parse_args() - diff_model_infer(args.env_config, args.model_config, args.model_def, args.num_gpus) + diff_model_infer(args.env_config, args.model_config, args.model_def, args.num_gpus, include_body_region=args.include_body_region) diff --git a/generation/maisi/scripts/diff_model_train.py b/generation/maisi/scripts/diff_model_train.py index abe36c4be..759e20295 100644 --- a/generation/maisi/scripts/diff_model_train.py +++ b/generation/maisi/scripts/diff_model_train.py @@ -27,6 +27,8 @@ from monai.data import DataLoader, partition_dataset from monai.transforms import Compose from monai.utils import first +from monai.networks.schedulers import RFlowScheduler +from monai.networks.schedulers.ddpm import DDPMPredictionType from .diff_model_setting import initialize_distributed, load_config, setup_logging from .utils import define_instance @@ -49,7 +51,12 @@ def load_filenames(data_list_path: str) -> list: def prepare_data( - train_files: list, device: torch.device, cache_rate: float, num_workers: int = 2, batch_size: int = 1 + train_files: list, + device: torch.device, + cache_rate: float, + num_workers: int = 2, + batch_size: int = 1, + include_body_region: bool = False ) -> DataLoader: """ Prepare training data. @@ -60,6 +67,7 @@ def prepare_data( cache_rate (float): Cache rate for dataset. num_workers (int): Number of workers for data loading. batch_size (int): Mini-batch size. + include_body_region (bool): Whether to include body region in data Returns: DataLoader: Data loader for training. @@ -69,22 +77,24 @@ def _load_data_from_file(file_path, key): with open(file_path) as f: return torch.FloatTensor(json.load(f)[key]) - train_transforms = Compose( - [ + train_transforms_list = [ monai.transforms.LoadImaged(keys=["image"]), - monai.transforms.EnsureChannelFirstd(keys=["image"]), + monai.transforms.EnsureChannelFirstd(keys=["image"]), + monai.transforms.Lambdad(keys="spacing", func=lambda x: _load_data_from_file(x, "spacing")), + monai.transforms.Lambdad(keys="spacing", func=lambda x: x * 1e2), + ] + if include_body_region: + train_transforms_list += [ monai.transforms.Lambdad( keys="top_region_index", func=lambda x: _load_data_from_file(x, "top_region_index") ), monai.transforms.Lambdad( keys="bottom_region_index", func=lambda x: _load_data_from_file(x, "bottom_region_index") ), - monai.transforms.Lambdad(keys="spacing", func=lambda x: _load_data_from_file(x, "spacing")), monai.transforms.Lambdad(keys="top_region_index", func=lambda x: x * 1e2), monai.transforms.Lambdad(keys="bottom_region_index", func=lambda x: x * 1e2), - monai.transforms.Lambdad(keys="spacing", func=lambda x: x * 1e2), ] - ) + train_transforms = Compose(train_transforms_list) train_ds = monai.data.CacheDataset( data=train_files, transform=train_transforms, cache_rate=cache_rate, num_workers=num_workers @@ -192,6 +202,7 @@ def train_one_epoch( logger: logging.Logger, local_rank: int, amp: bool = True, + include_body_region: bool = False ) -> torch.Tensor: """ Train the model for one epoch. @@ -212,6 +223,7 @@ def train_one_epoch( logger (logging.Logger): Logger for logging information. local_rank (int): Local rank for distributed training. amp (bool): Use automatic mixed precision training. + include_body_region (bool): Whether to include body region in data Returns: torch.Tensor: Training loss for the epoch. @@ -231,30 +243,50 @@ def train_one_epoch( images = train_data["image"].to(device) images = images * scale_factor - top_region_index_tensor = train_data["top_region_index"].to(device) - bottom_region_index_tensor = train_data["bottom_region_index"].to(device) + if include_body_region: + top_region_index_tensor = train_data["top_region_index"].to(device) + bottom_region_index_tensor = train_data["bottom_region_index"].to(device) spacing_tensor = train_data["spacing"].to(device) optimizer.zero_grad(set_to_none=True) with autocast("cuda", enabled=amp): - noise = torch.randn( - (num_images_per_batch, 4, images.size(-3), images.size(-2), images.size(-1)), device=device - ) + noise = torch.randn_like(images) - timesteps = torch.randint(0, num_train_timesteps, (images.shape[0],), device=images.device).long() + if isinstance(noise_scheduler, RFlowScheduler): + timesteps = noise_scheduler.sample_timesteps(images) + else: + timesteps = torch.randint(0, num_train_timesteps, (images.shape[0],), device=images.device).long() noisy_latent = noise_scheduler.add_noise(original_samples=images, noise=noise, timesteps=timesteps) - noise_pred = unet( - x=noisy_latent, - timesteps=timesteps, - top_region_index_tensor=top_region_index_tensor, - bottom_region_index_tensor=bottom_region_index_tensor, - spacing_tensor=spacing_tensor, - ) - - loss = loss_pt(noise_pred.float(), noise.float()) + if include_body_region: + model_output = unet( + x=noisy_latent, + timesteps=timesteps, + top_region_index_tensor=top_region_index_tensor, + bottom_region_index_tensor=bottom_region_index_tensor, + spacing_tensor=spacing_tensor, + ) + else: + model_output = unet( + x=noisy_latent, + timesteps=timesteps, + spacing_tensor=spacing_tensor, + ) + if noise_scheduler.prediction_type == DDPMPredictionType.EPSILON: + # predict noise + loss = loss_pt(model_output.float(), noise.float()) + elif noise_scheduler.prediction_type == DDPMPredictionType.SAMPLE: + # predict sample + loss = loss_pt(model_output.float(), images.float()) + elif noise_scheduler.prediction_type == DDPMPredictionType.V_PREDICTION: + # predict velocity + loss = loss_pt(model_output.float(), (images - noise).float()) + else: + raise ValueError("noise scheduler prediction type has to be chosen from ", + f"[{DDPMPredictionType.EPSILON},{DDPMPredictionType.SAMPLE},{DDPMPredictionType.V_PREDICTION}]" + ) if amp: scaler.scale(loss).backward() @@ -317,7 +349,7 @@ def save_checkpoint( def diff_model_train( - env_config_path: str, model_config_path: str, model_def_path: str, num_gpus: int, amp: bool = True + env_config_path: str, model_config_path: str, model_def_path: str, num_gpus: int, amp: bool = True, include_body_region: bool = False ) -> None: """ Main function to train a diffusion model. @@ -328,6 +360,7 @@ def diff_model_train( model_def_path (str): Path to the model definition file. num_gpus (int): Number of GPUs to use for training. amp (bool): Use automatic mixed precision training. + include_body_region (bool): Whether to include body region in data """ args = load_config(env_config_path, model_config_path, model_def_path) local_rank, world_size, device = initialize_distributed(num_gpus) @@ -356,16 +389,20 @@ def diff_model_train( continue str_info = os.path.join(args.embedding_base_dir, filenames_train[_i]) + ".json" - train_files.append( - {"image": str_img, "top_region_index": str_info, "bottom_region_index": str_info, "spacing": str_info} - ) + train_files_i = {"image": str_img, "spacing": str_info} + if include_body_region: + train_files_i["top_region_index"] = str_info + train_files_i["bottom_region_index"] = str_info + train_files.append(train_files_i) if dist.is_initialized(): train_files = partition_dataset( data=train_files, shuffle=True, num_partitions=dist.get_world_size(), even_divisible=True )[local_rank] train_loader = prepare_data( - train_files, device, args.diffusion_unet_train["cache_rate"], batch_size=args.diffusion_unet_train["batch_size"] + train_files, device, args.diffusion_unet_train["cache_rate"], + batch_size=args.diffusion_unet_train["batch_size"], + include_body_region = include_body_region ) unet = load_unet(args, device, logger) @@ -401,6 +438,7 @@ def diff_model_train( logger, local_rank, amp=amp, + include_body_region=include_body_region ) loss_torch = loss_torch.tolist() @@ -441,6 +479,7 @@ def diff_model_train( ) parser.add_argument("--num_gpus", type=int, default=1, help="Number of GPUs to use for training") parser.add_argument("--no_amp", dest="amp", action="store_false", help="Disable automatic mixed precision training") + parser.add_argument("--include_body_region", dest="include_body_region", action="store_true", help="Whether to include body region in data") args = parser.parse_args() - diff_model_train(args.env_config, args.model_config, args.model_def, args.num_gpus, args.amp) + diff_model_train(args.env_config, args.model_config, args.model_def, args.num_gpus, args.amp, args.include_body_region) diff --git a/generation/maisi/scripts/utils.py b/generation/maisi/scripts/utils.py index 13d9a240e..dd343ab24 100644 --- a/generation/maisi/scripts/utils.py +++ b/generation/maisi/scripts/utils.py @@ -709,4 +709,17 @@ def dynamic_infer(inferer, model, images): if torch.numel(images[0:1, 0:1, ...]) < math.prod(inferer.roi_size): return model(images) else: - return inferer(network=model, inputs=images) + # Extract the spatial dimensions from the images tensor (H, W, D) + spatial_dims = images.shape[2:] + orig_roi = inferer.roi_size + + # Check that roi has the same number of dimensions as spatial_dims + if len(orig_roi) != len(spatial_dims): + raise ValueError(f"ROI length ({len(orig_roi)}) does not match spatial dimensions ({len(spatial_dims)}).") + + # Iterate and adjust each ROI dimension + adjusted_roi = [min(roi_dim, img_dim) for roi_dim, img_dim in zip(orig_roi, spatial_dims)] + inferer.roi_size = adjusted_roi + output = inferer(network=model, inputs=images) + inferer.roi_size = orig_roi + return output \ No newline at end of file From 24c8573eaa76903400adbccce99b705b7535f874 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 11 Mar 2025 20:49:11 +0000 Subject: [PATCH 02/59] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- generation/maisi/scripts/diff_model_infer.py | 24 ++++++--- generation/maisi/scripts/diff_model_train.py | 57 ++++++++++++-------- generation/maisi/scripts/utils.py | 6 +-- 3 files changed, 55 insertions(+), 32 deletions(-) diff --git a/generation/maisi/scripts/diff_model_infer.py b/generation/maisi/scripts/diff_model_infer.py index 77aa0b229..48c8e43ab 100644 --- a/generation/maisi/scripts/diff_model_infer.py +++ b/generation/maisi/scripts/diff_model_infer.py @@ -149,7 +149,7 @@ def run_inference( if isinstance(noise_scheduler, RFlowScheduler): noise_scheduler.set_timesteps( num_inference_steps=args.diffusion_unet_inference["num_inference_steps"], - input_img_size_numel=torch.prod(torch.tensor(noise.shape[-3:])) + input_img_size_numel=torch.prod(torch.tensor(noise.shape[-3:])), ) else: noise_scheduler.set_timesteps(num_inference_steps=args.diffusion_unet_inference["num_inference_steps"]) @@ -161,9 +161,9 @@ def run_inference( all_timesteps = noise_scheduler.timesteps all_next_timesteps = torch.cat((all_timesteps[1:], torch.tensor([0], dtype=all_timesteps.dtype))) progress_bar = tqdm( - zip(all_timesteps, all_next_timesteps), - total=min(len(all_timesteps), len(all_next_timesteps)), - ) + zip(all_timesteps, all_next_timesteps), + total=min(len(all_timesteps), len(all_next_timesteps)), + ) with torch.amp.autocast("cuda", enabled=True): for t, next_t in progress_bar: model_output = unet( @@ -178,7 +178,6 @@ def run_inference( else: image, _ = noise_scheduler.step(model_output, t, image, next_t) # type: ignore - inferer = SlidingWindowInferer( roi_size=( min(output_size[0] // divisor // 4 * 3, 96), @@ -228,7 +227,9 @@ def save_image( @torch.inference_mode() -def diff_model_infer(env_config_path: str, model_config_path: str, model_def_path: str, num_gpus: int, include_body_region: bool = False ) -> None: +def diff_model_infer( + env_config_path: str, model_config_path: str, model_def_path: str, num_gpus: int, include_body_region: bool = False +) -> None: """ Main function to run the diffusion model inference. @@ -335,7 +336,14 @@ def diff_model_infer(env_config_path: str, model_config_path: str, model_def_pat default=1, help="Number of GPUs to use for distributed inference", ) - parser.add_argument("--include_body_region", dest="include_body_region", action="store_true", help="Whether to include body region in data") + parser.add_argument( + "--include_body_region", + dest="include_body_region", + action="store_true", + help="Whether to include body region in data", + ) args = parser.parse_args() - diff_model_infer(args.env_config, args.model_config, args.model_def, args.num_gpus, include_body_region=args.include_body_region) + diff_model_infer( + args.env_config, args.model_config, args.model_def, args.num_gpus, include_body_region=args.include_body_region + ) diff --git a/generation/maisi/scripts/diff_model_train.py b/generation/maisi/scripts/diff_model_train.py index 759e20295..ba2d269d7 100644 --- a/generation/maisi/scripts/diff_model_train.py +++ b/generation/maisi/scripts/diff_model_train.py @@ -51,12 +51,12 @@ def load_filenames(data_list_path: str) -> list: def prepare_data( - train_files: list, - device: torch.device, - cache_rate: float, - num_workers: int = 2, - batch_size: int = 1, - include_body_region: bool = False + train_files: list, + device: torch.device, + cache_rate: float, + num_workers: int = 2, + batch_size: int = 1, + include_body_region: bool = False, ) -> DataLoader: """ Prepare training data. @@ -78,11 +78,11 @@ def _load_data_from_file(file_path, key): return torch.FloatTensor(json.load(f)[key]) train_transforms_list = [ - monai.transforms.LoadImaged(keys=["image"]), - monai.transforms.EnsureChannelFirstd(keys=["image"]), - monai.transforms.Lambdad(keys="spacing", func=lambda x: _load_data_from_file(x, "spacing")), - monai.transforms.Lambdad(keys="spacing", func=lambda x: x * 1e2), - ] + monai.transforms.LoadImaged(keys=["image"]), + monai.transforms.EnsureChannelFirstd(keys=["image"]), + monai.transforms.Lambdad(keys="spacing", func=lambda x: _load_data_from_file(x, "spacing")), + monai.transforms.Lambdad(keys="spacing", func=lambda x: x * 1e2), + ] if include_body_region: train_transforms_list += [ monai.transforms.Lambdad( @@ -202,7 +202,7 @@ def train_one_epoch( logger: logging.Logger, local_rank: int, amp: bool = True, - include_body_region: bool = False + include_body_region: bool = False, ) -> torch.Tensor: """ Train the model for one epoch. @@ -284,9 +284,10 @@ def train_one_epoch( # predict velocity loss = loss_pt(model_output.float(), (images - noise).float()) else: - raise ValueError("noise scheduler prediction type has to be chosen from ", - f"[{DDPMPredictionType.EPSILON},{DDPMPredictionType.SAMPLE},{DDPMPredictionType.V_PREDICTION}]" - ) + raise ValueError( + "noise scheduler prediction type has to be chosen from ", + f"[{DDPMPredictionType.EPSILON},{DDPMPredictionType.SAMPLE},{DDPMPredictionType.V_PREDICTION}]", + ) if amp: scaler.scale(loss).backward() @@ -349,7 +350,12 @@ def save_checkpoint( def diff_model_train( - env_config_path: str, model_config_path: str, model_def_path: str, num_gpus: int, amp: bool = True, include_body_region: bool = False + env_config_path: str, + model_config_path: str, + model_def_path: str, + num_gpus: int, + amp: bool = True, + include_body_region: bool = False, ) -> None: """ Main function to train a diffusion model. @@ -400,9 +406,11 @@ def diff_model_train( )[local_rank] train_loader = prepare_data( - train_files, device, args.diffusion_unet_train["cache_rate"], + train_files, + device, + args.diffusion_unet_train["cache_rate"], batch_size=args.diffusion_unet_train["batch_size"], - include_body_region = include_body_region + include_body_region=include_body_region, ) unet = load_unet(args, device, logger) @@ -438,7 +446,7 @@ def diff_model_train( logger, local_rank, amp=amp, - include_body_region=include_body_region + include_body_region=include_body_region, ) loss_torch = loss_torch.tolist() @@ -479,7 +487,14 @@ def diff_model_train( ) parser.add_argument("--num_gpus", type=int, default=1, help="Number of GPUs to use for training") parser.add_argument("--no_amp", dest="amp", action="store_false", help="Disable automatic mixed precision training") - parser.add_argument("--include_body_region", dest="include_body_region", action="store_true", help="Whether to include body region in data") + parser.add_argument( + "--include_body_region", + dest="include_body_region", + action="store_true", + help="Whether to include body region in data", + ) args = parser.parse_args() - diff_model_train(args.env_config, args.model_config, args.model_def, args.num_gpus, args.amp, args.include_body_region) + diff_model_train( + args.env_config, args.model_config, args.model_def, args.num_gpus, args.amp, args.include_body_region + ) diff --git a/generation/maisi/scripts/utils.py b/generation/maisi/scripts/utils.py index dd343ab24..8086e6dff 100644 --- a/generation/maisi/scripts/utils.py +++ b/generation/maisi/scripts/utils.py @@ -712,14 +712,14 @@ def dynamic_infer(inferer, model, images): # Extract the spatial dimensions from the images tensor (H, W, D) spatial_dims = images.shape[2:] orig_roi = inferer.roi_size - + # Check that roi has the same number of dimensions as spatial_dims if len(orig_roi) != len(spatial_dims): raise ValueError(f"ROI length ({len(orig_roi)}) does not match spatial dimensions ({len(spatial_dims)}).") - + # Iterate and adjust each ROI dimension adjusted_roi = [min(roi_dim, img_dim) for roi_dim, img_dim in zip(orig_roi, spatial_dims)] inferer.roi_size = adjusted_roi output = inferer(network=model, inputs=images) inferer.roi_size = orig_roi - return output \ No newline at end of file + return output From 32400456e52398e025bebd04fefcf2f5197b0029 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Tue, 11 Mar 2025 20:52:45 +0000 Subject: [PATCH 03/59] rm redundant code Signed-off-by: Can-Zhao --- generation/maisi/scripts/diff_model_infer.py | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/generation/maisi/scripts/diff_model_infer.py b/generation/maisi/scripts/diff_model_infer.py index 77aa0b229..96da047e0 100644 --- a/generation/maisi/scripts/diff_model_infer.py +++ b/generation/maisi/scripts/diff_model_infer.py @@ -180,15 +180,11 @@ def run_inference( inferer = SlidingWindowInferer( - roi_size=( - min(output_size[0] // divisor // 4 * 3, 96), - min(output_size[1] // divisor // 4 * 3, 96), - min(output_size[2] // divisor // 4 * 3, 96), - ), + roi_size=[80,80,80], sw_batch_size=1, progress=True, mode="gaussian", - overlap=2.0 / 3.0, + overlap=0.4, sw_device=device, device=device, ) From b1385ba6a83148754d393fc34c3b890e21336582 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Tue, 11 Mar 2025 20:55:46 +0000 Subject: [PATCH 04/59] test and update notebook Signed-off-by: Can-Zhao --- .../maisi_diff_unet_training_tutorial.ipynb | 220 ++++++++++++------ 1 file changed, 147 insertions(+), 73 deletions(-) diff --git a/generation/maisi/maisi_diff_unet_training_tutorial.ipynb b/generation/maisi/maisi_diff_unet_training_tutorial.ipynb index 6effcee52..ab4e331a1 100644 --- a/generation/maisi/maisi_diff_unet_training_tutorial.ipynb +++ b/generation/maisi/maisi_diff_unet_training_tutorial.ipynb @@ -26,7 +26,33 @@ "\n", "![Generated image examples](https://developer-blogs.nvidia.com/wp-content/uploads/2024/06/image3.png)\n", "\n", - "In this notebook, we detail the procedure for training a 3D latent diffusion model to generate high-dimensional 3D medical images. Due to the potential for out-of-memory issues on most GPUs when generating large images (e.g., those with dimensions of 512 x 512 x 512 or greater), we have structured the training process into two primary steps: 1) generating image embeddings and 2) training 3D latent diffusion models. The subsequent sections will demonstrate the entire process using a simulated dataset." + "In this notebook, we detail the procedure for training a 3D latent diffusion model to generate high-dimensional 3D medical images. Due to the potential for out-of-memory issues on most GPUs when generating large images (e.g., those with dimensions of 512 x 512 x 512 or greater), we have structured the training process into two primary steps: 1) generating image embeddings and 2) training 3D latent diffusion models. The subsequent sections will demonstrate the entire process using a simulated dataset.\n", + "\n", + "`[Release Note (March 2025)]:` We are excited to announce the new MAISI Version `'maisi-rflow'`. Compared with the previous version `'maisi-ddpm'`, it accelerated latent diffusion model inference by 33x. Please see the detailed difference in the following section." + ] + }, + { + "cell_type": "markdown", + "id": "c2389853", + "metadata": {}, + "source": [ + "## Set up the MAISI version\n", + "\n", + "Choose between `'maisi-ddpm'` and `'maisi-rflow'`. The differences are:\n", + "- The maisi version `'maisi-ddpm'` uses basic noise scheduler DDPM. `'maisi-rflow'` uses Rectified Flow scheduler, can be 33 times faster during inference.\n", + "- The maisi version `'maisi-ddpm'` requires training images to be labeled with body region (`\"top_region_index\"` and `\"bottom_region_index\"`), while `'maisi-rflow'` does not have such requirement. In other words, it is easier to prepare training data for `'maisi-rflow'`.\n", + "- For the released model weights, `'maisi-rflow'` can generate images with better quality for head region and small output volumes, and comparable quality for other cases compared with `'maisi-ddpm'`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "828b9ece-7759-40e8-ac4c-6467c3399701", + "metadata": {}, + "outputs": [], + "source": [ + "maisi_version = 'maisi-ddpm'\n", + "assert maisi_version in ['maisi-ddpm', 'maisi-rflow']" ] }, { @@ -39,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "58cbde9b", "metadata": {}, "outputs": [], @@ -57,10 +83,47 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "e3bf0346", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MONAI version: 1.4.1rc1+31.g90de55b1\n", + "Numpy version: 1.26.4\n", + "Pytorch version: 2.5.0+cu124\n", + "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n", + "MONAI rev id: 90de55b1340ffc8eccf960d111229535bcb2f81d\n", + "MONAI __file__: /localhome//.local/lib/python3.10/site-packages/monai/__init__.py\n", + "\n", + "Optional dependencies:\n", + "Pytorch Ignite version: 0.4.11\n", + "ITK version: 5.4.0\n", + "Nibabel version: 5.3.2\n", + "scikit-image version: 0.24.0\n", + "scipy version: 1.14.1\n", + "Pillow version: 11.0.0\n", + "Tensorboard version: 2.18.0\n", + "gdown version: 5.2.0\n", + "TorchVision version: 0.20.0+cu124\n", + "tqdm version: 4.66.5\n", + "lmdb version: 1.5.1\n", + "psutil version: 6.1.0\n", + "pandas version: 2.2.3\n", + "einops version: 0.8.0\n", + "transformers version: 4.40.2\n", + "mlflow version: 2.17.1\n", + "pynrrd version: 1.0.0\n", + "clearml version: 1.16.5rc2\n", + "\n", + "For details about installing the optional dependencies, please visit:\n", + " https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n", + "\n" + ] + } + ], "source": [ "from scripts.diff_model_setting import setup_logging\n", "import copy\n", @@ -95,7 +158,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "fc32a7fe", "metadata": {}, "outputs": [], @@ -117,7 +180,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "1b199078", "metadata": {}, "outputs": [ @@ -125,7 +188,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:notebook:Generated simulated images.\n" + "[2025-03-11 20:54:15.384][ INFO](notebook) - Generated simulated images.\n" ] } ], @@ -154,7 +217,7 @@ }, { "cell_type": "markdown", - "id": "c2389853", + "id": "a059ddcf-8525-4241-9fe3-b661c4bdd336", "metadata": {}, "source": [ "### Set up directories and configurations\n", @@ -164,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "6c7b434c", "metadata": {}, "outputs": [ @@ -172,15 +235,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:notebook:files and folders under work_dir: ['environment_maisi_diff_model.json', 'config_maisi.json', 'sim_dataroot', 'sim_datalist.json', 'models', 'embeddings', 'config_maisi_diff_model.json', 'predictions'].\n", - "INFO:notebook:number of GPUs: 1.\n" + "[2025-03-11 20:54:15.397][ INFO](notebook) - files and folders under work_dir: ['predictions', 'config_maisi.json', 'models', 'sim_dataroot', 'config_maisi_diff_model.json', 'embeddings', 'environment_maisi_diff_model.json', 'sim_datalist.json'].\n", + "[2025-03-11 20:54:15.398][ INFO](notebook) - number of GPUs: 1.\n" ] } ], "source": [ "env_config_path = \"./configs/environment_maisi_diff_model.json\"\n", "model_config_path = \"./configs/config_maisi_diff_model.json\"\n", - "model_def_path = \"./configs/config_maisi.json\"\n", + "if maisi_version == 'maisi-ddpm':\n", + " model_def_path = \"./configs/config_maisi-ddpm.json\"\n", + " include_body_region = True\n", + "elif maisi_version == 'maisi-rflow':\n", + " model_def_path = \"./configs/config_maisi-rflow.json\"\n", + " include_body_region = False\n", + "else:\n", + " raise ValueError(f\"maisi_version has to be chosen from ['maisi-ddpm', 'maisi-rflow'], yet got {maisi_version}.\")\n", "\n", "# Load environment configuration, model configuration and model definition\n", "with open(env_config_path, \"r\") as f:\n", @@ -229,7 +299,7 @@ " json.dump(model_config_out, f, sort_keys=True, indent=4)\n", "\n", "# Update model definition for demo\n", - "model_def_out[\"autoencoder_def\"][\"num_splits\"] = 4\n", + "model_def_out[\"autoencoder_def\"][\"num_splits\"] = 2\n", "model_def_filepath = os.path.join(work_dir, \"config_maisi.json\")\n", "with open(model_def_filepath, \"w\") as f:\n", " json.dump(model_def_out, f, sort_keys=True, indent=4)\n", @@ -244,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "95ea6972", "metadata": {}, "outputs": [], @@ -304,7 +374,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "f45ea863", "metadata": {}, "outputs": [ @@ -312,7 +382,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:notebook:Creating training data...\n" + "[2025-03-11 20:54:15.409][ INFO](notebook) - Creating training data...\n" ] }, { @@ -320,8 +390,8 @@ "output_type": "stream", "text": [ "\n", - "INFO:creating training data:Using device cuda:0\n", - "INFO:creating training data:filenames_raw: ['tr_image_001.nii.gz', 'tr_image_002.nii.gz']\n", + "[2025-03-11 20:54:22.732][ INFO](creating training data) - Using device cuda:0\n", + "[2025-03-11 20:54:23.622][ INFO](creating training data) - filenames_raw: ['tr_image_001.nii.gz', 'tr_image_002.nii.gz']\n", "\n" ] } @@ -357,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "0221a658", "metadata": {}, "outputs": [ @@ -365,9 +435,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:notebook:data: {'dim': (64, 64, 32), 'spacing': [0.875, 0.875, 0.75], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}.\n", - "INFO:notebook:data: {'dim': (64, 64, 32), 'spacing': [0.875, 0.875, 0.75], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}.\n", - "INFO:notebook:Completed creating .json files for all embedding files.\n" + "[2025-03-11 20:54:25.753][ INFO](notebook) - data: {'dim': (64, 64, 32), 'spacing': [0.875, 0.875, 0.75], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}.\n", + "[2025-03-11 20:54:25.755][ INFO](notebook) - data: {'dim': (64, 64, 32), 'spacing': [0.875, 0.875, 0.75], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}.\n", + "[2025-03-11 20:54:25.757][ INFO](notebook) - Completed creating .json files for all embedding files.\n" ] } ], @@ -396,15 +466,16 @@ " spacing = [float(_item) for _item in spacing]\n", "\n", " # Create the dictionary with the specified keys and values\n", - " # The region can be selected from one of four regions from top to bottom.\n", - " # [1,0,0,0] is the head and neck, [0,1,0,0] is the chest region, [0,0,1,0]\n", - " # is the abdomen region, and [0,0,0,1] is the lower body region.\n", " data = {\n", - " \"dim\": dimensions,\n", - " \"spacing\": spacing,\n", - " \"top_region_index\": [0, 1, 0, 0], # chest region\n", - " \"bottom_region_index\": [0, 0, 1, 0], # abdomen region\n", + " \"dim\": dimensions,\n", + " \"spacing\": spacing\n", " }\n", + " if include_body_region:\n", + " # The region can be selected from one of four regions from top to bottom.\n", + " # [1,0,0,0] is the head and neck, [0,1,0,0] is the chest region, [0,0,1,0]\n", + " # is the abdomen region, and [0,0,0,1] is the lower body region.\n", + " data[\"top_region_index\"] = [0, 1, 0, 0] # chest region\n", + " data[\"bottom_region_index\"] = [0, 0, 1, 0] # abdomen region\n", " logger.info(f\"data: {data}.\")\n", "\n", " # Create the .json filename\n", @@ -438,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "ade6389d", "metadata": {}, "outputs": [ @@ -446,7 +517,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:notebook:Training the model...\n" + "[2025-03-11 20:54:25.762][ INFO](notebook) - Training the model...\n" ] }, { @@ -454,26 +525,26 @@ "output_type": "stream", "text": [ "\n", - "INFO:training:Using cuda:0 of 1\n", - "INFO:training:[config] ckpt_folder -> ./temp_work_dir/./models.\n", - "INFO:training:[config] data_root -> ./temp_work_dir/./embeddings.\n", - "INFO:training:[config] data_list -> ./temp_work_dir/sim_datalist.json.\n", - "INFO:training:[config] lr -> 0.0001.\n", - "INFO:training:[config] num_epochs -> 2.\n", - "INFO:training:[config] num_train_timesteps -> 1000.\n", - "INFO:training:num_files_train: 2\n", - "INFO:training:Training from scratch.\n", - "INFO:training:Scaling factor set to 0.8903454542160034.\n", - "INFO:training:scale_factor -> 0.8903454542160034.\n", - "INFO:training:torch.set_float32_matmul_precision -> highest.\n", - "INFO:training:Epoch 1, lr 0.0001.\n", - "INFO:training:[2024-09-30 06:30:33] epoch 1, iter 1/2, loss: 0.7974, lr: 0.000100000000.\n", - "INFO:training:[2024-09-30 06:30:33] epoch 1, iter 2/2, loss: 0.7939, lr: 0.000056250000.\n", - "INFO:training:epoch 1 average loss: 0.7957.\n", - "INFO:training:Epoch 2, lr 2.5e-05.\n", - "INFO:training:[2024-09-30 06:30:35] epoch 2, iter 1/2, loss: 0.7902, lr: 0.000025000000.\n", - "INFO:training:[2024-09-30 06:30:35] epoch 2, iter 2/2, loss: 0.7889, lr: 0.000006250000.\n", - "INFO:training:epoch 2 average loss: 0.7895.\n", + "[2025-03-11 20:54:35.079][ INFO](training) - Using cuda:0 of 1\n", + "[2025-03-11 20:54:35.079][ INFO](training) - [config] ckpt_folder -> ./temp_work_dir/./models.\n", + "[2025-03-11 20:54:35.080][ INFO](training) - [config] data_root -> ./temp_work_dir/./embeddings.\n", + "[2025-03-11 20:54:35.080][ INFO](training) - [config] data_list -> ./temp_work_dir/sim_datalist.json.\n", + "[2025-03-11 20:54:35.080][ INFO](training) - [config] lr -> 0.0001.\n", + "[2025-03-11 20:54:35.080][ INFO](training) - [config] num_epochs -> 2.\n", + "[2025-03-11 20:54:35.080][ INFO](training) - [config] num_train_timesteps -> 1000.\n", + "[2025-03-11 20:54:35.080][ INFO](training) - num_files_train: 2\n", + "[2025-03-11 20:54:36.672][ INFO](training) - Training from scratch.\n", + "[2025-03-11 20:54:37.017][ INFO](training) - Scaling factor set to 1.159693956375122.\n", + "[2025-03-11 20:54:37.017][ INFO](training) - scale_factor -> 1.159693956375122.\n", + "[2025-03-11 20:54:37.020][ INFO](training) - torch.set_float32_matmul_precision -> highest.\n", + "[2025-03-11 20:54:37.020][ INFO](training) - Epoch 1, lr 0.0001.\n", + "[2025-03-11 20:54:40.078][ INFO](training) - [2025-03-11 20:54:40] epoch 1, iter 1/2, loss: 0.7975, lr: 0.000100000000.\n", + "[2025-03-11 20:54:40.323][ INFO](training) - [2025-03-11 20:54:40] epoch 1, iter 2/2, loss: 0.7941, lr: 0.000056250000.\n", + "[2025-03-11 20:54:40.361][ INFO](training) - epoch 1 average loss: 0.7958.\n", + "[2025-03-11 20:54:42.460][ INFO](training) - Epoch 2, lr 2.5e-05.\n", + "[2025-03-11 20:54:42.819][ INFO](training) - [2025-03-11 20:54:42] epoch 2, iter 1/2, loss: 0.7904, lr: 0.000025000000.\n", + "[2025-03-11 20:54:42.920][ INFO](training) - [2025-03-11 20:54:42] epoch 2, iter 2/2, loss: 0.7901, lr: 0.000006250000.\n", + "[2025-03-11 20:54:42.988][ INFO](training) - epoch 2 average loss: 0.7903.\n", "\n" ] } @@ -493,6 +564,8 @@ " \"--num_gpus\",\n", " str(num_gpus),\n", "]\n", + "if include_body_region:\n", + " module_args.append(\"--include_body_region\")\n", "\n", "run_torchrun(module, module_args, num_gpus=num_gpus)" ] @@ -509,7 +582,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "1626526d", "metadata": {}, "outputs": [ @@ -517,8 +590,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:notebook:Running inference...\n", - "INFO:notebook:Completed all steps.\n" + "[2025-03-11 20:54:46.504][ INFO](notebook) - Running inference...\n", + "[2025-03-11 20:55:04.841][ INFO](notebook) - Completed all steps.\n" ] }, { @@ -526,24 +599,23 @@ "output_type": "stream", "text": [ "\n", - "INFO:inference:Using cuda:0 of 1 with random seed: 93612\n", - "INFO:inference:[config] ckpt_filepath -> ./temp_work_dir/./models/diff_unet_ckpt.pt.\n", - "INFO:inference:[config] random_seed -> 93612.\n", - "INFO:inference:[config] output_prefix -> unet_3d.\n", - "INFO:inference:[config] output_size -> (256, 256, 128).\n", - "INFO:inference:[config] out_spacing -> (1.0, 1.0, 0.75).\n", - "INFO:root:`controllable_anatomy_size` is not provided.\n", - "INFO:inference:checkpoints ./temp_work_dir/./models/diff_unet_ckpt.pt loaded.\n", - "INFO:inference:scale_factor -> 0.8903454542160034.\n", - "INFO:inference:num_downsample_level -> 4, divisor -> 4.\n", - "INFO:inference:noise: cuda:0, torch.float32, \n", + "[2025-03-11 20:54:57.016][ INFO](inference) - Using cuda:0 of 1 with random seed: 51319\n", + "[2025-03-11 20:54:57.016][ INFO](inference) - [config] ckpt_filepath -> ./temp_work_dir/./models/diff_unet_ckpt.pt.\n", + "[2025-03-11 20:54:57.016][ INFO](inference) - [config] random_seed -> 51319.\n", + "[2025-03-11 20:54:57.016][ INFO](inference) - [config] output_prefix -> unet_3d.\n", + "[2025-03-11 20:54:57.016][ INFO](inference) - [config] output_size -> (256, 256, 128).\n", + "[2025-03-11 20:54:57.016][ INFO](inference) - [config] out_spacing -> (1.0, 1.0, 0.75).\n", + "[2025-03-11 20:54:57.016][ INFO](root) - `controllable_anatomy_size` is not provided.\n", + "[2025-03-11 20:54:59.298][ INFO](inference) - checkpoints ./temp_work_dir/./models/diff_unet_ckpt.pt loaded.\n", + "[2025-03-11 20:54:59.300][ INFO](inference) - scale_factor -> 1.159693956375122.\n", + "[2025-03-11 20:54:59.301][ INFO](inference) - num_downsample_level -> 4, divisor -> 4.\n", + "[2025-03-11 20:54:59.302][ INFO](inference) - noise: cuda:0, torch.float32, \n", "\n", - " 0%| | 0/10 [00:00 Date: Tue, 11 Mar 2025 20:57:44 +0000 Subject: [PATCH 05/59] rm old json Signed-off-by: Can-Zhao --- generation/maisi/configs/config_maisi.json | 153 --------------------- 1 file changed, 153 deletions(-) delete mode 100644 generation/maisi/configs/config_maisi.json diff --git a/generation/maisi/configs/config_maisi.json b/generation/maisi/configs/config_maisi.json deleted file mode 100644 index 8a781ca5b..000000000 --- a/generation/maisi/configs/config_maisi.json +++ /dev/null @@ -1,153 +0,0 @@ -{ - "spatial_dims": 3, - "image_channels": 1, - "latent_channels": 4, - "mask_generation_latent_shape": [ - 4, - 64, - 64, - 64 - ], - "autoencoder_def": { - "_target_": "monai.apps.generation.maisi.networks.autoencoderkl_maisi.AutoencoderKlMaisi", - "spatial_dims": "@spatial_dims", - "in_channels": "@image_channels", - "out_channels": "@image_channels", - "latent_channels": "@latent_channels", - "num_channels": [ - 64, - 128, - 256 - ], - "num_res_blocks": [2,2,2], - "norm_num_groups": 32, - "norm_eps": 1e-06, - "attention_levels": [ - false, - false, - false - ], - "with_encoder_nonlocal_attn": false, - "with_decoder_nonlocal_attn": false, - "use_checkpointing": false, - "use_convtranspose": false, - "norm_float16": true, - "num_splits": 8, - "dim_split": 1 - }, - "diffusion_unet_def": { - "_target_": "monai.apps.generation.maisi.networks.diffusion_model_unet_maisi.DiffusionModelUNetMaisi", - "spatial_dims": "@spatial_dims", - "in_channels": "@latent_channels", - "out_channels": "@latent_channels", - "num_channels": [ - 64, - 128, - 256, - 512 - ], - "attention_levels": [ - false, - false, - true, - true - ], - "num_head_channels": [ - 0, - 0, - 32, - 32 - ], - "num_res_blocks": 2, - "use_flash_attention": true, - "include_top_region_index_input": true, - "include_bottom_region_index_input": true, - "include_spacing_input": true - }, - "controlnet_def": { - "_target_": "monai.apps.generation.maisi.networks.controlnet_maisi.ControlNetMaisi", - "spatial_dims": "@spatial_dims", - "in_channels": "@latent_channels", - "num_channels": [ - 64, - 128, - 256, - 512 - ], - "attention_levels": [ - false, - false, - true, - true - ], - "num_head_channels": [ - 0, - 0, - 32, - 32 - ], - "num_res_blocks": 2, - "use_flash_attention": true, - "conditioning_embedding_in_channels": 8, - "conditioning_embedding_num_channels": [8, 32, 64] - }, - "mask_generation_autoencoder_def": { - "_target_": "monai.apps.generation.maisi.networks.autoencoderkl_maisi.AutoencoderKlMaisi", - "spatial_dims": "@spatial_dims", - "in_channels": 8, - "out_channels": 125, - "latent_channels": "@latent_channels", - "num_channels": [ - 32, - 64, - 128 - ], - "num_res_blocks": [1, 2, 2], - "norm_num_groups": 32, - "norm_eps": 1e-06, - "attention_levels": [ - false, - false, - false - ], - "with_encoder_nonlocal_attn": false, - "with_decoder_nonlocal_attn": false, - "use_flash_attention": false, - "use_checkpointing": true, - "use_convtranspose": true, - "norm_float16": true, - "num_splits": 8, - "dim_split": 1 - }, - "mask_generation_diffusion_def": { - "_target_": "monai.networks.nets.diffusion_model_unet.DiffusionModelUNet", - "spatial_dims": "@spatial_dims", - "in_channels": "@latent_channels", - "out_channels": "@latent_channels", - "channels":[64, 128, 256, 512], - "attention_levels":[false, false, true, true], - "num_head_channels":[0, 0, 32, 32], - "num_res_blocks": 2, - "use_flash_attention": true, - "with_conditioning": true, - "upcast_attention": true, - "cross_attention_dim": 10 - }, - "mask_generation_scale_factor": 1.0055984258651733, - "noise_scheduler": { - "_target_": "monai.networks.schedulers.ddpm.DDPMScheduler", - "num_train_timesteps": 1000, - "beta_start": 0.0015, - "beta_end": 0.0195, - "schedule": "scaled_linear_beta", - "clip_sample": false - }, - "mask_generation_noise_scheduler": { - "_target_": "monai.networks.schedulers.ddpm.DDPMScheduler", - "num_train_timesteps": 1000, - "beta_start": 0.0015, - "beta_end": 0.0195, - "schedule": "scaled_linear_beta", - "clip_sample": false - } -} From 9be20ed68a9382f7fd4c5b681a266f52cae92518 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 11 Mar 2025 21:01:36 +0000 Subject: [PATCH 06/59] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../maisi/maisi_diff_unet_training_tutorial.ipynb | 13 +++++-------- generation/maisi/scripts/diff_model_infer.py | 2 +- 2 files changed, 6 insertions(+), 9 deletions(-) diff --git a/generation/maisi/maisi_diff_unet_training_tutorial.ipynb b/generation/maisi/maisi_diff_unet_training_tutorial.ipynb index ab4e331a1..9c0542972 100644 --- a/generation/maisi/maisi_diff_unet_training_tutorial.ipynb +++ b/generation/maisi/maisi_diff_unet_training_tutorial.ipynb @@ -51,8 +51,8 @@ "metadata": {}, "outputs": [], "source": [ - "maisi_version = 'maisi-ddpm'\n", - "assert maisi_version in ['maisi-ddpm', 'maisi-rflow']" + "maisi_version = \"maisi-ddpm\"\n", + "assert maisi_version in [\"maisi-ddpm\", \"maisi-rflow\"]" ] }, { @@ -243,10 +243,10 @@ "source": [ "env_config_path = \"./configs/environment_maisi_diff_model.json\"\n", "model_config_path = \"./configs/config_maisi_diff_model.json\"\n", - "if maisi_version == 'maisi-ddpm':\n", + "if maisi_version == \"maisi-ddpm\":\n", " model_def_path = \"./configs/config_maisi-ddpm.json\"\n", " include_body_region = True\n", - "elif maisi_version == 'maisi-rflow':\n", + "elif maisi_version == \"maisi-rflow\":\n", " model_def_path = \"./configs/config_maisi-rflow.json\"\n", " include_body_region = False\n", "else:\n", @@ -466,10 +466,7 @@ " spacing = [float(_item) for _item in spacing]\n", "\n", " # Create the dictionary with the specified keys and values\n", - " data = {\n", - " \"dim\": dimensions,\n", - " \"spacing\": spacing\n", - " }\n", + " data = {\"dim\": dimensions, \"spacing\": spacing}\n", " if include_body_region:\n", " # The region can be selected from one of four regions from top to bottom.\n", " # [1,0,0,0] is the head and neck, [0,1,0,0] is the chest region, [0,0,1,0]\n", diff --git a/generation/maisi/scripts/diff_model_infer.py b/generation/maisi/scripts/diff_model_infer.py index e34a4ee48..aa3996eae 100644 --- a/generation/maisi/scripts/diff_model_infer.py +++ b/generation/maisi/scripts/diff_model_infer.py @@ -179,7 +179,7 @@ def run_inference( image, _ = noise_scheduler.step(model_output, t, image, next_t) # type: ignore inferer = SlidingWindowInferer( - roi_size=[80,80,80], + roi_size=[80, 80, 80], sw_batch_size=1, progress=True, mode="gaussian", From 9e6a715aba512793ed7daa56b526fc07243efe87 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Tue, 11 Mar 2025 21:53:36 +0000 Subject: [PATCH 07/59] reformat Signed-off-by: Can-Zhao --- .../maisi_diff_unet_training_tutorial.ipynb | 146 +++++++++--------- 1 file changed, 73 insertions(+), 73 deletions(-) diff --git a/generation/maisi/maisi_diff_unet_training_tutorial.ipynb b/generation/maisi/maisi_diff_unet_training_tutorial.ipynb index ab4e331a1..f7c5ad4ac 100644 --- a/generation/maisi/maisi_diff_unet_training_tutorial.ipynb +++ b/generation/maisi/maisi_diff_unet_training_tutorial.ipynb @@ -31,30 +31,6 @@ "`[Release Note (March 2025)]:` We are excited to announce the new MAISI Version `'maisi-rflow'`. Compared with the previous version `'maisi-ddpm'`, it accelerated latent diffusion model inference by 33x. Please see the detailed difference in the following section." ] }, - { - "cell_type": "markdown", - "id": "c2389853", - "metadata": {}, - "source": [ - "## Set up the MAISI version\n", - "\n", - "Choose between `'maisi-ddpm'` and `'maisi-rflow'`. The differences are:\n", - "- The maisi version `'maisi-ddpm'` uses basic noise scheduler DDPM. `'maisi-rflow'` uses Rectified Flow scheduler, can be 33 times faster during inference.\n", - "- The maisi version `'maisi-ddpm'` requires training images to be labeled with body region (`\"top_region_index\"` and `\"bottom_region_index\"`), while `'maisi-rflow'` does not have such requirement. In other words, it is easier to prepare training data for `'maisi-rflow'`.\n", - "- For the released model weights, `'maisi-rflow'` can generate images with better quality for head region and small output volumes, and comparable quality for other cases compared with `'maisi-ddpm'`." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "828b9ece-7759-40e8-ac4c-6467c3399701", - "metadata": {}, - "outputs": [], - "source": [ - "maisi_version = 'maisi-ddpm'\n", - "assert maisi_version in ['maisi-ddpm', 'maisi-rflow']" - ] - }, { "cell_type": "markdown", "id": "c9ecfb90", @@ -65,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "58cbde9b", "metadata": {}, "outputs": [], @@ -83,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "e3bf0346", "metadata": {}, "outputs": [ @@ -144,6 +120,30 @@ "logger = setup_logging(\"notebook\")" ] }, + { + "cell_type": "markdown", + "id": "c2389853", + "metadata": {}, + "source": [ + "## Set up the MAISI version\n", + "\n", + "Choose between `'maisi-ddpm'` and `'maisi-rflow'`. The differences are:\n", + "- The maisi version `'maisi-ddpm'` uses basic noise scheduler DDPM. `'maisi-rflow'` uses Rectified Flow scheduler, can be 33 times faster during inference.\n", + "- The maisi version `'maisi-ddpm'` requires training images to be labeled with body region (`\"top_region_index\"` and `\"bottom_region_index\"`), while `'maisi-rflow'` does not have such requirement. In other words, it is easier to prepare training data for `'maisi-rflow'`.\n", + "- For the released model weights, `'maisi-rflow'` can generate images with better quality for head region and small output volumes, and comparable quality for other cases compared with `'maisi-ddpm'`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "828b9ece-7759-40e8-ac4c-6467c3399701", + "metadata": {}, + "outputs": [], + "source": [ + "maisi_version = 'maisi-ddpm'\n", + "assert maisi_version in ['maisi-ddpm', 'maisi-rflow']" + ] + }, { "cell_type": "markdown", "id": "d8e29c23", @@ -188,7 +188,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 20:54:15.384][ INFO](notebook) - Generated simulated images.\n" + "[2025-03-11 21:46:47.184][ INFO](notebook) - Generated simulated images.\n" ] } ], @@ -235,8 +235,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 20:54:15.397][ INFO](notebook) - files and folders under work_dir: ['predictions', 'config_maisi.json', 'models', 'sim_dataroot', 'config_maisi_diff_model.json', 'embeddings', 'environment_maisi_diff_model.json', 'sim_datalist.json'].\n", - "[2025-03-11 20:54:15.398][ INFO](notebook) - number of GPUs: 1.\n" + "[2025-03-11 21:46:47.199][ INFO](notebook) - files and folders under work_dir: ['predictions', 'config_maisi.json', 'models', 'sim_dataroot', 'config_maisi_diff_model.json', 'embeddings', 'environment_maisi_diff_model.json', 'sim_datalist.json'].\n", + "[2025-03-11 21:46:47.199][ INFO](notebook) - number of GPUs: 1.\n" ] } ], @@ -382,7 +382,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 20:54:15.409][ INFO](notebook) - Creating training data...\n" + "[2025-03-11 21:46:47.210][ INFO](notebook) - Creating training data...\n" ] }, { @@ -390,8 +390,8 @@ "output_type": "stream", "text": [ "\n", - "[2025-03-11 20:54:22.732][ INFO](creating training data) - Using device cuda:0\n", - "[2025-03-11 20:54:23.622][ INFO](creating training data) - filenames_raw: ['tr_image_001.nii.gz', 'tr_image_002.nii.gz']\n", + "[2025-03-11 21:46:57.369][ INFO](creating training data) - Using device cuda:0\n", + "[2025-03-11 21:46:58.170][ INFO](creating training data) - filenames_raw: ['tr_image_001.nii.gz', 'tr_image_002.nii.gz']\n", "\n" ] } @@ -435,9 +435,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 20:54:25.753][ INFO](notebook) - data: {'dim': (64, 64, 32), 'spacing': [0.875, 0.875, 0.75], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}.\n", - "[2025-03-11 20:54:25.755][ INFO](notebook) - data: {'dim': (64, 64, 32), 'spacing': [0.875, 0.875, 0.75], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}.\n", - "[2025-03-11 20:54:25.757][ INFO](notebook) - Completed creating .json files for all embedding files.\n" + "[2025-03-11 21:47:00.412][ INFO](notebook) - data: {'dim': (64, 64, 32), 'spacing': [0.875, 0.875, 0.75], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}.\n", + "[2025-03-11 21:47:00.414][ INFO](notebook) - data: {'dim': (64, 64, 32), 'spacing': [0.875, 0.875, 0.75], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}.\n", + "[2025-03-11 21:47:00.415][ INFO](notebook) - Completed creating .json files for all embedding files.\n" ] } ], @@ -517,7 +517,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 20:54:25.762][ INFO](notebook) - Training the model...\n" + "[2025-03-11 21:47:00.420][ INFO](notebook) - Training the model...\n" ] }, { @@ -525,26 +525,26 @@ "output_type": "stream", "text": [ "\n", - "[2025-03-11 20:54:35.079][ INFO](training) - Using cuda:0 of 1\n", - "[2025-03-11 20:54:35.079][ INFO](training) - [config] ckpt_folder -> ./temp_work_dir/./models.\n", - "[2025-03-11 20:54:35.080][ INFO](training) - [config] data_root -> ./temp_work_dir/./embeddings.\n", - "[2025-03-11 20:54:35.080][ INFO](training) - [config] data_list -> ./temp_work_dir/sim_datalist.json.\n", - "[2025-03-11 20:54:35.080][ INFO](training) - [config] lr -> 0.0001.\n", - "[2025-03-11 20:54:35.080][ INFO](training) - [config] num_epochs -> 2.\n", - "[2025-03-11 20:54:35.080][ INFO](training) - [config] num_train_timesteps -> 1000.\n", - "[2025-03-11 20:54:35.080][ INFO](training) - num_files_train: 2\n", - "[2025-03-11 20:54:36.672][ INFO](training) - Training from scratch.\n", - "[2025-03-11 20:54:37.017][ INFO](training) - Scaling factor set to 1.159693956375122.\n", - "[2025-03-11 20:54:37.017][ INFO](training) - scale_factor -> 1.159693956375122.\n", - "[2025-03-11 20:54:37.020][ INFO](training) - torch.set_float32_matmul_precision -> highest.\n", - "[2025-03-11 20:54:37.020][ INFO](training) - Epoch 1, lr 0.0001.\n", - "[2025-03-11 20:54:40.078][ INFO](training) - [2025-03-11 20:54:40] epoch 1, iter 1/2, loss: 0.7975, lr: 0.000100000000.\n", - "[2025-03-11 20:54:40.323][ INFO](training) - [2025-03-11 20:54:40] epoch 1, iter 2/2, loss: 0.7941, lr: 0.000056250000.\n", - "[2025-03-11 20:54:40.361][ INFO](training) - epoch 1 average loss: 0.7958.\n", - "[2025-03-11 20:54:42.460][ INFO](training) - Epoch 2, lr 2.5e-05.\n", - "[2025-03-11 20:54:42.819][ INFO](training) - [2025-03-11 20:54:42] epoch 2, iter 1/2, loss: 0.7904, lr: 0.000025000000.\n", - "[2025-03-11 20:54:42.920][ INFO](training) - [2025-03-11 20:54:42] epoch 2, iter 2/2, loss: 0.7901, lr: 0.000006250000.\n", - "[2025-03-11 20:54:42.988][ INFO](training) - epoch 2 average loss: 0.7903.\n", + "[2025-03-11 21:47:09.081][ INFO](training) - Using cuda:0 of 1\n", + "[2025-03-11 21:47:09.081][ INFO](training) - [config] ckpt_folder -> ./temp_work_dir/./models.\n", + "[2025-03-11 21:47:09.081][ INFO](training) - [config] data_root -> ./temp_work_dir/./embeddings.\n", + "[2025-03-11 21:47:09.081][ INFO](training) - [config] data_list -> ./temp_work_dir/sim_datalist.json.\n", + "[2025-03-11 21:47:09.081][ INFO](training) - [config] lr -> 0.0001.\n", + "[2025-03-11 21:47:09.081][ INFO](training) - [config] num_epochs -> 2.\n", + "[2025-03-11 21:47:09.081][ INFO](training) - [config] num_train_timesteps -> 1000.\n", + "[2025-03-11 21:47:09.081][ INFO](training) - num_files_train: 2\n", + "[2025-03-11 21:47:10.815][ INFO](training) - Training from scratch.\n", + "[2025-03-11 21:47:11.273][ INFO](training) - Scaling factor set to 1.159977912902832.\n", + "[2025-03-11 21:47:11.273][ INFO](training) - scale_factor -> 1.159977912902832.\n", + "[2025-03-11 21:47:11.276][ INFO](training) - torch.set_float32_matmul_precision -> highest.\n", + "[2025-03-11 21:47:11.276][ INFO](training) - Epoch 1, lr 0.0001.\n", + "[2025-03-11 21:47:12.253][ INFO](training) - [2025-03-11 21:47:12] epoch 1, iter 1/2, loss: 0.7979, lr: 0.000100000000.\n", + "[2025-03-11 21:47:12.535][ INFO](training) - [2025-03-11 21:47:12] epoch 1, iter 2/2, loss: 0.7931, lr: 0.000056250000.\n", + "[2025-03-11 21:47:12.572][ INFO](training) - epoch 1 average loss: 0.7955.\n", + "[2025-03-11 21:47:14.031][ INFO](training) - Epoch 2, lr 2.5e-05.\n", + "[2025-03-11 21:47:14.420][ INFO](training) - [2025-03-11 21:47:14] epoch 2, iter 1/2, loss: 0.7883, lr: 0.000025000000.\n", + "[2025-03-11 21:47:14.517][ INFO](training) - [2025-03-11 21:47:14] epoch 2, iter 2/2, loss: 0.7893, lr: 0.000006250000.\n", + "[2025-03-11 21:47:14.594][ INFO](training) - epoch 2 average loss: 0.7888.\n", "\n" ] } @@ -590,8 +590,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 20:54:46.504][ INFO](notebook) - Running inference...\n", - "[2025-03-11 20:55:04.841][ INFO](notebook) - Completed all steps.\n" + "[2025-03-11 21:47:18.262][ INFO](notebook) - Running inference...\n", + "[2025-03-11 21:47:35.148][ INFO](notebook) - Completed all steps.\n" ] }, { @@ -599,23 +599,23 @@ "output_type": "stream", "text": [ "\n", - "[2025-03-11 20:54:57.016][ INFO](inference) - Using cuda:0 of 1 with random seed: 51319\n", - "[2025-03-11 20:54:57.016][ INFO](inference) - [config] ckpt_filepath -> ./temp_work_dir/./models/diff_unet_ckpt.pt.\n", - "[2025-03-11 20:54:57.016][ INFO](inference) - [config] random_seed -> 51319.\n", - "[2025-03-11 20:54:57.016][ INFO](inference) - [config] output_prefix -> unet_3d.\n", - "[2025-03-11 20:54:57.016][ INFO](inference) - [config] output_size -> (256, 256, 128).\n", - "[2025-03-11 20:54:57.016][ INFO](inference) - [config] out_spacing -> (1.0, 1.0, 0.75).\n", - "[2025-03-11 20:54:57.016][ INFO](root) - `controllable_anatomy_size` is not provided.\n", - "[2025-03-11 20:54:59.298][ INFO](inference) - checkpoints ./temp_work_dir/./models/diff_unet_ckpt.pt loaded.\n", - "[2025-03-11 20:54:59.300][ INFO](inference) - scale_factor -> 1.159693956375122.\n", - "[2025-03-11 20:54:59.301][ INFO](inference) - num_downsample_level -> 4, divisor -> 4.\n", - "[2025-03-11 20:54:59.302][ INFO](inference) - noise: cuda:0, torch.float32, \n", + "[2025-03-11 21:47:27.859][ INFO](inference) - Using cuda:0 of 1 with random seed: 99760\n", + "[2025-03-11 21:47:27.859][ INFO](inference) - [config] ckpt_filepath -> ./temp_work_dir/./models/diff_unet_ckpt.pt.\n", + "[2025-03-11 21:47:27.860][ INFO](inference) - [config] random_seed -> 99760.\n", + "[2025-03-11 21:47:27.860][ INFO](inference) - [config] output_prefix -> unet_3d.\n", + "[2025-03-11 21:47:27.860][ INFO](inference) - [config] output_size -> (256, 256, 128).\n", + "[2025-03-11 21:47:27.860][ INFO](inference) - [config] out_spacing -> (1.0, 1.0, 0.75).\n", + "[2025-03-11 21:47:27.860][ INFO](root) - `controllable_anatomy_size` is not provided.\n", + "[2025-03-11 21:47:30.510][ INFO](inference) - checkpoints ./temp_work_dir/./models/diff_unet_ckpt.pt loaded.\n", + "[2025-03-11 21:47:30.512][ INFO](inference) - scale_factor -> 1.159977912902832.\n", + "[2025-03-11 21:47:30.512][ INFO](inference) - num_downsample_level -> 4, divisor -> 4.\n", + "[2025-03-11 21:47:30.514][ INFO](inference) - noise: cuda:0, torch.float32, \n", "\n", " 0%| | 0/10 [00:00 Date: Tue, 11 Mar 2025 22:02:57 +0000 Subject: [PATCH 08/59] reformat Signed-off-by: Can-Zhao --- generation/maisi/maisi_diff_unet_training_tutorial.ipynb | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/generation/maisi/maisi_diff_unet_training_tutorial.ipynb b/generation/maisi/maisi_diff_unet_training_tutorial.ipynb index f7c5ad4ac..d70ca974b 100644 --- a/generation/maisi/maisi_diff_unet_training_tutorial.ipynb +++ b/generation/maisi/maisi_diff_unet_training_tutorial.ipynb @@ -59,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 13, "id": "e3bf0346", "metadata": {}, "outputs": [ @@ -101,7 +101,6 @@ } ], "source": [ - "from scripts.diff_model_setting import setup_logging\n", "import copy\n", "import os\n", "import json\n", @@ -115,6 +114,8 @@ "\n", "from IPython.display import Image, display\n", "\n", + "from scripts.diff_model_setting import setup_logging\n", + "\n", "print_config()\n", "\n", "logger = setup_logging(\"notebook\")" From 4187565ec35e56d0eb4d2b05e1159882030e7847 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Tue, 11 Mar 2025 22:03:11 +0000 Subject: [PATCH 09/59] reformat Signed-off-by: Can-Zhao --- .../maisi/scripts/diff_model_create_training_data.py | 3 +-- generation/maisi/scripts/diff_model_infer.py | 9 ++++----- generation/maisi/scripts/diff_model_setting.py | 1 - generation/maisi/scripts/diff_model_train.py | 11 +++++------ generation/maisi/scripts/infer_controlnet.py | 2 +- generation/maisi/scripts/inference.py | 3 ++- generation/maisi/scripts/sample.py | 4 ++-- generation/maisi/scripts/utils.py | 4 ++-- 8 files changed, 17 insertions(+), 20 deletions(-) diff --git a/generation/maisi/scripts/diff_model_create_training_data.py b/generation/maisi/scripts/diff_model_create_training_data.py index ca44b43cc..177dfa34c 100644 --- a/generation/maisi/scripts/diff_model_create_training_data.py +++ b/generation/maisi/scripts/diff_model_create_training_data.py @@ -17,12 +17,11 @@ import os from pathlib import Path +import monai import nibabel as nib import numpy as np import torch import torch.distributed as dist - -import monai from monai.transforms import Compose from monai.utils import set_determinism diff --git a/generation/maisi/scripts/diff_model_infer.py b/generation/maisi/scripts/diff_model_infer.py index e34a4ee48..bc6b3539c 100644 --- a/generation/maisi/scripts/diff_model_infer.py +++ b/generation/maisi/scripts/diff_model_infer.py @@ -21,12 +21,11 @@ import numpy as np import torch import torch.distributed as dist -from tqdm import tqdm - from monai.inferers import sliding_window_inference -from monai.utils import set_determinism -from monai.networks.schedulers import RFlowScheduler from monai.inferers.inferer import SlidingWindowInferer +from monai.networks.schedulers import RFlowScheduler +from monai.utils import set_determinism +from tqdm import tqdm from .diff_model_setting import initialize_distributed, load_config, setup_logging from .sample import ReconModel, check_input @@ -179,7 +178,7 @@ def run_inference( image, _ = noise_scheduler.step(model_output, t, image, next_t) # type: ignore inferer = SlidingWindowInferer( - roi_size=[80,80,80], + roi_size=[80, 80, 80], sw_batch_size=1, progress=True, mode="gaussian", diff --git a/generation/maisi/scripts/diff_model_setting.py b/generation/maisi/scripts/diff_model_setting.py index 6ba468886..3118b56d0 100644 --- a/generation/maisi/scripts/diff_model_setting.py +++ b/generation/maisi/scripts/diff_model_setting.py @@ -17,7 +17,6 @@ import torch import torch.distributed as dist - from monai.utils import RankFilter diff --git a/generation/maisi/scripts/diff_model_train.py b/generation/maisi/scripts/diff_model_train.py index ba2d269d7..1fd8b003d 100644 --- a/generation/maisi/scripts/diff_model_train.py +++ b/generation/maisi/scripts/diff_model_train.py @@ -18,17 +18,16 @@ from datetime import datetime from pathlib import Path +import monai import torch import torch.distributed as dist -from torch.amp import GradScaler, autocast -from torch.nn.parallel import DistributedDataParallel - -import monai from monai.data import DataLoader, partition_dataset -from monai.transforms import Compose -from monai.utils import first from monai.networks.schedulers import RFlowScheduler from monai.networks.schedulers.ddpm import DDPMPredictionType +from monai.transforms import Compose +from monai.utils import first +from torch.amp import GradScaler, autocast +from torch.nn.parallel import DistributedDataParallel from .diff_model_setting import initialize_distributed, load_config, setup_logging from .utils import define_instance diff --git a/generation/maisi/scripts/infer_controlnet.py b/generation/maisi/scripts/infer_controlnet.py index bfb5246c5..f43078375 100644 --- a/generation/maisi/scripts/infer_controlnet.py +++ b/generation/maisi/scripts/infer_controlnet.py @@ -18,7 +18,7 @@ import torch import torch.distributed as dist -from monai.data import decollate_batch, MetaTensor +from monai.data import MetaTensor, decollate_batch from monai.networks.utils import copy_model_state from monai.transforms import SaveImage from monai.utils import RankFilter diff --git a/generation/maisi/scripts/inference.py b/generation/maisi/scripts/inference.py index 968d5bf49..5d549c5c9 100644 --- a/generation/maisi/scripts/inference.py +++ b/generation/maisi/scripts/inference.py @@ -14,8 +14,8 @@ import json import logging import os -import tempfile import sys +import tempfile import monai import torch @@ -23,6 +23,7 @@ from monai.config import print_config from monai.transforms import LoadImage, Orientation from monai.utils import set_determinism + from scripts.sample import LDMSampler, check_input from scripts.utils import define_instance from scripts.utils_plot import find_label_center_loc, get_xyz_plot, show_image diff --git a/generation/maisi/scripts/sample.py b/generation/maisi/scripts/sample.py index c1e2c8699..2f934b392 100644 --- a/generation/maisi/scripts/sample.py +++ b/generation/maisi/scripts/sample.py @@ -19,17 +19,17 @@ import monai import torch -from monai.inferers.inferer import DiffusionInferer from monai.data import MetaTensor from monai.inferers import sliding_window_inference +from monai.inferers.inferer import DiffusionInferer from monai.transforms import Compose, SaveImage from monai.utils import set_determinism from tqdm import tqdm from .augmentation import augmentation from .find_masks import find_masks -from .utils import binarize_labels, general_mask_generation_post_process, get_body_region_index_from_mask, remap_labels from .quality_check import is_outlier +from .utils import binarize_labels, general_mask_generation_post_process, get_body_region_index_from_mask, remap_labels class ReconModel(torch.nn.Module): diff --git a/generation/maisi/scripts/utils.py b/generation/maisi/scripts/utils.py index 8086e6dff..dbcc292bd 100644 --- a/generation/maisi/scripts/utils.py +++ b/generation/maisi/scripts/utils.py @@ -11,9 +11,9 @@ import copy import json +import logging import math import os -import logging from argparse import Namespace from datetime import timedelta from typing import Any, Sequence @@ -22,11 +22,11 @@ import skimage import torch import torch.distributed as dist -from monai.transforms.utils_morphological_ops import dilate, erode from monai.bundle import ConfigParser from monai.config import DtypeLike, NdarrayOrTensor from monai.data import CacheDataset, DataLoader, partition_dataset from monai.transforms import Compose, EnsureTyped, Lambdad, LoadImaged, Orientationd +from monai.transforms.utils_morphological_ops import dilate, erode from monai.utils import TransformBackends, convert_data_type, convert_to_dst_type, get_equivalent_dtype from scipy import stats from torch import Tensor From 81ea271bea4fb0aad97d940383d1fcbb974604e2 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 11 Mar 2025 22:08:53 +0000 Subject: [PATCH 10/59] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- generation/maisi/maisi_diff_unet_training_tutorial.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/generation/maisi/maisi_diff_unet_training_tutorial.ipynb b/generation/maisi/maisi_diff_unet_training_tutorial.ipynb index 99c1e13a1..7b0e3d1c9 100644 --- a/generation/maisi/maisi_diff_unet_training_tutorial.ipynb +++ b/generation/maisi/maisi_diff_unet_training_tutorial.ipynb @@ -165,8 +165,8 @@ "metadata": {}, "outputs": [], "source": [ - "maisi_version = 'maisi-ddpm'\n", - "assert maisi_version in ['maisi-ddpm', 'maisi-rflow']" + "maisi_version = \"maisi-ddpm\"\n", + "assert maisi_version in [\"maisi-ddpm\", \"maisi-rflow\"]" ] }, { From a90cbd0b8e3dd4e2194ca5ff389632b72d508fe9 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Tue, 11 Mar 2025 22:26:16 +0000 Subject: [PATCH 11/59] reformat Signed-off-by: Can-Zhao --- .../maisi_diff_unet_training_tutorial.ipynb | 133 +++++++++--------- 1 file changed, 66 insertions(+), 67 deletions(-) diff --git a/generation/maisi/maisi_diff_unet_training_tutorial.ipynb b/generation/maisi/maisi_diff_unet_training_tutorial.ipynb index 7b0e3d1c9..17d9babbd 100644 --- a/generation/maisi/maisi_diff_unet_training_tutorial.ipynb +++ b/generation/maisi/maisi_diff_unet_training_tutorial.ipynb @@ -28,7 +28,7 @@ "\n", "In this notebook, we detail the procedure for training a 3D latent diffusion model to generate high-dimensional 3D medical images. Due to the potential for out-of-memory issues on most GPUs when generating large images (e.g., those with dimensions of 512 x 512 x 512 or greater), we have structured the training process into two primary steps: 1) generating image embeddings and 2) training 3D latent diffusion models. The subsequent sections will demonstrate the entire process using a simulated dataset.\n", "\n", - "`[Release Note (March 2025)]:` We are excited to announce the new MAISI Version `'maisi-rflow'`. Compared with the previous version `'maisi-ddpm'`, it accelerated latent diffusion model inference by 33x. Please see the detailed difference in the following section." + "`[Release Note (March 2025)]:` We are excited to announce the new MAISI Version `'maisi3d-rflow'`. Compared with the previous version `'maisi3d-ddpm'`, it accelerated latent diffusion model inference by 33x. Please see the detailed difference in the following section." ] }, { @@ -38,10 +38,10 @@ "source": [ "## Set up the MAISI version\n", "\n", - "Choose between `'maisi-ddpm'` and `'maisi-rflow'`. The differences are:\n", - "- The maisi version `'maisi-ddpm'` uses basic noise scheduler DDPM. `'maisi-rflow'` uses Rectified Flow scheduler, can be 33 times faster during inference.\n", - "- The maisi version `'maisi-ddpm'` requires training images to be labeled with body region (`\"top_region_index\"` and `\"bottom_region_index\"`), while `'maisi-rflow'` does not have such requirement. In other words, it is easier to prepare training data for `'maisi-rflow'`.\n", - "- For the released model weights, `'maisi-rflow'` can generate images with better quality for head region and small output volumes, and comparable quality for other cases compared with `'maisi-ddpm'`." + "Choose between `'maisi3d-ddpm'` and `'maisi3d-rflow'`. The differences are:\n", + "- The maisi version `'maisi3d-ddpm'` uses basic noise scheduler DDPM. `'maisi3d-rflow'` uses Rectified Flow scheduler, can be 33 times faster during inference.\n", + "- The maisi version `'maisi3d-ddpm'` requires training images to be labeled with body region (`\"top_region_index\"` and `\"bottom_region_index\"`), while `'maisi3d-rflow'` does not have such requirement. In other words, it is easier to prepare training data for `'maisi3d-rflow'`.\n", + "- For the released model weights, `'maisi3d-rflow'` can generate images with better quality for head region and small output volumes, and comparable quality for other cases compared with `'maisi3d-ddpm'`." ] }, { @@ -51,8 +51,8 @@ "metadata": {}, "outputs": [], "source": [ - "maisi_version = \"maisi-ddpm\"\n", - "assert maisi_version in [\"maisi-ddpm\", \"maisi-rflow\"]" + "maisi_version = \"maisi3d-ddpm\"\n", + "assert maisi_version in [\"maisi3d-ddpm\", \"maisi3d-rflow\"]" ] }, { @@ -131,13 +131,12 @@ "import numpy as np\n", "import nibabel as nib\n", "import subprocess\n", + "from IPython.display import Image, display\n", "\n", "from monai.apps import download_url\n", "from monai.data import create_test_image_3d\n", "from monai.config import print_config\n", "\n", - "from IPython.display import Image, display\n", - "\n", "from scripts.diff_model_setting import setup_logging\n", "\n", "print_config()\n", @@ -152,10 +151,10 @@ "source": [ "## Set up the MAISI version\n", "\n", - "Choose between `'maisi-ddpm'` and `'maisi-rflow'`. The differences are:\n", - "- The maisi version `'maisi-ddpm'` uses basic noise scheduler DDPM. `'maisi-rflow'` uses Rectified Flow scheduler, can be 33 times faster during inference.\n", - "- The maisi version `'maisi-ddpm'` requires training images to be labeled with body region (`\"top_region_index\"` and `\"bottom_region_index\"`), while `'maisi-rflow'` does not have such requirement. In other words, it is easier to prepare training data for `'maisi-rflow'`.\n", - "- For the released model weights, `'maisi-rflow'` can generate images with better quality for head region and small output volumes, and comparable quality for other cases compared with `'maisi-ddpm'`." + "Choose between `'maisi3d-ddpm'` and `'maisi3d-rflow'`. The differences are:\n", + "- The maisi version `'maisi3d-ddpm'` uses basic noise scheduler DDPM. `'maisi3d-rflow'` uses Rectified Flow scheduler, can be 33 times faster during inference.\n", + "- The maisi version `'maisi3d-ddpm'` requires training images to be labeled with body region (`\"top_region_index\"` and `\"bottom_region_index\"`), while `'maisi3d-rflow'` does not have such requirement. In other words, it is easier to prepare training data for `'maisi3d-rflow'`.\n", + "- For the released model weights, `'maisi3d-rflow'` can generate images with better quality for head region and small output volumes, and comparable quality for other cases compared with `'maisi3d-ddpm'`." ] }, { @@ -165,8 +164,8 @@ "metadata": {}, "outputs": [], "source": [ - "maisi_version = \"maisi-ddpm\"\n", - "assert maisi_version in [\"maisi-ddpm\", \"maisi-rflow\"]" + "maisi_version = \"maisi3d-ddpm\"\n", + "assert maisi_version in [\"maisi3d-ddpm\", \"maisi3d-rflow\"]" ] }, { @@ -213,7 +212,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 22:05:02.952][ INFO](notebook) - Generated simulated images.\n" + "[2025-03-11 22:16:41.000][ INFO](notebook) - Generated simulated images.\n" ] } ], @@ -260,22 +259,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 22:05:02.966][ INFO](notebook) - files and folders under work_dir: ['predictions', 'config_maisi.json', 'models', 'sim_dataroot', 'config_maisi_diff_model.json', 'embeddings', 'environment_maisi_diff_model.json', 'sim_datalist.json'].\n", - "[2025-03-11 22:05:02.966][ INFO](notebook) - number of GPUs: 1.\n" + "[2025-03-11 22:16:41.012][ INFO](notebook) - files and folders under work_dir: ['predictions', 'config_maisi.json', 'models', 'sim_dataroot', 'config_maisi_diff_model.json', 'embeddings', 'environment_maisi_diff_model.json', 'sim_datalist.json'].\n", + "[2025-03-11 22:16:41.012][ INFO](notebook) - number of GPUs: 1.\n" ] } ], "source": [ "env_config_path = \"./configs/environment_maisi_diff_model.json\"\n", "model_config_path = \"./configs/config_maisi_diff_model.json\"\n", - "if maisi_version == \"maisi-ddpm\":\n", - " model_def_path = \"./configs/config_maisi-ddpm.json\"\n", + "if maisi_version == \"maisi3d-ddpm\":\n", + " model_def_path = \"./configs/config_maisi3d-ddpm.json\"\n", " include_body_region = True\n", - "elif maisi_version == \"maisi-rflow\":\n", - " model_def_path = \"./configs/config_maisi-rflow.json\"\n", + "elif maisi_version == \"maisi3d-rflow\":\n", + " model_def_path = \"./configs/config_maisi3d-rflow.json\"\n", " include_body_region = False\n", "else:\n", - " raise ValueError(f\"maisi_version has to be chosen from ['maisi-ddpm', 'maisi-rflow'], yet got {maisi_version}.\")\n", + " raise ValueError(f\"maisi_version has to be chosen from ['maisi3d-ddpm', 'maisi3d-rflow'], yet got {maisi_version}.\")\n", "\n", "# Load environment configuration, model configuration and model definition\n", "with open(env_config_path, \"r\") as f:\n", @@ -407,7 +406,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 22:05:02.977][ INFO](notebook) - Creating training data...\n" + "[2025-03-11 22:16:41.021][ INFO](notebook) - Creating training data...\n" ] }, { @@ -415,8 +414,8 @@ "output_type": "stream", "text": [ "\n", - "[2025-03-11 22:05:10.881][ INFO](creating training data) - Using device cuda:0\n", - "[2025-03-11 22:05:11.686][ INFO](creating training data) - filenames_raw: ['tr_image_001.nii.gz', 'tr_image_002.nii.gz']\n", + "[2025-03-11 22:16:50.396][ INFO](creating training data) - Using device cuda:0\n", + "[2025-03-11 22:16:51.402][ INFO](creating training data) - filenames_raw: ['tr_image_001.nii.gz', 'tr_image_002.nii.gz']\n", "\n" ] } @@ -460,9 +459,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 22:05:13.881][ INFO](notebook) - data: {'dim': (64, 64, 32), 'spacing': [0.875, 0.875, 0.75], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}.\n", - "[2025-03-11 22:05:13.884][ INFO](notebook) - data: {'dim': (64, 64, 32), 'spacing': [0.875, 0.875, 0.75], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}.\n", - "[2025-03-11 22:05:13.885][ INFO](notebook) - Completed creating .json files for all embedding files.\n" + "[2025-03-11 22:16:53.638][ INFO](notebook) - data: {'dim': (64, 64, 32), 'spacing': [0.875, 0.875, 0.75], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}.\n", + "[2025-03-11 22:16:53.640][ INFO](notebook) - data: {'dim': (64, 64, 32), 'spacing': [0.875, 0.875, 0.75], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}.\n", + "[2025-03-11 22:16:53.641][ INFO](notebook) - Completed creating .json files for all embedding files.\n" ] } ], @@ -539,7 +538,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 22:05:13.892][ INFO](notebook) - Training the model...\n" + "[2025-03-11 22:16:53.646][ INFO](notebook) - Training the model...\n" ] }, { @@ -547,26 +546,26 @@ "output_type": "stream", "text": [ "\n", - "[2025-03-11 22:05:24.419][ INFO](training) - Using cuda:0 of 1\n", - "[2025-03-11 22:05:24.419][ INFO](training) - [config] ckpt_folder -> ./temp_work_dir/./models.\n", - "[2025-03-11 22:05:24.419][ INFO](training) - [config] data_root -> ./temp_work_dir/./embeddings.\n", - "[2025-03-11 22:05:24.419][ INFO](training) - [config] data_list -> ./temp_work_dir/sim_datalist.json.\n", - "[2025-03-11 22:05:24.419][ INFO](training) - [config] lr -> 0.0001.\n", - "[2025-03-11 22:05:24.419][ INFO](training) - [config] num_epochs -> 2.\n", - "[2025-03-11 22:05:24.419][ INFO](training) - [config] num_train_timesteps -> 1000.\n", - "[2025-03-11 22:05:24.420][ INFO](training) - num_files_train: 2\n", - "[2025-03-11 22:05:26.152][ INFO](training) - Training from scratch.\n", - "[2025-03-11 22:05:26.539][ INFO](training) - Scaling factor set to 1.159977912902832.\n", - "[2025-03-11 22:05:26.539][ INFO](training) - scale_factor -> 1.159977912902832.\n", - "[2025-03-11 22:05:26.542][ INFO](training) - torch.set_float32_matmul_precision -> highest.\n", - "[2025-03-11 22:05:26.542][ INFO](training) - Epoch 1, lr 0.0001.\n", - "[2025-03-11 22:05:28.578][ INFO](training) - [2025-03-11 22:05:28] epoch 1, iter 1/2, loss: 0.7974, lr: 0.000100000000.\n", - "[2025-03-11 22:05:28.719][ INFO](training) - [2025-03-11 22:05:28] epoch 1, iter 2/2, loss: 0.7943, lr: 0.000056250000.\n", - "[2025-03-11 22:05:28.762][ INFO](training) - epoch 1 average loss: 0.7958.\n", - "[2025-03-11 22:05:30.615][ INFO](training) - Epoch 2, lr 2.5e-05.\n", - "[2025-03-11 22:05:31.002][ INFO](training) - [2025-03-11 22:05:31] epoch 2, iter 1/2, loss: 0.7898, lr: 0.000025000000.\n", - "[2025-03-11 22:05:31.105][ INFO](training) - [2025-03-11 22:05:31] epoch 2, iter 2/2, loss: 0.7886, lr: 0.000006250000.\n", - "[2025-03-11 22:05:31.168][ INFO](training) - epoch 2 average loss: 0.7892.\n", + "[2025-03-11 22:17:02.004][ INFO](training) - Using cuda:0 of 1\n", + "[2025-03-11 22:17:02.004][ INFO](training) - [config] ckpt_folder -> ./temp_work_dir/./models.\n", + "[2025-03-11 22:17:02.004][ INFO](training) - [config] data_root -> ./temp_work_dir/./embeddings.\n", + "[2025-03-11 22:17:02.004][ INFO](training) - [config] data_list -> ./temp_work_dir/sim_datalist.json.\n", + "[2025-03-11 22:17:02.004][ INFO](training) - [config] lr -> 0.0001.\n", + "[2025-03-11 22:17:02.004][ INFO](training) - [config] num_epochs -> 2.\n", + "[2025-03-11 22:17:02.004][ INFO](training) - [config] num_train_timesteps -> 1000.\n", + "[2025-03-11 22:17:02.005][ INFO](training) - num_files_train: 2\n", + "[2025-03-11 22:17:03.887][ INFO](training) - Training from scratch.\n", + "[2025-03-11 22:17:04.338][ INFO](training) - Scaling factor set to 1.159977912902832.\n", + "[2025-03-11 22:17:04.339][ INFO](training) - scale_factor -> 1.159977912902832.\n", + "[2025-03-11 22:17:04.341][ INFO](training) - torch.set_float32_matmul_precision -> highest.\n", + "[2025-03-11 22:17:04.341][ INFO](training) - Epoch 1, lr 0.0001.\n", + "[2025-03-11 22:17:05.278][ INFO](training) - [2025-03-11 22:17:05] epoch 1, iter 1/2, loss: 0.7973, lr: 0.000100000000.\n", + "[2025-03-11 22:17:05.673][ INFO](training) - [2025-03-11 22:17:05] epoch 1, iter 2/2, loss: 0.7969, lr: 0.000056250000.\n", + "[2025-03-11 22:17:05.718][ INFO](training) - epoch 1 average loss: 0.7971.\n", + "[2025-03-11 22:17:07.383][ INFO](training) - Epoch 2, lr 2.5e-05.\n", + "[2025-03-11 22:17:07.777][ INFO](training) - [2025-03-11 22:17:07] epoch 2, iter 1/2, loss: 0.7932, lr: 0.000025000000.\n", + "[2025-03-11 22:17:07.881][ INFO](training) - [2025-03-11 22:17:07] epoch 2, iter 2/2, loss: 0.7904, lr: 0.000006250000.\n", + "[2025-03-11 22:17:07.942][ INFO](training) - epoch 2 average loss: 0.7918.\n", "\n" ] } @@ -612,8 +611,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 22:05:35.033][ INFO](notebook) - Running inference...\n", - "[2025-03-11 22:05:50.259][ INFO](notebook) - Completed all steps.\n" + "[2025-03-11 22:17:11.993][ INFO](notebook) - Running inference...\n", + "[2025-03-11 22:17:27.730][ INFO](notebook) - Completed all steps.\n" ] }, { @@ -621,23 +620,23 @@ "output_type": "stream", "text": [ "\n", - "[2025-03-11 22:05:43.502][ INFO](inference) - Using cuda:0 of 1 with random seed: 7854\n", - "[2025-03-11 22:05:43.502][ INFO](inference) - [config] ckpt_filepath -> ./temp_work_dir/./models/diff_unet_ckpt.pt.\n", - "[2025-03-11 22:05:43.502][ INFO](inference) - [config] random_seed -> 7854.\n", - "[2025-03-11 22:05:43.502][ INFO](inference) - [config] output_prefix -> unet_3d.\n", - "[2025-03-11 22:05:43.502][ INFO](inference) - [config] output_size -> (256, 256, 128).\n", - "[2025-03-11 22:05:43.502][ INFO](inference) - [config] out_spacing -> (1.0, 1.0, 0.75).\n", - "[2025-03-11 22:05:43.502][ INFO](root) - `controllable_anatomy_size` is not provided.\n", - "[2025-03-11 22:05:45.793][ INFO](inference) - checkpoints ./temp_work_dir/./models/diff_unet_ckpt.pt loaded.\n", - "[2025-03-11 22:05:45.795][ INFO](inference) - scale_factor -> 1.159977912902832.\n", - "[2025-03-11 22:05:45.796][ INFO](inference) - num_downsample_level -> 4, divisor -> 4.\n", - "[2025-03-11 22:05:45.798][ INFO](inference) - noise: cuda:0, torch.float32, \n", + "[2025-03-11 22:17:20.465][ INFO](inference) - Using cuda:0 of 1 with random seed: 23141\n", + "[2025-03-11 22:17:20.466][ INFO](inference) - [config] ckpt_filepath -> ./temp_work_dir/./models/diff_unet_ckpt.pt.\n", + "[2025-03-11 22:17:20.466][ INFO](inference) - [config] random_seed -> 23141.\n", + "[2025-03-11 22:17:20.466][ INFO](inference) - [config] output_prefix -> unet_3d.\n", + "[2025-03-11 22:17:20.466][ INFO](inference) - [config] output_size -> (256, 256, 128).\n", + "[2025-03-11 22:17:20.466][ INFO](inference) - [config] out_spacing -> (1.0, 1.0, 0.75).\n", + "[2025-03-11 22:17:20.466][ INFO](root) - `controllable_anatomy_size` is not provided.\n", + "[2025-03-11 22:17:23.065][ INFO](inference) - checkpoints ./temp_work_dir/./models/diff_unet_ckpt.pt loaded.\n", + "[2025-03-11 22:17:23.067][ INFO](inference) - scale_factor -> 1.159977912902832.\n", + "[2025-03-11 22:17:23.068][ INFO](inference) - num_downsample_level -> 4, divisor -> 4.\n", + "[2025-03-11 22:17:23.070][ INFO](inference) - noise: cuda:0, torch.float32, \n", "\n", " 0%| | 0/10 [00:00 Date: Tue, 11 Mar 2025 22:41:48 +0000 Subject: [PATCH 12/59] reformat Signed-off-by: Can-Zhao --- .../maisi/configs/config_maisi3d-ddpm.json | 153 ++++++++++++++++++ .../maisi/configs/config_maisi3d-rflow.json | 149 +++++++++++++++++ 2 files changed, 302 insertions(+) create mode 100644 generation/maisi/configs/config_maisi3d-ddpm.json create mode 100644 generation/maisi/configs/config_maisi3d-rflow.json diff --git a/generation/maisi/configs/config_maisi3d-ddpm.json b/generation/maisi/configs/config_maisi3d-ddpm.json new file mode 100644 index 000000000..8a781ca5b --- /dev/null +++ b/generation/maisi/configs/config_maisi3d-ddpm.json @@ -0,0 +1,153 @@ +{ + "spatial_dims": 3, + "image_channels": 1, + "latent_channels": 4, + "mask_generation_latent_shape": [ + 4, + 64, + 64, + 64 + ], + "autoencoder_def": { + "_target_": "monai.apps.generation.maisi.networks.autoencoderkl_maisi.AutoencoderKlMaisi", + "spatial_dims": "@spatial_dims", + "in_channels": "@image_channels", + "out_channels": "@image_channels", + "latent_channels": "@latent_channels", + "num_channels": [ + 64, + 128, + 256 + ], + "num_res_blocks": [2,2,2], + "norm_num_groups": 32, + "norm_eps": 1e-06, + "attention_levels": [ + false, + false, + false + ], + "with_encoder_nonlocal_attn": false, + "with_decoder_nonlocal_attn": false, + "use_checkpointing": false, + "use_convtranspose": false, + "norm_float16": true, + "num_splits": 8, + "dim_split": 1 + }, + "diffusion_unet_def": { + "_target_": "monai.apps.generation.maisi.networks.diffusion_model_unet_maisi.DiffusionModelUNetMaisi", + "spatial_dims": "@spatial_dims", + "in_channels": "@latent_channels", + "out_channels": "@latent_channels", + "num_channels": [ + 64, + 128, + 256, + 512 + ], + "attention_levels": [ + false, + false, + true, + true + ], + "num_head_channels": [ + 0, + 0, + 32, + 32 + ], + "num_res_blocks": 2, + "use_flash_attention": true, + "include_top_region_index_input": true, + "include_bottom_region_index_input": true, + "include_spacing_input": true + }, + "controlnet_def": { + "_target_": "monai.apps.generation.maisi.networks.controlnet_maisi.ControlNetMaisi", + "spatial_dims": "@spatial_dims", + "in_channels": "@latent_channels", + "num_channels": [ + 64, + 128, + 256, + 512 + ], + "attention_levels": [ + false, + false, + true, + true + ], + "num_head_channels": [ + 0, + 0, + 32, + 32 + ], + "num_res_blocks": 2, + "use_flash_attention": true, + "conditioning_embedding_in_channels": 8, + "conditioning_embedding_num_channels": [8, 32, 64] + }, + "mask_generation_autoencoder_def": { + "_target_": "monai.apps.generation.maisi.networks.autoencoderkl_maisi.AutoencoderKlMaisi", + "spatial_dims": "@spatial_dims", + "in_channels": 8, + "out_channels": 125, + "latent_channels": "@latent_channels", + "num_channels": [ + 32, + 64, + 128 + ], + "num_res_blocks": [1, 2, 2], + "norm_num_groups": 32, + "norm_eps": 1e-06, + "attention_levels": [ + false, + false, + false + ], + "with_encoder_nonlocal_attn": false, + "with_decoder_nonlocal_attn": false, + "use_flash_attention": false, + "use_checkpointing": true, + "use_convtranspose": true, + "norm_float16": true, + "num_splits": 8, + "dim_split": 1 + }, + "mask_generation_diffusion_def": { + "_target_": "monai.networks.nets.diffusion_model_unet.DiffusionModelUNet", + "spatial_dims": "@spatial_dims", + "in_channels": "@latent_channels", + "out_channels": "@latent_channels", + "channels":[64, 128, 256, 512], + "attention_levels":[false, false, true, true], + "num_head_channels":[0, 0, 32, 32], + "num_res_blocks": 2, + "use_flash_attention": true, + "with_conditioning": true, + "upcast_attention": true, + "cross_attention_dim": 10 + }, + "mask_generation_scale_factor": 1.0055984258651733, + "noise_scheduler": { + "_target_": "monai.networks.schedulers.ddpm.DDPMScheduler", + "num_train_timesteps": 1000, + "beta_start": 0.0015, + "beta_end": 0.0195, + "schedule": "scaled_linear_beta", + "clip_sample": false + }, + "mask_generation_noise_scheduler": { + "_target_": "monai.networks.schedulers.ddpm.DDPMScheduler", + "num_train_timesteps": 1000, + "beta_start": 0.0015, + "beta_end": 0.0195, + "schedule": "scaled_linear_beta", + "clip_sample": false + } +} diff --git a/generation/maisi/configs/config_maisi3d-rflow.json b/generation/maisi/configs/config_maisi3d-rflow.json new file mode 100644 index 000000000..56d769284 --- /dev/null +++ b/generation/maisi/configs/config_maisi3d-rflow.json @@ -0,0 +1,149 @@ +{ + "spatial_dims": 3, + "image_channels": 1, + "latent_channels": 4, + "mask_generation_latent_shape": [ + 4, + 64, + 64, + 64 + ], + "autoencoder_def": { + "_target_": "monai.apps.generation.maisi.networks.autoencoderkl_maisi.AutoencoderKlMaisi", + "spatial_dims": "@spatial_dims", + "in_channels": "@image_channels", + "out_channels": "@image_channels", + "latent_channels": "@latent_channels", + "num_channels": [ + 64, + 128, + 256 + ], + "num_res_blocks": [2,2,2], + "norm_num_groups": 32, + "norm_eps": 1e-06, + "attention_levels": [ + false, + false, + false + ], + "with_encoder_nonlocal_attn": false, + "with_decoder_nonlocal_attn": false, + "use_checkpointing": false, + "use_convtranspose": false, + "norm_float16": true, + "num_splits": 4, + "dim_split": 1 + }, + "diffusion_unet_def": { + "_target_": "monai.apps.generation.maisi.networks.diffusion_model_unet_maisi.DiffusionModelUNetMaisi", + "spatial_dims": "@spatial_dims", + "in_channels": "@latent_channels", + "out_channels": "@latent_channels", + "num_channels": [64, 128, 256, 512], + "attention_levels": [ + false, + false, + true, + true + ], + "num_head_channels": [ + 0, + 0, + 32, + 32 + ], + "num_res_blocks": 2, + "use_flash_attention": true, + "include_top_region_index_input": false, + "include_bottom_region_index_input": false, + "include_spacing_input": true, + "num_class_embeds": 128, + "resblock_updown": true, + "include_fc": true + }, + "controlnet_def": { + "_target_": "monai.apps.generation.maisi.networks.controlnet_maisi.ControlNetMaisi", + "spatial_dims": "@spatial_dims", + "in_channels": "@latent_channels", + "num_channels": [64, 128, 256, 512], + "attention_levels": [ + false, + false, + true, + true + ], + "num_head_channels": [ + 0, + 0, + 32, + 32 + ], + "num_res_blocks": 2, + "use_flash_attention": true, + "conditioning_embedding_in_channels": 8, + "conditioning_embedding_num_channels": [8, 32, 64], + "num_class_embeds": 128, + "resblock_updown": true, + "include_fc": true + }, + "mask_generation_autoencoder_def": { + "_target_": "monai.apps.generation.maisi.networks.autoencoderkl_maisi.AutoencoderKlMaisi", + "spatial_dims": "@spatial_dims", + "in_channels": 8, + "out_channels": 125, + "latent_channels": "@latent_channels", + "num_channels": [ + 32, + 64, + 128 + ], + "num_res_blocks": [1, 2, 2], + "norm_num_groups": 32, + "norm_eps": 1e-06, + "attention_levels": [ + false, + false, + false + ], + "with_encoder_nonlocal_attn": false, + "with_decoder_nonlocal_attn": false, + "use_flash_attention": false, + "use_checkpointing": true, + "use_convtranspose": true, + "norm_float16": true, + "num_splits": 8, + "dim_split": 1 + }, + "mask_generation_diffusion_def": { + "_target_": "monai.networks.nets.diffusion_model_unet.DiffusionModelUNet", + "spatial_dims": "@spatial_dims", + "in_channels": "@latent_channels", + "out_channels": "@latent_channels", + "channels":[64, 128, 256, 512], + "attention_levels":[false, false, true, true], + "num_head_channels":[0, 0, 32, 32], + "num_res_blocks": 2, + "use_flash_attention": true, + "with_conditioning": true, + "upcast_attention": true, + "cross_attention_dim": 10 + }, + "mask_generation_scale_factor": 1.0055984258651733, + "noise_scheduler": { + "_target_": "monai.networks.schedulers.rectified_flow.RFlowScheduler", + "num_train_timesteps": 1000, + "use_discrete_timesteps": false, + "use_timestep_transform": true, + "sample_method": "uniform", + "scale":1.4 + }, + "mask_generation_noise_scheduler": { + "_target_": "monai.networks.schedulers.ddpm.DDPMScheduler", + "num_train_timesteps": 1000, + "beta_start": 0.0015, + "beta_end": 0.0195, + "schedule": "scaled_linear_beta", + "clip_sample": false + } +} From 198399c9626269500be4bf846f5cf7caa2dc0ec3 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Tue, 11 Mar 2025 22:42:16 +0000 Subject: [PATCH 13/59] reformat Signed-off-by: Can-Zhao --- .../maisi/configs/config_maisi-ddpm.json | 153 ------------------ .../maisi/configs/config_maisi-rflow.json | 149 ----------------- 2 files changed, 302 deletions(-) delete mode 100644 generation/maisi/configs/config_maisi-ddpm.json delete mode 100644 generation/maisi/configs/config_maisi-rflow.json diff --git a/generation/maisi/configs/config_maisi-ddpm.json b/generation/maisi/configs/config_maisi-ddpm.json deleted file mode 100644 index 8a781ca5b..000000000 --- a/generation/maisi/configs/config_maisi-ddpm.json +++ /dev/null @@ -1,153 +0,0 @@ -{ - "spatial_dims": 3, - "image_channels": 1, - "latent_channels": 4, - "mask_generation_latent_shape": [ - 4, - 64, - 64, - 64 - ], - "autoencoder_def": { - "_target_": "monai.apps.generation.maisi.networks.autoencoderkl_maisi.AutoencoderKlMaisi", - "spatial_dims": "@spatial_dims", - "in_channels": "@image_channels", - "out_channels": "@image_channels", - "latent_channels": "@latent_channels", - "num_channels": [ - 64, - 128, - 256 - ], - "num_res_blocks": [2,2,2], - "norm_num_groups": 32, - "norm_eps": 1e-06, - "attention_levels": [ - false, - false, - false - ], - "with_encoder_nonlocal_attn": false, - "with_decoder_nonlocal_attn": false, - "use_checkpointing": false, - "use_convtranspose": false, - "norm_float16": true, - "num_splits": 8, - "dim_split": 1 - }, - "diffusion_unet_def": { - "_target_": "monai.apps.generation.maisi.networks.diffusion_model_unet_maisi.DiffusionModelUNetMaisi", - "spatial_dims": "@spatial_dims", - "in_channels": "@latent_channels", - "out_channels": "@latent_channels", - "num_channels": [ - 64, - 128, - 256, - 512 - ], - "attention_levels": [ - false, - false, - true, - true - ], - "num_head_channels": [ - 0, - 0, - 32, - 32 - ], - "num_res_blocks": 2, - "use_flash_attention": true, - "include_top_region_index_input": true, - "include_bottom_region_index_input": true, - "include_spacing_input": true - }, - "controlnet_def": { - "_target_": "monai.apps.generation.maisi.networks.controlnet_maisi.ControlNetMaisi", - "spatial_dims": "@spatial_dims", - "in_channels": "@latent_channels", - "num_channels": [ - 64, - 128, - 256, - 512 - ], - "attention_levels": [ - false, - false, - true, - true - ], - "num_head_channels": [ - 0, - 0, - 32, - 32 - ], - "num_res_blocks": 2, - "use_flash_attention": true, - "conditioning_embedding_in_channels": 8, - "conditioning_embedding_num_channels": [8, 32, 64] - }, - "mask_generation_autoencoder_def": { - "_target_": "monai.apps.generation.maisi.networks.autoencoderkl_maisi.AutoencoderKlMaisi", - "spatial_dims": "@spatial_dims", - "in_channels": 8, - "out_channels": 125, - "latent_channels": "@latent_channels", - "num_channels": [ - 32, - 64, - 128 - ], - "num_res_blocks": [1, 2, 2], - "norm_num_groups": 32, - "norm_eps": 1e-06, - "attention_levels": [ - false, - false, - false - ], - "with_encoder_nonlocal_attn": false, - "with_decoder_nonlocal_attn": false, - "use_flash_attention": false, - "use_checkpointing": true, - "use_convtranspose": true, - "norm_float16": true, - "num_splits": 8, - "dim_split": 1 - }, - "mask_generation_diffusion_def": { - "_target_": "monai.networks.nets.diffusion_model_unet.DiffusionModelUNet", - "spatial_dims": "@spatial_dims", - "in_channels": "@latent_channels", - "out_channels": "@latent_channels", - "channels":[64, 128, 256, 512], - "attention_levels":[false, false, true, true], - "num_head_channels":[0, 0, 32, 32], - "num_res_blocks": 2, - "use_flash_attention": true, - "with_conditioning": true, - "upcast_attention": true, - "cross_attention_dim": 10 - }, - "mask_generation_scale_factor": 1.0055984258651733, - "noise_scheduler": { - "_target_": "monai.networks.schedulers.ddpm.DDPMScheduler", - "num_train_timesteps": 1000, - "beta_start": 0.0015, - "beta_end": 0.0195, - "schedule": "scaled_linear_beta", - "clip_sample": false - }, - "mask_generation_noise_scheduler": { - "_target_": "monai.networks.schedulers.ddpm.DDPMScheduler", - "num_train_timesteps": 1000, - "beta_start": 0.0015, - "beta_end": 0.0195, - "schedule": "scaled_linear_beta", - "clip_sample": false - } -} diff --git a/generation/maisi/configs/config_maisi-rflow.json b/generation/maisi/configs/config_maisi-rflow.json deleted file mode 100644 index 56d769284..000000000 --- a/generation/maisi/configs/config_maisi-rflow.json +++ /dev/null @@ -1,149 +0,0 @@ -{ - "spatial_dims": 3, - "image_channels": 1, - "latent_channels": 4, - "mask_generation_latent_shape": [ - 4, - 64, - 64, - 64 - ], - "autoencoder_def": { - "_target_": "monai.apps.generation.maisi.networks.autoencoderkl_maisi.AutoencoderKlMaisi", - "spatial_dims": "@spatial_dims", - "in_channels": "@image_channels", - "out_channels": "@image_channels", - "latent_channels": "@latent_channels", - "num_channels": [ - 64, - 128, - 256 - ], - "num_res_blocks": [2,2,2], - "norm_num_groups": 32, - "norm_eps": 1e-06, - "attention_levels": [ - false, - false, - false - ], - "with_encoder_nonlocal_attn": false, - "with_decoder_nonlocal_attn": false, - "use_checkpointing": false, - "use_convtranspose": false, - "norm_float16": true, - "num_splits": 4, - "dim_split": 1 - }, - "diffusion_unet_def": { - "_target_": "monai.apps.generation.maisi.networks.diffusion_model_unet_maisi.DiffusionModelUNetMaisi", - "spatial_dims": "@spatial_dims", - "in_channels": "@latent_channels", - "out_channels": "@latent_channels", - "num_channels": [64, 128, 256, 512], - "attention_levels": [ - false, - false, - true, - true - ], - "num_head_channels": [ - 0, - 0, - 32, - 32 - ], - "num_res_blocks": 2, - "use_flash_attention": true, - "include_top_region_index_input": false, - "include_bottom_region_index_input": false, - "include_spacing_input": true, - "num_class_embeds": 128, - "resblock_updown": true, - "include_fc": true - }, - "controlnet_def": { - "_target_": "monai.apps.generation.maisi.networks.controlnet_maisi.ControlNetMaisi", - "spatial_dims": "@spatial_dims", - "in_channels": "@latent_channels", - "num_channels": [64, 128, 256, 512], - "attention_levels": [ - false, - false, - true, - true - ], - "num_head_channels": [ - 0, - 0, - 32, - 32 - ], - "num_res_blocks": 2, - "use_flash_attention": true, - "conditioning_embedding_in_channels": 8, - "conditioning_embedding_num_channels": [8, 32, 64], - "num_class_embeds": 128, - "resblock_updown": true, - "include_fc": true - }, - "mask_generation_autoencoder_def": { - "_target_": "monai.apps.generation.maisi.networks.autoencoderkl_maisi.AutoencoderKlMaisi", - "spatial_dims": "@spatial_dims", - "in_channels": 8, - "out_channels": 125, - "latent_channels": "@latent_channels", - "num_channels": [ - 32, - 64, - 128 - ], - "num_res_blocks": [1, 2, 2], - "norm_num_groups": 32, - "norm_eps": 1e-06, - "attention_levels": [ - false, - false, - false - ], - "with_encoder_nonlocal_attn": false, - "with_decoder_nonlocal_attn": false, - "use_flash_attention": false, - "use_checkpointing": true, - "use_convtranspose": true, - "norm_float16": true, - "num_splits": 8, - "dim_split": 1 - }, - "mask_generation_diffusion_def": { - "_target_": "monai.networks.nets.diffusion_model_unet.DiffusionModelUNet", - "spatial_dims": "@spatial_dims", - "in_channels": "@latent_channels", - "out_channels": "@latent_channels", - "channels":[64, 128, 256, 512], - "attention_levels":[false, false, true, true], - "num_head_channels":[0, 0, 32, 32], - "num_res_blocks": 2, - "use_flash_attention": true, - "with_conditioning": true, - "upcast_attention": true, - "cross_attention_dim": 10 - }, - "mask_generation_scale_factor": 1.0055984258651733, - "noise_scheduler": { - "_target_": "monai.networks.schedulers.rectified_flow.RFlowScheduler", - "num_train_timesteps": 1000, - "use_discrete_timesteps": false, - "use_timestep_transform": true, - "sample_method": "uniform", - "scale":1.4 - }, - "mask_generation_noise_scheduler": { - "_target_": "monai.networks.schedulers.ddpm.DDPMScheduler", - "num_train_timesteps": 1000, - "beta_start": 0.0015, - "beta_end": 0.0195, - "schedule": "scaled_linear_beta", - "clip_sample": false - } -} From e3eb5855db1ca7d872dd1bae8d431f4e80ed521d Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Tue, 11 Mar 2025 23:20:42 +0000 Subject: [PATCH 14/59] add controlnet notebook Signed-off-by: Can-Zhao --- .../maisi_train_controlnet_tutorial.ipynb | 207 +++++++++++------- ...b => maisi_train_diff_unet_tutorial.ipynb} | 163 +++++++------- 2 files changed, 202 insertions(+), 168 deletions(-) rename generation/maisi/{maisi_diff_unet_training_tutorial.ipynb => maisi_train_diff_unet_tutorial.ipynb} (98%) diff --git a/generation/maisi/maisi_train_controlnet_tutorial.ipynb b/generation/maisi/maisi_train_controlnet_tutorial.ipynb index 37becc2e4..2ece732ea 100644 --- a/generation/maisi/maisi_train_controlnet_tutorial.ipynb +++ b/generation/maisi/maisi_train_controlnet_tutorial.ipynb @@ -26,7 +26,9 @@ "\n", "![Generated image examples and input mask](https://developer.download.nvidia.com/assets/Clara/Images/monai_maisi_ct_generative_example_synthetic_data.png)\n", "\n", - "In this notebook, we detail the procedure for training a 3D ControlNet to generate high-dimensional 3D medical images. Due to the potential for out-of-memory issues on most GPUs when generating large images (e.g., those with dimensions of 512 x 512 x 512 or greater), we have structured the training process into two primary steps: 1) preparing training data, 2) training config preparation, and 3) launch training of 3D ControlNet. The subsequent sections will demonstrate the entire process using a simulated dataset. We also provide the real preprocessed dataset used in the finetuning config `environment_maisi_controlnet_train.json`. More instructions about how to preprocess real data can be found in the [README](./data/README.md) in `data` folder.\n" + "In this notebook, we detail the procedure for training a 3D ControlNet to generate high-dimensional 3D medical images. Due to the potential for out-of-memory issues on most GPUs when generating large images (e.g., those with dimensions of 512 x 512 x 512 or greater), we have structured the training process into two primary steps: 1) preparing training data, 2) training config preparation, and 3) launch training of 3D ControlNet. The subsequent sections will demonstrate the entire process using a simulated dataset. We also provide the real preprocessed dataset used in the finetuning config `environment_maisi_controlnet_train.json`. More instructions about how to preprocess real data can be found in the [README](./data/README.md) in `data` folder.\n", + "\n", + "`[Release Note (March 2025)]:` We are excited to announce the new MAISI Version `'maisi3d-rflow'`. Compared with the previous version `'maisi3d-ddpm'`, it accelerated latent diffusion model inference by 33x. Please see the detailed difference in the following section." ] }, { @@ -57,46 +59,38 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "e3bf0346", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - }, { "name": "stdout", "output_type": "stream", "text": [ - "MONAI version: 1.4.0rc12\n", - "Numpy version: 1.24.4\n", - "Pytorch version: 2.5.0a0+872d972e41.nv24.08\n", + "MONAI version: 1.4.1rc1+31.g90de55b1\n", + "Numpy version: 1.26.4\n", + "Pytorch version: 2.5.0+cu124\n", "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n", - "MONAI rev id: 76ef9f40c8da626928238c91eacddc789b0b4530\n", - "MONAI __file__: /workspace/Code/MONAI/monai/__init__.py\n", + "MONAI rev id: 90de55b1340ffc8eccf960d111229535bcb2f81d\n", + "MONAI __file__: /localhome//.local/lib/python3.10/site-packages/monai/__init__.py\n", "\n", "Optional dependencies:\n", "Pytorch Ignite version: 0.4.11\n", "ITK version: 5.4.0\n", - "Nibabel version: 5.2.1\n", - "scikit-image version: 0.23.2\n", - "scipy version: 1.14.0\n", - "Pillow version: 10.4.0\n", - "Tensorboard version: 2.16.2\n", + "Nibabel version: 5.3.2\n", + "scikit-image version: 0.24.0\n", + "scipy version: 1.14.1\n", + "Pillow version: 11.0.0\n", + "Tensorboard version: 2.18.0\n", "gdown version: 5.2.0\n", - "TorchVision version: 0.20.0a0\n", + "TorchVision version: 0.20.0+cu124\n", "tqdm version: 4.66.5\n", "lmdb version: 1.5.1\n", - "psutil version: 6.0.0\n", - "pandas version: 2.2.2\n", + "psutil version: 6.1.0\n", + "pandas version: 2.2.3\n", "einops version: 0.8.0\n", "transformers version: 4.40.2\n", - "mlflow version: 2.16.2\n", + "mlflow version: 2.17.1\n", "pynrrd version: 1.0.0\n", "clearml version: 1.16.5rc2\n", "\n", @@ -124,6 +118,45 @@ "logger = setup_logging(\"notebook\")" ] }, + { + "cell_type": "markdown", + "id": "b16b92a4-2039-42b7-bf77-68851d25701b", + "metadata": {}, + "source": [ + "## Set up the MAISI version\n", + "\n", + "Choose between `'maisi3d-ddpm'` and `'maisi3d-rflow'`. The differences are:\n", + "- The maisi version `'maisi3d-ddpm'` uses basic noise scheduler DDPM. `'maisi3d-rflow'` uses Rectified Flow scheduler, can be 33 times faster during inference.\n", + "- The maisi version `'maisi3d-ddpm'` requires training images to be labeled with body region (`\"top_region_index\"` and `\"bottom_region_index\"`), while `'maisi3d-rflow'` does not have such requirement. In other words, it is easier to prepare training data for `'maisi3d-rflow'`.\n", + "- For the released model weights, `'maisi3d-rflow'` can generate images with better quality for head region and small output volumes, and comparable quality for other cases compared with `'maisi3d-ddpm'`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3d233abe-d69c-4b57-9655-33c2c3da6c96", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2025-03-11 23:19:19.279][ INFO](notebook) - Using MAISI version maisi3d-ddpm. Will need body region as data input.\n" + ] + } + ], + "source": [ + "maisi_version = \"maisi3d-ddpm\"\n", + "if maisi_version == \"maisi3d-ddpm\":\n", + " include_body_region = True\n", + " logger.info(\"Using MAISI version maisi3d-ddpm. Will need body region as data input.\")\n", + "elif maisi_version == \"maisi3d-rflow\":\n", + " include_body_region = False\n", + " logger.info(\"Using MAISI version maisi3d-rflow. Does not need body region as data input.\")\n", + "else:\n", + " raise ValueError(f\"maisi_version has to be chosen from ['maisi3d-ddpm', 'maisi3d-rflow'], yet got {maisi_version}.\")" + ] + }, { "cell_type": "markdown", "id": "671e7f10", @@ -154,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "id": "fc32a7fe", "metadata": {}, "outputs": [], @@ -169,8 +202,6 @@ " \"fold\": 0, # fold index for cross validation, fold 0 is used for training\n", " \"dim\": sim_dim, # the dimension of image\n", " \"spacing\": [1.5, 1.5, 1.5], # the spacing of image\n", - " \"top_region_index\": [0, 1, 0, 0], # the top region index of the image\n", - " \"bottom_region_index\": [0, 0, 0, 1], # the bottom region index of the image\n", " },\n", " {\n", " \"image\": \"tr_image_002_emb.nii.gz\",\n", @@ -178,8 +209,6 @@ " \"fold\": 1,\n", " \"dim\": sim_dim,\n", " \"spacing\": [1.5, 1.5, 1.5],\n", - " \"top_region_index\": [0, 1, 0, 0],\n", - " \"bottom_region_index\": [0, 0, 0, 1],\n", " },\n", " {\n", " \"image\": \"tr_image_003_emb.nii.gz\",\n", @@ -187,11 +216,14 @@ " \"fold\": 1,\n", " \"dim\": sim_dim,\n", " \"spacing\": [1.5, 1.5, 1.5],\n", - " \"top_region_index\": [0, 1, 0, 0],\n", - " \"bottom_region_index\": [0, 0, 0, 1],\n", " },\n", " ]\n", - "}" + "}\n", + "if include_body_region:\n", + " for i in range(len(sim_datalist[\"training\"])):\n", + " # body region index\n", + " sim_datalist[\"training\"][i][\"top_region_index\"] = [0, 1, 0, 0] # the top region index of the image\n", + " sim_datalist[\"training\"][i][\"bottom_region_index\"] = [0, 0, 0, 1] # the bottom region index of the image" ] }, { @@ -206,7 +238,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "id": "1b199078", "metadata": {}, "outputs": [ @@ -214,9 +246,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:notebook:Generated simulated images.\n", - "INFO:notebook:img_emb shape: (64, 64, 32, 4)\n", - "INFO:notebook:label shape: (256, 256, 128)\n" + "[2025-03-11 23:19:21.615][ INFO](notebook) - Generated simulated images.\n", + "[2025-03-11 23:19:21.616][ INFO](notebook) - img_emb shape: (64, 64, 32, 4)\n", + "[2025-03-11 23:19:21.617][ INFO](notebook) - label shape: (256, 256, 128)\n" ] } ], @@ -280,7 +312,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "id": "6c7b434c", "metadata": {}, "outputs": [ @@ -288,15 +320,20 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:notebook:files and folders under work_dir: ['config_maisi.json', 'sim_dataroot', 'sim_datalist.json', 'models', 'outputs', 'environment_maisi_controlnet_train.json', 'config_maisi_controlnet_train.json'].\n", - "INFO:notebook:number of GPUs: 1.\n" + "[2025-03-11 23:19:21.631][ INFO](notebook) - files and folders under work_dir: ['config_maisi.json', 'models', 'config_maisi_controlnet_train.json', 'outputs', 'sim_dataroot', 'environment_maisi_controlnet_train.json', 'sim_datalist.json'].\n", + "[2025-03-11 23:19:21.632][ INFO](notebook) - number of GPUs: 1.\n" ] } ], "source": [ "env_config_path = \"./configs/environment_maisi_controlnet_train.json\"\n", "train_config_path = \"./configs/config_maisi_controlnet_train.json\"\n", - "model_def_path = \"./configs/config_maisi.json\"\n", + "if maisi_version == \"maisi3d-ddpm\":\n", + " model_def_path = \"./configs/config_maisi3d-ddpm.json\"\n", + "elif maisi_version == \"maisi3d-rflow\":\n", + " model_def_path = \"./configs/config_maisi3d-rflow.json\"\n", + "else:\n", + " raise ValueError(f\"maisi_version has to be chosen from ['maisi3d-ddpm', 'maisi3d-rflow'], yet got {maisi_version}.\")\n", "\n", "# Load environment configuration, model configuration and model definition\n", "with open(env_config_path, \"r\") as f:\n", @@ -345,7 +382,7 @@ "# we want to apply more penalty during training.\n", "train_config_out[\"controlnet_train\"][\"weighted_loss_label\"] = [None]\n", "# We set it as a small number for demo\n", - "train_config_out[\"controlnet_infer\"][\"num_inference_steps\"] = 1\n", + "train_config_out[\"controlnet_infer\"][\"num_inference_steps\"] = 10\n", "\n", "train_config_filepath = os.path.join(work_dir, \"config_maisi_controlnet_train.json\")\n", "with open(train_config_filepath, \"w\") as f:\n", @@ -367,7 +404,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "95ea6972", "metadata": {}, "outputs": [], @@ -427,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "ade6389d", "metadata": {}, "outputs": [ @@ -435,30 +472,30 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:notebook:Training the model...\n" + "[2025-03-11 23:19:21.642][ INFO](notebook) - Training the model...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2024-09-24 02:33:40,881 - INFO - 'dst' model updated: 158 of 206 variables.\n", - "\n", - "INFO:maisi.controlnet.training:Number of GPUs: 2\n", - "INFO:maisi.controlnet.training:World_size: 1\n", - "INFO:maisi.controlnet.training:trained diffusion model is not loaded.\n", - "INFO:maisi.controlnet.training:set scale_factor -> 1.0.\n", - "INFO:maisi.controlnet.training:train controlnet model from scratch.\n", - "INFO:maisi.controlnet.training:total number of training steps: 4.0.\n", - "INFO:maisi.controlnet.training:\n", - "[Epoch 1/2] [Batch 1/2] [LR: 0.00000563] [loss: 0.7981] ETA: 0:00:01.501654 \n", - "INFO:maisi.controlnet.training:\n", - "[Epoch 1/2] [Batch 2/2] [LR: 0.00000250] [loss: 0.7976] ETA: 0:00:00 \n", - "INFO:maisi.controlnet.training:best loss -> 0.7978459596633911.\n", - "INFO:maisi.controlnet.training:\n", - "[Epoch 2/2] [Batch 1/2] [LR: 0.00000063] [loss: 0.7982] ETA: 0:00:01.988772 \n", - "INFO:maisi.controlnet.training:\n", - "[Epoch 2/2] [Batch 2/2] [LR: 0.00000000] [loss: 0.7998] ETA: 0:00:00 \n", + "[2025-03-11 23:19:30.675][ INFO](maisi.controlnet.training) - Number of GPUs: 8\n", + "[2025-03-11 23:19:30.675][ INFO](maisi.controlnet.training) - World_size: 1\n", + "[2025-03-11 23:19:32.378][ INFO](maisi.controlnet.training) - trained diffusion model is not loaded.\n", + "[2025-03-11 23:19:32.379][ INFO](maisi.controlnet.training) - set scale_factor -> 1.0.\n", + "2025-03-11 23:19:32,885 - INFO - 'dst' model updated: 158 of 206 variables.\n", + "[2025-03-11 23:19:32.890][ INFO](maisi.controlnet.training) - train controlnet model from scratch.\n", + "[2025-03-11 23:19:32.910][ INFO](maisi.controlnet.training) - total number of training steps: 4.0.\n", + "[2025-03-11 23:19:36.855][ INFO](maisi.controlnet.training) -\n", + "[Epoch 1/2] [Batch 1/2] [LR: 0.00000563] [loss: 0.7995] ETA: 0:00:03.943022\n", + "[2025-03-11 23:19:36.999][ INFO](maisi.controlnet.training) -\n", + "[Epoch 1/2] [Batch 2/2] [LR: 0.00000250] [loss: 0.7989] ETA: 0:00:00\n", + "[2025-03-11 23:19:37.770][ INFO](maisi.controlnet.training) - best loss -> 0.7991659045219421.\n", + "[2025-03-11 23:19:39.101][ INFO](maisi.controlnet.training) -\n", + "[Epoch 2/2] [Batch 1/2] [LR: 0.00000063] [loss: 0.7983] ETA: 0:00:02.101929\n", + "[2025-03-11 23:19:39.233][ INFO](maisi.controlnet.training) -\n", + "[Epoch 2/2] [Batch 2/2] [LR: 0.00000000] [loss: 0.7979] ETA: 0:00:00\n", + "[2025-03-11 23:19:39.830][ INFO](maisi.controlnet.training) - best loss -> 0.7980948090553284.\n", "\n" ] } @@ -476,6 +513,8 @@ " \"--training-config\",\n", " train_config_filepath,\n", "]\n", + "if include_body_region:\n", + " module_args.append(\"--include_body_region\")\n", "\n", "run_torchrun(module, module_args, num_gpus=num_gpus)" ] @@ -493,7 +532,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "936360c8", "metadata": {}, "outputs": [ @@ -501,32 +540,36 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:notebook:Inference...\n" + "[2025-03-11 23:19:42.173][ INFO](notebook) - Inference...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2024-09-24 02:34:03,472 - INFO - 'dst' model updated: 158 of 206 variables.\n", - "2024-09-24 02:34:06,052 INFO image_writer.py:197 - writing: temp_work_dir_controlnet_train_demo/outputs/sample_20240924_023406_038072_image.nii.gz\n", - "2024-09-24 02:34:06,437 INFO image_writer.py:197 - writing: temp_work_dir_controlnet_train_demo/outputs/sample_20240924_023406_038072_label.nii.gz\n", + "[2025-03-11 23:19:51.737][ INFO](maisi.controlnet.infer) - Number of GPUs: 8\n", + "[2025-03-11 23:19:51.738][ INFO](maisi.controlnet.infer) - World_size: 1\n", + "[2025-03-11 23:19:52.727][ INFO](maisi.controlnet.infer) - trained autoencoder model is not loaded.\n", + "[2025-03-11 23:19:53.928][ INFO](maisi.controlnet.infer) - trained diffusion model is not loaded.\n", + "[2025-03-11 23:19:53.928][ INFO](maisi.controlnet.infer) - set scale_factor -> 1.0.\n", + "2025-03-11 23:19:54,436 - INFO - 'dst' model updated: 158 of 206 variables.\n", + "[2025-03-11 23:19:54.441][ INFO](maisi.controlnet.infer) - trained controlnet is not loaded.\n", + "[2025-03-11 23:19:55.156][ INFO](root) - `controllable_anatomy_size` is not provided.\n", + "[2025-03-11 23:19:55.158][ INFO](root) - ---- Start generating latent features... ----\n", + "[2025-03-11 23:19:56.264][ INFO](root) - ---- Latent features generation time: 1.1059284210205078 seconds ----\n", + "[2025-03-11 23:19:56.270][ INFO](root) - ---- Start decoding latent features into images... ----\n", + "[2025-03-11 23:19:57.066][ INFO](root) - ---- Image decoding time: 0.7959444522857666 seconds ----\n", + "2025-03-11 23:19:57,373 INFO image_writer.py:197 - writing: temp_work_dir_controlnet_train_demo/outputs/sample_20250311_231957_354982_image.nii.gz\n", + "2025-03-11 23:19:57,723 INFO image_writer.py:197 - writing: temp_work_dir_controlnet_train_demo/outputs/sample_20250311_231957_354982_label.nii.gz\n", "\n", - "INFO:maisi.controlnet.infer:Number of GPUs: 2\n", - "INFO:maisi.controlnet.infer:World_size: 1\n", - "INFO:maisi.controlnet.infer:trained autoencoder model is not loaded.\n", - "INFO:maisi.controlnet.infer:trained diffusion model is not loaded.\n", - "INFO:maisi.controlnet.infer:set scale_factor -> 1.0.\n", - "INFO:maisi.controlnet.infer:trained controlnet is not loaded.\n", - "INFO:root:`controllable_anatomy_size` is not provided.\n", - "INFO:root:---- Start generating latent features... ----\n", "\n", - " 0%| | 0/1 [00:00 ./temp_work_dir/./models.\n", - "[2025-03-11 22:17:02.004][ INFO](training) - [config] data_root -> ./temp_work_dir/./embeddings.\n", - "[2025-03-11 22:17:02.004][ INFO](training) - [config] data_list -> ./temp_work_dir/sim_datalist.json.\n", - "[2025-03-11 22:17:02.004][ INFO](training) - [config] lr -> 0.0001.\n", - "[2025-03-11 22:17:02.004][ INFO](training) - [config] num_epochs -> 2.\n", - "[2025-03-11 22:17:02.004][ INFO](training) - [config] num_train_timesteps -> 1000.\n", - "[2025-03-11 22:17:02.005][ INFO](training) - num_files_train: 2\n", - "[2025-03-11 22:17:03.887][ INFO](training) - Training from scratch.\n", - "[2025-03-11 22:17:04.338][ INFO](training) - Scaling factor set to 1.159977912902832.\n", - "[2025-03-11 22:17:04.339][ INFO](training) - scale_factor -> 1.159977912902832.\n", - "[2025-03-11 22:17:04.341][ INFO](training) - torch.set_float32_matmul_precision -> highest.\n", - "[2025-03-11 22:17:04.341][ INFO](training) - Epoch 1, lr 0.0001.\n", - "[2025-03-11 22:17:05.278][ INFO](training) - [2025-03-11 22:17:05] epoch 1, iter 1/2, loss: 0.7973, lr: 0.000100000000.\n", - "[2025-03-11 22:17:05.673][ INFO](training) - [2025-03-11 22:17:05] epoch 1, iter 2/2, loss: 0.7969, lr: 0.000056250000.\n", - "[2025-03-11 22:17:05.718][ INFO](training) - epoch 1 average loss: 0.7971.\n", - "[2025-03-11 22:17:07.383][ INFO](training) - Epoch 2, lr 2.5e-05.\n", - "[2025-03-11 22:17:07.777][ INFO](training) - [2025-03-11 22:17:07] epoch 2, iter 1/2, loss: 0.7932, lr: 0.000025000000.\n", - "[2025-03-11 22:17:07.881][ INFO](training) - [2025-03-11 22:17:07] epoch 2, iter 2/2, loss: 0.7904, lr: 0.000006250000.\n", - "[2025-03-11 22:17:07.942][ INFO](training) - epoch 2 average loss: 0.7918.\n", + "[2025-03-11 22:59:49.447][ INFO](training) - Using cuda:0 of 1\n", + "[2025-03-11 22:59:49.448][ INFO](training) - [config] ckpt_folder -> ./temp_work_dir/./models.\n", + "[2025-03-11 22:59:49.448][ INFO](training) - [config] data_root -> ./temp_work_dir/./embeddings.\n", + "[2025-03-11 22:59:49.448][ INFO](training) - [config] data_list -> ./temp_work_dir/sim_datalist.json.\n", + "[2025-03-11 22:59:49.448][ INFO](training) - [config] lr -> 0.0001.\n", + "[2025-03-11 22:59:49.448][ INFO](training) - [config] num_epochs -> 2.\n", + "[2025-03-11 22:59:49.448][ INFO](training) - [config] num_train_timesteps -> 1000.\n", + "[2025-03-11 22:59:49.454][ INFO](training) - num_files_train: 2\n", + "[2025-03-11 22:59:51.369][ INFO](training) - Training from scratch.\n", + "[2025-03-11 22:59:51.843][ INFO](training) - Scaling factor set to 1.159693956375122.\n", + "[2025-03-11 22:59:51.844][ INFO](training) - scale_factor -> 1.159693956375122.\n", + "[2025-03-11 22:59:51.846][ INFO](training) - torch.set_float32_matmul_precision -> highest.\n", + "[2025-03-11 22:59:51.846][ INFO](training) - Epoch 1, lr 0.0001.\n", + "[2025-03-11 22:59:53.869][ INFO](training) - [2025-03-11 22:59:53] epoch 1, iter 1/2, loss: 0.7971, lr: 0.000100000000.\n", + "[2025-03-11 22:59:53.995][ INFO](training) - [2025-03-11 22:59:53] epoch 1, iter 2/2, loss: 0.7942, lr: 0.000056250000.\n", + "[2025-03-11 22:59:54.038][ INFO](training) - epoch 1 average loss: 0.7956.\n", + "[2025-03-11 22:59:55.795][ INFO](training) - Epoch 2, lr 2.5e-05.\n", + "[2025-03-11 22:59:56.159][ INFO](training) - [2025-03-11 22:59:56] epoch 2, iter 1/2, loss: 0.7891, lr: 0.000025000000.\n", + "[2025-03-11 22:59:56.260][ INFO](training) - [2025-03-11 22:59:56] epoch 2, iter 2/2, loss: 0.7888, lr: 0.000006250000.\n", + "[2025-03-11 22:59:56.315][ INFO](training) - epoch 2 average loss: 0.7889.\n", "\n" ] } @@ -603,7 +592,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "1626526d", "metadata": {}, "outputs": [ @@ -611,8 +600,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 22:17:11.993][ INFO](notebook) - Running inference...\n", - "[2025-03-11 22:17:27.730][ INFO](notebook) - Completed all steps.\n" + "[2025-03-11 23:00:00.296][ INFO](notebook) - Running inference...\n", + "[2025-03-11 23:00:19.011][ INFO](notebook) - Completed all steps.\n" ] }, { @@ -620,23 +609,23 @@ "output_type": "stream", "text": [ "\n", - "[2025-03-11 22:17:20.465][ INFO](inference) - Using cuda:0 of 1 with random seed: 23141\n", - "[2025-03-11 22:17:20.466][ INFO](inference) - [config] ckpt_filepath -> ./temp_work_dir/./models/diff_unet_ckpt.pt.\n", - "[2025-03-11 22:17:20.466][ INFO](inference) - [config] random_seed -> 23141.\n", - "[2025-03-11 22:17:20.466][ INFO](inference) - [config] output_prefix -> unet_3d.\n", - "[2025-03-11 22:17:20.466][ INFO](inference) - [config] output_size -> (256, 256, 128).\n", - "[2025-03-11 22:17:20.466][ INFO](inference) - [config] out_spacing -> (1.0, 1.0, 0.75).\n", - "[2025-03-11 22:17:20.466][ INFO](root) - `controllable_anatomy_size` is not provided.\n", - "[2025-03-11 22:17:23.065][ INFO](inference) - checkpoints ./temp_work_dir/./models/diff_unet_ckpt.pt loaded.\n", - "[2025-03-11 22:17:23.067][ INFO](inference) - scale_factor -> 1.159977912902832.\n", - "[2025-03-11 22:17:23.068][ INFO](inference) - num_downsample_level -> 4, divisor -> 4.\n", - "[2025-03-11 22:17:23.070][ INFO](inference) - noise: cuda:0, torch.float32, \n", + "[2025-03-11 23:00:11.173][ INFO](inference) - Using cuda:0 of 1 with random seed: 17942\n", + "[2025-03-11 23:00:11.173][ INFO](inference) - [config] ckpt_filepath -> ./temp_work_dir/./models/diff_unet_ckpt.pt.\n", + "[2025-03-11 23:00:11.173][ INFO](inference) - [config] random_seed -> 17942.\n", + "[2025-03-11 23:00:11.173][ INFO](inference) - [config] output_prefix -> unet_3d.\n", + "[2025-03-11 23:00:11.173][ INFO](inference) - [config] output_size -> (256, 256, 128).\n", + "[2025-03-11 23:00:11.173][ INFO](inference) - [config] out_spacing -> (1.0, 1.0, 0.75).\n", + "[2025-03-11 23:00:11.173][ INFO](root) - `controllable_anatomy_size` is not provided.\n", + "[2025-03-11 23:00:13.699][ INFO](inference) - checkpoints ./temp_work_dir/./models/diff_unet_ckpt.pt loaded.\n", + "[2025-03-11 23:00:13.701][ INFO](inference) - scale_factor -> 1.159693956375122.\n", + "[2025-03-11 23:00:13.702][ INFO](inference) - num_downsample_level -> 4, divisor -> 4.\n", + "[2025-03-11 23:00:13.704][ INFO](inference) - noise: cuda:0, torch.float32, \n", "\n", " 0%| | 0/10 [00:00 Date: Tue, 11 Mar 2025 23:21:59 +0000 Subject: [PATCH 15/59] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- generation/maisi/maisi_train_controlnet_tutorial.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/generation/maisi/maisi_train_controlnet_tutorial.ipynb b/generation/maisi/maisi_train_controlnet_tutorial.ipynb index 2ece732ea..729b27da9 100644 --- a/generation/maisi/maisi_train_controlnet_tutorial.ipynb +++ b/generation/maisi/maisi_train_controlnet_tutorial.ipynb @@ -222,8 +222,8 @@ "if include_body_region:\n", " for i in range(len(sim_datalist[\"training\"])):\n", " # body region index\n", - " sim_datalist[\"training\"][i][\"top_region_index\"] = [0, 1, 0, 0] # the top region index of the image\n", - " sim_datalist[\"training\"][i][\"bottom_region_index\"] = [0, 0, 0, 1] # the bottom region index of the image" + " sim_datalist[\"training\"][i][\"top_region_index\"] = [0, 1, 0, 0] # the top region index of the image\n", + " sim_datalist[\"training\"][i][\"bottom_region_index\"] = [0, 0, 0, 1] # the bottom region index of the image" ] }, { From 1c42a8311291f092f6e541ab152cdcc29474676a Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Tue, 11 Mar 2025 23:40:47 +0000 Subject: [PATCH 16/59] add inference notebook Signed-off-by: Can-Zhao --- .../maisi/maisi_inference_tutorial.ipynb | 431 ++++++++++++------ .../maisi_train_controlnet_tutorial.ipynb | 89 ++-- 2 files changed, 341 insertions(+), 179 deletions(-) diff --git a/generation/maisi/maisi_inference_tutorial.ipynb b/generation/maisi/maisi_inference_tutorial.ipynb index d121f886b..2cace32ac 100644 --- a/generation/maisi/maisi_inference_tutorial.ipynb +++ b/generation/maisi/maisi_inference_tutorial.ipynb @@ -18,7 +18,9 @@ "\n", "# MAISI Inference Tutorial\n", "\n", - "This tutorial illustrates how to use trained MAISI model and codebase to generate synthetic 3D images and paired masks." + "This tutorial illustrates how to use trained MAISI model and codebase to generate synthetic 3D images and paired masks.\n", + "\n", + "`[Release Note (March 2025)]:` We are excited to announce the new MAISI Version `'maisi3d-rflow'`. Compared with the previous version `'maisi3d-ddpm'`, it accelerated latent diffusion model inference by 33x. Please see the detailed difference in the following section." ] }, { @@ -61,32 +63,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "MONAI version: 1.4.0rc10\n", - "Numpy version: 1.24.4\n", - "Pytorch version: 2.5.0a0+872d972e41.nv24.08.01\n", + "MONAI version: 1.4.1rc1+31.g90de55b1\n", + "Numpy version: 1.26.4\n", + "Pytorch version: 2.5.0+cu124\n", "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n", - "MONAI rev id: cac21f6936a2e8d6e4e57e4e958f8e32aae1585e\n", - "MONAI __file__: /usr/local/lib/python3.10/dist-packages/monai/__init__.py\n", + "MONAI rev id: 90de55b1340ffc8eccf960d111229535bcb2f81d\n", + "MONAI __file__: /localhome//.local/lib/python3.10/site-packages/monai/__init__.py\n", "\n", "Optional dependencies:\n", "Pytorch Ignite version: 0.4.11\n", "ITK version: 5.4.0\n", - "Nibabel version: 5.2.1\n", - "scikit-image version: 0.23.2\n", - "scipy version: 1.13.1\n", - "Pillow version: 10.4.0\n", - "Tensorboard version: 2.17.0\n", + "Nibabel version: 5.3.2\n", + "scikit-image version: 0.24.0\n", + "scipy version: 1.14.1\n", + "Pillow version: 11.0.0\n", + "Tensorboard version: 2.18.0\n", "gdown version: 5.2.0\n", - "TorchVision version: 0.20.0a0\n", - "tqdm version: 4.66.4\n", + "TorchVision version: 0.20.0+cu124\n", + "tqdm version: 4.66.5\n", "lmdb version: 1.5.1\n", - "psutil version: 5.9.8\n", - "pandas version: 2.2.2\n", - "einops version: 0.7.0\n", + "psutil version: 6.1.0\n", + "pandas version: 2.2.3\n", + "einops version: 0.8.0\n", "transformers version: 4.40.2\n", - "mlflow version: 2.16.0\n", + "mlflow version: 2.17.1\n", "pynrrd version: 1.0.0\n", - "clearml version: 1.16.3\n", + "clearml version: 1.16.5rc2\n", "\n", "For details about installing the optional dependencies, please visit:\n", " https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n", @@ -109,8 +111,50 @@ "from scripts.sample import LDMSampler, check_input\n", "from scripts.utils import define_instance\n", "from scripts.utils_plot import find_label_center_loc, get_xyz_plot, show_image\n", + "from scripts.diff_model_setting import setup_logging\n", + "\n", + "print_config()\n", + "\n", + "logger = setup_logging(\"notebook\")" + ] + }, + { + "cell_type": "markdown", + "id": "37c2759e-d1fa-42d7-b208-fbe306ac1e06", + "metadata": {}, + "source": [ + "## Set up the MAISI version\n", "\n", - "print_config()" + "Choose between `'maisi3d-ddpm'` and `'maisi3d-rflow'`. The differences are:\n", + "- The maisi version `'maisi3d-ddpm'` uses basic noise scheduler DDPM. `'maisi3d-rflow'` uses Rectified Flow scheduler, can be 33 times faster during inference.\n", + "- The maisi version `'maisi3d-ddpm'` requires training images to be labeled with body region (`\"top_region_index\"` and `\"bottom_region_index\"`), while `'maisi3d-rflow'` does not have such requirement. In other words, it is easier to prepare training data for `'maisi3d-rflow'`.\n", + "- For the released model weights, `'maisi3d-rflow'` can generate images with better quality for head region and small output volumes, and comparable quality for other cases compared with `'maisi3d-ddpm'`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "bf4252b1-089d-48c1-b6d6-aa24a93a5839", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2025-03-11 23:28:16.158][ INFO](notebook) - Using MAISI version maisi3d-ddpm. Will need body region as data input.\n" + ] + } + ], + "source": [ + "maisi_version = \"maisi3d-ddpm\"\n", + "if maisi_version == \"maisi3d-ddpm\":\n", + " include_body_region = True\n", + " logger.info(\"Using MAISI version maisi3d-ddpm. Will need body region as data input.\")\n", + "elif maisi_version == \"maisi3d-rflow\":\n", + " include_body_region = False\n", + " logger.info(\"Using MAISI version maisi3d-rflow. Does not need body region as data input.\")\n", + "else:\n", + " raise ValueError(f\"maisi_version has to be chosen from ['maisi3d-ddpm', 'maisi3d-rflow'], yet got {maisi_version}.\")" ] }, { @@ -126,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "e3c12dcc", "metadata": {}, "outputs": [ @@ -134,22 +178,122 @@ "name": "stdout", "output_type": "stream", "text": [ - "2024-09-30 06:40:49,932 - INFO - Expected md5 is None, skip md5 check for file models/autoencoder_epoch273.pt.\n", - "2024-09-30 06:40:49,933 - INFO - File exists: models/autoencoder_epoch273.pt, skipped downloading.\n", - "2024-09-30 06:40:49,933 - INFO - Expected md5 is None, skip md5 check for file models/input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1.pt.\n", - "2024-09-30 06:40:49,933 - INFO - File exists: models/input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1.pt, skipped downloading.\n", - "2024-09-30 06:40:49,934 - INFO - Expected md5 is None, skip md5 check for file models/controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current.pt.\n", - "2024-09-30 06:40:49,934 - INFO - File exists: models/controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current.pt, skipped downloading.\n", - "2024-09-30 06:40:49,934 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_autoencoder.pt.\n", - "2024-09-30 06:40:49,934 - INFO - File exists: models/mask_generation_autoencoder.pt, skipped downloading.\n", - "2024-09-30 06:40:49,935 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_diffusion_unet.pt.\n", - "2024-09-30 06:40:49,935 - INFO - File exists: models/mask_generation_diffusion_unet.pt, skipped downloading.\n", - "2024-09-30 06:40:49,935 - INFO - Expected md5 is None, skip md5 check for file configs/candidate_masks_flexible_size_and_spacing_3000.json.\n", - "2024-09-30 06:40:49,935 - INFO - File exists: configs/candidate_masks_flexible_size_and_spacing_3000.json, skipped downloading.\n", - "2024-09-30 06:40:49,936 - INFO - Expected md5 is None, skip md5 check for file configs/all_anatomy_size_condtions.json.\n", - "2024-09-30 06:40:49,936 - INFO - File exists: configs/all_anatomy_size_condtions.json, skipped downloading.\n", - "2024-09-30 06:40:49,936 - INFO - Expected md5 is None, skip md5 check for file /workspace/data/datasets/all_masks_flexible_size_and_spacing_3000.zip.\n", - "2024-09-30 06:40:49,936 - INFO - File exists: /workspace/data/datasets/all_masks_flexible_size_and_spacing_3000.zip, skipped downloading.\n" + "2025-03-11 23:28:16,166 - INFO - Expected md5 is None, skip md5 check for file models/autoencoder_epoch273.pt.\n", + "2025-03-11 23:28:16,167 - INFO - File exists: models/autoencoder_epoch273.pt, skipped downloading.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1.pt: 654MB [00:13, 51.1MB/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-11 23:28:30,219 - INFO - Downloaded: models/input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1.pt\n", + "2025-03-11 23:28:30,221 - INFO - Expected md5 is None, skip md5 check for file models/input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1.pt.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current.pt: 265MB [00:11, 23.3MB/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-11 23:28:42,451 - INFO - Downloaded: models/controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current.pt\n", + "2025-03-11 23:28:42,453 - INFO - Expected md5 is None, skip md5 check for file models/controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current.pt.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "mask_generation_autoencoder.pt: 20.1MB [00:00, 24.1MB/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-11 23:28:43,367 - INFO - Downloaded: models/mask_generation_autoencoder.pt\n", + "2025-03-11 23:28:43,368 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_autoencoder.pt.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "mask_generation_diffusion_unet.pt: 752MB [00:33, 23.4MB/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-11 23:29:17,932 - INFO - Downloaded: models/mask_generation_diffusion_unet.pt\n", + "2025-03-11 23:29:17,934 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_diffusion_unet.pt.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "candidate_masks_flexible_size_and_spacing_3000.json: 4.80MB [00:00, 5.61MB/s] " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-11 23:29:18,848 - INFO - Downloaded: configs/candidate_masks_flexible_size_and_spacing_3000.json\n", + "2025-03-11 23:29:18,849 - INFO - Expected md5 is None, skip md5 check for file configs/candidate_masks_flexible_size_and_spacing_3000.json.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "all_anatomy_size_condtions.json: 352kB [00:00, 1.38MB/s] " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-11 23:29:19,116 - INFO - Downloaded: configs/all_anatomy_size_condtions.json\n", + "2025-03-11 23:29:19,117 - INFO - Expected md5 is None, skip md5 check for file configs/all_anatomy_size_condtions.json.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "all_masks_flexible_size_and_spacing_3000.zip: 8.41GB [05:00, 30.0MB/s] " + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-03-11 23:34:20,049 - INFO - Downloaded: /tmp/tmpm89_6ifv/datasets/all_masks_flexible_size_and_spacing_3000.zip\n", + "2025-03-11 23:34:20,050 - INFO - Expected md5 is None, skip md5 check for file /tmp/tmpm89_6ifv/datasets/all_masks_flexible_size_and_spacing_3000.zip.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" ] } ], @@ -218,28 +362,28 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "c38b4c33", "metadata": { "scrolled": true }, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "output_dir: output\n", - "trained_autoencoder_path: models/autoencoder_epoch273.pt\n", - "trained_diffusion_path: models/input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1.pt\n", - "trained_controlnet_path: models/controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current.pt\n", - "trained_mask_generation_autoencoder_path: models/mask_generation_autoencoder.pt\n", - "trained_mask_generation_diffusion_path: models/mask_generation_diffusion_unet.pt\n", - "all_mask_files_base_dir: /workspace/data/datasets/all_masks_flexible_size_and_spacing_3000\n", - "all_mask_files_json: ./configs/candidate_masks_flexible_size_and_spacing_3000.json\n", - "all_anatomy_size_conditions_json: ./configs/all_anatomy_size_condtions.json\n", - "label_dict_json: ./configs/label_dict.json\n", - "label_dict_remap_json: ./configs/label_dict_124_to_132.json\n", - "Global config variables have been loaded.\n" + "[2025-03-11 23:34:20.066][ INFO](notebook) - output_dir: output\n", + "[2025-03-11 23:34:20.068][ INFO](notebook) - trained_autoencoder_path: models/autoencoder_epoch273.pt\n", + "[2025-03-11 23:34:20.068][ INFO](notebook) - trained_diffusion_path: models/input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1.pt\n", + "[2025-03-11 23:34:20.069][ INFO](notebook) - trained_controlnet_path: models/controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current.pt\n", + "[2025-03-11 23:34:20.070][ INFO](notebook) - trained_mask_generation_autoencoder_path: models/mask_generation_autoencoder.pt\n", + "[2025-03-11 23:34:20.070][ INFO](notebook) - trained_mask_generation_diffusion_path: models/mask_generation_diffusion_unet.pt\n", + "[2025-03-11 23:34:20.071][ INFO](notebook) - all_mask_files_base_dir: /tmp/tmpm89_6ifv/datasets/all_masks_flexible_size_and_spacing_3000\n", + "[2025-03-11 23:34:20.072][ INFO](notebook) - all_mask_files_json: ./configs/candidate_masks_flexible_size_and_spacing_3000.json\n", + "[2025-03-11 23:34:20.072][ INFO](notebook) - all_anatomy_size_conditions_json: ./configs/all_anatomy_size_condtions.json\n", + "[2025-03-11 23:34:20.073][ INFO](notebook) - label_dict_json: ./configs/label_dict.json\n", + "[2025-03-11 23:34:20.075][ INFO](notebook) - label_dict_remap_json: ./configs/label_dict_124_to_132.json\n", + "[2025-03-11 23:34:20.075][ INFO](notebook) - Global config variables have been loaded.\n" ] } ], @@ -253,8 +397,8 @@ " # Update the path to the downloaded dataset in MONAI_DATA_DIRECTORY\n", " val = v if \"datasets/\" not in v else os.path.join(root_dir, v)\n", " setattr(args, k, val)\n", - " print(f\"{k}: {val}\")\n", - "print(\"Global config variables have been loaded.\")" + " logger.info(f\"{k}: {val}\")\n", + "logger.info(\"Global config variables have been loaded.\")" ] }, { @@ -269,7 +413,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "533414f3-bef5-49f7-b082-f803b5e494bf", "metadata": {}, "outputs": [ @@ -277,36 +421,41 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:root:`controllable_anatomy_size` is empty.\n", + "[2025-03-11 23:34:20.090][ INFO](notebook) - num_output_samples: 1\n", + "[2025-03-11 23:34:20.091][ INFO](notebook) - body_region: ['abdomen']\n", + "[2025-03-11 23:34:20.091][ INFO](notebook) - anatomy_list: ['liver', 'hepatic tumor']\n", + "[2025-03-11 23:34:20.092][ INFO](notebook) - controllable_anatomy_size: []\n", + "[2025-03-11 23:34:20.093][ INFO](notebook) - num_inference_steps: 1000\n", + "[2025-03-11 23:34:20.093][ INFO](notebook) - mask_generation_num_inference_steps: 1000\n", + "[2025-03-11 23:34:20.094][ INFO](notebook) - output_size: [256, 256, 256]\n", + "[2025-03-11 23:34:20.094][ INFO](notebook) - image_output_ext: .nii.gz\n", + "[2025-03-11 23:34:20.095][ INFO](notebook) - label_output_ext: .nii.gz\n", + "[2025-03-11 23:34:20.095][ INFO](notebook) - spacing: [1.5, 1.5, 2.0]\n", + "[2025-03-11 23:34:20.096][ INFO](notebook) - autoencoder_sliding_window_infer_size: [48, 48, 48]\n", + "[2025-03-11 23:34:20.096][ INFO](notebook) - autoencoder_sliding_window_infer_overlap: 0.25\n", + "[2025-03-11 23:34:20.097][ INFO](notebook) - controlnet: $@controlnet_def\n", + "[2025-03-11 23:34:20.098][ INFO](notebook) - diffusion_unet: $@diffusion_unet_def\n", + "[2025-03-11 23:34:20.098][ INFO](notebook) - autoencoder: $@autoencoder_def\n", + "[2025-03-11 23:34:20.099][ INFO](notebook) - mask_generation_autoencoder: $@mask_generation_autoencoder_def\n", + "[2025-03-11 23:34:20.099][ INFO](notebook) - mask_generation_diffusion: $@mask_generation_diffusion_def\n", + "[2025-03-11 23:34:20.100][ INFO](root) - `controllable_anatomy_size` is empty.\n", "We will synthesize based on `body_region`: (['abdomen']) and `anatomy_list`: (['liver', 'hepatic tumor']).\n", - "INFO:root:The generate results will have voxel size to be [1.5, 1.5, 2.0]mm, volume size to be [256, 256, 256].\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "num_output_samples: 1\n", - "body_region: ['abdomen']\n", - "anatomy_list: ['liver', 'hepatic tumor']\n", - "controllable_anatomy_size: []\n", - "num_inference_steps: 1000\n", - "mask_generation_num_inference_steps: 1000\n", - "output_size: [256, 256, 256]\n", - "image_output_ext: .nii.gz\n", - "label_output_ext: .nii.gz\n", - "spacing: [1.5, 1.5, 2.0]\n", - "autoencoder_sliding_window_infer_size: [48, 48, 48]\n", - "autoencoder_sliding_window_infer_overlap: 0.25\n", - "Network definition and inference inputs have been loaded.\n" + "[2025-03-11 23:34:20.103][ INFO](root) - The generate results will have voxel size to be [1.5, 1.5, 2.0]mm, volume size to be [256, 256, 256].\n", + "[2025-03-11 23:34:20.103][ INFO](notebook) - Network definition and inference inputs have been loaded.\n" ] } ], "source": [ - "config_file = \"./configs/config_maisi.json\"\n", - "with open(config_file, \"r\") as f:\n", - " config_dict = json.load(f)\n", - "for k, v in config_dict.items():\n", + "if maisi_version == \"maisi3d-ddpm\":\n", + " model_def_path = \"./configs/config_maisi3d-ddpm.json\"\n", + "elif maisi_version == \"maisi3d-rflow\":\n", + " model_def_path = \"./configs/config_maisi3d-rflow.json\"\n", + "else:\n", + " raise ValueError(f\"maisi_version has to be chosen from ['maisi3d-ddpm', 'maisi3d-rflow'], yet got {maisi_version}.\")\n", + " \n", + "with open(model_def_path, \"r\") as f:\n", + " model_def = json.load(f) \n", + "for k, v in model_def.items():\n", " setattr(args, k, v)\n", "\n", "# check the format of inference inputs\n", @@ -315,7 +464,7 @@ " config_infer_dict = json.load(f)\n", "for k, v in config_infer_dict.items():\n", " setattr(args, k, v)\n", - " print(f\"{k}: {v}\")\n", + " logger.info(f\"{k}: {v}\")\n", "\n", "check_input(\n", " args.body_region,\n", @@ -326,7 +475,7 @@ " args.controllable_anatomy_size,\n", ")\n", "latent_shape = [args.latent_channels, args.output_size[0] // 4, args.output_size[1] // 4, args.output_size[2] // 4]\n", - "print(\"Network definition and inference inputs have been loaded.\")" + "logger.info(\"Network definition and inference inputs have been loaded.\")" ] }, { @@ -339,7 +488,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "87ba613d-a2f5-4afc-95df-65ad21fafedd", "metadata": {}, "outputs": [], @@ -360,7 +509,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "d499f7b1", "metadata": { "lines_to_next_cell": 2 @@ -370,8 +519,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "2024-09-30 06:34:48,434 - INFO - 'dst' model updated: 158 of 206 variables.\n", - "All the trained model weights have been loaded.\n" + "2025-03-11 23:34:25,873 - INFO - 'dst' model updated: 158 of 206 variables.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2025-03-11 23:34:28.137][ INFO](notebook) - All the trained model weights have been loaded.\n" ] } ], @@ -404,7 +559,7 @@ "mask_generation_diffusion_unet.load_state_dict(checkpoint_mask_generation_diffusion_unet[\"unet_state_dict\"])\n", "mask_generation_scale_factor = checkpoint_mask_generation_diffusion_unet[\"scale_factor\"]\n", "\n", - "print(\"All the trained model weights have been loaded.\")" + "logger.info(\"All the trained model weights have been loaded.\")" ] }, { @@ -417,7 +572,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "8685da6e", "metadata": {}, "outputs": [ @@ -425,7 +580,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:root:LDM sampler initialized.\n" + "[2025-03-11 23:34:28.157][ INFO](root) - LDM sampler initialized.\n" ] } ], @@ -477,59 +632,65 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "271f91bf-1c55-46e2-ae56-8677cd8eb81f", "metadata": { "scrolled": true }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2025-03-11 23:34:28.161][ INFO](notebook) - The generated image/mask pairs will be saved in output.\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "The generated image/mask pairs will be saved in output.\n", - "Extracting /workspace/data/datasets/all_masks_flexible_size_and_spacing_3000.zip to /workspace/data/datasets\n", - "2024-09-30 06:34:50,652 - INFO - Writing into directory: /workspace/data/datasets.\n" + "Extracting /tmp/tmpm89_6ifv/datasets/all_masks_flexible_size_and_spacing_3000.zip to /tmp/tmpm89_6ifv/datasets\n", + "2025-03-11 23:34:28,163 - INFO - Writing into directory: /tmp/tmpm89_6ifv/datasets.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:root:Resample mask file to get desired output size and spacing\n" + "[2025-03-11 23:35:31.412][ INFO](root) - Resample mask file to get desired output size and spacing\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Unzipped /workspace/data/datasets/all_masks_flexible_size_and_spacing_3000.zip to /workspace/data/datasets/all_masks_flexible_size_and_spacing_3000.\n" + "Unzipped /tmp/tmpm89_6ifv/datasets/all_masks_flexible_size_and_spacing_3000.zip to /tmp/tmpm89_6ifv/datasets/all_masks_flexible_size_and_spacing_3000.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "INFO:root:Resampling mask to target shape and spacing\n", - "INFO:root:Resize Spacing: [tensor(0.7988, dtype=torch.float64), tensor(0.7988, dtype=torch.float64), tensor(1.9062, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "INFO:root:Output size: [512, 512, 256] -> [256, 256, 256]\n", - "INFO:root:Resampling mask to target shape and spacing\n", - "INFO:root:Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "INFO:root:Output size: [512, 512, 256] -> [256, 256, 256]\n", - "INFO:root:Resampling mask to target shape and spacing\n", - "INFO:root:Resize Spacing: [tensor(0.8027, dtype=torch.float64), tensor(0.8027, dtype=torch.float64), tensor(1.8672, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "INFO:root:Output size: [512, 512, 256] -> [256, 256, 256]\n", - "INFO:root:Resampling mask to target shape and spacing\n", - "INFO:root:Resize Spacing: [tensor(0.9062, dtype=torch.float64), tensor(0.9062, dtype=torch.float64), tensor(2.3438, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "INFO:root:Output size: [512, 512, 256] -> [256, 256, 256]\n", - "INFO:root:Resampling mask to target shape and spacing\n", - "INFO:root:Resize Spacing: [tensor(0.9551, dtype=torch.float64), tensor(0.9551, dtype=torch.float64), tensor(2.4805, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "INFO:root:Output size: [512, 512, 256] -> [256, 256, 256]\n", - "INFO:root:Images will be generated based on [{'mask_file': {'pseudo_label': '/workspace/data/datasets/all_masks_flexible_size_and_spacing_3000/./Task03/labelsTr/liver_56_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}].\n", - "INFO:root:---- Start preparing masks... ----\n", - "INFO:root:Resampling mask to target shape and spacing\n", - "INFO:root:Resize Spacing: [tensor(0.8027, dtype=torch.float64), tensor(0.8027, dtype=torch.float64), tensor(1.8672, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "INFO:root:Output size: [512, 512, 256] -> [256, 256, 256]\n" + "[2025-03-11 23:35:32.575][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-11 23:35:32.579][ INFO](root) - Resize Spacing: [tensor(0.7988, dtype=torch.float64), tensor(0.7988, dtype=torch.float64), tensor(1.9062, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-11 23:35:32.580][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-11 23:35:35.398][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-11 23:35:35.401][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-11 23:35:35.401][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-11 23:35:38.378][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-11 23:35:38.380][ INFO](root) - Resize Spacing: [tensor(0.8027, dtype=torch.float64), tensor(0.8027, dtype=torch.float64), tensor(1.8672, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-11 23:35:38.381][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-11 23:35:41.219][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-11 23:35:41.222][ INFO](root) - Resize Spacing: [tensor(0.9062, dtype=torch.float64), tensor(0.9062, dtype=torch.float64), tensor(2.3438, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-11 23:35:41.222][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-11 23:35:44.278][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-11 23:35:44.282][ INFO](root) - Resize Spacing: [tensor(0.9551, dtype=torch.float64), tensor(0.9551, dtype=torch.float64), tensor(2.4805, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-11 23:35:44.283][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-11 23:35:46.311][ INFO](root) - Images will be generated based on [{'mask_file': {'pseudo_label': '/tmp/tmpm89_6ifv/datasets/all_masks_flexible_size_and_spacing_3000/./Task03/labelsTr/liver_56_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}].\n", + "[2025-03-11 23:35:46.313][ INFO](root) - ---- Start preparing masks... ----\n", + "[2025-03-11 23:35:47.553][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-11 23:35:47.555][ INFO](root) - Resize Spacing: [tensor(0.8027, dtype=torch.float64), tensor(0.8027, dtype=torch.float64), tensor(1.8672, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-11 23:35:47.556][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n" ] }, { @@ -543,29 +704,35 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:root:---- Mask preparation time: 10.283801794052124 seconds ----\n", - "INFO:root:---- Start generating latent features... ----\n", - "100%|█████████████████████████████████████████████████████████████████████| 1000/1000 [02:15<00:00, 7.41it/s]\n", - "INFO:root:---- Latent features generation time: 135.05768537521362 seconds ----\n", - "INFO:root:---- Start decoding latent features into images... ----\n", - "100%|██████████| 8/8 [00:08<00:00, 1.09s/it]\n", - "INFO:root:---- Image decoding time: 8.780551671981812 seconds ----\n" + "[2025-03-11 23:35:51.553][ INFO](root) - ---- Mask preparation time: 5.239385604858398 seconds ----\n", + "[2025-03-11 23:35:51.562][ INFO](root) - ---- Start generating latent features... ----\n", + "100%|█████████████████████████████████████████████████████████████████████| 1000/1000 [01:21<00:00, 12.25it/s]\n", + "[2025-03-11 23:37:13.231][ INFO](root) - ---- Latent features generation time: 81.66785907745361 seconds ----\n", + "[2025-03-11 23:37:13.249][ INFO](root) - ---- Start decoding latent features into images... ----\n", + "100%|█████████████████████████████████████████████| 8/8 [00:06<00:00, 1.22it/s]\n", + "[2025-03-11 23:37:19.839][ INFO](root) - ---- Image decoding time: 6.5896477699279785 seconds ----\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2024-09-30 06:38:43,064 INFO image_writer.py:197 - writing: output/sample_20240930_063843_052831_image.nii.gz\n", - "2024-09-30 06:38:44,661 INFO image_writer.py:197 - writing: output/sample_20240930_063843_052831_label.nii.gz\n", - "MAISI image/mask generation finished\n" + "2025-03-11 23:37:20,191 INFO image_writer.py:197 - writing: output/sample_20250311_233720_171269_image.nii.gz\n", + "2025-03-11 23:37:21,993 INFO image_writer.py:197 - writing: output/sample_20250311_233720_171269_label.nii.gz\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2025-03-11 23:37:22.955][ INFO](notebook) - MAISI image/mask generation finished\n" ] } ], "source": [ - "print(f\"The generated image/mask pairs will be saved in {args.output_dir}.\")\n", + "logger.info(f\"The generated image/mask pairs will be saved in {args.output_dir}.\")\n", "output_filenames = ldm_sampler.sample_multiple_images(args.num_output_samples)\n", - "print(\"MAISI image/mask generation finished\")" + "logger.info(\"MAISI image/mask generation finished\")" ] }, { @@ -578,22 +745,22 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "id": "e0453d9f-1614-4c84-aef1-77b6339d8c12", "metadata": { "scrolled": true }, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Visualizing output/sample_20240930_063843_052831_image.nii.gz and output/sample_20240930_063843_052831_label.nii.gz...\n" + "[2025-03-11 23:37:22.964][ INFO](notebook) - Visualizing output/sample_20250311_233720_171269_image.nii.gz and output/sample_20250311_233720_171269_label.nii.gz...\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -603,7 +770,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -615,7 +782,7 @@ "source": [ "visualize_image_filename = output_filenames[0][0]\n", "visualize_mask_filename = output_filenames[0][1]\n", - "print(f\"Visualizing {visualize_image_filename} and {visualize_mask_filename}...\")\n", + "logger.info(f\"Visualizing {visualize_image_filename} and {visualize_mask_filename}...\")\n", "\n", "# load image/mask pairs\n", "loader = LoadImage(image_only=True, ensure_channel_first=True)\n", @@ -651,7 +818,7 @@ "formats": "py:percent,ipynb" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/generation/maisi/maisi_train_controlnet_tutorial.ipynb b/generation/maisi/maisi_train_controlnet_tutorial.ipynb index 2ece732ea..f1f68fc50 100644 --- a/generation/maisi/maisi_train_controlnet_tutorial.ipynb +++ b/generation/maisi/maisi_train_controlnet_tutorial.ipynb @@ -141,7 +141,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 23:19:19.279][ INFO](notebook) - Using MAISI version maisi3d-ddpm. Will need body region as data input.\n" + "[2025-03-11 23:38:43.304][ INFO](notebook) - Using MAISI version maisi3d-ddpm. Will need body region as data input.\n" ] } ], @@ -246,9 +246,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 23:19:21.615][ INFO](notebook) - Generated simulated images.\n", - "[2025-03-11 23:19:21.616][ INFO](notebook) - img_emb shape: (64, 64, 32, 4)\n", - "[2025-03-11 23:19:21.617][ INFO](notebook) - label shape: (256, 256, 128)\n" + "[2025-03-11 23:38:45.473][ INFO](notebook) - Generated simulated images.\n", + "[2025-03-11 23:38:45.474][ INFO](notebook) - img_emb shape: (64, 64, 32, 4)\n", + "[2025-03-11 23:38:45.475][ INFO](notebook) - label shape: (256, 256, 128)\n" ] } ], @@ -320,8 +320,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 23:19:21.631][ INFO](notebook) - files and folders under work_dir: ['config_maisi.json', 'models', 'config_maisi_controlnet_train.json', 'outputs', 'sim_dataroot', 'environment_maisi_controlnet_train.json', 'sim_datalist.json'].\n", - "[2025-03-11 23:19:21.632][ INFO](notebook) - number of GPUs: 1.\n" + "[2025-03-11 23:38:45.489][ INFO](notebook) - files and folders under work_dir: ['config_maisi.json', 'models', 'config_maisi_controlnet_train.json', 'outputs', 'sim_dataroot', 'environment_maisi_controlnet_train.json', 'sim_datalist.json'].\n", + "[2025-03-11 23:38:45.490][ INFO](notebook) - number of GPUs: 1.\n" ] } ], @@ -382,7 +382,7 @@ "# we want to apply more penalty during training.\n", "train_config_out[\"controlnet_train\"][\"weighted_loss_label\"] = [None]\n", "# We set it as a small number for demo\n", - "train_config_out[\"controlnet_infer\"][\"num_inference_steps\"] = 10\n", + "train_config_out[\"controlnet_infer\"][\"num_inference_steps\"] = 1\n", "\n", "train_config_filepath = os.path.join(work_dir, \"config_maisi_controlnet_train.json\")\n", "with open(train_config_filepath, \"w\") as f:\n", @@ -472,30 +472,29 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 23:19:21.642][ INFO](notebook) - Training the model...\n" + "[2025-03-11 23:38:45.501][ INFO](notebook) - Training the model...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[2025-03-11 23:19:30.675][ INFO](maisi.controlnet.training) - Number of GPUs: 8\n", - "[2025-03-11 23:19:30.675][ INFO](maisi.controlnet.training) - World_size: 1\n", - "[2025-03-11 23:19:32.378][ INFO](maisi.controlnet.training) - trained diffusion model is not loaded.\n", - "[2025-03-11 23:19:32.379][ INFO](maisi.controlnet.training) - set scale_factor -> 1.0.\n", - "2025-03-11 23:19:32,885 - INFO - 'dst' model updated: 158 of 206 variables.\n", - "[2025-03-11 23:19:32.890][ INFO](maisi.controlnet.training) - train controlnet model from scratch.\n", - "[2025-03-11 23:19:32.910][ INFO](maisi.controlnet.training) - total number of training steps: 4.0.\n", - "[2025-03-11 23:19:36.855][ INFO](maisi.controlnet.training) -\n", - "[Epoch 1/2] [Batch 1/2] [LR: 0.00000563] [loss: 0.7995] ETA: 0:00:03.943022\n", - "[2025-03-11 23:19:36.999][ INFO](maisi.controlnet.training) -\n", - "[Epoch 1/2] [Batch 2/2] [LR: 0.00000250] [loss: 0.7989] ETA: 0:00:00\n", - "[2025-03-11 23:19:37.770][ INFO](maisi.controlnet.training) - best loss -> 0.7991659045219421.\n", - "[2025-03-11 23:19:39.101][ INFO](maisi.controlnet.training) -\n", - "[Epoch 2/2] [Batch 1/2] [LR: 0.00000063] [loss: 0.7983] ETA: 0:00:02.101929\n", - "[2025-03-11 23:19:39.233][ INFO](maisi.controlnet.training) -\n", - "[Epoch 2/2] [Batch 2/2] [LR: 0.00000000] [loss: 0.7979] ETA: 0:00:00\n", - "[2025-03-11 23:19:39.830][ INFO](maisi.controlnet.training) - best loss -> 0.7980948090553284.\n", + "[2025-03-11 23:38:54.835][ INFO](maisi.controlnet.training) - Number of GPUs: 8\n", + "[2025-03-11 23:38:54.835][ INFO](maisi.controlnet.training) - World_size: 1\n", + "[2025-03-11 23:38:56.401][ INFO](maisi.controlnet.training) - trained diffusion model is not loaded.\n", + "[2025-03-11 23:38:56.401][ INFO](maisi.controlnet.training) - set scale_factor -> 1.0.\n", + "2025-03-11 23:38:56,899 - INFO - 'dst' model updated: 158 of 206 variables.\n", + "[2025-03-11 23:38:56.903][ INFO](maisi.controlnet.training) - train controlnet model from scratch.\n", + "[2025-03-11 23:38:56.925][ INFO](maisi.controlnet.training) - total number of training steps: 4.0.\n", + "[2025-03-11 23:38:58.871][ INFO](maisi.controlnet.training) -\n", + "[Epoch 1/2] [Batch 1/2] [LR: 0.00000563] [loss: 0.7972] ETA: 0:00:01.944427\n", + "[2025-03-11 23:38:59.018][ INFO](maisi.controlnet.training) -\n", + "[Epoch 1/2] [Batch 2/2] [LR: 0.00000250] [loss: 0.7981] ETA: 0:00:00\n", + "[2025-03-11 23:38:59.775][ INFO](maisi.controlnet.training) - best loss -> 0.7976870536804199.\n", + "[2025-03-11 23:39:00.998][ INFO](maisi.controlnet.training) -\n", + "[Epoch 2/2] [Batch 1/2] [LR: 0.00000063] [loss: 0.7971] ETA: 0:00:01.979231\n", + "[2025-03-11 23:39:01.129][ INFO](maisi.controlnet.training) -\n", + "[Epoch 2/2] [Batch 2/2] [LR: 0.00000000] [loss: 0.7994] ETA: 0:00:00\n", "\n" ] } @@ -540,36 +539,32 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 23:19:42.173][ INFO](notebook) - Inference...\n" + "[2025-03-11 23:39:03.635][ INFO](notebook) - Inference...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[2025-03-11 23:19:51.737][ INFO](maisi.controlnet.infer) - Number of GPUs: 8\n", - "[2025-03-11 23:19:51.738][ INFO](maisi.controlnet.infer) - World_size: 1\n", - "[2025-03-11 23:19:52.727][ INFO](maisi.controlnet.infer) - trained autoencoder model is not loaded.\n", - "[2025-03-11 23:19:53.928][ INFO](maisi.controlnet.infer) - trained diffusion model is not loaded.\n", - "[2025-03-11 23:19:53.928][ INFO](maisi.controlnet.infer) - set scale_factor -> 1.0.\n", - "2025-03-11 23:19:54,436 - INFO - 'dst' model updated: 158 of 206 variables.\n", - "[2025-03-11 23:19:54.441][ INFO](maisi.controlnet.infer) - trained controlnet is not loaded.\n", - "[2025-03-11 23:19:55.156][ INFO](root) - `controllable_anatomy_size` is not provided.\n", - "[2025-03-11 23:19:55.158][ INFO](root) - ---- Start generating latent features... ----\n", - "[2025-03-11 23:19:56.264][ INFO](root) - ---- Latent features generation time: 1.1059284210205078 seconds ----\n", - "[2025-03-11 23:19:56.270][ INFO](root) - ---- Start decoding latent features into images... ----\n", - "[2025-03-11 23:19:57.066][ INFO](root) - ---- Image decoding time: 0.7959444522857666 seconds ----\n", - "2025-03-11 23:19:57,373 INFO image_writer.py:197 - writing: temp_work_dir_controlnet_train_demo/outputs/sample_20250311_231957_354982_image.nii.gz\n", - "2025-03-11 23:19:57,723 INFO image_writer.py:197 - writing: temp_work_dir_controlnet_train_demo/outputs/sample_20250311_231957_354982_label.nii.gz\n", + "[2025-03-11 23:39:13.628][ INFO](maisi.controlnet.infer) - Number of GPUs: 8\n", + "[2025-03-11 23:39:13.628][ INFO](maisi.controlnet.infer) - World_size: 1\n", + "[2025-03-11 23:39:14.205][ INFO](maisi.controlnet.infer) - trained autoencoder model is not loaded.\n", + "[2025-03-11 23:39:15.418][ INFO](maisi.controlnet.infer) - trained diffusion model is not loaded.\n", + "[2025-03-11 23:39:15.418][ INFO](maisi.controlnet.infer) - set scale_factor -> 1.0.\n", + "2025-03-11 23:39:15,917 - INFO - 'dst' model updated: 158 of 206 variables.\n", + "[2025-03-11 23:39:15.922][ INFO](maisi.controlnet.infer) - trained controlnet is not loaded.\n", + "[2025-03-11 23:39:16.582][ INFO](root) - `controllable_anatomy_size` is not provided.\n", + "[2025-03-11 23:39:16.584][ INFO](root) - ---- Start generating latent features... ----\n", + "[2025-03-11 23:39:17.178][ INFO](root) - ---- Latent features generation time: 0.5939664840698242 seconds ----\n", + "[2025-03-11 23:39:17.180][ INFO](root) - ---- Start decoding latent features into images... ----\n", + "[2025-03-11 23:39:18.003][ INFO](root) - ---- Image decoding time: 0.8231167793273926 seconds ----\n", + "2025-03-11 23:39:18,299 INFO image_writer.py:197 - writing: temp_work_dir_controlnet_train_demo/outputs/sample_20250311_233918_283950_image.nii.gz\n", + "2025-03-11 23:39:18,649 INFO image_writer.py:197 - writing: temp_work_dir_controlnet_train_demo/outputs/sample_20250311_233918_283950_label.nii.gz\n", "\n", "\n", - " 0%| | 0/10 [00:00 Date: Tue, 11 Mar 2025 23:42:17 +0000 Subject: [PATCH 17/59] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- generation/maisi/maisi_inference_tutorial.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/generation/maisi/maisi_inference_tutorial.ipynb b/generation/maisi/maisi_inference_tutorial.ipynb index 2cace32ac..5c5ac78dd 100644 --- a/generation/maisi/maisi_inference_tutorial.ipynb +++ b/generation/maisi/maisi_inference_tutorial.ipynb @@ -452,9 +452,9 @@ " model_def_path = \"./configs/config_maisi3d-rflow.json\"\n", "else:\n", " raise ValueError(f\"maisi_version has to be chosen from ['maisi3d-ddpm', 'maisi3d-rflow'], yet got {maisi_version}.\")\n", - " \n", + "\n", "with open(model_def_path, \"r\") as f:\n", - " model_def = json.load(f) \n", + " model_def = json.load(f)\n", "for k, v in model_def.items():\n", " setattr(args, k, v)\n", "\n", From de69a559ed28d53f61c38b9b1a0cbfe689fd7dbe Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Tue, 11 Mar 2025 23:45:05 +0000 Subject: [PATCH 18/59] update code Signed-off-by: Can-Zhao --- generation/maisi/scripts/diff_model_infer.py | 23 +++-- generation/maisi/scripts/diff_model_train.py | 9 +- generation/maisi/scripts/infer_controlnet.py | 37 +++++--- generation/maisi/scripts/sample.py | 12 +-- generation/maisi/scripts/train_controlnet.py | 88 ++++++++++++++------ 5 files changed, 113 insertions(+), 56 deletions(-) diff --git a/generation/maisi/scripts/diff_model_infer.py b/generation/maisi/scripts/diff_model_infer.py index bc6b3539c..048ac7ea7 100644 --- a/generation/maisi/scripts/diff_model_infer.py +++ b/generation/maisi/scripts/diff_model_infer.py @@ -111,6 +111,7 @@ def run_inference( output_size: tuple, divisor: int, logger: logging.Logger, + include_body_region: bool = False, ) -> np.ndarray: """ Run the inference to generate synthetic images. @@ -165,13 +166,20 @@ def run_inference( ) with torch.amp.autocast("cuda", enabled=True): for t, next_t in progress_bar: - model_output = unet( - x=image, - timesteps=torch.Tensor((t,)).to(device), - top_region_index_tensor=top_region_index_tensor, - bottom_region_index_tensor=bottom_region_index_tensor, - spacing_tensor=spacing_tensor, - ) + if include_body_region: + model_output = unet( + x=image, + timesteps=torch.Tensor((t,)).to(device), + top_region_index_tensor=top_region_index_tensor, + bottom_region_index_tensor=bottom_region_index_tensor, + spacing_tensor=spacing_tensor, + ) + else: + model_output = unet( + x=image, + timesteps=torch.Tensor((t,)).to(device), + spacing_tensor=spacing_tensor, + ) if not isinstance(noise_scheduler, RFlowScheduler): image, _ = noise_scheduler.step(model_output, t, image) # type: ignore else: @@ -283,6 +291,7 @@ def diff_model_infer( output_size, divisor, logger, + include_body_region = include_body_region, ) timestamp = datetime.now().strftime("%Y%m%d%H%M%S") diff --git a/generation/maisi/scripts/diff_model_train.py b/generation/maisi/scripts/diff_model_train.py index 1fd8b003d..7ee31d551 100644 --- a/generation/maisi/scripts/diff_model_train.py +++ b/generation/maisi/scripts/diff_model_train.py @@ -273,20 +273,23 @@ def train_one_epoch( timesteps=timesteps, spacing_tensor=spacing_tensor, ) + if noise_scheduler.prediction_type == DDPMPredictionType.EPSILON: # predict noise - loss = loss_pt(model_output.float(), noise.float()) + model_gt = noise elif noise_scheduler.prediction_type == DDPMPredictionType.SAMPLE: # predict sample - loss = loss_pt(model_output.float(), images.float()) + model_gt = images elif noise_scheduler.prediction_type == DDPMPredictionType.V_PREDICTION: # predict velocity - loss = loss_pt(model_output.float(), (images - noise).float()) + model_gt = images - noise else: raise ValueError( "noise scheduler prediction type has to be chosen from ", f"[{DDPMPredictionType.EPSILON},{DDPMPredictionType.SAMPLE},{DDPMPredictionType.V_PREDICTION}]", ) + + loss = loss_pt(model_output.float(), model_gt.float()) if amp: scaler.scale(loss).backward() diff --git a/generation/maisi/scripts/infer_controlnet.py b/generation/maisi/scripts/infer_controlnet.py index f43078375..6a00228c6 100644 --- a/generation/maisi/scripts/infer_controlnet.py +++ b/generation/maisi/scripts/infer_controlnet.py @@ -49,7 +49,14 @@ def main(): help="config json file that stores training hyper-parameters", ) parser.add_argument("-g", "--gpus", default=1, type=int, help="number of gpus per node") + parser.add_argument( + "--include_body_region", + dest="include_body_region", + action="store_true", + help="Whether to include body region in data", + ) args = parser.parse_args() + include_body_region = args.include_body_region # Step 0: configuration logger = logging.getLogger("maisi.controlnet.infer") @@ -150,8 +157,12 @@ def main(): # get label mask labels = batch["label"].to(device) # get corresponding conditions - top_region_index_tensor = batch["top_region_index"].to(device) - bottom_region_index_tensor = batch["bottom_region_index"].to(device) + if include_body_region: + top_region_index_tensor = batch["top_region_index"].to(device) + bottom_region_index_tensor = batch["bottom_region_index"].to(device) + else: + top_region_index_tensor = None + bottom_region_index_tensor = None spacing_tensor = batch["spacing"].to(device) out_spacing = tuple((batch["spacing"].squeeze().numpy() / 100).tolist()) # get target dimension @@ -162,22 +173,22 @@ def main(): check_input(None, None, None, output_size, out_spacing, None) # generate a single synthetic image using a latent diffusion model with controlnet. synthetic_images, _ = ldm_conditional_sample_one_image( - autoencoder, - unet, - controlnet, - noise_scheduler, - scale_factor, - device, - labels, - top_region_index_tensor, - bottom_region_index_tensor, - spacing_tensor, + autoencoder=autoencoder, + diffusion_unet=unet, + controlnet=controlnet, + noise_scheduler=noise_scheduler, + scale_factor=scale_factor, + device=device, + combine_label_or=labels, + top_region_index_tensor=top_region_index_tensor, + bottom_region_index_tensor=bottom_region_index_tensor, + spacing_tensor=spacing_tensor, latent_shape=latent_shape, output_size=output_size, noise_factor=1.0, num_inference_steps=args.controlnet_infer["num_inference_steps"], - # reduce it when GPU memory is limited autoencoder_sliding_window_infer_size=args.controlnet_infer["autoencoder_sliding_window_infer_size"], + autoencoder_sliding_window_infer_overlap=args.controlnet_infer["autoencoder_sliding_window_infer_overlap"] ) # save image/label pairs labels = decollate_batch(batch)[0]["label"] diff --git a/generation/maisi/scripts/sample.py b/generation/maisi/scripts/sample.py index 2f934b392..36b323a37 100644 --- a/generation/maisi/scripts/sample.py +++ b/generation/maisi/scripts/sample.py @@ -182,13 +182,13 @@ def ldm_conditional_sample_one_image( noise_scheduler, scale_factor, device, - combine_label_or, - top_region_index_tensor, - bottom_region_index_tensor, + combine_label_or, spacing_tensor, latent_shape, output_size, noise_factor, + top_region_index_tensor = None, + bottom_region_index_tensor = None, num_inference_steps=1000, autoencoder_sliding_window_infer_size=[96, 96, 96], autoencoder_sliding_window_infer_overlap=0.6667, @@ -203,13 +203,13 @@ def ldm_conditional_sample_one_image( noise_scheduler: The noise scheduler for the diffusion process. scale_factor (float): Scaling factor for the latent space. device (torch.device): The device to run the computation on. - combine_label_or (torch.Tensor): The combined label tensor. - top_region_index_tensor (torch.Tensor): Tensor specifying the top region index. - bottom_region_index_tensor (torch.Tensor): Tensor specifying the bottom region index. + combine_label_or (torch.Tensor): The combined label tensor. spacing_tensor (torch.Tensor): Tensor specifying the spacing. latent_shape (tuple): The shape of the latent space. output_size (tuple): The desired output size of the image. noise_factor (float): Factor to scale the initial noise. + top_region_index_tensor (torch.Tensor): Tensor specifying the top region index. Defaults to None. + bottom_region_index_tensor (torch.Tensor): Tensor specifying the bottom region index. Defaults to None. num_inference_steps (int): Number of inference steps for the diffusion process. autoencoder_sliding_window_infer_size (list, optional): Size of the sliding window for inference. Defaults to [96, 96, 96]. autoencoder_sliding_window_infer_overlap (float, optional): Overlap ratio for sliding window inference. Defaults to 0.6667. diff --git a/generation/maisi/scripts/train_controlnet.py b/generation/maisi/scripts/train_controlnet.py index 33e09cc76..0e9c83462 100644 --- a/generation/maisi/scripts/train_controlnet.py +++ b/generation/maisi/scripts/train_controlnet.py @@ -23,6 +23,8 @@ import torch.nn.functional as F from monai.networks.utils import copy_model_state from monai.utils import RankFilter +from monai.networks.schedulers import RFlowScheduler +from monai.networks.schedulers.ddpm import DDPMPredictionType from torch.amp import GradScaler, autocast from torch.nn.parallel import DistributedDataParallel as DDP from torch.utils.tensorboard import SummaryWriter @@ -49,9 +51,16 @@ def main(): "--training-config", default="./configs/config_maisi_controlnet_train.json", help="config json file that stores training hyper-parameters", - ) + ) parser.add_argument("-g", "--gpus", default=1, type=int, help="number of gpus per node") + parser.add_argument( + "--include_body_region", + dest="include_body_region", + action="store_true", + help="Whether to include body region in data", + ) args = parser.parse_args() + include_body_region = args.include_body_region # Step 0: configuration logger = logging.getLogger("maisi.controlnet.training") @@ -168,58 +177,83 @@ def main(): epoch_loss_ = 0 for step, batch in enumerate(train_loader): # get image embedding and label mask and scale image embedding by the provided scale_factor - inputs = batch["image"].to(device) * scale_factor + images = batch["image"].to(device) * scale_factor labels = batch["label"].to(device) # get corresponding conditions - top_region_index_tensor = batch["top_region_index"].to(device) - bottom_region_index_tensor = batch["bottom_region_index"].to(device) + if include_body_region: + top_region_index_tensor = batch["top_region_index"].to(device) + bottom_region_index_tensor = batch["bottom_region_index"].to(device) spacing_tensor = batch["spacing"].to(device) optimizer.zero_grad(set_to_none=True) with autocast("cuda", enabled=True): # generate random noise - noise_shape = list(inputs.shape) - noise = torch.randn(noise_shape, dtype=inputs.dtype).to(device) + noise_shape = list(images.shape) + noise = torch.randn(noise_shape, dtype=images.dtype).to(device) # use binary encoding to encode segmentation mask controlnet_cond = binarize_labels(labels.as_tensor().to(torch.uint8)).float() # create timesteps - timesteps = torch.randint( - 0, noise_scheduler.num_train_timesteps, (inputs.shape[0],), device=device - ).long() + if isinstance(noise_scheduler, RFlowScheduler): + timesteps = noise_scheduler.sample_timesteps(images) + else: + timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (images.shape[0],), device=images.device).long() # create noisy latent - noisy_latent = noise_scheduler.add_noise(original_samples=inputs, noise=noise, timesteps=timesteps) + noisy_latent = noise_scheduler.add_noise(original_samples=images, noise=noise, timesteps=timesteps) # get controlnet output down_block_res_samples, mid_block_res_sample = controlnet( x=noisy_latent, timesteps=timesteps, controlnet_cond=controlnet_cond ) - # get noise prediction from diffusion unet - noise_pred = unet( - x=noisy_latent, - timesteps=timesteps, - top_region_index_tensor=top_region_index_tensor, - bottom_region_index_tensor=bottom_region_index_tensor, - spacing_tensor=spacing_tensor, - down_block_additional_residuals=down_block_res_samples, - mid_block_additional_residual=mid_block_res_sample, - ) + if include_body_region: + model_output = unet( + x=noisy_latent, + timesteps=timesteps, + top_region_index_tensor=top_region_index_tensor, + bottom_region_index_tensor=bottom_region_index_tensor, + spacing_tensor=spacing_tensor, + down_block_additional_residuals=down_block_res_samples, + mid_block_additional_residual=mid_block_res_sample, + ) + else: + model_output = unet( + x=noisy_latent, + timesteps=timesteps, + spacing_tensor=spacing_tensor, + down_block_additional_residuals=down_block_res_samples, + mid_block_additional_residual=mid_block_res_sample, + ) + if noise_scheduler.prediction_type == DDPMPredictionType.EPSILON: + # predict noise + model_gt = noise + elif noise_scheduler.prediction_type == DDPMPredictionType.SAMPLE: + # predict sample + model_gt = images + elif noise_scheduler.prediction_type == DDPMPredictionType.V_PREDICTION: + # predict velocity + model_gt = images - noise + else: + raise ValueError( + "noise scheduler prediction type has to be chosen from ", + f"[{DDPMPredictionType.EPSILON},{DDPMPredictionType.SAMPLE},{DDPMPredictionType.V_PREDICTION}]", + ) + if weighted_loss > 1.0: - weights = torch.ones_like(inputs).to(inputs.device) - roi = torch.zeros([noise_shape[0]] + [1] + noise_shape[2:]).to(inputs.device) - interpolate_label = F.interpolate(labels, size=inputs.shape[2:], mode="nearest") + weights = torch.ones_like(images).to(images.device) + roi = torch.zeros([noise_shape[0]] + [1] + noise_shape[2:]).to(images.device) + interpolate_label = F.interpolate(labels, size=images.shape[2:], mode="nearest") # assign larger weights for ROI (tumor) for label in weighted_loss_label: roi[interpolate_label == label] = 1 - weights[roi.repeat(1, inputs.shape[1], 1, 1, 1) == 1] = weighted_loss - loss = (F.l1_loss(noise_pred.float(), noise.float(), reduction="none") * weights).mean() + weights[roi.repeat(1, images.shape[1], 1, 1, 1) == 1] = weighted_loss + loss = (F.l1_loss(noise_pred.float(), model_gt.float(), reduction="none") * weights).mean() else: - loss = F.l1_loss(noise_pred.float(), noise.float()) - + loss = F.l1_loss(model_output.float(), model_gt.float()) + scaler.scale(loss).backward() scaler.step(optimizer) scaler.update() From f93d2fb2d36af5f8cb0a3550ad7b7454c7637e0d Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 11 Mar 2025 23:46:43 +0000 Subject: [PATCH 19/59] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- generation/maisi/scripts/diff_model_infer.py | 2 +- generation/maisi/scripts/diff_model_train.py | 2 +- generation/maisi/scripts/infer_controlnet.py | 2 +- generation/maisi/scripts/sample.py | 8 ++++---- generation/maisi/scripts/train_controlnet.py | 10 ++++++---- 5 files changed, 13 insertions(+), 11 deletions(-) diff --git a/generation/maisi/scripts/diff_model_infer.py b/generation/maisi/scripts/diff_model_infer.py index 048ac7ea7..b7ad8b5aa 100644 --- a/generation/maisi/scripts/diff_model_infer.py +++ b/generation/maisi/scripts/diff_model_infer.py @@ -291,7 +291,7 @@ def diff_model_infer( output_size, divisor, logger, - include_body_region = include_body_region, + include_body_region=include_body_region, ) timestamp = datetime.now().strftime("%Y%m%d%H%M%S") diff --git a/generation/maisi/scripts/diff_model_train.py b/generation/maisi/scripts/diff_model_train.py index 7ee31d551..95c1d10fe 100644 --- a/generation/maisi/scripts/diff_model_train.py +++ b/generation/maisi/scripts/diff_model_train.py @@ -288,7 +288,7 @@ def train_one_epoch( "noise scheduler prediction type has to be chosen from ", f"[{DDPMPredictionType.EPSILON},{DDPMPredictionType.SAMPLE},{DDPMPredictionType.V_PREDICTION}]", ) - + loss = loss_pt(model_output.float(), model_gt.float()) if amp: diff --git a/generation/maisi/scripts/infer_controlnet.py b/generation/maisi/scripts/infer_controlnet.py index 6a00228c6..da55f33f8 100644 --- a/generation/maisi/scripts/infer_controlnet.py +++ b/generation/maisi/scripts/infer_controlnet.py @@ -188,7 +188,7 @@ def main(): noise_factor=1.0, num_inference_steps=args.controlnet_infer["num_inference_steps"], autoencoder_sliding_window_infer_size=args.controlnet_infer["autoencoder_sliding_window_infer_size"], - autoencoder_sliding_window_infer_overlap=args.controlnet_infer["autoencoder_sliding_window_infer_overlap"] + autoencoder_sliding_window_infer_overlap=args.controlnet_infer["autoencoder_sliding_window_infer_overlap"], ) # save image/label pairs labels = decollate_batch(batch)[0]["label"] diff --git a/generation/maisi/scripts/sample.py b/generation/maisi/scripts/sample.py index 36b323a37..3b5dc4474 100644 --- a/generation/maisi/scripts/sample.py +++ b/generation/maisi/scripts/sample.py @@ -182,13 +182,13 @@ def ldm_conditional_sample_one_image( noise_scheduler, scale_factor, device, - combine_label_or, + combine_label_or, spacing_tensor, latent_shape, output_size, noise_factor, - top_region_index_tensor = None, - bottom_region_index_tensor = None, + top_region_index_tensor=None, + bottom_region_index_tensor=None, num_inference_steps=1000, autoencoder_sliding_window_infer_size=[96, 96, 96], autoencoder_sliding_window_infer_overlap=0.6667, @@ -203,7 +203,7 @@ def ldm_conditional_sample_one_image( noise_scheduler: The noise scheduler for the diffusion process. scale_factor (float): Scaling factor for the latent space. device (torch.device): The device to run the computation on. - combine_label_or (torch.Tensor): The combined label tensor. + combine_label_or (torch.Tensor): The combined label tensor. spacing_tensor (torch.Tensor): Tensor specifying the spacing. latent_shape (tuple): The shape of the latent space. output_size (tuple): The desired output size of the image. diff --git a/generation/maisi/scripts/train_controlnet.py b/generation/maisi/scripts/train_controlnet.py index 0e9c83462..a43ffdd5d 100644 --- a/generation/maisi/scripts/train_controlnet.py +++ b/generation/maisi/scripts/train_controlnet.py @@ -51,7 +51,7 @@ def main(): "--training-config", default="./configs/config_maisi_controlnet_train.json", help="config json file that stores training hyper-parameters", - ) + ) parser.add_argument("-g", "--gpus", default=1, type=int, help="number of gpus per node") parser.add_argument( "--include_body_region", @@ -199,7 +199,9 @@ def main(): if isinstance(noise_scheduler, RFlowScheduler): timesteps = noise_scheduler.sample_timesteps(images) else: - timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (images.shape[0],), device=images.device).long() + timesteps = torch.randint( + 0, noise_scheduler.num_train_timesteps, (images.shape[0],), device=images.device + ).long() # create noisy latent noisy_latent = noise_scheduler.add_noise(original_samples=images, noise=noise, timesteps=timesteps) @@ -241,7 +243,7 @@ def main(): "noise scheduler prediction type has to be chosen from ", f"[{DDPMPredictionType.EPSILON},{DDPMPredictionType.SAMPLE},{DDPMPredictionType.V_PREDICTION}]", ) - + if weighted_loss > 1.0: weights = torch.ones_like(images).to(images.device) roi = torch.zeros([noise_shape[0]] + [1] + noise_shape[2:]).to(images.device) @@ -253,7 +255,7 @@ def main(): loss = (F.l1_loss(noise_pred.float(), model_gt.float(), reduction="none") * weights).mean() else: loss = F.l1_loss(model_output.float(), model_gt.float()) - + scaler.scale(loss).backward() scaler.step(optimizer) scaler.update() From adc5292426bffcae653c9fa683a305b8350f3365 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Wed, 12 Mar 2025 00:39:17 +0000 Subject: [PATCH 20/59] update readme Signed-off-by: Can-Zhao --- generation/maisi/README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/generation/maisi/README.md b/generation/maisi/README.md index f51cdf61f..733b5949c 100644 --- a/generation/maisi/README.md +++ b/generation/maisi/README.md @@ -29,7 +29,8 @@ We retrained several state-of-the-art diffusion model-based methods using our da | [DDPM](https://proceedings.neurips.cc/paper_files/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf) | 18.524 | 23.696 | 25.604 | 22.608 | | [LDM](https://openaccess.thecvf.com/content/CVPR2022/papers/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.pdf) | 16.853 | 10.191 | 10.093 | 12.379 | | [HA-GAN](https://ieeexplore.ieee.org/document/9770375) | 17.432 | 10.266 | 13.572 | 13.757 | -| MAISI | 3.301 | 5.838 | 9.109 | 6.083 | +| MAISI-DDPM | 3.301 | 5.838 | 9.109 | 6.083 | +| MAISI-RFlow | 2.685 | 4.723 | 7.963 | 5.124 | **Table 1.** Comparison of Fréchet Inception Distance scores between our foundation model and retrained baseline methods
using the unseen public dataset [autoPET 2023](https://www.nature.com/articles/s41597-022-01718-3) as the reference. From 57fb6105a91e716a8eb1c45b5cefa81b96c6f39e Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Wed, 12 Mar 2025 18:43:43 +0000 Subject: [PATCH 21/59] update readme Signed-off-by: Can-Zhao --- generation/maisi/README.md | 8 ++++---- generation/maisi/maisi_train_controlnet_tutorial.ipynb | 2 +- generation/maisi/maisi_train_diff_unet_tutorial.ipynb | 2 +- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/generation/maisi/README.md b/generation/maisi/README.md index 733b5949c..73dcdfb43 100644 --- a/generation/maisi/README.md +++ b/generation/maisi/README.md @@ -168,7 +168,7 @@ For example, To run the inference script, please run: ```bash export MONAI_DATA_DIRECTORY= -python -m scripts.inference -c ./configs/config_maisi.json -i ./configs/config_infer.json -e ./configs/environment.json --random-seed 0 +python -m scripts.inference -c ./configs/config_maisi3d-ddpm.json -i ./configs/config_infer.json -e ./configs/environment.json --random-seed 0 ``` Please refer to [maisi_inference_tutorial.ipynb](maisi_inference_tutorial.ipynb) for the tutorial for MAISI model inference. @@ -216,7 +216,7 @@ Please refer to [maisi_train_vae_tutorial.ipynb](maisi_train_vae_tutorial.ipynb) #### [3.2 3D Latent Diffusion Training](./scripts/diff_model_train.py) -Please refer to [maisi_diff_unet_training_tutorial.ipynb](maisi_diff_unet_training_tutorial.ipynb) for the tutorial for MAISI diffusion model training. +Please refer to [maisi_train_diff_unet_tutorial.ipynb](maisi_train_diff_unet_tutorial.ipynb) for the tutorial for MAISI diffusion model training. #### [3.3 3D ControlNet Training](./scripts/train_controlnet.py) @@ -234,7 +234,7 @@ The training was performed with the following: #### Execute Training: To train with a single GPU, please run: ```bash -python -m scripts.train_controlnet -c ./configs/config_maisi.json -t ./configs/config_maisi_controlnet_train.json -e ./configs/environment_maisi_controlnet_train.json -g 1 +python -m scripts.train_controlnet -c ./configs/config_maisi3d-ddpm.json -t ./configs/config_maisi_controlnet_train.json -e ./configs/environment_maisi_controlnet_train.json -g 1 ``` The training script also enables multi-GPU training. For instance, if you are using eight GPUs, you can run the training script with the following command: @@ -244,7 +244,7 @@ torchrun \ --nproc_per_node=${NUM_GPUS_PER_NODE} \ --nnodes=1 \ --master_addr=localhost --master_port=1234 \ - -m scripts.train_controlnet -c ./configs/config_maisi.json -t ./configs/config_maisi_controlnet_train.json -e ./configs/environment_maisi_controlnet_train.json -g ${NUM_GPUS_PER_NODE} + -m scripts.train_controlnet -c ./configs/config_maisi3d-ddpm.json -t ./configs/config_maisi_controlnet_train.json -e ./configs/environment_maisi_controlnet_train.json -g ${NUM_GPUS_PER_NODE} ``` Please also check [maisi_train_controlnet_tutorial.ipynb](./maisi_train_controlnet_tutorial.ipynb) for more details about data preparation and training parameters. diff --git a/generation/maisi/maisi_train_controlnet_tutorial.ipynb b/generation/maisi/maisi_train_controlnet_tutorial.ipynb index 855ac887a..8f1d1368c 100644 --- a/generation/maisi/maisi_train_controlnet_tutorial.ipynb +++ b/generation/maisi/maisi_train_controlnet_tutorial.ipynb @@ -127,7 +127,7 @@ "\n", "Choose between `'maisi3d-ddpm'` and `'maisi3d-rflow'`. The differences are:\n", "- The maisi version `'maisi3d-ddpm'` uses basic noise scheduler DDPM. `'maisi3d-rflow'` uses Rectified Flow scheduler, can be 33 times faster during inference.\n", - "- The maisi version `'maisi3d-ddpm'` requires training images to be labeled with body region (`\"top_region_index\"` and `\"bottom_region_index\"`), while `'maisi3d-rflow'` does not have such requirement. In other words, it is easier to prepare training data for `'maisi3d-rflow'`.\n", + "- The maisi version `'maisi3d-ddpm'` requires training images to be labeled with body regions (`\"top_region_index\"` and `\"bottom_region_index\"`), while `'maisi3d-rflow'` does not have such requirement. In other words, it is easier to prepare training data for `'maisi3d-rflow'`.\n", "- For the released model weights, `'maisi3d-rflow'` can generate images with better quality for head region and small output volumes, and comparable quality for other cases compared with `'maisi3d-ddpm'`." ] }, diff --git a/generation/maisi/maisi_train_diff_unet_tutorial.ipynb b/generation/maisi/maisi_train_diff_unet_tutorial.ipynb index 9d603d8dd..5b38141ff 100644 --- a/generation/maisi/maisi_train_diff_unet_tutorial.ipynb +++ b/generation/maisi/maisi_train_diff_unet_tutorial.ipynb @@ -129,7 +129,7 @@ "\n", "Choose between `'maisi3d-ddpm'` and `'maisi3d-rflow'`. The differences are:\n", "- The maisi version `'maisi3d-ddpm'` uses basic noise scheduler DDPM. `'maisi3d-rflow'` uses Rectified Flow scheduler, can be 33 times faster during inference.\n", - "- The maisi version `'maisi3d-ddpm'` requires training images to be labeled with body region (`\"top_region_index\"` and `\"bottom_region_index\"`), while `'maisi3d-rflow'` does not have such requirement. In other words, it is easier to prepare training data for `'maisi3d-rflow'`.\n", + "- The maisi version `'maisi3d-ddpm'` requires training images to be labeled with body regions (`\"top_region_index\"` and `\"bottom_region_index\"`), while `'maisi3d-rflow'` does not have such requirement. In other words, it is easier to prepare training data for `'maisi3d-rflow'`.\n", "- For the released model weights, `'maisi3d-rflow'` can generate images with better quality for head region and small output volumes, and comparable quality for other cases compared with `'maisi3d-ddpm'`." ] }, From 2dc8039473795eace93f4534af6928e0b774582f Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Wed, 12 Mar 2025 20:01:37 +0000 Subject: [PATCH 22/59] update environment Signed-off-by: Can-Zhao --- .../maisi/configs/environment_maisi3d-ddpm.json | 13 +++++++++++++ .../maisi/configs/environment_maisi3d-rflow.json | 13 +++++++++++++ 2 files changed, 26 insertions(+) create mode 100644 generation/maisi/configs/environment_maisi3d-ddpm.json create mode 100644 generation/maisi/configs/environment_maisi3d-rflow.json diff --git a/generation/maisi/configs/environment_maisi3d-ddpm.json b/generation/maisi/configs/environment_maisi3d-ddpm.json new file mode 100644 index 000000000..97ba2b590 --- /dev/null +++ b/generation/maisi/configs/environment_maisi3d-ddpm.json @@ -0,0 +1,13 @@ +{ + "output_dir": "output", + "trained_autoencoder_path": "models/autoencoder_epoch273.pt", + "trained_diffusion_path": "models/diff_unet_3d_ddpm.pt", + "trained_controlnet_path": "models/controlnet_3d_ddpm.pt", + "trained_mask_generation_autoencoder_path": "models/mask_generation_autoencoder.pt", + "trained_mask_generation_diffusion_path": "models/mask_generation_diffusion_unet.pt", + "all_mask_files_base_dir": "datasets/all_masks_flexible_size_and_spacing_3000", + "all_mask_files_json": "./configs/candidate_masks_flexible_size_and_spacing_3000.json", + "all_anatomy_size_conditions_json": "./configs/all_anatomy_size_condtions.json", + "label_dict_json": "./configs/label_dict.json", + "label_dict_remap_json": "./configs/label_dict_124_to_132.json" +} diff --git a/generation/maisi/configs/environment_maisi3d-rflow.json b/generation/maisi/configs/environment_maisi3d-rflow.json new file mode 100644 index 000000000..c1be37b59 --- /dev/null +++ b/generation/maisi/configs/environment_maisi3d-rflow.json @@ -0,0 +1,13 @@ +{ + "output_dir": "output", + "trained_autoencoder_path": "models/autoencoder_epoch273.pt", + "trained_diffusion_path": "models/diff_unet_3d_rflow.pt", + "trained_controlnet_path": "models/controlnet_3d_rflow.pt", + "trained_mask_generation_autoencoder_path": "models/mask_generation_autoencoder.pt", + "trained_mask_generation_diffusion_path": "models/mask_generation_diffusion_unet.pt", + "all_mask_files_base_dir": "datasets/all_masks_flexible_size_and_spacing_4000", + "all_mask_files_json": "./configs/candidate_masks_flexible_size_and_spacing_4000.json", + "all_anatomy_size_conditions_json": "./configs/all_anatomy_size_condtions.json", + "label_dict_json": "./configs/label_dict.json", + "label_dict_remap_json": "./configs/label_dict_124_to_132.json" +} From 151177d2322f7ad1bd1b9ab5aedaef19f6fcf445 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Thu, 13 Mar 2025 05:35:14 +0000 Subject: [PATCH 23/59] add modality as input, make inference notebook excutable for ddpm and rflow Signed-off-by: Can-Zhao --- generation/maisi/configs/config_infer.json | 5 +- .../maisi/configs/config_maisi3d-ddpm.json | 5 +- .../maisi/configs/config_maisi3d-rflow.json | 5 +- .../config_maisi_controlnet_train.json | 6 +- .../configs/config_maisi_diff_model.json | 3 +- .../maisi/maisi_inference_tutorial.ipynb | 405 +++++------- .../maisi_train_controlnet_tutorial.ipynb | 104 ++-- .../maisi_train_diff_unet_tutorial.ipynb | 121 ++-- generation/maisi/scripts/augmentation.py | 575 ++++++------------ generation/maisi/scripts/diff_model_infer.py | 60 +- generation/maisi/scripts/diff_model_train.py | 59 +- generation/maisi/scripts/infer_controlnet.py | 11 +- generation/maisi/scripts/quality_check.py | 10 +- generation/maisi/scripts/sample.py | 379 +++++++----- generation/maisi/scripts/train_controlnet.py | 63 +- 15 files changed, 821 insertions(+), 990 deletions(-) diff --git a/generation/maisi/configs/config_infer.json b/generation/maisi/configs/config_infer.json index fc08a7bda..65769f20f 100644 --- a/generation/maisi/configs/config_infer.json +++ b/generation/maisi/configs/config_infer.json @@ -3,7 +3,7 @@ "body_region": ["abdomen"], "anatomy_list": ["liver","hepatic tumor"], "controllable_anatomy_size": [], - "num_inference_steps": 1000, + "num_inference_steps": 30, "mask_generation_num_inference_steps": 1000, "output_size": [ 256, @@ -23,5 +23,6 @@ "diffusion_unet": "$@diffusion_unet_def", "autoencoder": "$@autoencoder_def", "mask_generation_autoencoder": "$@mask_generation_autoencoder_def", - "mask_generation_diffusion": "$@mask_generation_diffusion_def" + "mask_generation_diffusion": "$@mask_generation_diffusion_def", + "modality": 1 } diff --git a/generation/maisi/configs/config_maisi3d-ddpm.json b/generation/maisi/configs/config_maisi3d-ddpm.json index 8a781ca5b..67b38e9c1 100644 --- a/generation/maisi/configs/config_maisi3d-ddpm.json +++ b/generation/maisi/configs/config_maisi3d-ddpm.json @@ -2,6 +2,7 @@ "spatial_dims": 3, "image_channels": 1, "latent_channels": 4, + "include_body_region": true, "mask_generation_latent_shape": [ 4, 64, @@ -60,8 +61,8 @@ ], "num_res_blocks": 2, "use_flash_attention": true, - "include_top_region_index_input": true, - "include_bottom_region_index_input": true, + "include_top_region_index_input": "@include_body_region", + "include_bottom_region_index_input": "@include_body_region", "include_spacing_input": true }, "controlnet_def": { diff --git a/generation/maisi/configs/config_maisi3d-rflow.json b/generation/maisi/configs/config_maisi3d-rflow.json index 56d769284..d76da08bf 100644 --- a/generation/maisi/configs/config_maisi3d-rflow.json +++ b/generation/maisi/configs/config_maisi3d-rflow.json @@ -2,6 +2,7 @@ "spatial_dims": 3, "image_channels": 1, "latent_channels": 4, + "include_body_region": false, "mask_generation_latent_shape": [ 4, 64, @@ -55,8 +56,8 @@ ], "num_res_blocks": 2, "use_flash_attention": true, - "include_top_region_index_input": false, - "include_bottom_region_index_input": false, + "include_top_region_index_input": "@include_body_region", + "include_bottom_region_index_input": "@include_body_region", "include_spacing_input": true, "num_class_embeds": 128, "resblock_updown": true, diff --git a/generation/maisi/configs/config_maisi_controlnet_train.json b/generation/maisi/configs/config_maisi_controlnet_train.json index 4ac94efe6..9bca444bd 100644 --- a/generation/maisi/configs/config_maisi_controlnet_train.json +++ b/generation/maisi/configs/config_maisi_controlnet_train.json @@ -9,7 +9,9 @@ "weighted_loss": 100 }, "controlnet_infer": { - "num_inference_steps": 1000, - "autoencoder_sliding_window_infer_size": [96, 96, 96] + "num_inference_steps": 10, + "autoencoder_sliding_window_infer_size": [80, 80, 80], + "autoencoder_sliding_window_infer_overlap": 0.4, + "modality": 1 } } diff --git a/generation/maisi/configs/config_maisi_diff_model.json b/generation/maisi/configs/config_maisi_diff_model.json index 8407dbdcc..f97a749c8 100644 --- a/generation/maisi/configs/config_maisi_diff_model.json +++ b/generation/maisi/configs/config_maisi_diff_model.json @@ -29,6 +29,7 @@ 0 ], "random_seed": 0, - "num_inference_steps": 10 + "num_inference_steps": 10, + "modality": 1 } } diff --git a/generation/maisi/maisi_inference_tutorial.ipynb b/generation/maisi/maisi_inference_tutorial.ipynb index 5c5ac78dd..9e6e63456 100644 --- a/generation/maisi/maisi_inference_tutorial.ipynb +++ b/generation/maisi/maisi_inference_tutorial.ipynb @@ -141,20 +141,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 23:28:16.158][ INFO](notebook) - Using MAISI version maisi3d-ddpm. Will need body region as data input.\n" + "[2025-03-13 05:29:42.156][ INFO](notebook) - MAISI version is maisi3d-rflow, whether to use body_region is False\n" ] } ], "source": [ - "maisi_version = \"maisi3d-ddpm\"\n", + "maisi_version = \"maisi3d-rflow\"\n", "if maisi_version == \"maisi3d-ddpm\":\n", - " include_body_region = True\n", - " logger.info(\"Using MAISI version maisi3d-ddpm. Will need body region as data input.\")\n", + " model_def_path = \"./configs/config_maisi3d-ddpm.json\"\n", "elif maisi_version == \"maisi3d-rflow\":\n", - " include_body_region = False\n", - " logger.info(\"Using MAISI version maisi3d-rflow. Does not need body region as data input.\")\n", + " model_def_path = \"./configs/config_maisi3d-rflow.json\"\n", "else:\n", - " raise ValueError(f\"maisi_version has to be chosen from ['maisi3d-ddpm', 'maisi3d-rflow'], yet got {maisi_version}.\")" + " raise ValueError(f\"maisi_version has to be chosen from ['maisi3d-ddpm', 'maisi3d-rflow'], yet got {maisi_version}.\")\n", + "with open(model_def_path, \"r\") as f:\n", + " model_def = json.load(f)\n", + "include_body_region = model_def[\"include_body_region\"]\n", + "logger.info(f\"MAISI version is {maisi_version}, whether to use body_region is {include_body_region}\")" ] }, { @@ -178,130 +180,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-11 23:28:16,166 - INFO - Expected md5 is None, skip md5 check for file models/autoencoder_epoch273.pt.\n", - "2025-03-11 23:28:16,167 - INFO - File exists: models/autoencoder_epoch273.pt, skipped downloading.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1.pt: 654MB [00:13, 51.1MB/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-03-11 23:28:30,219 - INFO - Downloaded: models/input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1.pt\n", - "2025-03-11 23:28:30,221 - INFO - Expected md5 is None, skip md5 check for file models/input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1.pt.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current.pt: 265MB [00:11, 23.3MB/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-03-11 23:28:42,451 - INFO - Downloaded: models/controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current.pt\n", - "2025-03-11 23:28:42,453 - INFO - Expected md5 is None, skip md5 check for file models/controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current.pt.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "mask_generation_autoencoder.pt: 20.1MB [00:00, 24.1MB/s] \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-03-11 23:28:43,367 - INFO - Downloaded: models/mask_generation_autoencoder.pt\n", - "2025-03-11 23:28:43,368 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_autoencoder.pt.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "mask_generation_diffusion_unet.pt: 752MB [00:33, 23.4MB/s] \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-03-11 23:29:17,932 - INFO - Downloaded: models/mask_generation_diffusion_unet.pt\n", - "2025-03-11 23:29:17,934 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_diffusion_unet.pt.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "candidate_masks_flexible_size_and_spacing_3000.json: 4.80MB [00:00, 5.61MB/s] " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-03-11 23:29:18,848 - INFO - Downloaded: configs/candidate_masks_flexible_size_and_spacing_3000.json\n", - "2025-03-11 23:29:18,849 - INFO - Expected md5 is None, skip md5 check for file configs/candidate_masks_flexible_size_and_spacing_3000.json.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "all_anatomy_size_condtions.json: 352kB [00:00, 1.38MB/s] " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-03-11 23:29:19,116 - INFO - Downloaded: configs/all_anatomy_size_condtions.json\n", - "2025-03-11 23:29:19,117 - INFO - Expected md5 is None, skip md5 check for file configs/all_anatomy_size_condtions.json.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n", - "all_masks_flexible_size_and_spacing_3000.zip: 8.41GB [05:00, 30.0MB/s] " - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2025-03-11 23:34:20,049 - INFO - Downloaded: /tmp/tmpm89_6ifv/datasets/all_masks_flexible_size_and_spacing_3000.zip\n", - "2025-03-11 23:34:20,050 - INFO - Expected md5 is None, skip md5 check for file /tmp/tmpm89_6ifv/datasets/all_masks_flexible_size_and_spacing_3000.zip.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "\n" + "2025-03-13 05:29:42,164 - INFO - Expected md5 is None, skip md5 check for file models/autoencoder_epoch273.pt.\n", + "2025-03-13 05:29:42,166 - INFO - File exists: models/autoencoder_epoch273.pt, skipped downloading.\n", + "2025-03-13 05:29:42,166 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_autoencoder.pt.\n", + "2025-03-13 05:29:42,167 - INFO - File exists: models/mask_generation_autoencoder.pt, skipped downloading.\n", + "2025-03-13 05:29:42,167 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_diffusion_unet.pt.\n", + "2025-03-13 05:29:42,168 - INFO - File exists: models/mask_generation_diffusion_unet.pt, skipped downloading.\n", + "2025-03-13 05:29:42,169 - INFO - Expected md5 is None, skip md5 check for file configs/all_anatomy_size_condtions.json.\n", + "2025-03-13 05:29:42,169 - INFO - File exists: configs/all_anatomy_size_condtions.json, skipped downloading.\n", + "2025-03-13 05:29:42,170 - INFO - Expected md5 is None, skip md5 check for file models/diff_unet_3d_rflow.pt.\n", + "2025-03-13 05:29:42,170 - INFO - File exists: models/diff_unet_3d_rflow.pt, skipped downloading.\n", + "2025-03-13 05:29:42,171 - INFO - Expected md5 is None, skip md5 check for file models/controlnet_3d_rflow.pt.\n", + "2025-03-13 05:29:42,171 - INFO - File exists: models/controlnet_3d_rflow.pt, skipped downloading.\n", + "2025-03-13 05:29:42,172 - INFO - Expected md5 is None, skip md5 check for file configs/candidate_masks_flexible_size_and_spacing_4000.json.\n", + "2025-03-13 05:29:42,172 - INFO - File exists: configs/candidate_masks_flexible_size_and_spacing_4000.json, skipped downloading.\n", + "2025-03-13 05:29:42,173 - INFO - Expected md5 is None, skip md5 check for file temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip.\n", + "2025-03-13 05:29:42,173 - INFO - File exists: temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip, skipped downloading.\n" ] } ], "source": [ - "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", - "if directory is not None:\n", - " os.makedirs(directory, exist_ok=True)\n", - "root_dir = tempfile.mkdtemp() if directory is None else directory\n", + "root_dir = \"./temp_work_dir_inference_demo\"\n", + "if not os.path.isdir(root_dir):\n", + " os.makedirs(root_dir)\n", "\n", "# TODO: remove the `files` after the files are uploaded to the NGC\n", "files = [\n", @@ -309,17 +210,7 @@ " \"path\": \"models/autoencoder_epoch273.pt\",\n", " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials\"\n", " \"/model_zoo/model_maisi_autoencoder_epoch273_alternative.pt\",\n", - " },\n", - " {\n", - " \"path\": \"models/input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1.pt\",\n", - " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo\"\n", - " \"/model_maisi_input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1_alternative.pt\",\n", - " },\n", - " {\n", - " \"path\": \"models/controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current.pt\",\n", - " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo\"\n", - " \"/model_maisi_controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current_alternative.pt\",\n", - " },\n", + " }, \n", " {\n", " \"path\": \"models/mask_generation_autoencoder.pt\",\n", " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\" \"/tutorials/mask_generation_autoencoder.pt\",\n", @@ -328,23 +219,61 @@ " \"path\": \"models/mask_generation_diffusion_unet.pt\",\n", " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n", " \"/tutorials/model_zoo/model_maisi_mask_generation_diffusion_unet_v2.pt\",\n", - " },\n", - " {\n", - " \"path\": \"configs/candidate_masks_flexible_size_and_spacing_3000.json\",\n", - " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n", - " \"/tutorials/candidate_masks_flexible_size_and_spacing_3000.json\",\n", - " },\n", + " }, \n", " {\n", " \"path\": \"configs/all_anatomy_size_condtions.json\",\n", " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/all_anatomy_size_condtions.json\",\n", " },\n", - " {\n", - " \"path\": \"datasets/all_masks_flexible_size_and_spacing_3000.zip\",\n", - " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n", - " \"/tutorials/model_zoo/model_maisi_all_masks_flexible_size_and_spacing_3000.zip\",\n", - " },\n", + " \n", "]\n", "\n", + "if maisi_version == \"maisi3d-ddpm\":\n", + " files += [\n", + " {\n", + " \"path\": \"models/diff_unet_3d_ddpm.pt\",\n", + " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo\"\n", + " \"/model_maisi_input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1_alternative.pt\",\n", + " },\n", + " {\n", + " \"path\": \"models/controlnet_3d_ddpm.pt\",\n", + " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo\"\n", + " \"/model_maisi_controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current_alternative.pt\",\n", + " },\n", + " {\n", + " \"path\": \"configs/candidate_masks_flexible_size_and_spacing_3000.json\",\n", + " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n", + " \"/tutorials/candidate_masks_flexible_size_and_spacing_3000.json\",\n", + " },\n", + " {\n", + " \"path\": \"datasets/all_masks_flexible_size_and_spacing_3000.zip\",\n", + " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n", + " \"/tutorials/model_zoo/model_maisi_all_masks_flexible_size_and_spacing_3000.zip\",\n", + " },\n", + " ]\n", + "elif maisi_version == \"maisi3d-rflow\":\n", + " files += [\n", + " {\n", + " \"path\": \"models/diff_unet_3d_rflow.pt\",\n", + " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/\"\n", + " \"diff_unet_ckpt_rflow_epoch19350.pt\",\n", + " },\n", + " {\n", + " \"path\": \"models/controlnet_3d_rflow.pt\",\n", + " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/\"\n", + " \"controlnet_rflow_epoch208.pt\",\n", + " },\n", + " {\n", + " \"path\": \"configs/candidate_masks_flexible_size_and_spacing_4000.json\",\n", + " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n", + " \"/tutorials/candidate_masks_flexible_size_and_spacing_4000.json\",\n", + " },\n", + " {\n", + " \"path\": \"datasets/all_masks_flexible_size_and_spacing_4000.zip\",\n", + " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n", + " \"/tutorials/all_masks_flexible_size_and_spacing_4000.zip\",\n", + " },\n", + " ]\n", + "\n", "for file in files:\n", " file[\"path\"] = file[\"path\"] if \"datasets/\" not in file[\"path\"] else os.path.join(root_dir, file[\"path\"])\n", " download_url(url=file[\"url\"], filepath=file[\"path\"])" @@ -372,25 +301,31 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 23:34:20.066][ INFO](notebook) - output_dir: output\n", - "[2025-03-11 23:34:20.068][ INFO](notebook) - trained_autoencoder_path: models/autoencoder_epoch273.pt\n", - "[2025-03-11 23:34:20.068][ INFO](notebook) - trained_diffusion_path: models/input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1.pt\n", - "[2025-03-11 23:34:20.069][ INFO](notebook) - trained_controlnet_path: models/controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current.pt\n", - "[2025-03-11 23:34:20.070][ INFO](notebook) - trained_mask_generation_autoencoder_path: models/mask_generation_autoencoder.pt\n", - "[2025-03-11 23:34:20.070][ INFO](notebook) - trained_mask_generation_diffusion_path: models/mask_generation_diffusion_unet.pt\n", - "[2025-03-11 23:34:20.071][ INFO](notebook) - all_mask_files_base_dir: /tmp/tmpm89_6ifv/datasets/all_masks_flexible_size_and_spacing_3000\n", - "[2025-03-11 23:34:20.072][ INFO](notebook) - all_mask_files_json: ./configs/candidate_masks_flexible_size_and_spacing_3000.json\n", - "[2025-03-11 23:34:20.072][ INFO](notebook) - all_anatomy_size_conditions_json: ./configs/all_anatomy_size_condtions.json\n", - "[2025-03-11 23:34:20.073][ INFO](notebook) - label_dict_json: ./configs/label_dict.json\n", - "[2025-03-11 23:34:20.075][ INFO](notebook) - label_dict_remap_json: ./configs/label_dict_124_to_132.json\n", - "[2025-03-11 23:34:20.075][ INFO](notebook) - Global config variables have been loaded.\n" + "[2025-03-13 05:29:42.181][ INFO](notebook) - output_dir: output\n", + "[2025-03-13 05:29:42.182][ INFO](notebook) - trained_autoencoder_path: models/autoencoder_epoch273.pt\n", + "[2025-03-13 05:29:42.183][ INFO](notebook) - trained_diffusion_path: models/diff_unet_3d_rflow.pt\n", + "[2025-03-13 05:29:42.183][ INFO](notebook) - trained_controlnet_path: models/controlnet_3d_rflow.pt\n", + "[2025-03-13 05:29:42.184][ INFO](notebook) - trained_mask_generation_autoencoder_path: models/mask_generation_autoencoder.pt\n", + "[2025-03-13 05:29:42.184][ INFO](notebook) - trained_mask_generation_diffusion_path: models/mask_generation_diffusion_unet.pt\n", + "[2025-03-13 05:29:42.185][ INFO](notebook) - all_mask_files_base_dir: ./temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000\n", + "[2025-03-13 05:29:42.185][ INFO](notebook) - all_mask_files_json: ./configs/candidate_masks_flexible_size_and_spacing_4000.json\n", + "[2025-03-13 05:29:42.186][ INFO](notebook) - all_anatomy_size_conditions_json: ./configs/all_anatomy_size_condtions.json\n", + "[2025-03-13 05:29:42.187][ INFO](notebook) - label_dict_json: ./configs/label_dict.json\n", + "[2025-03-13 05:29:42.187][ INFO](notebook) - label_dict_remap_json: ./configs/label_dict_124_to_132.json\n", + "[2025-03-13 05:29:42.188][ INFO](notebook) - Global config variables have been loaded.\n" ] } ], "source": [ "args = argparse.Namespace()\n", "\n", - "environment_file = \"./configs/environment.json\"\n", + "if maisi_version == \"maisi3d-ddpm\":\n", + " environment_file = \"./configs/environment_maisi3d-ddpm.json\"\n", + "elif maisi_version == \"maisi3d-rflow\":\n", + " environment_file = \"./configs/environment_maisi3d-rflow.json\"\n", + "else:\n", + " raise ValueError(f\"maisi_version has to be chosen from ['maisi3d-ddpm', 'maisi3d-rflow'], yet got {maisi_version}.\")\n", + "\n", "with open(environment_file, \"r\") as f:\n", " env_dict = json.load(f)\n", "for k, v in env_dict.items():\n", @@ -421,38 +356,32 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 23:34:20.090][ INFO](notebook) - num_output_samples: 1\n", - "[2025-03-11 23:34:20.091][ INFO](notebook) - body_region: ['abdomen']\n", - "[2025-03-11 23:34:20.091][ INFO](notebook) - anatomy_list: ['liver', 'hepatic tumor']\n", - "[2025-03-11 23:34:20.092][ INFO](notebook) - controllable_anatomy_size: []\n", - "[2025-03-11 23:34:20.093][ INFO](notebook) - num_inference_steps: 1000\n", - "[2025-03-11 23:34:20.093][ INFO](notebook) - mask_generation_num_inference_steps: 1000\n", - "[2025-03-11 23:34:20.094][ INFO](notebook) - output_size: [256, 256, 256]\n", - "[2025-03-11 23:34:20.094][ INFO](notebook) - image_output_ext: .nii.gz\n", - "[2025-03-11 23:34:20.095][ INFO](notebook) - label_output_ext: .nii.gz\n", - "[2025-03-11 23:34:20.095][ INFO](notebook) - spacing: [1.5, 1.5, 2.0]\n", - "[2025-03-11 23:34:20.096][ INFO](notebook) - autoencoder_sliding_window_infer_size: [48, 48, 48]\n", - "[2025-03-11 23:34:20.096][ INFO](notebook) - autoencoder_sliding_window_infer_overlap: 0.25\n", - "[2025-03-11 23:34:20.097][ INFO](notebook) - controlnet: $@controlnet_def\n", - "[2025-03-11 23:34:20.098][ INFO](notebook) - diffusion_unet: $@diffusion_unet_def\n", - "[2025-03-11 23:34:20.098][ INFO](notebook) - autoencoder: $@autoencoder_def\n", - "[2025-03-11 23:34:20.099][ INFO](notebook) - mask_generation_autoencoder: $@mask_generation_autoencoder_def\n", - "[2025-03-11 23:34:20.099][ INFO](notebook) - mask_generation_diffusion: $@mask_generation_diffusion_def\n", - "[2025-03-11 23:34:20.100][ INFO](root) - `controllable_anatomy_size` is empty.\n", + "[2025-03-13 05:29:42.196][ INFO](notebook) - num_output_samples: 1\n", + "[2025-03-13 05:29:42.196][ INFO](notebook) - body_region: ['abdomen']\n", + "[2025-03-13 05:29:42.197][ INFO](notebook) - anatomy_list: ['liver', 'hepatic tumor']\n", + "[2025-03-13 05:29:42.198][ INFO](notebook) - controllable_anatomy_size: []\n", + "[2025-03-13 05:29:42.198][ INFO](notebook) - num_inference_steps: 30\n", + "[2025-03-13 05:29:42.199][ INFO](notebook) - mask_generation_num_inference_steps: 1000\n", + "[2025-03-13 05:29:42.200][ INFO](notebook) - output_size: [256, 256, 256]\n", + "[2025-03-13 05:29:42.200][ INFO](notebook) - image_output_ext: .nii.gz\n", + "[2025-03-13 05:29:42.201][ INFO](notebook) - label_output_ext: .nii.gz\n", + "[2025-03-13 05:29:42.201][ INFO](notebook) - spacing: [1.5, 1.5, 2.0]\n", + "[2025-03-13 05:29:42.202][ INFO](notebook) - autoencoder_sliding_window_infer_size: [48, 48, 48]\n", + "[2025-03-13 05:29:42.202][ INFO](notebook) - autoencoder_sliding_window_infer_overlap: 0.25\n", + "[2025-03-13 05:29:42.203][ INFO](notebook) - controlnet: $@controlnet_def\n", + "[2025-03-13 05:29:42.203][ INFO](notebook) - diffusion_unet: $@diffusion_unet_def\n", + "[2025-03-13 05:29:42.204][ INFO](notebook) - autoencoder: $@autoencoder_def\n", + "[2025-03-13 05:29:42.205][ INFO](notebook) - mask_generation_autoencoder: $@mask_generation_autoencoder_def\n", + "[2025-03-13 05:29:42.205][ INFO](notebook) - mask_generation_diffusion: $@mask_generation_diffusion_def\n", + "[2025-03-13 05:29:42.207][ INFO](notebook) - modality: 1\n", + "[2025-03-13 05:29:42.208][ INFO](root) - `controllable_anatomy_size` is empty.\n", "We will synthesize based on `body_region`: (['abdomen']) and `anatomy_list`: (['liver', 'hepatic tumor']).\n", - "[2025-03-11 23:34:20.103][ INFO](root) - The generate results will have voxel size to be [1.5, 1.5, 2.0]mm, volume size to be [256, 256, 256].\n", - "[2025-03-11 23:34:20.103][ INFO](notebook) - Network definition and inference inputs have been loaded.\n" + "[2025-03-13 05:29:42.209][ INFO](root) - The generate results will have voxel size to be [1.5, 1.5, 2.0]mm, volume size to be [256, 256, 256].\n", + "[2025-03-13 05:29:42.210][ INFO](notebook) - Network definition and inference inputs have been loaded.\n" ] } ], "source": [ - "if maisi_version == \"maisi3d-ddpm\":\n", - " model_def_path = \"./configs/config_maisi3d-ddpm.json\"\n", - "elif maisi_version == \"maisi3d-rflow\":\n", - " model_def_path = \"./configs/config_maisi3d-rflow.json\"\n", - "else:\n", - " raise ValueError(f\"maisi_version has to be chosen from ['maisi3d-ddpm', 'maisi3d-rflow'], yet got {maisi_version}.\")\n", - "\n", "with open(model_def_path, \"r\") as f:\n", " model_def = json.load(f)\n", "for k, v in model_def.items():\n", @@ -519,14 +448,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-11 23:34:25,873 - INFO - 'dst' model updated: 158 of 206 variables.\n" + "2025-03-13 05:29:50,519 - INFO - 'dst' model updated: 180 of 231 variables.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 23:34:28.137][ INFO](notebook) - All the trained model weights have been loaded.\n" + "[2025-03-13 05:29:52.881][ INFO](notebook) - All the trained model weights have been loaded.\n" ] } ], @@ -580,7 +509,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 23:34:28.157][ INFO](root) - LDM sampler initialized.\n" + "[2025-03-13 05:29:52.911][ INFO](root) - LDM sampler initialized.\n" ] } ], @@ -611,11 +540,12 @@ " image_output_ext=args.image_output_ext,\n", " label_output_ext=args.label_output_ext,\n", " spacing=args.spacing,\n", + " modality=args.modality,\n", " num_inference_steps=args.num_inference_steps,\n", " mask_generation_num_inference_steps=args.mask_generation_num_inference_steps,\n", " random_seed=args.random_seed,\n", " autoencoder_sliding_window_infer_size=args.autoencoder_sliding_window_infer_size,\n", - " autoencoder_sliding_window_infer_overlap=args.autoencoder_sliding_window_infer_overlap,\n", + " autoencoder_sliding_window_infer_overlap=args.autoencoder_sliding_window_infer_overlap\n", ")" ] }, @@ -642,90 +572,77 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 23:34:28.161][ INFO](notebook) - The generated image/mask pairs will be saved in output.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Extracting /tmp/tmpm89_6ifv/datasets/all_masks_flexible_size_and_spacing_3000.zip to /tmp/tmpm89_6ifv/datasets\n", - "2025-03-11 23:34:28,163 - INFO - Writing into directory: /tmp/tmpm89_6ifv/datasets.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2025-03-11 23:35:31.412][ INFO](root) - Resample mask file to get desired output size and spacing\n" + "[2025-03-13 05:29:52.915][ INFO](notebook) - The generated image/mask pairs will be saved in output.\n", + "[2025-03-13 05:29:52.965][ INFO](root) - Resample mask file to get desired output size and spacing\n", + "[2025-03-13 05:29:54.196][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 05:29:54.199][ INFO](root) - Resize Spacing: [tensor(0.7539, dtype=torch.float64), tensor(0.7539, dtype=torch.float64), tensor(1.9141, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 05:29:54.200][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-13 05:29:57.100][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 05:29:57.103][ INFO](root) - Resize Spacing: [tensor(0.7578, dtype=torch.float64), tensor(0.7578, dtype=torch.float64), tensor(1.8848, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 05:29:57.103][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-13 05:29:59.889][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 05:29:59.893][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 05:29:59.893][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-13 05:30:04.161][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 05:30:04.163][ INFO](root) - Resize Spacing: [tensor(0.7754, dtype=torch.float64), tensor(0.7754, dtype=torch.float64), tensor(1.0098, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 05:30:04.164][ INFO](root) - Output size: [512, 512, 512] -> [256, 256, 256]\n", + "[2025-03-13 05:30:08.605][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 05:30:08.607][ INFO](root) - Resize Spacing: [tensor(0.7480, dtype=torch.float64), tensor(0.7480, dtype=torch.float64), tensor(1.0566, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 05:30:08.608][ INFO](root) - Output size: [512, 512, 512] -> [256, 256, 256]\n", + "[2025-03-13 05:30:10.409][ INFO](root) - Images will be generated based on [{'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_78_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}].\n", + "[2025-03-13 05:30:10.410][ INFO](root) - ---- Start preparing masks... ----\n", + "[2025-03-13 05:30:11.596][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 05:30:11.598][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 05:30:11.599][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Unzipped /tmp/tmpm89_6ifv/datasets/all_masks_flexible_size_and_spacing_3000.zip to /tmp/tmpm89_6ifv/datasets/all_masks_flexible_size_and_spacing_3000.\n" + "augmenting liver tumor\n", + "metatensor(3216., device='cuda:0') | metatensor(3474.4001, device='cuda:0')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 23:35:32.575][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-11 23:35:32.579][ INFO](root) - Resize Spacing: [tensor(0.7988, dtype=torch.float64), tensor(0.7988, dtype=torch.float64), tensor(1.9062, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-11 23:35:32.580][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", - "[2025-03-11 23:35:35.398][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-11 23:35:35.401][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-11 23:35:35.401][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", - "[2025-03-11 23:35:38.378][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-11 23:35:38.380][ INFO](root) - Resize Spacing: [tensor(0.8027, dtype=torch.float64), tensor(0.8027, dtype=torch.float64), tensor(1.8672, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-11 23:35:38.381][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", - "[2025-03-11 23:35:41.219][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-11 23:35:41.222][ INFO](root) - Resize Spacing: [tensor(0.9062, dtype=torch.float64), tensor(0.9062, dtype=torch.float64), tensor(2.3438, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-11 23:35:41.222][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", - "[2025-03-11 23:35:44.278][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-11 23:35:44.282][ INFO](root) - Resize Spacing: [tensor(0.9551, dtype=torch.float64), tensor(0.9551, dtype=torch.float64), tensor(2.4805, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-11 23:35:44.283][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", - "[2025-03-11 23:35:46.311][ INFO](root) - Images will be generated based on [{'mask_file': {'pseudo_label': '/tmp/tmpm89_6ifv/datasets/all_masks_flexible_size_and_spacing_3000/./Task03/labelsTr/liver_56_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}].\n", - "[2025-03-11 23:35:46.313][ INFO](root) - ---- Start preparing masks... ----\n", - "[2025-03-11 23:35:47.553][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-11 23:35:47.555][ INFO](root) - Resize Spacing: [tensor(0.8027, dtype=torch.float64), tensor(0.8027, dtype=torch.float64), tensor(1.8672, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-11 23:35:47.556][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n" + "[2025-03-13 05:30:14.302][ INFO](root) - ---- Mask preparation time: 3.8906569480895996 seconds ----\n", + "[2025-03-13 05:30:14.310][ INFO](root) - ---- Start generating latent features... ----\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "augmenting liver tumor\n" + "metatensor(5213., device='cuda:0') | metatensor(3474.4001, device='cuda:0')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 23:35:51.553][ INFO](root) - ---- Mask preparation time: 5.239385604858398 seconds ----\n", - "[2025-03-11 23:35:51.562][ INFO](root) - ---- Start generating latent features... ----\n", - "100%|█████████████████████████████████████████████████████████████████████| 1000/1000 [01:21<00:00, 12.25it/s]\n", - "[2025-03-11 23:37:13.231][ INFO](root) - ---- Latent features generation time: 81.66785907745361 seconds ----\n", - "[2025-03-11 23:37:13.249][ INFO](root) - ---- Start decoding latent features into images... ----\n", - "100%|█████████████████████████████████████████████| 8/8 [00:06<00:00, 1.22it/s]\n", - "[2025-03-11 23:37:19.839][ INFO](root) - ---- Image decoding time: 6.5896477699279785 seconds ----\n" + "100%|███████████████████████████████████████████| 30/30 [00:02<00:00, 10.26it/s]\n", + "[2025-03-13 05:30:17.278][ INFO](root) - ---- Latent features generation time: 2.967771530151367 seconds ----\n", + "[2025-03-13 05:30:17.302][ INFO](root) - ---- Start decoding latent features into images... ----\n", + "100%|█████████████████████████████████████████████| 8/8 [00:07<00:00, 1.05it/s]\n", + "[2025-03-13 05:30:24.970][ INFO](root) - ---- Image decoding time: 7.6675684452056885 seconds ----\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2025-03-11 23:37:20,191 INFO image_writer.py:197 - writing: output/sample_20250311_233720_171269_image.nii.gz\n", - "2025-03-11 23:37:21,993 INFO image_writer.py:197 - writing: output/sample_20250311_233720_171269_label.nii.gz\n" + "2025-03-13 05:30:25,361 INFO image_writer.py:197 - writing: output/sample_20250313_053025_342138_image.nii.gz\n", + "2025-03-13 05:30:27,034 INFO image_writer.py:197 - writing: output/sample_20250313_053025_342138_label.nii.gz\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 23:37:22.955][ INFO](notebook) - MAISI image/mask generation finished\n" + "[2025-03-13 05:30:28.003][ INFO](notebook) - MAISI image/mask generation finished\n" ] } ], @@ -755,12 +672,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 23:37:22.964][ INFO](notebook) - Visualizing output/sample_20250311_233720_171269_image.nii.gz and output/sample_20250311_233720_171269_label.nii.gz...\n" + "[2025-03-13 05:30:28.011][ INFO](notebook) - Visualizing output/sample_20250313_053025_342138_image.nii.gz and output/sample_20250313_053025_342138_label.nii.gz...\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -770,7 +687,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] diff --git a/generation/maisi/maisi_train_controlnet_tutorial.ipynb b/generation/maisi/maisi_train_controlnet_tutorial.ipynb index 8f1d1368c..62a0a26f9 100644 --- a/generation/maisi/maisi_train_controlnet_tutorial.ipynb +++ b/generation/maisi/maisi_train_controlnet_tutorial.ipynb @@ -141,20 +141,22 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 23:38:43.304][ INFO](notebook) - Using MAISI version maisi3d-ddpm. Will need body region as data input.\n" + "[2025-03-12 22:27:22.838][ INFO](notebook) - MAISI version is maisi3d-ddpm, whether to use body_region is True\n" ] } ], "source": [ "maisi_version = \"maisi3d-ddpm\"\n", "if maisi_version == \"maisi3d-ddpm\":\n", - " include_body_region = True\n", - " logger.info(\"Using MAISI version maisi3d-ddpm. Will need body region as data input.\")\n", + " model_def_path = \"./configs/config_maisi3d-ddpm.json\"\n", "elif maisi_version == \"maisi3d-rflow\":\n", - " include_body_region = False\n", - " logger.info(\"Using MAISI version maisi3d-rflow. Does not need body region as data input.\")\n", + " model_def_path = \"./configs/config_maisi3d-rflow.json\"\n", "else:\n", - " raise ValueError(f\"maisi_version has to be chosen from ['maisi3d-ddpm', 'maisi3d-rflow'], yet got {maisi_version}.\")" + " raise ValueError(f\"maisi_version has to be chosen from ['maisi3d-ddpm', 'maisi3d-rflow'], yet got {maisi_version}.\")\n", + "with open(model_def_path, \"r\") as f:\n", + " model_def = json.load(f)\n", + "include_body_region = model_def[\"include_body_region\"]\n", + "logger.info(f\"MAISI version is {maisi_version}, whether to use body_region is {include_body_region}\")" ] }, { @@ -246,9 +248,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 23:38:45.473][ INFO](notebook) - Generated simulated images.\n", - "[2025-03-11 23:38:45.474][ INFO](notebook) - img_emb shape: (64, 64, 32, 4)\n", - "[2025-03-11 23:38:45.475][ INFO](notebook) - label shape: (256, 256, 128)\n" + "[2025-03-12 22:27:25.046][ INFO](notebook) - Generated simulated images.\n", + "[2025-03-12 22:27:25.047][ INFO](notebook) - img_emb shape: (64, 64, 32, 4)\n", + "[2025-03-12 22:27:25.048][ INFO](notebook) - label shape: (256, 256, 128)\n" ] } ], @@ -320,20 +322,14 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 23:38:45.489][ INFO](notebook) - files and folders under work_dir: ['config_maisi.json', 'models', 'config_maisi_controlnet_train.json', 'outputs', 'sim_dataroot', 'environment_maisi_controlnet_train.json', 'sim_datalist.json'].\n", - "[2025-03-11 23:38:45.490][ INFO](notebook) - number of GPUs: 1.\n" + "[2025-03-12 22:27:25.062][ INFO](notebook) - files and folders under work_dir: ['config_maisi.json', 'models', 'config_maisi_controlnet_train.json', 'outputs', 'sim_dataroot', 'environment_maisi_controlnet_train.json', 'sim_datalist.json'].\n", + "[2025-03-12 22:27:25.063][ INFO](notebook) - number of GPUs: 1.\n" ] } ], "source": [ "env_config_path = \"./configs/environment_maisi_controlnet_train.json\"\n", "train_config_path = \"./configs/config_maisi_controlnet_train.json\"\n", - "if maisi_version == \"maisi3d-ddpm\":\n", - " model_def_path = \"./configs/config_maisi3d-ddpm.json\"\n", - "elif maisi_version == \"maisi3d-rflow\":\n", - " model_def_path = \"./configs/config_maisi3d-rflow.json\"\n", - "else:\n", - " raise ValueError(f\"maisi_version has to be chosen from ['maisi3d-ddpm', 'maisi3d-rflow'], yet got {maisi_version}.\")\n", "\n", "# Load environment configuration, model configuration and model definition\n", "with open(env_config_path, \"r\") as f:\n", @@ -472,29 +468,29 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 23:38:45.501][ INFO](notebook) - Training the model...\n" + "[2025-03-12 22:27:25.074][ INFO](notebook) - Training the model...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[2025-03-11 23:38:54.835][ INFO](maisi.controlnet.training) - Number of GPUs: 8\n", - "[2025-03-11 23:38:54.835][ INFO](maisi.controlnet.training) - World_size: 1\n", - "[2025-03-11 23:38:56.401][ INFO](maisi.controlnet.training) - trained diffusion model is not loaded.\n", - "[2025-03-11 23:38:56.401][ INFO](maisi.controlnet.training) - set scale_factor -> 1.0.\n", - "2025-03-11 23:38:56,899 - INFO - 'dst' model updated: 158 of 206 variables.\n", - "[2025-03-11 23:38:56.903][ INFO](maisi.controlnet.training) - train controlnet model from scratch.\n", - "[2025-03-11 23:38:56.925][ INFO](maisi.controlnet.training) - total number of training steps: 4.0.\n", - "[2025-03-11 23:38:58.871][ INFO](maisi.controlnet.training) -\n", - "[Epoch 1/2] [Batch 1/2] [LR: 0.00000563] [loss: 0.7972] ETA: 0:00:01.944427\n", - "[2025-03-11 23:38:59.018][ INFO](maisi.controlnet.training) -\n", - "[Epoch 1/2] [Batch 2/2] [LR: 0.00000250] [loss: 0.7981] ETA: 0:00:00\n", - "[2025-03-11 23:38:59.775][ INFO](maisi.controlnet.training) - best loss -> 0.7976870536804199.\n", - "[2025-03-11 23:39:00.998][ INFO](maisi.controlnet.training) -\n", - "[Epoch 2/2] [Batch 1/2] [LR: 0.00000063] [loss: 0.7971] ETA: 0:00:01.979231\n", - "[2025-03-11 23:39:01.129][ INFO](maisi.controlnet.training) -\n", - "[Epoch 2/2] [Batch 2/2] [LR: 0.00000000] [loss: 0.7994] ETA: 0:00:00\n", + "[2025-03-12 22:27:33.707][ INFO](maisi.controlnet.training) - Number of GPUs: 8\n", + "[2025-03-12 22:27:33.708][ INFO](maisi.controlnet.training) - World_size: 1\n", + "[2025-03-12 22:27:35.410][ INFO](maisi.controlnet.training) - trained diffusion model is not loaded.\n", + "[2025-03-12 22:27:35.410][ INFO](maisi.controlnet.training) - set scale_factor -> 1.0.\n", + "2025-03-12 22:27:35,902 - INFO - 'dst' model updated: 158 of 206 variables.\n", + "[2025-03-12 22:27:35.907][ INFO](maisi.controlnet.training) - train controlnet model from scratch.\n", + "[2025-03-12 22:27:35.930][ INFO](maisi.controlnet.training) - total number of training steps: 4.0.\n", + "[2025-03-12 22:27:38.006][ INFO](maisi.controlnet.training) -\n", + "[Epoch 1/2] [Batch 1/2] [LR: 0.00000563] [loss: 0.7976] ETA: 0:00:02.073507\n", + "[2025-03-12 22:27:38.147][ INFO](maisi.controlnet.training) -\n", + "[Epoch 1/2] [Batch 2/2] [LR: 0.00000250] [loss: 0.7985] ETA: 0:00:00\n", + "[2025-03-12 22:27:38.683][ INFO](maisi.controlnet.training) - best loss -> 0.7980280518531799.\n", + "[2025-03-12 22:27:39.955][ INFO](maisi.controlnet.training) -\n", + "[Epoch 2/2] [Batch 1/2] [LR: 0.00000063] [loss: 0.7992] ETA: 0:00:01.807460\n", + "[2025-03-12 22:27:40.086][ INFO](maisi.controlnet.training) -\n", + "[Epoch 2/2] [Batch 2/2] [LR: 0.00000000] [loss: 0.7980] ETA: 0:00:00\n", "\n" ] } @@ -512,8 +508,6 @@ " \"--training-config\",\n", " train_config_filepath,\n", "]\n", - "if include_body_region:\n", - " module_args.append(\"--include_body_region\")\n", "\n", "run_torchrun(module, module_args, num_gpus=num_gpus)" ] @@ -539,32 +533,32 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 23:39:03.635][ INFO](notebook) - Inference...\n" + "[2025-03-12 22:27:42.632][ INFO](notebook) - Inference...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[2025-03-11 23:39:13.628][ INFO](maisi.controlnet.infer) - Number of GPUs: 8\n", - "[2025-03-11 23:39:13.628][ INFO](maisi.controlnet.infer) - World_size: 1\n", - "[2025-03-11 23:39:14.205][ INFO](maisi.controlnet.infer) - trained autoencoder model is not loaded.\n", - "[2025-03-11 23:39:15.418][ INFO](maisi.controlnet.infer) - trained diffusion model is not loaded.\n", - "[2025-03-11 23:39:15.418][ INFO](maisi.controlnet.infer) - set scale_factor -> 1.0.\n", - "2025-03-11 23:39:15,917 - INFO - 'dst' model updated: 158 of 206 variables.\n", - "[2025-03-11 23:39:15.922][ INFO](maisi.controlnet.infer) - trained controlnet is not loaded.\n", - "[2025-03-11 23:39:16.582][ INFO](root) - `controllable_anatomy_size` is not provided.\n", - "[2025-03-11 23:39:16.584][ INFO](root) - ---- Start generating latent features... ----\n", - "[2025-03-11 23:39:17.178][ INFO](root) - ---- Latent features generation time: 0.5939664840698242 seconds ----\n", - "[2025-03-11 23:39:17.180][ INFO](root) - ---- Start decoding latent features into images... ----\n", - "[2025-03-11 23:39:18.003][ INFO](root) - ---- Image decoding time: 0.8231167793273926 seconds ----\n", - "2025-03-11 23:39:18,299 INFO image_writer.py:197 - writing: temp_work_dir_controlnet_train_demo/outputs/sample_20250311_233918_283950_image.nii.gz\n", - "2025-03-11 23:39:18,649 INFO image_writer.py:197 - writing: temp_work_dir_controlnet_train_demo/outputs/sample_20250311_233918_283950_label.nii.gz\n", + "[2025-03-12 22:27:53.399][ INFO](maisi.controlnet.infer) - Number of GPUs: 8\n", + "[2025-03-12 22:27:53.400][ INFO](maisi.controlnet.infer) - World_size: 1\n", + "[2025-03-12 22:27:54.101][ INFO](maisi.controlnet.infer) - trained autoencoder model is not loaded.\n", + "[2025-03-12 22:27:55.286][ INFO](maisi.controlnet.infer) - trained diffusion model is not loaded.\n", + "[2025-03-12 22:27:55.286][ INFO](maisi.controlnet.infer) - set scale_factor -> 1.0.\n", + "2025-03-12 22:27:55,756 - INFO - 'dst' model updated: 158 of 206 variables.\n", + "[2025-03-12 22:27:55.761][ INFO](maisi.controlnet.infer) - trained controlnet is not loaded.\n", + "[2025-03-12 22:27:56.340][ INFO](root) - `controllable_anatomy_size` is not provided.\n", + "[2025-03-12 22:27:56.344][ INFO](root) - ---- Start generating latent features... ----\n", + "[2025-03-12 22:27:58.065][ INFO](root) - ---- Latent features generation time: 1.7215001583099365 seconds ----\n", + "[2025-03-12 22:27:58.066][ INFO](root) - ---- Start decoding latent features into images... ----\n", + "[2025-03-12 22:27:58.838][ INFO](root) - ---- Image decoding time: 0.7712326049804688 seconds ----\n", + "2025-03-12 22:27:59,142 INFO image_writer.py:197 - writing: temp_work_dir_controlnet_train_demo/outputs/sample_20250312_222759_124463_image.nii.gz\n", + "2025-03-12 22:27:59,487 INFO image_writer.py:197 - writing: temp_work_dir_controlnet_train_demo/outputs/sample_20250312_222759_124463_label.nii.gz\n", "\n", "\n", - " 0%| | 0/1 [00:00 ./temp_work_dir/./models.\n", - "[2025-03-11 22:59:49.448][ INFO](training) - [config] data_root -> ./temp_work_dir/./embeddings.\n", - "[2025-03-11 22:59:49.448][ INFO](training) - [config] data_list -> ./temp_work_dir/sim_datalist.json.\n", - "[2025-03-11 22:59:49.448][ INFO](training) - [config] lr -> 0.0001.\n", - "[2025-03-11 22:59:49.448][ INFO](training) - [config] num_epochs -> 2.\n", - "[2025-03-11 22:59:49.448][ INFO](training) - [config] num_train_timesteps -> 1000.\n", - "[2025-03-11 22:59:49.454][ INFO](training) - num_files_train: 2\n", - "[2025-03-11 22:59:51.369][ INFO](training) - Training from scratch.\n", - "[2025-03-11 22:59:51.843][ INFO](training) - Scaling factor set to 1.159693956375122.\n", - "[2025-03-11 22:59:51.844][ INFO](training) - scale_factor -> 1.159693956375122.\n", - "[2025-03-11 22:59:51.846][ INFO](training) - torch.set_float32_matmul_precision -> highest.\n", - "[2025-03-11 22:59:51.846][ INFO](training) - Epoch 1, lr 0.0001.\n", - "[2025-03-11 22:59:53.869][ INFO](training) - [2025-03-11 22:59:53] epoch 1, iter 1/2, loss: 0.7971, lr: 0.000100000000.\n", - "[2025-03-11 22:59:53.995][ INFO](training) - [2025-03-11 22:59:53] epoch 1, iter 2/2, loss: 0.7942, lr: 0.000056250000.\n", - "[2025-03-11 22:59:54.038][ INFO](training) - epoch 1 average loss: 0.7956.\n", - "[2025-03-11 22:59:55.795][ INFO](training) - Epoch 2, lr 2.5e-05.\n", - "[2025-03-11 22:59:56.159][ INFO](training) - [2025-03-11 22:59:56] epoch 2, iter 1/2, loss: 0.7891, lr: 0.000025000000.\n", - "[2025-03-11 22:59:56.260][ INFO](training) - [2025-03-11 22:59:56] epoch 2, iter 2/2, loss: 0.7888, lr: 0.000006250000.\n", - "[2025-03-11 22:59:56.315][ INFO](training) - epoch 2 average loss: 0.7889.\n", + "[2025-03-12 22:14:49.047][ INFO](training) - Using cuda:0 of 1\n", + "[2025-03-12 22:14:49.048][ INFO](training) - [config] ckpt_folder -> ./temp_work_dir/./models.\n", + "[2025-03-12 22:14:49.048][ INFO](training) - [config] data_root -> ./temp_work_dir/./embeddings.\n", + "[2025-03-12 22:14:49.048][ INFO](training) - [config] data_list -> ./temp_work_dir/sim_datalist.json.\n", + "[2025-03-12 22:14:49.048][ INFO](training) - [config] lr -> 0.0001.\n", + "[2025-03-12 22:14:49.048][ INFO](training) - [config] num_epochs -> 2.\n", + "[2025-03-12 22:14:49.048][ INFO](training) - [config] num_train_timesteps -> 1000.\n", + "[2025-03-12 22:14:50.943][ INFO](training) - Training from scratch.\n", + "[2025-03-12 22:14:50.961][ INFO](training) - num_files_train: 2\n", + "[2025-03-12 22:14:51.403][ INFO](training) - Scaling factor set to 1.159693956375122.\n", + "[2025-03-12 22:14:51.403][ INFO](training) - scale_factor -> 1.159693956375122.\n", + "[2025-03-12 22:14:51.405][ INFO](training) - torch.set_float32_matmul_precision -> highest.\n", + "[2025-03-12 22:14:51.405][ INFO](training) - Epoch 1, lr 0.0001.\n", + "[2025-03-12 22:14:53.068][ INFO](training) - [2025-03-12 22:14:53] epoch 1, iter 1/2, loss: 0.7966, lr: 0.000100000000.\n", + "[2025-03-12 22:14:53.363][ INFO](training) - [2025-03-12 22:14:53] epoch 1, iter 2/2, loss: 0.7920, lr: 0.000056250000.\n", + "[2025-03-12 22:14:53.414][ INFO](training) - epoch 1 average loss: 0.7943.\n", + "[2025-03-12 22:14:54.826][ INFO](training) - Epoch 2, lr 2.5e-05.\n", + "[2025-03-12 22:14:55.220][ INFO](training) - [2025-03-12 22:14:55] epoch 2, iter 1/2, loss: 0.7901, lr: 0.000025000000.\n", + "[2025-03-12 22:14:55.326][ INFO](training) - [2025-03-12 22:14:55] epoch 2, iter 2/2, loss: 0.7977, lr: 0.000006250000.\n", + "[2025-03-12 22:14:55.385][ INFO](training) - epoch 2 average loss: 0.7939.\n", "\n" ] } @@ -574,8 +567,6 @@ " \"--num_gpus\",\n", " str(num_gpus),\n", "]\n", - "if include_body_region:\n", - " module_args.append(\"--include_body_region\")\n", "\n", "run_torchrun(module, module_args, num_gpus=num_gpus)" ] @@ -600,8 +591,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-11 23:00:00.296][ INFO](notebook) - Running inference...\n", - "[2025-03-11 23:00:19.011][ INFO](notebook) - Completed all steps.\n" + "[2025-03-12 22:14:58.601][ INFO](notebook) - Running inference...\n", + "[2025-03-12 22:15:16.609][ INFO](notebook) - Completed all steps.\n" ] }, { @@ -609,23 +600,23 @@ "output_type": "stream", "text": [ "\n", - "[2025-03-11 23:00:11.173][ INFO](inference) - Using cuda:0 of 1 with random seed: 17942\n", - "[2025-03-11 23:00:11.173][ INFO](inference) - [config] ckpt_filepath -> ./temp_work_dir/./models/diff_unet_ckpt.pt.\n", - "[2025-03-11 23:00:11.173][ INFO](inference) - [config] random_seed -> 17942.\n", - "[2025-03-11 23:00:11.173][ INFO](inference) - [config] output_prefix -> unet_3d.\n", - "[2025-03-11 23:00:11.173][ INFO](inference) - [config] output_size -> (256, 256, 128).\n", - "[2025-03-11 23:00:11.173][ INFO](inference) - [config] out_spacing -> (1.0, 1.0, 0.75).\n", - "[2025-03-11 23:00:11.173][ INFO](root) - `controllable_anatomy_size` is not provided.\n", - "[2025-03-11 23:00:13.699][ INFO](inference) - checkpoints ./temp_work_dir/./models/diff_unet_ckpt.pt loaded.\n", - "[2025-03-11 23:00:13.701][ INFO](inference) - scale_factor -> 1.159693956375122.\n", - "[2025-03-11 23:00:13.702][ INFO](inference) - num_downsample_level -> 4, divisor -> 4.\n", - "[2025-03-11 23:00:13.704][ INFO](inference) - noise: cuda:0, torch.float32, \n", + "[2025-03-12 22:15:08.281][ INFO](inference) - Using cuda:0 of 1 with random seed: 22762\n", + "[2025-03-12 22:15:08.281][ INFO](inference) - [config] ckpt_filepath -> ./temp_work_dir/./models/diff_unet_ckpt.pt.\n", + "[2025-03-12 22:15:08.281][ INFO](inference) - [config] random_seed -> 22762.\n", + "[2025-03-12 22:15:08.282][ INFO](inference) - [config] output_prefix -> unet_3d.\n", + "[2025-03-12 22:15:08.282][ INFO](inference) - [config] output_size -> (256, 256, 128).\n", + "[2025-03-12 22:15:08.282][ INFO](inference) - [config] out_spacing -> (1.0, 1.0, 0.75).\n", + "[2025-03-12 22:15:08.282][ INFO](root) - `controllable_anatomy_size` is not provided.\n", + "[2025-03-12 22:15:11.078][ INFO](inference) - checkpoints ./temp_work_dir/./models/diff_unet_ckpt.pt loaded.\n", + "[2025-03-12 22:15:11.080][ INFO](inference) - scale_factor -> 1.159693956375122.\n", + "[2025-03-12 22:15:11.081][ INFO](inference) - num_downsample_level -> 4, divisor -> 4.\n", + "[2025-03-12 22:15:11.083][ INFO](inference) - noise: cuda:0, torch.float32, \n", "\n", " 0%| | 0/10 [00:00 Tensor: - """ - Initialize tumor mask for tumor augmentation. +def erode3d(input_tensor, erosion=3): + # Define the structuring element + erosion = ensure_tuple_rep(erosion, 3) + structuring_element = torch.ones(1, 1, erosion[0], erosion[1], erosion[2]).to(input_tensor.device) - Args: - volume: input 3D multi-label mask, [1,H,W,D] torch tensor. - tumor_label: tumor label in whole_mask, list of int. - - Return: - tumor_mask_, initialized tumor mask, [1,H,W,D] torch tensor. - """ - tumor_mask_ = torch.zeros_like(volume, dtype=torch.uint8) - for idx, label in enumerate(tumor_label): - tumor_mask_[volume == label] = idx + 1 - return tumor_mask_ + # Pad the input tensor to handle border pixels + input_padded = F.pad( + input_tensor.float().unsqueeze(0).unsqueeze(0), + (erosion[0] // 2, erosion[0] // 2, erosion[1] // 2, erosion[1] // 2, erosion[2] // 2, erosion[2] // 2), + mode="constant", + value=1.0, + ) + # Apply erosion operation + output = F.conv3d(input_padded, structuring_element, padding=0) -def finalize_tumor_mask(augmented_mask: Tensor, organ_mask: Tensor, threshold_tumor_size: float): - """ - Try to generate the final tumor mask by combining the augmented tumor mask and organ mask. - Need to make sure tumor is inside of organ and is larger than threshold_tumor_size. + # Set output values based on the minimum value within the structuring element + output = torch.where(output == torch.sum(structuring_element), 1.0, 0.0) - Args: - augmented_mask: input 3D binary tumor mask, [1,H,W,D] torch tensor. - organ_mask: input 3D binary organ mask, [1,H,W,D] torch tensor. - threshold_tumor_size: threshold tumor size, float + return output.squeeze(0).squeeze(0) - Return: - tumor_mask, [H,W,D] torch tensor; or None if the size did not qualify - """ - tumor_mask = augmented_mask * organ_mask - if torch.sum(tumor_mask) >= threshold_tumor_size: - tumor_mask = dilate_one_img(tumor_mask.squeeze(0), filter_size=5, pad_value=1.0) - tumor_mask = erode_one_img(tumor_mask, filter_size=5, pad_value=1.0).unsqueeze(0).to(torch.uint8) - return tumor_mask - else: - return None +def dilate3d(input_tensor, erosion=3): + # Define the structuring element + erosion = ensure_tuple_rep(erosion, 3) + structuring_element = torch.ones(1, 1, erosion[0], erosion[1], erosion[2]).to(input_tensor.device) -def augmentation_bone_tumor(whole_mask: Tensor, spatial_size: tuple[int, int, int] | int | None = None) -> Tensor: - """ - Bone tumor augmentation. + # Pad the input tensor to handle border pixels + input_padded = F.pad( + input_tensor.float().unsqueeze(0).unsqueeze(0), + (erosion[0] // 2, erosion[0] // 2, erosion[1] // 2, erosion[1] // 2, erosion[2] // 2, erosion[2] // 2), + mode="constant", + value=1.0, + ) - Args: - whole_mask: input 3D multi-label mask, [1,1,H,W,D] torch tensor. - spatial_size: output image spatial size, used in random transform. - If not defined, will use (H,W,D). If some components are non-positive values, - the transform will use the corresponding components of whole_mask size. - For example, spatial_size=(128, 128, -1) will be adapted to (128, 128, 64) - if the third spatial dimension size of whole_mask is 64. + # Apply erosion operation + output = F.conv3d(input_padded, structuring_element, padding=0) - Return: - augmented mask, with shape of spatial_size and data type as whole_mask. + # Set output values based on the minimum value within the structuring element + output = torch.where(output > 0, 1.0, 0.0) - Example: + return output.squeeze(0).squeeze(0) - .. code-block:: python - # define a multi-label mask - whole_mask = torch.zeros([1,1,128,128,128]) - whole_mask[0,0, 90:110, 90:110, 90:110]=127 - whole_mask[0,0, 97:103, 97:103, 97:103]=128 - augmented_whole_mask = augmentation_bone_tumor(whole_mask) - """ - # Initialize binary tumor mask - device = whole_mask.device - volume = whole_mask.squeeze(0).cuda() if not whole_mask.is_cuda else whole_mask.squeeze(0) - tumor_label = [128] - tumor_mask_ = initialize_tumor_mask(volume, tumor_label) +def augmentation_tumor_bone(pt_nda, output_size, random_seed): + volume = pt_nda.squeeze(0) + real_l_volume_ = torch.zeros_like(volume) + real_l_volume_[volume == 128] = 1 + real_l_volume_ = real_l_volume_.to(torch.uint8) - # Define augmentation transform elastic = RandAffine( mode="nearest", prob=1.0, @@ -105,78 +74,52 @@ def augmentation_bone_tumor(whole_mask: Tensor, spatial_size: tuple[int, int, in scale_range=(0.15, 0.15, 0), padding_mode="zeros", ) + elastic.set_random_state(seed=random_seed) - tumor_size = torch.sum((tumor_mask_ > 0).float()) + tumor_szie = torch.sum((real_l_volume_ > 0).float()) ########################### # remove pred in pseudo_label in real lesion region - volume[tumor_mask_ > 0] = 200 + volume[real_l_volume_ > 0] = 200 ########################### - if tumor_size > 0: + if tumor_szie > 0: # get organ mask organ_mask = ( torch.logical_and(33 <= volume, volume <= 56).float() + torch.logical_and(63 <= volume, volume <= 97).float() + (volume == 127).float() + (volume == 114).float() - + tumor_mask_ + + real_l_volume_ ) organ_mask = (organ_mask > 0).float() - - # augment mask - count = 0 + cnt = 0 while True: - threshold = 0.8 if count < 40 else 0.75 - tumor_mask = tumor_mask_ - # apply random augmentation - augmented_mask = elastic(tumor_mask > 0, spatial_size=spatial_size).as_tensor() - # generate final tumor mask - count += 1 - tumor_mask = finalize_tumor_mask(augmented_mask, organ_mask, tumor_size * threshold) - if tumor_mask is not None: + threshold = 0.8 if cnt < 40 else 0.75 + real_l_volume = real_l_volume_ + # random distor mask + distored_mask = elastic((real_l_volume > 0).cuda(), spatial_size=tuple(output_size)).as_tensor() + real_l_volume = distored_mask * organ_mask + cnt += 1 + print(torch.sum(real_l_volume), "|", tumor_szie * threshold) + if torch.sum(real_l_volume) >= tumor_szie * threshold: + real_l_volume = dilate3d(real_l_volume.squeeze(0), erosion=5) + real_l_volume = erode3d(real_l_volume, erosion=5).unsqueeze(0).to(torch.uint8) break - if count > MAX_COUNT: - raise ValueError("Please check if bone lesion is inside bone.") else: - tumor_mask = tumor_mask_ - - # update the new tumor mask - volume[tumor_mask == 1] = tumor_label[0] - - return volume.unsqueeze(0).to(device) - - -def augmentation_liver_tumor(whole_mask: Tensor, spatial_size: tuple[int, int, int] | int | None = None) -> Tensor: - """ - Bone liver augmentation. + real_l_volume = real_l_volume_ - Args: - whole_mask: input 3D multi-label mask, [1,1,H,W,D] torch tensor. - spatial_size: output image spatial size, used in random transform. - If not defined, will use (H,W,D). If some components are non-positive values, - the transform will use the corresponding components of whole_mask size. - For example, spatial_size=(128, 128, -1) will be adapted to (128, 128, 64) - if the third spatial dimension size of whole_mask is 64. + volume[real_l_volume == 1] = 128 - Return: - augmented mask, with shape of spatial_size and data type as whole_mask. + pt_nda = volume.unsqueeze(0) + return pt_nda - Example: - .. code-block:: python +def augmentation_tumor_liver(pt_nda, output_size, random_seed): + volume = pt_nda.squeeze(0) + real_l_volume_ = torch.zeros_like(volume) + real_l_volume_[volume == 1] = 1 + real_l_volume_[volume == 26] = 2 + real_l_volume_ = real_l_volume_.to(torch.uint8) - # define a multi-label mask - whole_mask = torch.zeros([1,1,128,128,128]) - whole_mask[0,0, 90:110, 90:110, 90:110]=1 - whole_mask[0,0, 97:103, 97:103, 97:103]=26 - augmented_whole_mask = augment_liver_tumor(whole_mask) - """ - # Initialize binary tumor mask - device = whole_mask.device - volume = whole_mask.squeeze(0).cuda() if not whole_mask.is_cuda else whole_mask.squeeze(0) - tumor_label = [1, 26] - tumor_mask_ = initialize_tumor_mask(volume, tumor_label) - - # Define augmentation transform elastic = Rand3DElastic( mode="nearest", prob=1.0, @@ -187,74 +130,45 @@ def augmentation_liver_tumor(whole_mask: Tensor, spatial_size: tuple[int, int, i scale_range=(0.2, 0.2, 0.2), padding_mode="zeros", ) + elastic.set_random_state(seed=random_seed) - tumor_size = torch.sum(tumor_mask_ == 2) + tumor_szie = torch.sum(real_l_volume_ == 2) ########################### # remove pred organ labels volume[volume == 1] = 0 volume[volume == 26] = 0 # before move tumor maks, full the original location by organ labels - volume[tumor_mask_ == 1] = 1 - volume[tumor_mask_ == 2] = 1 + volume[real_l_volume_ == 1] = 1 + volume[real_l_volume_ == 2] = 1 ########################### - if tumor_size > 0: - count = 0 + while True: + real_l_volume = real_l_volume_ + # random distor mask + real_l_volume = elastic((real_l_volume == 2).cuda(), spatial_size=tuple(output_size)).as_tensor() # get organ mask - organ_mask = (tumor_mask_ == 1).float() + (tumor_mask_ == 2).float() - organ_mask = dilate_one_img(organ_mask.squeeze(0), filter_size=5, pad_value=1.0) - organ_mask = erode_one_img(organ_mask, filter_size=5, pad_value=1.0).unsqueeze(0) - while True: - tumor_mask = tumor_mask_ - # apply random augmentation - augmented_mask = elastic((tumor_mask == 2), spatial_size=spatial_size).as_tensor() - - # generate final tumor mask - count += 1 - tumor_mask = finalize_tumor_mask(augmented_mask, organ_mask, tumor_size * 0.80) - if tumor_mask is not None: - break - if count > MAX_COUNT: - raise ValueError("Please check if liver tumor is inside liver.") - else: - tumor_mask = tumor_mask_ - - volume[tumor_mask == 1] = 26 - - return volume.unsqueeze(0).to(device) - + organ_mask = (real_l_volume_ == 1).float() + (real_l_volume_ == 2).float() -def augmentation_lung_tumor(whole_mask: Tensor, spatial_size: tuple[int, int, int] | int | None = None) -> Tensor: - """ - Lung tumor augmentation. + organ_mask = dilate3d(organ_mask.squeeze(0), erosion=5) + organ_mask = erode3d(organ_mask, erosion=5).unsqueeze(0) + real_l_volume = real_l_volume * organ_mask + print(torch.sum(real_l_volume), "|", tumor_szie * 0.80) + if torch.sum(real_l_volume) >= tumor_szie * 0.80: + real_l_volume = dilate3d(real_l_volume.squeeze(0), erosion=5) + real_l_volume = erode3d(real_l_volume, erosion=5).unsqueeze(0) + break - Args: - whole_mask: input 3D multi-label mask, [1,1,H,W,D] torch tensor. - spatial_size: output image spatial size, used in random transform. - If not defined, will use (H,W,D). If some components are non-positive values, - the transform will use the corresponding components of whole_mask size. - For example, spatial_size=(128, 128, -1) will be adapted to (128, 128, 64) - if the third spatial dimension size of whole_mask is 64. + volume[real_l_volume == 1] = 26 - Return: - augmented mask, with shape of spatial_size and data type as whole_mask. + pt_nda = volume.unsqueeze(0) + return pt_nda - Example: - .. code-block:: python +def augmentation_tumor_lung(pt_nda, output_size, random_seed): + volume = pt_nda.squeeze(0) + real_l_volume_ = torch.zeros_like(volume) + real_l_volume_[volume == 23] = 1 + real_l_volume_ = real_l_volume_.to(torch.uint8) - # define a multi-label mask - whole_mask = torch.zeros([1,1,128,128,128]) - whole_mask[0,0, 90:110, 90:110, 90:110]=28 - whole_mask[0,0, 97:103, 97:103, 97:103]=23 - augmented_whole_mask = augmentation_lung_tumor(whole_mask) - """ - # Initialize binary tumor mask - device = whole_mask.device - volume = whole_mask.squeeze(0).cuda() if not whole_mask.is_cuda else whole_mask.squeeze(0) - tumor_label = [23] - tumor_mask_ = initialize_tumor_mask(volume, tumor_label) - - # Define augmentation transform elastic = Rand3DElastic( mode="nearest", prob=1.0, @@ -265,87 +179,61 @@ def augmentation_lung_tumor(whole_mask: Tensor, spatial_size: tuple[int, int, in scale_range=(0.15, 0.15, 0.15), padding_mode="zeros", ) + elastic.set_random_state(seed=random_seed) - tumor_size = torch.sum(tumor_mask_) + tumor_szie = torch.sum(real_l_volume_) # before move lung tumor maks, full the original location by lung labels - new_tumor_mask_ = dilate_one_img(tumor_mask_.squeeze(0), filter_size=3, pad_value=1.0) - new_tumor_mask_ = new_tumor_mask_.unsqueeze(0) - new_tumor_mask_[tumor_mask_ > 0] = 0 - new_tumor_mask_[volume < 28] = 0 - new_tumor_mask_[volume > 32] = 0 - tmp = volume[(volume * new_tumor_mask_).nonzero(as_tuple=True)].view(-1) + new_real_l_volume_ = dilate3d(real_l_volume_.squeeze(0), erosion=3) + new_real_l_volume_ = new_real_l_volume_.unsqueeze(0) + new_real_l_volume_[real_l_volume_ > 0] = 0 + new_real_l_volume_[volume < 28] = 0 + new_real_l_volume_[volume > 32] = 0 + tmp = volume[(volume * new_real_l_volume_).nonzero(as_tuple=True)].view(-1) mode = torch.mode(tmp, 0)[0].item() + print(mode) assert 28 <= mode <= 32 - volume[tumor_mask_.bool()] = mode + volume[real_l_volume_.bool()] = mode ########################### - - if tumor_size > 0: - count = 0 - # get lung mask v2 (133 order) - organ_mask = ( - (volume == 28).float() - + (volume == 29).float() - + (volume == 30).float() - + (volume == 31).float() - + (volume == 32).float() - ) - organ_mask = dilate_one_img(organ_mask.squeeze(0), filter_size=5, pad_value=1.0) - organ_mask = erode_one_img(organ_mask, filter_size=5, pad_value=1.0).unsqueeze(0) - + if tumor_szie > 0: # aug while True: - tumor_mask = tumor_mask_ - # apply random augmentation - augmented_mask = elastic(tumor_mask, spatial_size=spatial_size).as_tensor() - - # generate final tumor mask - count += 1 - tumor_mask = finalize_tumor_mask(augmented_mask, organ_mask, tumor_size * 0.85) - if tumor_mask is not None: + real_l_volume = real_l_volume_ + # random distor mask + real_l_volume = elastic(real_l_volume, spatial_size=tuple(output_size)).as_tensor() + # get lung mask v2 (133 order) + lung_mask = ( + (volume == 28).float() + + (volume == 29).float() + + (volume == 30).float() + + (volume == 31).float() + + (volume == 32).float() + ) + + lung_mask = dilate3d(lung_mask.squeeze(0), erosion=5) + lung_mask = erode3d(lung_mask, erosion=5).unsqueeze(0) + real_l_volume = real_l_volume * lung_mask + print(torch.sum(real_l_volume), "|", tumor_szie * 0.85) + if torch.sum(real_l_volume) >= tumor_szie * 0.85: + real_l_volume = dilate3d(real_l_volume.squeeze(0), erosion=5) + real_l_volume = erode3d(real_l_volume, erosion=5).unsqueeze(0).to(torch.uint8) break - if count > MAX_COUNT: - raise ValueError("Please check if lung tumor is inside lung.") else: - tumor_mask = tumor_mask_ - - volume[tumor_mask == 1] = tumor_label[0] + real_l_volume = real_l_volume_ - return volume.unsqueeze(0).to(device) + volume[real_l_volume == 1] = 23 + pt_nda = volume.unsqueeze(0) + return pt_nda -def augmentation_pancreas_tumor(whole_mask: Tensor, spatial_size: tuple[int, int, int] | int | None = None) -> Tensor: - """ - Pancreas tumor augmentation. - Args: - whole_mask: input 3D multi-label mask, [1,1,H,W,D] torch tensor. - spatial_size: output image spatial size, used in random transform. - If not defined, will use (H,W,D). If some components are non-positive values, - the transform will use the corresponding components of whole_mask size. - For example, spatial_size=(128, 128, -1) will be adapted to (128, 128, 64) - if the third spatial dimension size of whole_mask is 64. +def augmentation_tumor_pancreas(pt_nda, output_size, random_seed): + volume = pt_nda.squeeze(0) + real_l_volume_ = torch.zeros_like(volume) + real_l_volume_[volume == 4] = 1 + real_l_volume_[volume == 24] = 2 + real_l_volume_ = real_l_volume_.to(torch.uint8) - Return: - augmented mask, with shape of spatial_size and data type as whole_mask. - - Example: - - .. code-block:: python - - # define a multi-label mask - whole_mask = torch.zeros([1,1,128,128,128]) - whole_mask[0,0, 90:110, 90:110, 90:110]=24 - whole_mask[0,0, 97:103, 97:103, 97:103]=4 - augmented_whole_mask = augmentation_pancreas_tumor(whole_mask) - """ - # Initialize binary tumor mask - device = whole_mask.device - volume = whole_mask.squeeze(0).cuda() if not whole_mask.is_cuda else whole_mask.squeeze(0) - tumor_label = [4, 24] - tumor_mask_ = initialize_tumor_mask(volume, tumor_label) - - # Define augmentation transform elastic = Rand3DElastic( mode="nearest", prob=1.0, @@ -356,74 +244,45 @@ def augmentation_pancreas_tumor(whole_mask: Tensor, spatial_size: tuple[int, int scale_range=(0.1, 0.1, 0.1), padding_mode="zeros", ) + elastic.set_random_state(seed=random_seed) - tumor_size = torch.sum(tumor_mask_ == 2) + tumor_szie = torch.sum(real_l_volume_ == 2) ########################### # remove pred organ labels volume[volume == 24] = 0 volume[volume == 4] = 0 # before move tumor maks, full the original location by organ labels - volume[tumor_mask_ == 1] = 4 - volume[tumor_mask_ == 2] = 4 + volume[real_l_volume_ == 1] = 4 + volume[real_l_volume_ == 2] = 4 ########################### - if tumor_size > 0: - count = 0 + while True: + real_l_volume = real_l_volume_ + # random distor mask + real_l_volume = elastic((real_l_volume == 2).cuda(), spatial_size=tuple(output_size)).as_tensor() # get organ mask - organ_mask = (tumor_mask_ == 1).float() + (tumor_mask_ == 2).float() - organ_mask = dilate_one_img(organ_mask.squeeze(0), filter_size=5, pad_value=1.0) - organ_mask = erode_one_img(organ_mask, filter_size=5, pad_value=1.0).unsqueeze(0) - while True: - tumor_mask = tumor_mask_ - # apply random augmentation - augmented_mask = elastic((tumor_mask == 2), spatial_size=spatial_size).as_tensor() - - # generate final tumor mask - count += 1 - tumor_mask = finalize_tumor_mask(augmented_mask, organ_mask, tumor_size * 0.80) - if tumor_mask is not None: - break - if count > MAX_COUNT: - raise ValueError("Please check if pancreas tumor is inside pancreas.") - else: - tumor_mask = tumor_mask_ + organ_mask = (real_l_volume_ == 1).float() + (real_l_volume_ == 2).float() - volume[tumor_mask == 1] = 24 + organ_mask = dilate3d(organ_mask.squeeze(0), erosion=5) + organ_mask = erode3d(organ_mask, erosion=5).unsqueeze(0) + real_l_volume = real_l_volume * organ_mask + print(torch.sum(real_l_volume), "|", tumor_szie * 0.80) + if torch.sum(real_l_volume) >= tumor_szie * 0.80: + real_l_volume = dilate3d(real_l_volume.squeeze(0), erosion=5) + real_l_volume = erode3d(real_l_volume, erosion=5).unsqueeze(0) + break - return volume.unsqueeze(0).to(device) + volume[real_l_volume == 1] = 24 + pt_nda = volume.unsqueeze(0) + return pt_nda -def augmentation_colon_tumor(whole_mask: Tensor, spatial_size: tuple[int, int, int] | int | None = None) -> Tensor: - """ - Colon tumor augmentation. - Args: - whole_mask: input 3D multi-label mask, [1,1,H,W,D] torch tensor. - spatial_size: output image spatial size, used in random transform. - If not defined, will use (H,W,D). If some components are non-positive values, - the transform will use the corresponding components of whole_mask size. - For example, spatial_size=(128, 128, -1) will be adapted to (128, 128, 64) - if the third spatial dimension size of whole_mask is 64. +def augmentation_tumor_colon(pt_nda, output_size, random_seed): + volume = pt_nda.squeeze(0) + real_l_volume_ = torch.zeros_like(volume) + real_l_volume_[volume == 27] = 1 + real_l_volume_ = real_l_volume_.to(torch.uint8) - Return: - augmented mask, with shape of spatial_size and data type as whole_mask. - - Example: - - .. code-block:: python - - # define a multi-label mask - whole_mask = torch.zeros([1,1,128,128,128]) - whole_mask[0,0, 90:110, 90:110, 90:110]=62 - whole_mask[0,0, 97:103, 97:103, 97:103]=27 - augmented_whole_mask = augmentation_colon_tumor(whole_mask) - """ - # Initialize binary tumor mask - device = whole_mask.device - volume = whole_mask.squeeze(0).cuda() if not whole_mask.is_cuda else whole_mask.squeeze(0) - tumor_label = [27] - tumor_mask_ = initialize_tumor_mask(volume, tumor_label) - - # Define augmentation transform elastic = Rand3DElastic( mode="nearest", prob=1.0, @@ -434,125 +293,81 @@ def augmentation_colon_tumor(whole_mask: Tensor, spatial_size: tuple[int, int, i scale_range=(0.1, 0.1, 0.1), padding_mode="zeros", ) + elastic.set_random_state(seed=random_seed) - tumor_size = torch.sum(tumor_mask_) + tumor_szie = torch.sum(real_l_volume_) ########################### # before move tumor maks, full the original location by organ labels - volume[tumor_mask_.bool()] = 62 + volume[real_l_volume_.bool()] = 62 ########################### - if tumor_size > 0: + if tumor_szie > 0: # get organ mask organ_mask = (volume == 62).float() - organ_mask = dilate_one_img(organ_mask.squeeze(0), filter_size=5, pad_value=1.0) - organ_mask = erode_one_img(organ_mask, filter_size=5, pad_value=1.0).unsqueeze(0) - - count = 0 + organ_mask = dilate3d(organ_mask.squeeze(0), erosion=5) + organ_mask = erode3d(organ_mask, erosion=5).unsqueeze(0) + # cnt = 0 + cnt = 0 while True: threshold = 0.8 - tumor_mask = tumor_mask_ - if count < 20: - # apply random augmentation - augmented_mask = elastic((tumor_mask == 1), spatial_size=spatial_size).as_tensor() - tumor_mask = augmented_mask * organ_mask - elif 20 <= count < 40: + real_l_volume = real_l_volume_ + if cnt < 20: + # random distor mask + distored_mask = elastic((real_l_volume == 1).cuda(), spatial_size=tuple(output_size)).as_tensor() + real_l_volume = distored_mask * organ_mask + elif 20 <= cnt < 40: threshold = 0.75 else: break - # generate final tumor mask - count += 1 - tumor_mask = finalize_tumor_mask(tumor_mask, organ_mask, tumor_size * threshold) - if tumor_mask is not None: + real_l_volume = real_l_volume * organ_mask + print(torch.sum(real_l_volume), "|", tumor_szie * threshold) + cnt += 1 + if torch.sum(real_l_volume) >= tumor_szie * threshold: + real_l_volume = dilate3d(real_l_volume.squeeze(0), erosion=5) + real_l_volume = erode3d(real_l_volume, erosion=5).unsqueeze(0).to(torch.uint8) break - if count > MAX_COUNT: - raise ValueError("Please check if colon tumor is inside colon.") else: - tumor_mask = tumor_mask_ - - volume[tumor_mask == 1] = tumor_label[0] + real_l_volume = real_l_volume_ + # break + volume[real_l_volume == 1] = 27 - return volume.unsqueeze(0).to(device) + pt_nda = volume.unsqueeze(0) + return pt_nda -def augmentation_body(whole_mask: Tensor) -> Tensor: - """ - Whole body mask augmentation. +def augmentation_body(pt_nda, random_seed): + volume = pt_nda.squeeze(0) - Args: - whole_mask: input 3D multi-label mask, [1,1,H,W,D] torch tensor. + zoom = RandZoom(min_zoom=0.99, max_zoom=1.01, mode="nearest", align_corners=None, prob=1.0) + zoom.set_random_state(seed=random_seed) - Return: - augmented mask, with same shape and data type as whole_mask. - - Example: - - .. code-block:: python - - # define a multi-label mask - whole_mask = torch.zeros([1,1,128,128,128]) - whole_mask[0,0, 90:110, 90:110, 90:110]=127 - whole_mask[0,0, 97:103, 97:103, 97:103]=128 - augmented_whole_mask = augmentation_body(whole_mask) - """ - device = whole_mask.device - volume = whole_mask.squeeze(0).cuda() if not whole_mask.is_cuda else whole_mask.squeeze(0) - - # Define augmentation transform - zoom = RandZoom( - min_zoom=0.99, - max_zoom=1.01, - mode="nearest", - align_corners=None, - prob=1.0, - ) - # apply random augmentation volume = zoom(volume) - return volume.unsqueeze(0).to(device) - - -def augmentation(whole_mask: Tensor, spatial_size: tuple[int, int, int] | int | None = None) -> Tensor: - """ - Tumor or whole body mask augmentation. If tumor exist, augment tumor mask; if not, augment whole body mask - - Args: - whole_mask: input 3D multi-label mask, [1,1,H,W,D] torch tensor. - spatial_size: output image spatial size, used in random transform. If not defined, will use (H,W,D). If some components are non-positive values, the transform will use the corresponding components of whole_mask size. For example, spatial_size=(128, 128, -1) will be adapted to (128, 128, 64) if the third spatial dimension size of whole_mask is 64. - - Return: - augmented mask, with shape of spatial_size and data type as whole_mask. - - Example: + pt_nda = volume.unsqueeze(0) + return pt_nda - .. code-block:: python - # define a multi-label mask - whole_mask = torch.zeros([1,1, 128,128,128]) - whole_mask[0,0, 90:110, 90:110, 90:110]=127 - whole_mask[0,0, 97:103, 97:103, 97:103]=128 - augmented_whole_mask = augmentation(whole_mask) - """ - label_list = torch.unique(whole_mask) +def augmentation(pt_nda, output_size, random_seed): + label_list = torch.unique(pt_nda) label_list = list(label_list.cpu().numpy()) - # Note that we only augment one type of tumor. if 128 in label_list: print("augmenting bone lesion/tumor") - whole_mask = augmentation_bone_tumor(whole_mask, spatial_size) + pt_nda = augmentation_tumor_bone(pt_nda, output_size, random_seed) elif 26 in label_list: print("augmenting liver tumor") - whole_mask = augmentation_liver_tumor(whole_mask, spatial_size) + pt_nda = augmentation_tumor_liver(pt_nda, output_size, random_seed) elif 23 in label_list: print("augmenting lung tumor") - whole_mask = augmentation_lung_tumor(whole_mask, spatial_size) + pt_nda = augmentation_tumor_lung(pt_nda, output_size, random_seed) elif 24 in label_list: print("augmenting pancreas tumor") - whole_mask = augmentation_pancreas_tumor(whole_mask, spatial_size) + pt_nda = augmentation_tumor_pancreas(pt_nda, output_size, random_seed) elif 27 in label_list: print("augmenting colon tumor") - whole_mask = augmentation_colon_tumor(whole_mask, spatial_size) + pt_nda = augmentation_tumor_colon(pt_nda, output_size, random_seed) else: print("augmenting body") - whole_mask = augmentation_body(whole_mask) + pt_nda = augmentation_body(pt_nda, random_seed) - return whole_mask + return pt_nda \ No newline at end of file diff --git a/generation/maisi/scripts/diff_model_infer.py b/generation/maisi/scripts/diff_model_infer.py index b7ad8b5aa..c9a0b0b3f 100644 --- a/generation/maisi/scripts/diff_model_infer.py +++ b/generation/maisi/scripts/diff_model_infer.py @@ -91,12 +91,14 @@ def prepare_tensors(args: argparse.Namespace, device: torch.device) -> tuple: top_region_index_tensor = np.array(args.diffusion_unet_inference["top_region_index"]).astype(float) * 1e2 bottom_region_index_tensor = np.array(args.diffusion_unet_inference["bottom_region_index"]).astype(float) * 1e2 spacing_tensor = np.array(args.diffusion_unet_inference["spacing"]).astype(float) * 1e2 + modality_tensor = np.array([args.diffusion_unet_inference["modality"]]).astype(int) top_region_index_tensor = torch.from_numpy(top_region_index_tensor[np.newaxis, :]).half().to(device) bottom_region_index_tensor = torch.from_numpy(bottom_region_index_tensor[np.newaxis, :]).half().to(device) spacing_tensor = torch.from_numpy(spacing_tensor[np.newaxis, :]).half().to(device) + modality_tensor = torch.from_numpy(modality_tensor[np.newaxis, :]).long().to(device) - return top_region_index_tensor, bottom_region_index_tensor, spacing_tensor + return top_region_index_tensor, bottom_region_index_tensor, spacing_tensor, modality_tensor def run_inference( @@ -108,10 +110,10 @@ def run_inference( top_region_index_tensor: torch.Tensor, bottom_region_index_tensor: torch.Tensor, spacing_tensor: torch.Tensor, + modality_tensor: torch.Tensor, output_size: tuple, divisor: int, logger: logging.Logger, - include_body_region: bool = False, ) -> np.ndarray: """ Run the inference to generate synthetic images. @@ -125,6 +127,7 @@ def run_inference( top_region_index_tensor (torch.Tensor): Top region index tensor. bottom_region_index_tensor (torch.Tensor): Bottom region index tensor. spacing_tensor (torch.Tensor): Spacing tensor. + modality_tensor (torch.Tensor): Modality tensor. output_size (tuple): Output size of the synthetic image. divisor (int): Divisor for downsample level. logger (logging.Logger): Logger for logging information. @@ -132,6 +135,9 @@ def run_inference( Returns: np.ndarray: Generated synthetic image data. """ + include_body_region = unet.include_top_region_index_input + include_modality = (unet.num_class_embeds is not None) + noise = torch.randn( ( 1, @@ -149,7 +155,7 @@ def run_inference( if isinstance(noise_scheduler, RFlowScheduler): noise_scheduler.set_timesteps( num_inference_steps=args.diffusion_unet_inference["num_inference_steps"], - input_img_size_numel=torch.prod(torch.tensor(noise.shape[-3:])), + input_img_size_numel=torch.prod(torch.tensor(noise.shape[2:])), ) else: noise_scheduler.set_timesteps(num_inference_steps=args.diffusion_unet_inference["num_inference_steps"]) @@ -166,20 +172,25 @@ def run_inference( ) with torch.amp.autocast("cuda", enabled=True): for t, next_t in progress_bar: + # Create a dictionary to store the inputs + unet_inputs = { + "x": image, + "timesteps": torch.Tensor((t,)).to(device), + "spacing_tensor": spacing_tensor, + } + + # Add extra arguments if include_body_region is True if include_body_region: - model_output = unet( - x=image, - timesteps=torch.Tensor((t,)).to(device), - top_region_index_tensor=top_region_index_tensor, - bottom_region_index_tensor=bottom_region_index_tensor, - spacing_tensor=spacing_tensor, - ) - else: - model_output = unet( - x=image, - timesteps=torch.Tensor((t,)).to(device), - spacing_tensor=spacing_tensor, - ) + unet_inputs.update({ + "top_region_index_tensor": top_region_index_tensor, + "bottom_region_index_tensor": bottom_region_index_tensor + }) + + if include_modality: + unet_inputs.update({ + "class_labels": modality_tensor, + }) + model_output = unet(**unet_inputs) if not isinstance(noise_scheduler, RFlowScheduler): image, _ = noise_scheduler.step(model_output, t, image) # type: ignore else: @@ -231,7 +242,7 @@ def save_image( @torch.inference_mode() def diff_model_infer( - env_config_path: str, model_config_path: str, model_def_path: str, num_gpus: int, include_body_region: bool = False + env_config_path: str, model_config_path: str, model_def_path: str, num_gpus: int ) -> None: """ Main function to run the diffusion model inference. @@ -240,7 +251,6 @@ def diff_model_infer( env_config_path (str): Path to the environment configuration file. model_config_path (str): Path to the model configuration file. model_def_path (str): Path to the model definition file. - include_body_region (bool): Whether to include body region in data """ args = load_config(env_config_path, model_config_path, model_def_path) local_rank, world_size, device = initialize_distributed(num_gpus) @@ -278,7 +288,7 @@ def diff_model_infer( divisor = 2 ** (num_downsample_level - 2) logger.info(f"num_downsample_level -> {num_downsample_level}, divisor -> {divisor}.") - top_region_index_tensor, bottom_region_index_tensor, spacing_tensor = prepare_tensors(args, device) + top_region_index_tensor, bottom_region_index_tensor, spacing_tensor, modality_tensor = prepare_tensors(args, device) data = run_inference( args, device, @@ -288,10 +298,10 @@ def diff_model_infer( top_region_index_tensor, bottom_region_index_tensor, spacing_tensor, + modality_tensor, output_size, divisor, - logger, - include_body_region=include_body_region, + logger ) timestamp = datetime.now().strftime("%Y%m%d%H%M%S") @@ -340,14 +350,8 @@ def diff_model_infer( default=1, help="Number of GPUs to use for distributed inference", ) - parser.add_argument( - "--include_body_region", - dest="include_body_region", - action="store_true", - help="Whether to include body region in data", - ) args = parser.parse_args() diff_model_infer( - args.env_config, args.model_config, args.model_def, args.num_gpus, include_body_region=args.include_body_region + args.env_config, args.model_config, args.model_def, args.num_gpus ) diff --git a/generation/maisi/scripts/diff_model_train.py b/generation/maisi/scripts/diff_model_train.py index 95c1d10fe..4b38256e3 100644 --- a/generation/maisi/scripts/diff_model_train.py +++ b/generation/maisi/scripts/diff_model_train.py @@ -201,7 +201,6 @@ def train_one_epoch( logger: logging.Logger, local_rank: int, amp: bool = True, - include_body_region: bool = False, ) -> torch.Tensor: """ Train the model for one epoch. @@ -222,11 +221,13 @@ def train_one_epoch( logger (logging.Logger): Logger for logging information. local_rank (int): Local rank for distributed training. amp (bool): Use automatic mixed precision training. - include_body_region (bool): Whether to include body region in data Returns: torch.Tensor: Training loss for the epoch. """ + include_body_region = unet.include_top_region_index_input + include_modality = (unet.num_class_embeds is not None) + if local_rank == 0: current_lr = optimizer.param_groups[0]["lr"] logger.info(f"Epoch {epoch + 1}, lr {current_lr}.") @@ -245,6 +246,9 @@ def train_one_epoch( if include_body_region: top_region_index_tensor = train_data["top_region_index"].to(device) bottom_region_index_tensor = train_data["bottom_region_index"].to(device) + # We trained with only CT in this version + if include_modality: + modality_tensor = torch.ones((len(images),),dtype=torch.long).to(device) spacing_tensor = train_data["spacing"].to(device) optimizer.zero_grad(set_to_none=True) @@ -259,20 +263,23 @@ def train_one_epoch( noisy_latent = noise_scheduler.add_noise(original_samples=images, noise=noise, timesteps=timesteps) + # Create a dictionary to store the inputs + unet_inputs = { + "x": noisy_latent, + "timesteps": timesteps, + "spacing_tensor": spacing_tensor, + } + # Add extra arguments if include_body_region is True if include_body_region: - model_output = unet( - x=noisy_latent, - timesteps=timesteps, - top_region_index_tensor=top_region_index_tensor, - bottom_region_index_tensor=bottom_region_index_tensor, - spacing_tensor=spacing_tensor, - ) - else: - model_output = unet( - x=noisy_latent, - timesteps=timesteps, - spacing_tensor=spacing_tensor, - ) + unet_inputs.update({ + "top_region_index_tensor": top_region_index_tensor, + "bottom_region_index_tensor": bottom_region_index_tensor + }) + if include_modality: + unet_inputs.update({ + "class_labels": modality_tensor, + }) + model_output = unet(**unet_inputs) if noise_scheduler.prediction_type == DDPMPredictionType.EPSILON: # predict noise @@ -356,8 +363,7 @@ def diff_model_train( model_config_path: str, model_def_path: str, num_gpus: int, - amp: bool = True, - include_body_region: bool = False, + amp: bool = True ) -> None: """ Main function to train a diffusion model. @@ -368,7 +374,6 @@ def diff_model_train( model_def_path (str): Path to the model definition file. num_gpus (int): Number of GPUs to use for training. amp (bool): Use automatic mixed precision training. - include_body_region (bool): Whether to include body region in data """ args = load_config(env_config_path, model_config_path, model_def_path) local_rank, world_size, device = initialize_distributed(num_gpus) @@ -386,6 +391,10 @@ def diff_model_train( Path(args.model_dir).mkdir(parents=True, exist_ok=True) + unet = load_unet(args, device, logger) + noise_scheduler = define_instance(args, "noise_scheduler") + include_body_region = unet.include_top_region_index_input + filenames_train = load_filenames(args.json_data_list) if local_rank == 0: logger.info(f"num_files_train: {len(filenames_train)}") @@ -415,8 +424,7 @@ def diff_model_train( include_body_region=include_body_region, ) - unet = load_unet(args, device, logger) - noise_scheduler = define_instance(args, "noise_scheduler") + scale_factor = calculate_scale_factor(train_loader, device, logger) optimizer = create_optimizer(unet, args.diffusion_unet_train["lr"]) @@ -447,8 +455,7 @@ def diff_model_train( device, logger, local_rank, - amp=amp, - include_body_region=include_body_region, + amp=amp ) loss_torch = loss_torch.tolist() @@ -489,14 +496,8 @@ def diff_model_train( ) parser.add_argument("--num_gpus", type=int, default=1, help="Number of GPUs to use for training") parser.add_argument("--no_amp", dest="amp", action="store_false", help="Disable automatic mixed precision training") - parser.add_argument( - "--include_body_region", - dest="include_body_region", - action="store_true", - help="Whether to include body region in data", - ) args = parser.parse_args() diff_model_train( - args.env_config, args.model_config, args.model_def, args.num_gpus, args.amp, args.include_body_region + args.env_config, args.model_config, args.model_def, args.num_gpus, args.amp ) diff --git a/generation/maisi/scripts/infer_controlnet.py b/generation/maisi/scripts/infer_controlnet.py index da55f33f8..a8f12617d 100644 --- a/generation/maisi/scripts/infer_controlnet.py +++ b/generation/maisi/scripts/infer_controlnet.py @@ -49,14 +49,8 @@ def main(): help="config json file that stores training hyper-parameters", ) parser.add_argument("-g", "--gpus", default=1, type=int, help="number of gpus per node") - parser.add_argument( - "--include_body_region", - dest="include_body_region", - action="store_true", - help="Whether to include body region in data", - ) + args = parser.parse_args() - include_body_region = args.include_body_region # Step 0: configuration logger = logging.getLogger("maisi.controlnet.infer") @@ -116,6 +110,9 @@ def main(): # define diffusion Model unet = define_instance(args, "diffusion_unet_def").to(device) + include_body_region = unet.include_top_region_index_input + include_modality = (unet.num_class_embeds is not None) + # load trained diffusion model if args.trained_diffusion_path is not None: if not os.path.exists(args.trained_diffusion_path): diff --git a/generation/maisi/scripts/quality_check.py b/generation/maisi/scripts/quality_check.py index 223732761..993bfeec4 100644 --- a/generation/maisi/scripts/quality_check.py +++ b/generation/maisi/scripts/quality_check.py @@ -9,7 +9,6 @@ # See the License for the specific language governing permissions and # limitations under the License. -import nibabel as nib import numpy as np @@ -110,8 +109,11 @@ def is_outlier(statistics, image_data, label_data, label_int_dict): for label_name, stats in statistics.items(): # Get the thresholds from the statistics - low_thresh = stats["sigma_6_low"] # or "sigma_12_low" depending on your needs - high_thresh = stats["sigma_6_high"] # or "sigma_12_high" depending on your needs + low_thresh = min(stats["sigma_6_low"], stats["percentile_0_5"]) # or "sigma_12_low" depending on your needs + high_thresh = max(stats["sigma_6_high"], stats["percentile_99_5"]) # or "sigma_12_high" depending on your needs + + if label_name == "bone": + high_thresh = 1000. # Retrieve the corresponding label integers labels = label_int_dict.get(label_name, []) @@ -144,4 +146,4 @@ def is_outlier(statistics, image_data, label_data, label_int_dict): "high_thresh": high_thresh, } - return outlier_results + return outlier_results \ No newline at end of file diff --git a/generation/maisi/scripts/sample.py b/generation/maisi/scripts/sample.py index 3b5dc4474..a1addb086 100644 --- a/generation/maisi/scripts/sample.py +++ b/generation/maisi/scripts/sample.py @@ -16,20 +16,22 @@ import random import time from datetime import datetime +import warnings import monai import torch from monai.data import MetaTensor -from monai.inferers import sliding_window_inference from monai.inferers.inferer import DiffusionInferer from monai.transforms import Compose, SaveImage from monai.utils import set_determinism from tqdm import tqdm +from monai.inferers.inferer import SlidingWindowInferer +from monai.networks.schedulers import RFlowScheduler, DDPMScheduler from .augmentation import augmentation from .find_masks import find_masks from .quality_check import is_outlier -from .utils import binarize_labels, general_mask_generation_post_process, get_body_region_index_from_mask, remap_labels +from .utils import binarize_labels, general_mask_generation_post_process, get_body_region_index_from_mask, remap_labels, dynamic_infer class ReconModel(torch.nn.Module): @@ -122,7 +124,14 @@ def ldm_conditional_sample_one_mask( latents = initialize_noise_latents(latent_shape, device) anatomy_size = torch.FloatTensor(anatomy_size).unsqueeze(0).unsqueeze(0).half().to(device) # synthesize latents + if isinstance(noise_scheduler,DDPMScheduler) and num_inference_steps None: """ Initialize the LDMSampler with various parameters and models. @@ -522,6 +560,7 @@ def __init__( Args: Various parameters related to model configuration, input settings, and output specifications. """ + self.random_seed = random_seed if random_seed is not None: set_determinism(seed=random_seed) @@ -562,7 +601,7 @@ def __init__( self.mask_generation_num_inference_steps = ( mask_generation_num_inference_steps if mask_generation_num_inference_steps is not None else 1000 ) - + if any(size % 16 != 0 for size in autoencoder_sliding_window_infer_size): raise ValueError( f"autoencoder_sliding_window_infer_size must be divisible by 16.\n Got {autoencoder_sliding_window_infer_size}" @@ -601,21 +640,28 @@ def __init__( self.mask_generation_diffusion_unet.eval() self.spacing = spacing - - self.val_transforms = Compose( - [ - monai.transforms.LoadImaged(keys=["pseudo_label"]), - monai.transforms.EnsureChannelFirstd(keys=["pseudo_label"]), - monai.transforms.Orientationd(keys=["pseudo_label"], axcodes="RAS"), - monai.transforms.EnsureTyped(keys=["pseudo_label"], dtype=torch.uint8), + self.modality_tensor = modality * torch.ones((1,),dtype=torch.long).to(device) + self.include_body_region = self.diffusion_unet.include_top_region_index_input + self.include_modality = (self.diffusion_unet.num_class_embeds is not None) + + val_transforms_list = [ + monai.transforms.LoadImaged(keys=["pseudo_label"]), + monai.transforms.EnsureChannelFirstd(keys=["pseudo_label"]), + monai.transforms.Orientationd(keys=["pseudo_label"], axcodes="RAS"), + monai.transforms.EnsureTyped(keys=["pseudo_label"], dtype=torch.uint8), + monai.transforms.Lambdad(keys="spacing", func=lambda x: torch.FloatTensor(x)), + monai.transforms.Lambdad(keys="spacing", func=lambda x: x * 1e2), + + ] + if self.include_body_region: + val_transforms_list += [ monai.transforms.Lambdad(keys="top_region_index", func=lambda x: torch.FloatTensor(x)), monai.transforms.Lambdad(keys="bottom_region_index", func=lambda x: torch.FloatTensor(x)), - monai.transforms.Lambdad(keys="spacing", func=lambda x: torch.FloatTensor(x)), monai.transforms.Lambdad(keys="top_region_index", func=lambda x: x * 1e2), - monai.transforms.Lambdad(keys="bottom_region_index", func=lambda x: x * 1e2), - monai.transforms.Lambdad(keys="spacing", func=lambda x: x * 1e2), + monai.transforms.Lambdad(keys="bottom_region_index", func=lambda x: x * 1e2), ] - ) + + self.val_transforms = Compose(val_transforms_list) logging.info("LDM sampler initialized.") def sample_multiple_images(self, num_img): @@ -625,6 +671,7 @@ def sample_multiple_images(self, num_img): Args: num_img (int): Number of images to generate. """ + modality_tensor = self.modality_tensor output_filenames = [] if len(self.controllable_anatomy_size) > 0: # we will use mask generation instead of finding candidate masks @@ -657,7 +704,12 @@ def sample_multiple_images(self, num_img): raise ValueError( f"len(selected_mask_files) ({len(selected_mask_files)}) != num_img ({num_img}). This should not happen. Please revisit function select_mask(self, candidate_mask_files, num_img)." ) - for item in selected_mask_files: + + num_generated_img = 0 + for index_s in range(len(selected_mask_files)): + item = selected_mask_files[index_s] + if num_generated_img >= num_img: + break logging.info("---- Start preparing masks... ----") start_time = time.time() if len(self.controllable_anatomy_size) > 0: @@ -682,60 +734,66 @@ def sample_multiple_images(self, num_img): combine_label_or = self.ensure_output_size_and_spacing(combine_label_or) # mask augmentation if if_aug: - combine_label_or = augmentation(combine_label_or, self.output_size) + combine_label_or = augmentation(combine_label_or, self.output_size, self.random_seed) end_time = time.time() logging.info(f"---- Mask preparation time: {end_time - start_time} seconds ----") torch.cuda.empty_cache() # generate image/label pairs to_generate = True try_time = 0 - while to_generate: - synthetic_images, synthetic_labels = self.sample_one_pair( + # start generation + synthetic_images, synthetic_labels = self.sample_one_pair( combine_label_or, top_region_index_tensor, bottom_region_index_tensor, spacing_tensor, + modality_tensor, ) - # synthetic image quality check - pass_quality_check = self.quality_check( - synthetic_images.cpu().detach().numpy(), combine_label_or.cpu().detach().numpy() - ) - if pass_quality_check or try_time > self.max_try_time: - # save image/label pairs - output_postfix = datetime.now().strftime("%Y%m%d_%H%M%S_%f") - synthetic_labels.meta["filename_or_obj"] = "sample.nii.gz" - synthetic_images = MetaTensor(synthetic_images, meta=synthetic_labels.meta) - img_saver = SaveImage( - output_dir=self.output_dir, - output_postfix=output_postfix + "_image", - output_ext=self.image_output_ext, - separate_folder=False, - ) - img_saver(synthetic_images[0]) - synthetic_images_filename = os.path.join( - self.output_dir, "sample_" + output_postfix + "_image" + self.image_output_ext - ) - # filter out the organs that are not in anatomy_list - synthetic_labels = filter_mask_with_organs(synthetic_labels, self.anatomy_list) - label_saver = SaveImage( - output_dir=self.output_dir, - output_postfix=output_postfix + "_label", - output_ext=self.label_output_ext, - separate_folder=False, - ) - label_saver(synthetic_labels[0]) - synthetic_labels_filename = os.path.join( - self.output_dir, "sample_" + output_postfix + "_label" + self.label_output_ext - ) - output_filenames.append([synthetic_images_filename, synthetic_labels_filename]) - to_generate = False - else: + # synthetic image quality check + pass_quality_check = self.quality_check( + synthetic_images.cpu().detach().numpy(), combine_label_or.cpu().detach().numpy() + ) + if pass_quality_check or (num_img - num_generated_img)>=(len(selected_mask_files)-index_s): + if not pass_quality_check: logging.info( - "Generated image/label pair did not pass quality check, will re-generate another pair." - ) - try_time += 1 + "Generated image/label pair did not pass quality check, but will still save them. " + "Please consider changing spacing and output_size to facilitate a more realistic setting." + ) + num_generated_img = num_generated_img +1 + # save image/label pairs + output_postfix = datetime.now().strftime("%Y%m%d_%H%M%S_%f") + synthetic_labels.meta["filename_or_obj"] = "sample.nii.gz" + synthetic_images = MetaTensor(synthetic_images, meta=synthetic_labels.meta) + img_saver = SaveImage( + output_dir=self.output_dir, + output_postfix=output_postfix + "_image", + output_ext=self.image_output_ext, + separate_folder=False, + ) + img_saver(synthetic_images[0]) + synthetic_images_filename = os.path.join( + self.output_dir, "sample_" + output_postfix + "_image" + self.image_output_ext + ) + # filter out the organs that are not in anatomy_list + synthetic_labels = filter_mask_with_organs(synthetic_labels, self.anatomy_list) + label_saver = SaveImage( + output_dir=self.output_dir, + output_postfix=output_postfix + "_label", + output_ext=self.label_output_ext, + separate_folder=False, + ) + label_saver(synthetic_labels[0]) + synthetic_labels_filename = os.path.join( + self.output_dir, "sample_" + output_postfix + "_label" + self.label_output_ext + ) + output_filenames.append([synthetic_images_filename, synthetic_labels_filename]) + to_generate = False + else: + logging.info( + "Generated image/label pair did not pass quality check, will re-generate another pair." + ) return output_filenames - + def select_mask(self, candidate_mask_files, num_img): """ Select mask files for image generation. @@ -761,6 +819,7 @@ def sample_one_pair( top_region_index_tensor, bottom_region_index_tensor, spacing_tensor, + modality_tensor, ): """ Generate a single pair of synthetic image and mask. @@ -770,6 +829,7 @@ def sample_one_pair( top_region_index_tensor (torch.Tensor): Tensor specifying the top region index. bottom_region_index_tensor (torch.Tensor): Tensor specifying the bottom region index. spacing_tensor (torch.Tensor): Tensor specifying the spacing. + modality_tensor (torch.Tensor): Int Tensor specifying the modality. Returns: tuple: A tuple containing the synthetic image and its corresponding label. @@ -786,12 +846,13 @@ def sample_one_pair( top_region_index_tensor=top_region_index_tensor, bottom_region_index_tensor=bottom_region_index_tensor, spacing_tensor=spacing_tensor, + modality_tensor=modality_tensor, latent_shape=self.latent_shape, output_size=self.output_size, noise_factor=self.noise_factor, num_inference_steps=self.num_inference_steps, autoencoder_sliding_window_infer_size=self.autoencoder_sliding_window_infer_size, - autoencoder_sliding_window_infer_overlap=self.autoencoder_sliding_window_infer_overlap, + autoencoder_sliding_window_infer_overlap=self.autoencoder_sliding_window_infer_overlap ) return synthetic_images, synthetic_labels @@ -963,9 +1024,12 @@ def read_mask_information(self, mask_file): "pseudo_label", "spacing", "top_region_index", - "bottom_region_index", + "bottom_region_index" ]: - val_data[key] = val_data[key].unsqueeze(0).to(self.device) + if isinstance(val_data[key],torch.Tensor): + val_data[key] = val_data[key].unsqueeze(0).to(self.device) + else: + val_data[key] = None return ( val_data["pseudo_label"], @@ -1000,42 +1064,73 @@ def find_closest_masks(self, num_img): if len(candidates) < num_img: raise ValueError(f"candidate masks are less than {num_img}).") + # loop through the database and find closest combinations new_candidates = [] for c in candidates: diff = 0 + include_c = True for axis in range(3): + if abs(c["dim"][axis]) < self.output_size[axis]-64: + # we cannot upsample the mask too much + include_c = False + break + # check diff in FOV, major metric + diff += abs((abs(c["dim"][axis]*c["spacing"][axis]) - self.output_size[axis]*self.spacing[axis]) / 10) # check diff in dim - diff += abs((c["dim"][axis] - self.output_size[axis]) / 100) + diff += abs((abs(c["dim"][axis]) - self.output_size[axis]) / 100) # check diff in spacing - diff += abs(c["spacing"][axis] - self.spacing[axis]) - new_candidates.append((c, diff)) + diff += abs(abs(c["spacing"][axis]) - self.spacing[axis]) + if include_c: + new_candidates.append((c, diff)) + # choose top-2*num_img candidates (at least 5) - new_candidates = sorted(new_candidates, key=lambda x: x[1])[: max(2 * num_img, 5)] + num_candidates = max(2 * num_img, 5) + new_candidates = sorted(new_candidates, key=lambda x: x[1]) + final_candidates = [] # check top-2*num_img candidates and update spacing after resampling - image_loader = monai.transforms.LoadImage(image_only=True, ensure_channel_first=True) for c, _ in new_candidates: - label = image_loader(c["pseudo_label"]) - try: - label = self.ensure_output_size_and_spacing(label.unsqueeze(0)) - except ValueError as e: - if "Resampled mask does not contain required class labels" in str(e): - continue - else: - raise e - # get region_index after resample - top_region_index, bottom_region_index = get_body_region_index_from_mask(label) - c["top_region_index"] = top_region_index - c["bottom_region_index"] = bottom_region_index - c["spacing"] = self.spacing - c["dim"] = self.output_size - - final_candidates.append(c) + c = self.resample_mask_check_organ_list(c) + if c is not None: + final_candidates.append(c) + if len(final_candidates)>=num_candidates: + break if len(final_candidates) == 0: raise ValueError("Cannot find body region with given organ list.") return final_candidates + def resample_mask_check_organ_list(self, mask): + """ + Resample mask and check if the resampled mask contains the required organ list. + + Args: + mask (dict): input mask. + + Returns: + dict: resampled mask. If None, means the resampled mask does not contain the required organ list + + Raises: + ValueError: If suitable candidates cannot be found. + """ + + image_loader = monai.transforms.LoadImage(image_only=True, ensure_channel_first=True) + label = image_loader(mask["pseudo_label"]) + try: + label = self.ensure_output_size_and_spacing(label.unsqueeze(0)) + except ValueError as e: + if "Resampled mask does not contain required class labels" in str(e): + return None + else: + raise e + # get region_index after resample + top_region_index, bottom_region_index = get_body_region_index_from_mask(label) + mask["top_region_index"] = top_region_index + mask["bottom_region_index"] = bottom_region_index + mask["spacing"] = self.spacing + mask["dim"] = self.output_size + return mask + def quality_check(self, image_data, label_data): """ Perform a quality check on the generated image. diff --git a/generation/maisi/scripts/train_controlnet.py b/generation/maisi/scripts/train_controlnet.py index a43ffdd5d..f96d00a33 100644 --- a/generation/maisi/scripts/train_controlnet.py +++ b/generation/maisi/scripts/train_controlnet.py @@ -53,14 +53,8 @@ def main(): help="config json file that stores training hyper-parameters", ) parser.add_argument("-g", "--gpus", default=1, type=int, help="number of gpus per node") - parser.add_argument( - "--include_body_region", - dest="include_body_region", - action="store_true", - help="Whether to include body region in data", - ) + args = parser.parse_args() - include_body_region = args.include_body_region # Step 0: configuration logger = logging.getLogger("maisi.controlnet.training") @@ -114,6 +108,9 @@ def main(): # Step 2: define diffusion model and controlnet # define diffusion Model unet = define_instance(args, "diffusion_unet_def").to(device) + include_body_region = unet.include_top_region_index_input + include_modality = (unet.num_class_embeds is not None) + # load trained diffusion model if args.trained_diffusion_path is not None: if not os.path.exists(args.trained_diffusion_path): @@ -183,6 +180,9 @@ def main(): if include_body_region: top_region_index_tensor = batch["top_region_index"].to(device) bottom_region_index_tensor = batch["bottom_region_index"].to(device) + # We trained with only CT in this version + if include_modality: + modality_tensor = torch.ones((len(images),),dtype=torch.long).to(device) spacing_tensor = batch["spacing"].to(device) optimizer.zero_grad(set_to_none=True) @@ -207,27 +207,40 @@ def main(): noisy_latent = noise_scheduler.add_noise(original_samples=images, noise=noise, timesteps=timesteps) # get controlnet output + # Create a dictionary to store the inputs + controlnet_inputs = { + "x": noisy_latent, + "timesteps": timesteps, + "controlnet_cond": controlnet_cond, + } + if include_modality: + controlnet_inputs.update({ + "class_labels": modality_tensor, + }) down_block_res_samples, mid_block_res_sample = controlnet( - x=noisy_latent, timesteps=timesteps, controlnet_cond=controlnet_cond + **controlnet_inputs ) + + # get diffusion network output + # Create a dictionary to store the inputs + unet_inputs = { + "x": noisy_latent, + "timesteps": timesteps, + "spacing_tensor": spacing_tensor, + "down_block_additional_residuals": down_block_res_samples, + "mid_block_additional_residual": mid_block_res_sample + } + # Add extra arguments if include_body_region is True if include_body_region: - model_output = unet( - x=noisy_latent, - timesteps=timesteps, - top_region_index_tensor=top_region_index_tensor, - bottom_region_index_tensor=bottom_region_index_tensor, - spacing_tensor=spacing_tensor, - down_block_additional_residuals=down_block_res_samples, - mid_block_additional_residual=mid_block_res_sample, - ) - else: - model_output = unet( - x=noisy_latent, - timesteps=timesteps, - spacing_tensor=spacing_tensor, - down_block_additional_residuals=down_block_res_samples, - mid_block_additional_residual=mid_block_res_sample, - ) + unet_inputs.update({ + "top_region_index_tensor": top_region_index_tensor, + "bottom_region_index_tensor": bottom_region_index_tensor + }) + if include_modality: + unet_inputs.update({ + "class_labels": modality_tensor, + }) + model_output = unet(**unet_inputs) if noise_scheduler.prediction_type == DDPMPredictionType.EPSILON: # predict noise From cfd563678fb1a4a4203d16a3af38458ca27ab4fc Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 13 Mar 2025 05:36:45 +0000 Subject: [PATCH 24/59] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../maisi/maisi_inference_tutorial.ipynb | 12 +- generation/maisi/scripts/augmentation.py | 2 +- generation/maisi/scripts/diff_model_infer.py | 34 ++-- generation/maisi/scripts/diff_model_train.py | 42 +++-- generation/maisi/scripts/infer_controlnet.py | 4 +- generation/maisi/scripts/quality_check.py | 4 +- generation/maisi/scripts/sample.py | 150 +++++++++--------- generation/maisi/scripts/train_controlnet.py | 44 ++--- 8 files changed, 149 insertions(+), 143 deletions(-) diff --git a/generation/maisi/maisi_inference_tutorial.ipynb b/generation/maisi/maisi_inference_tutorial.ipynb index 9e6e63456..01b1defca 100644 --- a/generation/maisi/maisi_inference_tutorial.ipynb +++ b/generation/maisi/maisi_inference_tutorial.ipynb @@ -210,7 +210,7 @@ " \"path\": \"models/autoencoder_epoch273.pt\",\n", " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials\"\n", " \"/model_zoo/model_maisi_autoencoder_epoch273_alternative.pt\",\n", - " }, \n", + " },\n", " {\n", " \"path\": \"models/mask_generation_autoencoder.pt\",\n", " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\" \"/tutorials/mask_generation_autoencoder.pt\",\n", @@ -219,12 +219,11 @@ " \"path\": \"models/mask_generation_diffusion_unet.pt\",\n", " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n", " \"/tutorials/model_zoo/model_maisi_mask_generation_diffusion_unet_v2.pt\",\n", - " }, \n", + " },\n", " {\n", " \"path\": \"configs/all_anatomy_size_condtions.json\",\n", " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/all_anatomy_size_condtions.json\",\n", " },\n", - " \n", "]\n", "\n", "if maisi_version == \"maisi3d-ddpm\":\n", @@ -259,15 +258,14 @@ " },\n", " {\n", " \"path\": \"models/controlnet_3d_rflow.pt\",\n", - " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/\"\n", - " \"controlnet_rflow_epoch208.pt\",\n", + " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/\" \"controlnet_rflow_epoch208.pt\",\n", " },\n", " {\n", " \"path\": \"configs/candidate_masks_flexible_size_and_spacing_4000.json\",\n", " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n", " \"/tutorials/candidate_masks_flexible_size_and_spacing_4000.json\",\n", " },\n", - " {\n", + " {\n", " \"path\": \"datasets/all_masks_flexible_size_and_spacing_4000.zip\",\n", " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n", " \"/tutorials/all_masks_flexible_size_and_spacing_4000.zip\",\n", @@ -545,7 +543,7 @@ " mask_generation_num_inference_steps=args.mask_generation_num_inference_steps,\n", " random_seed=args.random_seed,\n", " autoencoder_sliding_window_infer_size=args.autoencoder_sliding_window_infer_size,\n", - " autoencoder_sliding_window_infer_overlap=args.autoencoder_sliding_window_infer_overlap\n", + " autoencoder_sliding_window_infer_overlap=args.autoencoder_sliding_window_infer_overlap,\n", ")" ] }, diff --git a/generation/maisi/scripts/augmentation.py b/generation/maisi/scripts/augmentation.py index c56e7cb73..64469403a 100644 --- a/generation/maisi/scripts/augmentation.py +++ b/generation/maisi/scripts/augmentation.py @@ -370,4 +370,4 @@ def augmentation(pt_nda, output_size, random_seed): print("augmenting body") pt_nda = augmentation_body(pt_nda, random_seed) - return pt_nda \ No newline at end of file + return pt_nda diff --git a/generation/maisi/scripts/diff_model_infer.py b/generation/maisi/scripts/diff_model_infer.py index c9a0b0b3f..f63ecd36b 100644 --- a/generation/maisi/scripts/diff_model_infer.py +++ b/generation/maisi/scripts/diff_model_infer.py @@ -136,8 +136,8 @@ def run_inference( np.ndarray: Generated synthetic image data. """ include_body_region = unet.include_top_region_index_input - include_modality = (unet.num_class_embeds is not None) - + include_modality = unet.num_class_embeds is not None + noise = torch.randn( ( 1, @@ -178,18 +178,22 @@ def run_inference( "timesteps": torch.Tensor((t,)).to(device), "spacing_tensor": spacing_tensor, } - + # Add extra arguments if include_body_region is True if include_body_region: - unet_inputs.update({ - "top_region_index_tensor": top_region_index_tensor, - "bottom_region_index_tensor": bottom_region_index_tensor - }) + unet_inputs.update( + { + "top_region_index_tensor": top_region_index_tensor, + "bottom_region_index_tensor": bottom_region_index_tensor, + } + ) if include_modality: - unet_inputs.update({ - "class_labels": modality_tensor, - }) + unet_inputs.update( + { + "class_labels": modality_tensor, + } + ) model_output = unet(**unet_inputs) if not isinstance(noise_scheduler, RFlowScheduler): image, _ = noise_scheduler.step(model_output, t, image) # type: ignore @@ -241,9 +245,7 @@ def save_image( @torch.inference_mode() -def diff_model_infer( - env_config_path: str, model_config_path: str, model_def_path: str, num_gpus: int -) -> None: +def diff_model_infer(env_config_path: str, model_config_path: str, model_def_path: str, num_gpus: int) -> None: """ Main function to run the diffusion model inference. @@ -301,7 +303,7 @@ def diff_model_infer( modality_tensor, output_size, divisor, - logger + logger, ) timestamp = datetime.now().strftime("%Y%m%d%H%M%S") @@ -352,6 +354,4 @@ def diff_model_infer( ) args = parser.parse_args() - diff_model_infer( - args.env_config, args.model_config, args.model_def, args.num_gpus - ) + diff_model_infer(args.env_config, args.model_config, args.model_def, args.num_gpus) diff --git a/generation/maisi/scripts/diff_model_train.py b/generation/maisi/scripts/diff_model_train.py index 4b38256e3..a2bbc6ce7 100644 --- a/generation/maisi/scripts/diff_model_train.py +++ b/generation/maisi/scripts/diff_model_train.py @@ -226,8 +226,8 @@ def train_one_epoch( torch.Tensor: Training loss for the epoch. """ include_body_region = unet.include_top_region_index_input - include_modality = (unet.num_class_embeds is not None) - + include_modality = unet.num_class_embeds is not None + if local_rank == 0: current_lr = optimizer.param_groups[0]["lr"] logger.info(f"Epoch {epoch + 1}, lr {current_lr}.") @@ -248,7 +248,7 @@ def train_one_epoch( bottom_region_index_tensor = train_data["bottom_region_index"].to(device) # We trained with only CT in this version if include_modality: - modality_tensor = torch.ones((len(images),),dtype=torch.long).to(device) + modality_tensor = torch.ones((len(images),), dtype=torch.long).to(device) spacing_tensor = train_data["spacing"].to(device) optimizer.zero_grad(set_to_none=True) @@ -268,18 +268,22 @@ def train_one_epoch( "x": noisy_latent, "timesteps": timesteps, "spacing_tensor": spacing_tensor, - } + } # Add extra arguments if include_body_region is True if include_body_region: - unet_inputs.update({ - "top_region_index_tensor": top_region_index_tensor, - "bottom_region_index_tensor": bottom_region_index_tensor - }) + unet_inputs.update( + { + "top_region_index_tensor": top_region_index_tensor, + "bottom_region_index_tensor": bottom_region_index_tensor, + } + ) if include_modality: - unet_inputs.update({ - "class_labels": modality_tensor, - }) - model_output = unet(**unet_inputs) + unet_inputs.update( + { + "class_labels": modality_tensor, + } + ) + model_output = unet(**unet_inputs) if noise_scheduler.prediction_type == DDPMPredictionType.EPSILON: # predict noise @@ -359,11 +363,7 @@ def save_checkpoint( def diff_model_train( - env_config_path: str, - model_config_path: str, - model_def_path: str, - num_gpus: int, - amp: bool = True + env_config_path: str, model_config_path: str, model_def_path: str, num_gpus: int, amp: bool = True ) -> None: """ Main function to train a diffusion model. @@ -424,8 +424,6 @@ def diff_model_train( include_body_region=include_body_region, ) - - scale_factor = calculate_scale_factor(train_loader, device, logger) optimizer = create_optimizer(unet, args.diffusion_unet_train["lr"]) @@ -455,7 +453,7 @@ def diff_model_train( device, logger, local_rank, - amp=amp + amp=amp, ) loss_torch = loss_torch.tolist() @@ -498,6 +496,4 @@ def diff_model_train( parser.add_argument("--no_amp", dest="amp", action="store_false", help="Disable automatic mixed precision training") args = parser.parse_args() - diff_model_train( - args.env_config, args.model_config, args.model_def, args.num_gpus, args.amp - ) + diff_model_train(args.env_config, args.model_config, args.model_def, args.num_gpus, args.amp) diff --git a/generation/maisi/scripts/infer_controlnet.py b/generation/maisi/scripts/infer_controlnet.py index a8f12617d..fde17ae3f 100644 --- a/generation/maisi/scripts/infer_controlnet.py +++ b/generation/maisi/scripts/infer_controlnet.py @@ -111,8 +111,8 @@ def main(): # define diffusion Model unet = define_instance(args, "diffusion_unet_def").to(device) include_body_region = unet.include_top_region_index_input - include_modality = (unet.num_class_embeds is not None) - + include_modality = unet.num_class_embeds is not None + # load trained diffusion model if args.trained_diffusion_path is not None: if not os.path.exists(args.trained_diffusion_path): diff --git a/generation/maisi/scripts/quality_check.py b/generation/maisi/scripts/quality_check.py index 993bfeec4..bff49b6da 100644 --- a/generation/maisi/scripts/quality_check.py +++ b/generation/maisi/scripts/quality_check.py @@ -113,7 +113,7 @@ def is_outlier(statistics, image_data, label_data, label_int_dict): high_thresh = max(stats["sigma_6_high"], stats["percentile_99_5"]) # or "sigma_12_high" depending on your needs if label_name == "bone": - high_thresh = 1000. + high_thresh = 1000.0 # Retrieve the corresponding label integers labels = label_int_dict.get(label_name, []) @@ -146,4 +146,4 @@ def is_outlier(statistics, image_data, label_data, label_int_dict): "high_thresh": high_thresh, } - return outlier_results \ No newline at end of file + return outlier_results diff --git a/generation/maisi/scripts/sample.py b/generation/maisi/scripts/sample.py index a1addb086..f992ff719 100644 --- a/generation/maisi/scripts/sample.py +++ b/generation/maisi/scripts/sample.py @@ -31,7 +31,13 @@ from .augmentation import augmentation from .find_masks import find_masks from .quality_check import is_outlier -from .utils import binarize_labels, general_mask_generation_post_process, get_body_region_index_from_mask, remap_labels, dynamic_infer +from .utils import ( + binarize_labels, + general_mask_generation_post_process, + get_body_region_index_from_mask, + remap_labels, + dynamic_infer, +) class ReconModel(torch.nn.Module): @@ -124,12 +130,14 @@ def ldm_conditional_sample_one_mask( latents = initialize_noise_latents(latent_shape, device) anatomy_size = torch.FloatTensor(anatomy_size).unsqueeze(0).unsqueeze(0).half().to(device) # synthesize latents - if isinstance(noise_scheduler,DDPMScheduler) and num_inference_steps None: """ Initialize the LDMSampler with various parameters and models. @@ -601,7 +615,7 @@ def __init__( self.mask_generation_num_inference_steps = ( mask_generation_num_inference_steps if mask_generation_num_inference_steps is not None else 1000 ) - + if any(size % 16 != 0 for size in autoencoder_sliding_window_infer_size): raise ValueError( f"autoencoder_sliding_window_infer_size must be divisible by 16.\n Got {autoencoder_sliding_window_infer_size}" @@ -640,27 +654,26 @@ def __init__( self.mask_generation_diffusion_unet.eval() self.spacing = spacing - self.modality_tensor = modality * torch.ones((1,),dtype=torch.long).to(device) + self.modality_tensor = modality * torch.ones((1,), dtype=torch.long).to(device) self.include_body_region = self.diffusion_unet.include_top_region_index_input - self.include_modality = (self.diffusion_unet.num_class_embeds is not None) + self.include_modality = self.diffusion_unet.num_class_embeds is not None val_transforms_list = [ monai.transforms.LoadImaged(keys=["pseudo_label"]), monai.transforms.EnsureChannelFirstd(keys=["pseudo_label"]), monai.transforms.Orientationd(keys=["pseudo_label"], axcodes="RAS"), - monai.transforms.EnsureTyped(keys=["pseudo_label"], dtype=torch.uint8), - monai.transforms.Lambdad(keys="spacing", func=lambda x: torch.FloatTensor(x)), + monai.transforms.EnsureTyped(keys=["pseudo_label"], dtype=torch.uint8), + monai.transforms.Lambdad(keys="spacing", func=lambda x: torch.FloatTensor(x)), monai.transforms.Lambdad(keys="spacing", func=lambda x: x * 1e2), - ] if self.include_body_region: val_transforms_list += [ monai.transforms.Lambdad(keys="top_region_index", func=lambda x: torch.FloatTensor(x)), monai.transforms.Lambdad(keys="bottom_region_index", func=lambda x: torch.FloatTensor(x)), monai.transforms.Lambdad(keys="top_region_index", func=lambda x: x * 1e2), - monai.transforms.Lambdad(keys="bottom_region_index", func=lambda x: x * 1e2), + monai.transforms.Lambdad(keys="bottom_region_index", func=lambda x: x * 1e2), ] - + self.val_transforms = Compose(val_transforms_list) logging.info("LDM sampler initialized.") @@ -743,23 +756,23 @@ def sample_multiple_images(self, num_img): try_time = 0 # start generation synthetic_images, synthetic_labels = self.sample_one_pair( - combine_label_or, - top_region_index_tensor, - bottom_region_index_tensor, - spacing_tensor, - modality_tensor, - ) + combine_label_or, + top_region_index_tensor, + bottom_region_index_tensor, + spacing_tensor, + modality_tensor, + ) # synthetic image quality check pass_quality_check = self.quality_check( synthetic_images.cpu().detach().numpy(), combine_label_or.cpu().detach().numpy() ) - if pass_quality_check or (num_img - num_generated_img)>=(len(selected_mask_files)-index_s): + if pass_quality_check or (num_img - num_generated_img) >= (len(selected_mask_files) - index_s): if not pass_quality_check: logging.info( - "Generated image/label pair did not pass quality check, but will still save them. " - "Please consider changing spacing and output_size to facilitate a more realistic setting." - ) - num_generated_img = num_generated_img +1 + "Generated image/label pair did not pass quality check, but will still save them. " + "Please consider changing spacing and output_size to facilitate a more realistic setting." + ) + num_generated_img = num_generated_img + 1 # save image/label pairs output_postfix = datetime.now().strftime("%Y%m%d_%H%M%S_%f") synthetic_labels.meta["filename_or_obj"] = "sample.nii.gz" @@ -789,11 +802,9 @@ def sample_multiple_images(self, num_img): output_filenames.append([synthetic_images_filename, synthetic_labels_filename]) to_generate = False else: - logging.info( - "Generated image/label pair did not pass quality check, will re-generate another pair." - ) + logging.info("Generated image/label pair did not pass quality check, will re-generate another pair.") return output_filenames - + def select_mask(self, candidate_mask_files, num_img): """ Select mask files for image generation. @@ -852,7 +863,7 @@ def sample_one_pair( noise_factor=self.noise_factor, num_inference_steps=self.num_inference_steps, autoencoder_sliding_window_infer_size=self.autoencoder_sliding_window_infer_size, - autoencoder_sliding_window_infer_overlap=self.autoencoder_sliding_window_infer_overlap + autoencoder_sliding_window_infer_overlap=self.autoencoder_sliding_window_infer_overlap, ) return synthetic_images, synthetic_labels @@ -1020,13 +1031,8 @@ def read_mask_information(self, mask_file): """ val_data = self.val_transforms(mask_file) - for key in [ - "pseudo_label", - "spacing", - "top_region_index", - "bottom_region_index" - ]: - if isinstance(val_data[key],torch.Tensor): + for key in ["pseudo_label", "spacing", "top_region_index", "bottom_region_index"]: + if isinstance(val_data[key], torch.Tensor): val_data[key] = val_data[key].unsqueeze(0).to(self.device) else: val_data[key] = None @@ -1064,26 +1070,28 @@ def find_closest_masks(self, num_img): if len(candidates) < num_img: raise ValueError(f"candidate masks are less than {num_img}).") - + # loop through the database and find closest combinations new_candidates = [] for c in candidates: diff = 0 include_c = True for axis in range(3): - if abs(c["dim"][axis]) < self.output_size[axis]-64: + if abs(c["dim"][axis]) < self.output_size[axis] - 64: # we cannot upsample the mask too much include_c = False break # check diff in FOV, major metric - diff += abs((abs(c["dim"][axis]*c["spacing"][axis]) - self.output_size[axis]*self.spacing[axis]) / 10) + diff += abs( + (abs(c["dim"][axis] * c["spacing"][axis]) - self.output_size[axis] * self.spacing[axis]) / 10 + ) # check diff in dim diff += abs((abs(c["dim"][axis]) - self.output_size[axis]) / 100) # check diff in spacing diff += abs(abs(c["spacing"][axis]) - self.spacing[axis]) if include_c: new_candidates.append((c, diff)) - + # choose top-2*num_img candidates (at least 5) num_candidates = max(2 * num_img, 5) new_candidates = sorted(new_candidates, key=lambda x: x[1]) @@ -1094,7 +1102,7 @@ def find_closest_masks(self, num_img): c = self.resample_mask_check_organ_list(c) if c is not None: final_candidates.append(c) - if len(final_candidates)>=num_candidates: + if len(final_candidates) >= num_candidates: break if len(final_candidates) == 0: raise ValueError("Cannot find body region with given organ list.") @@ -1113,7 +1121,7 @@ def resample_mask_check_organ_list(self, mask): Raises: ValueError: If suitable candidates cannot be found. """ - + image_loader = monai.transforms.LoadImage(image_only=True, ensure_channel_first=True) label = image_loader(mask["pseudo_label"]) try: diff --git a/generation/maisi/scripts/train_controlnet.py b/generation/maisi/scripts/train_controlnet.py index f96d00a33..71703fbb7 100644 --- a/generation/maisi/scripts/train_controlnet.py +++ b/generation/maisi/scripts/train_controlnet.py @@ -53,7 +53,7 @@ def main(): help="config json file that stores training hyper-parameters", ) parser.add_argument("-g", "--gpus", default=1, type=int, help="number of gpus per node") - + args = parser.parse_args() # Step 0: configuration @@ -109,8 +109,8 @@ def main(): # define diffusion Model unet = define_instance(args, "diffusion_unet_def").to(device) include_body_region = unet.include_top_region_index_input - include_modality = (unet.num_class_embeds is not None) - + include_modality = unet.num_class_embeds is not None + # load trained diffusion model if args.trained_diffusion_path is not None: if not os.path.exists(args.trained_diffusion_path): @@ -182,7 +182,7 @@ def main(): bottom_region_index_tensor = batch["bottom_region_index"].to(device) # We trained with only CT in this version if include_modality: - modality_tensor = torch.ones((len(images),),dtype=torch.long).to(device) + modality_tensor = torch.ones((len(images),), dtype=torch.long).to(device) spacing_tensor = batch["spacing"].to(device) optimizer.zero_grad(set_to_none=True) @@ -212,14 +212,14 @@ def main(): "x": noisy_latent, "timesteps": timesteps, "controlnet_cond": controlnet_cond, - } + } if include_modality: - controlnet_inputs.update({ - "class_labels": modality_tensor, - }) - down_block_res_samples, mid_block_res_sample = controlnet( - **controlnet_inputs - ) + controlnet_inputs.update( + { + "class_labels": modality_tensor, + } + ) + down_block_res_samples, mid_block_res_sample = controlnet(**controlnet_inputs) # get diffusion network output # Create a dictionary to store the inputs @@ -228,18 +228,22 @@ def main(): "timesteps": timesteps, "spacing_tensor": spacing_tensor, "down_block_additional_residuals": down_block_res_samples, - "mid_block_additional_residual": mid_block_res_sample - } + "mid_block_additional_residual": mid_block_res_sample, + } # Add extra arguments if include_body_region is True if include_body_region: - unet_inputs.update({ - "top_region_index_tensor": top_region_index_tensor, - "bottom_region_index_tensor": bottom_region_index_tensor - }) + unet_inputs.update( + { + "top_region_index_tensor": top_region_index_tensor, + "bottom_region_index_tensor": bottom_region_index_tensor, + } + ) if include_modality: - unet_inputs.update({ - "class_labels": modality_tensor, - }) + unet_inputs.update( + { + "class_labels": modality_tensor, + } + ) model_output = unet(**unet_inputs) if noise_scheduler.prediction_type == DDPMPredictionType.EPSILON: From f085a287b15dfa4787b55699424ca2b8990aaa5f Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Thu, 13 Mar 2025 05:53:08 +0000 Subject: [PATCH 25/59] add modality as input to config Signed-off-by: Can-Zhao --- .../configs/config_infer_16g_256x256x128.json | 5 +- .../configs/config_infer_16g_256x256x256.json | 5 +- .../configs/config_infer_16g_512x512x128.json | 5 +- .../configs/config_infer_24g_256x256x256.json | 5 +- .../configs/config_infer_24g_512x512x128.json | 5 +- .../configs/config_infer_24g_512x512x512.json | 5 +- .../configs/config_infer_32g_512x512x512.json | 5 +- .../configs/config_infer_80g_512x512x128.json | 5 +- .../configs/config_infer_80g_512x512x512.json | 5 +- .../configs/config_infer_80g_512x512x768.json | 5 +- .../maisi/maisi_inference_tutorial.ipynb | 192 +++++++++--------- 11 files changed, 122 insertions(+), 120 deletions(-) diff --git a/generation/maisi/configs/config_infer_16g_256x256x128.json b/generation/maisi/configs/config_infer_16g_256x256x128.json index 72933304b..c4d98b146 100644 --- a/generation/maisi/configs/config_infer_16g_256x256x128.json +++ b/generation/maisi/configs/config_infer_16g_256x256x128.json @@ -3,7 +3,7 @@ "body_region": ["abdomen"], "anatomy_list": ["liver","hepatic tumor"], "controllable_anatomy_size": [], - "num_inference_steps": 1000, + "num_inference_steps": 30, "mask_generation_num_inference_steps": 1000, "output_size": [ 256, @@ -19,5 +19,6 @@ ], "autoencoder_sliding_window_infer_size": [96,96,96], "autoencoder_sliding_window_infer_overlap": 0.25, - "autoencoder_tp_num_splits": 2 + "autoencoder_tp_num_splits": 2, + "modality": 1 } diff --git a/generation/maisi/configs/config_infer_16g_256x256x256.json b/generation/maisi/configs/config_infer_16g_256x256x256.json index d4ec9e1a8..9af24e0b8 100644 --- a/generation/maisi/configs/config_infer_16g_256x256x256.json +++ b/generation/maisi/configs/config_infer_16g_256x256x256.json @@ -3,7 +3,7 @@ "body_region": ["abdomen"], "anatomy_list": ["liver","hepatic tumor"], "controllable_anatomy_size": [], - "num_inference_steps": 1000, + "num_inference_steps": 30, "mask_generation_num_inference_steps": 1000, "output_size": [ 256, @@ -19,5 +19,6 @@ ], "autoencoder_sliding_window_infer_size": [48,48,64], "autoencoder_sliding_window_infer_overlap": 0.25, - "autoencoder_tp_num_splits": 2 + "autoencoder_tp_num_splits": 2, + "modality": 1 } diff --git a/generation/maisi/configs/config_infer_16g_512x512x128.json b/generation/maisi/configs/config_infer_16g_512x512x128.json index 5e067cd4b..62df0c047 100644 --- a/generation/maisi/configs/config_infer_16g_512x512x128.json +++ b/generation/maisi/configs/config_infer_16g_512x512x128.json @@ -3,7 +3,7 @@ "body_region": ["abdomen"], "anatomy_list": ["liver","hepatic tumor"], "controllable_anatomy_size": [], - "num_inference_steps": 1000, + "num_inference_steps": 30, "mask_generation_num_inference_steps": 1000, "output_size": [ 512, @@ -19,5 +19,6 @@ ], "autoencoder_sliding_window_infer_size": [64,64,32], "autoencoder_sliding_window_infer_overlap": 0.25, - "autoencoder_tp_num_splits": 1 + "autoencoder_tp_num_splits": 1, + "modality": 1 } diff --git a/generation/maisi/configs/config_infer_24g_256x256x256.json b/generation/maisi/configs/config_infer_24g_256x256x256.json index bb0806f63..80d0d9e6f 100644 --- a/generation/maisi/configs/config_infer_24g_256x256x256.json +++ b/generation/maisi/configs/config_infer_24g_256x256x256.json @@ -3,7 +3,7 @@ "body_region": ["abdomen"], "anatomy_list": ["liver","hepatic tumor"], "controllable_anatomy_size": [], - "num_inference_steps": 1000, + "num_inference_steps": 30, "mask_generation_num_inference_steps": 1000, "output_size": [ 256, @@ -19,5 +19,6 @@ ], "autoencoder_sliding_window_infer_size": [64,64,64], "autoencoder_sliding_window_infer_overlap": 0.25, - "autoencoder_tp_num_splits": 4 + "autoencoder_tp_num_splits": 4, + "modality": 1 } diff --git a/generation/maisi/configs/config_infer_24g_512x512x128.json b/generation/maisi/configs/config_infer_24g_512x512x128.json index 6d2b9d7ea..012d9ba6e 100644 --- a/generation/maisi/configs/config_infer_24g_512x512x128.json +++ b/generation/maisi/configs/config_infer_24g_512x512x128.json @@ -3,7 +3,7 @@ "body_region": ["abdomen"], "anatomy_list": ["liver","hepatic tumor"], "controllable_anatomy_size": [], - "num_inference_steps": 1000, + "num_inference_steps": 30, "mask_generation_num_inference_steps": 1000, "output_size": [ 512, @@ -19,5 +19,6 @@ ], "autoencoder_sliding_window_infer_size": [80,80,32], "autoencoder_sliding_window_infer_overlap": 0.25, - "autoencoder_tp_num_splits": 1 + "autoencoder_tp_num_splits": 1, + "modality": 1 } diff --git a/generation/maisi/configs/config_infer_24g_512x512x512.json b/generation/maisi/configs/config_infer_24g_512x512x512.json index 2cbfb9573..27164e671 100644 --- a/generation/maisi/configs/config_infer_24g_512x512x512.json +++ b/generation/maisi/configs/config_infer_24g_512x512x512.json @@ -3,7 +3,7 @@ "body_region": ["abdomen"], "anatomy_list": ["liver","hepatic tumor"], "controllable_anatomy_size": [], - "num_inference_steps": 1000, + "num_inference_steps": 30, "mask_generation_num_inference_steps": 1000, "output_size": [ 512, @@ -19,5 +19,6 @@ ], "autoencoder_sliding_window_infer_size": [64,64,48], "autoencoder_sliding_window_infer_overlap": 0.25, - "autoencoder_tp_num_splits": 2 + "autoencoder_tp_num_splits": 2, + "modality": 1 } diff --git a/generation/maisi/configs/config_infer_32g_512x512x512.json b/generation/maisi/configs/config_infer_32g_512x512x512.json index 5dcbcacbe..a3c569947 100644 --- a/generation/maisi/configs/config_infer_32g_512x512x512.json +++ b/generation/maisi/configs/config_infer_32g_512x512x512.json @@ -3,7 +3,7 @@ "body_region": ["abdomen"], "anatomy_list": ["liver","hepatic tumor"], "controllable_anatomy_size": [], - "num_inference_steps": 1000, + "num_inference_steps": 30, "mask_generation_num_inference_steps": 1000, "output_size": [ 512, @@ -19,5 +19,6 @@ ], "autoencoder_sliding_window_infer_size": [64,64,64], "autoencoder_sliding_window_infer_overlap": 0.25, - "autoencoder_tp_num_splits": 2 + "autoencoder_tp_num_splits": 2, + "modality": 1 } diff --git a/generation/maisi/configs/config_infer_80g_512x512x128.json b/generation/maisi/configs/config_infer_80g_512x512x128.json index d20dbbc76..f85ebd513 100644 --- a/generation/maisi/configs/config_infer_80g_512x512x128.json +++ b/generation/maisi/configs/config_infer_80g_512x512x128.json @@ -3,7 +3,7 @@ "body_region": ["abdomen"], "anatomy_list": ["liver","hepatic tumor"], "controllable_anatomy_size": [], - "num_inference_steps": 1000, + "num_inference_steps": 30, "mask_generation_num_inference_steps": 1000, "output_size": [ 512, @@ -19,5 +19,6 @@ ], "autoencoder_sliding_window_infer_size": [128,128,32], "autoencoder_sliding_window_infer_overlap": 0.25, - "autoencoder_tp_num_splits": 4 + "autoencoder_tp_num_splits": 4, + "modality": 1 } diff --git a/generation/maisi/configs/config_infer_80g_512x512x512.json b/generation/maisi/configs/config_infer_80g_512x512x512.json index bfcd6b7dc..887beaeb4 100644 --- a/generation/maisi/configs/config_infer_80g_512x512x512.json +++ b/generation/maisi/configs/config_infer_80g_512x512x512.json @@ -3,7 +3,7 @@ "body_region": ["abdomen"], "anatomy_list": ["liver","hepatic tumor"], "controllable_anatomy_size": [], - "num_inference_steps": 1000, + "num_inference_steps": 30, "mask_generation_num_inference_steps": 1000, "output_size": [ 512, @@ -19,5 +19,6 @@ ], "autoencoder_sliding_window_infer_size": [80,80,80], "autoencoder_sliding_window_infer_overlap": 0.25, - "autoencoder_tp_num_splits": 2 + "autoencoder_tp_num_splits": 2, + "modality": 1 } diff --git a/generation/maisi/configs/config_infer_80g_512x512x768.json b/generation/maisi/configs/config_infer_80g_512x512x768.json index 9cb7e61b6..9ca4e82d2 100644 --- a/generation/maisi/configs/config_infer_80g_512x512x768.json +++ b/generation/maisi/configs/config_infer_80g_512x512x768.json @@ -3,7 +3,7 @@ "body_region": ["abdomen"], "anatomy_list": ["liver","hepatic tumor"], "controllable_anatomy_size": [], - "num_inference_steps": 1000, + "num_inference_steps": 30, "mask_generation_num_inference_steps": 1000, "output_size": [ 512, @@ -19,5 +19,6 @@ ], "autoencoder_sliding_window_infer_size": [80,80,112], "autoencoder_sliding_window_infer_overlap": 0.25, - "autoencoder_tp_num_splits": 4 + "autoencoder_tp_num_splits": 4, + "modality": 1 } diff --git a/generation/maisi/maisi_inference_tutorial.ipynb b/generation/maisi/maisi_inference_tutorial.ipynb index 9e6e63456..cede31cf9 100644 --- a/generation/maisi/maisi_inference_tutorial.ipynb +++ b/generation/maisi/maisi_inference_tutorial.ipynb @@ -100,7 +100,6 @@ "import argparse\n", "import json\n", "import os\n", - "import tempfile\n", "\n", "import monai\n", "import torch\n", @@ -141,7 +140,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 05:29:42.156][ INFO](notebook) - MAISI version is maisi3d-rflow, whether to use body_region is False\n" + "[2025-03-13 05:49:57.930][ INFO](notebook) - MAISI version is maisi3d-rflow, whether to use body_region is False\n" ] } ], @@ -180,22 +179,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-13 05:29:42,164 - INFO - Expected md5 is None, skip md5 check for file models/autoencoder_epoch273.pt.\n", - "2025-03-13 05:29:42,166 - INFO - File exists: models/autoencoder_epoch273.pt, skipped downloading.\n", - "2025-03-13 05:29:42,166 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_autoencoder.pt.\n", - "2025-03-13 05:29:42,167 - INFO - File exists: models/mask_generation_autoencoder.pt, skipped downloading.\n", - "2025-03-13 05:29:42,167 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_diffusion_unet.pt.\n", - "2025-03-13 05:29:42,168 - INFO - File exists: models/mask_generation_diffusion_unet.pt, skipped downloading.\n", - "2025-03-13 05:29:42,169 - INFO - Expected md5 is None, skip md5 check for file configs/all_anatomy_size_condtions.json.\n", - "2025-03-13 05:29:42,169 - INFO - File exists: configs/all_anatomy_size_condtions.json, skipped downloading.\n", - "2025-03-13 05:29:42,170 - INFO - Expected md5 is None, skip md5 check for file models/diff_unet_3d_rflow.pt.\n", - "2025-03-13 05:29:42,170 - INFO - File exists: models/diff_unet_3d_rflow.pt, skipped downloading.\n", - "2025-03-13 05:29:42,171 - INFO - Expected md5 is None, skip md5 check for file models/controlnet_3d_rflow.pt.\n", - "2025-03-13 05:29:42,171 - INFO - File exists: models/controlnet_3d_rflow.pt, skipped downloading.\n", - "2025-03-13 05:29:42,172 - INFO - Expected md5 is None, skip md5 check for file configs/candidate_masks_flexible_size_and_spacing_4000.json.\n", - "2025-03-13 05:29:42,172 - INFO - File exists: configs/candidate_masks_flexible_size_and_spacing_4000.json, skipped downloading.\n", - "2025-03-13 05:29:42,173 - INFO - Expected md5 is None, skip md5 check for file temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip.\n", - "2025-03-13 05:29:42,173 - INFO - File exists: temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip, skipped downloading.\n" + "2025-03-13 05:49:57,939 - INFO - Expected md5 is None, skip md5 check for file models/autoencoder_epoch273.pt.\n", + "2025-03-13 05:49:57,940 - INFO - File exists: models/autoencoder_epoch273.pt, skipped downloading.\n", + "2025-03-13 05:49:57,940 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_autoencoder.pt.\n", + "2025-03-13 05:49:57,941 - INFO - File exists: models/mask_generation_autoencoder.pt, skipped downloading.\n", + "2025-03-13 05:49:57,942 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_diffusion_unet.pt.\n", + "2025-03-13 05:49:57,942 - INFO - File exists: models/mask_generation_diffusion_unet.pt, skipped downloading.\n", + "2025-03-13 05:49:57,943 - INFO - Expected md5 is None, skip md5 check for file configs/all_anatomy_size_condtions.json.\n", + "2025-03-13 05:49:57,943 - INFO - File exists: configs/all_anatomy_size_condtions.json, skipped downloading.\n", + "2025-03-13 05:49:57,944 - INFO - Expected md5 is None, skip md5 check for file temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip.\n", + "2025-03-13 05:49:57,944 - INFO - File exists: temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip, skipped downloading.\n", + "2025-03-13 05:49:57,945 - INFO - Expected md5 is None, skip md5 check for file models/diff_unet_3d_rflow.pt.\n", + "2025-03-13 05:49:57,946 - INFO - File exists: models/diff_unet_3d_rflow.pt, skipped downloading.\n", + "2025-03-13 05:49:57,948 - INFO - Expected md5 is None, skip md5 check for file models/controlnet_3d_rflow.pt.\n", + "2025-03-13 05:49:57,948 - INFO - File exists: models/controlnet_3d_rflow.pt, skipped downloading.\n", + "2025-03-13 05:49:57,949 - INFO - Expected md5 is None, skip md5 check for file configs/candidate_masks_flexible_size_and_spacing_4000.json.\n", + "2025-03-13 05:49:57,950 - INFO - File exists: configs/candidate_masks_flexible_size_and_spacing_4000.json, skipped downloading.\n" ] } ], @@ -224,6 +223,11 @@ " \"path\": \"configs/all_anatomy_size_condtions.json\",\n", " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/all_anatomy_size_condtions.json\",\n", " },\n", + " {\n", + " \"path\": \"datasets/all_masks_flexible_size_and_spacing_4000.zip\",\n", + " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n", + " \"/tutorials/all_masks_flexible_size_and_spacing_4000.zip\",\n", + " },\n", " \n", "]\n", "\n", @@ -244,11 +248,6 @@ " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n", " \"/tutorials/candidate_masks_flexible_size_and_spacing_3000.json\",\n", " },\n", - " {\n", - " \"path\": \"datasets/all_masks_flexible_size_and_spacing_3000.zip\",\n", - " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n", - " \"/tutorials/model_zoo/model_maisi_all_masks_flexible_size_and_spacing_3000.zip\",\n", - " },\n", " ]\n", "elif maisi_version == \"maisi3d-rflow\":\n", " files += [\n", @@ -266,13 +265,10 @@ " \"path\": \"configs/candidate_masks_flexible_size_and_spacing_4000.json\",\n", " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n", " \"/tutorials/candidate_masks_flexible_size_and_spacing_4000.json\",\n", - " },\n", - " {\n", - " \"path\": \"datasets/all_masks_flexible_size_and_spacing_4000.zip\",\n", - " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n", - " \"/tutorials/all_masks_flexible_size_and_spacing_4000.zip\",\n", - " },\n", + " }, \n", " ]\n", + "else:\n", + " raise ValueError(f\"maisi_version has to be chosen from ['maisi3d-ddpm', 'maisi3d-rflow'], yet got {maisi_version}.\")\n", "\n", "for file in files:\n", " file[\"path\"] = file[\"path\"] if \"datasets/\" not in file[\"path\"] else os.path.join(root_dir, file[\"path\"])\n", @@ -301,18 +297,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 05:29:42.181][ INFO](notebook) - output_dir: output\n", - "[2025-03-13 05:29:42.182][ INFO](notebook) - trained_autoencoder_path: models/autoencoder_epoch273.pt\n", - "[2025-03-13 05:29:42.183][ INFO](notebook) - trained_diffusion_path: models/diff_unet_3d_rflow.pt\n", - "[2025-03-13 05:29:42.183][ INFO](notebook) - trained_controlnet_path: models/controlnet_3d_rflow.pt\n", - "[2025-03-13 05:29:42.184][ INFO](notebook) - trained_mask_generation_autoencoder_path: models/mask_generation_autoencoder.pt\n", - "[2025-03-13 05:29:42.184][ INFO](notebook) - trained_mask_generation_diffusion_path: models/mask_generation_diffusion_unet.pt\n", - "[2025-03-13 05:29:42.185][ INFO](notebook) - all_mask_files_base_dir: ./temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000\n", - "[2025-03-13 05:29:42.185][ INFO](notebook) - all_mask_files_json: ./configs/candidate_masks_flexible_size_and_spacing_4000.json\n", - "[2025-03-13 05:29:42.186][ INFO](notebook) - all_anatomy_size_conditions_json: ./configs/all_anatomy_size_condtions.json\n", - "[2025-03-13 05:29:42.187][ INFO](notebook) - label_dict_json: ./configs/label_dict.json\n", - "[2025-03-13 05:29:42.187][ INFO](notebook) - label_dict_remap_json: ./configs/label_dict_124_to_132.json\n", - "[2025-03-13 05:29:42.188][ INFO](notebook) - Global config variables have been loaded.\n" + "[2025-03-13 05:49:57.956][ INFO](notebook) - output_dir: output\n", + "[2025-03-13 05:49:57.956][ INFO](notebook) - trained_autoencoder_path: models/autoencoder_epoch273.pt\n", + "[2025-03-13 05:49:57.957][ INFO](notebook) - trained_diffusion_path: models/diff_unet_3d_rflow.pt\n", + "[2025-03-13 05:49:57.958][ INFO](notebook) - trained_controlnet_path: models/controlnet_3d_rflow.pt\n", + "[2025-03-13 05:49:57.958][ INFO](notebook) - trained_mask_generation_autoencoder_path: models/mask_generation_autoencoder.pt\n", + "[2025-03-13 05:49:57.959][ INFO](notebook) - trained_mask_generation_diffusion_path: models/mask_generation_diffusion_unet.pt\n", + "[2025-03-13 05:49:57.960][ INFO](notebook) - all_mask_files_base_dir: ./temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000\n", + "[2025-03-13 05:49:57.961][ INFO](notebook) - all_mask_files_json: ./configs/candidate_masks_flexible_size_and_spacing_4000.json\n", + "[2025-03-13 05:49:57.961][ INFO](notebook) - all_anatomy_size_conditions_json: ./configs/all_anatomy_size_condtions.json\n", + "[2025-03-13 05:49:57.962][ INFO](notebook) - label_dict_json: ./configs/label_dict.json\n", + "[2025-03-13 05:49:57.963][ INFO](notebook) - label_dict_remap_json: ./configs/label_dict_124_to_132.json\n", + "[2025-03-13 05:49:57.963][ INFO](notebook) - Global config variables have been loaded.\n" ] } ], @@ -356,28 +352,24 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 05:29:42.196][ INFO](notebook) - num_output_samples: 1\n", - "[2025-03-13 05:29:42.196][ INFO](notebook) - body_region: ['abdomen']\n", - "[2025-03-13 05:29:42.197][ INFO](notebook) - anatomy_list: ['liver', 'hepatic tumor']\n", - "[2025-03-13 05:29:42.198][ INFO](notebook) - controllable_anatomy_size: []\n", - "[2025-03-13 05:29:42.198][ INFO](notebook) - num_inference_steps: 30\n", - "[2025-03-13 05:29:42.199][ INFO](notebook) - mask_generation_num_inference_steps: 1000\n", - "[2025-03-13 05:29:42.200][ INFO](notebook) - output_size: [256, 256, 256]\n", - "[2025-03-13 05:29:42.200][ INFO](notebook) - image_output_ext: .nii.gz\n", - "[2025-03-13 05:29:42.201][ INFO](notebook) - label_output_ext: .nii.gz\n", - "[2025-03-13 05:29:42.201][ INFO](notebook) - spacing: [1.5, 1.5, 2.0]\n", - "[2025-03-13 05:29:42.202][ INFO](notebook) - autoencoder_sliding_window_infer_size: [48, 48, 48]\n", - "[2025-03-13 05:29:42.202][ INFO](notebook) - autoencoder_sliding_window_infer_overlap: 0.25\n", - "[2025-03-13 05:29:42.203][ INFO](notebook) - controlnet: $@controlnet_def\n", - "[2025-03-13 05:29:42.203][ INFO](notebook) - diffusion_unet: $@diffusion_unet_def\n", - "[2025-03-13 05:29:42.204][ INFO](notebook) - autoencoder: $@autoencoder_def\n", - "[2025-03-13 05:29:42.205][ INFO](notebook) - mask_generation_autoencoder: $@mask_generation_autoencoder_def\n", - "[2025-03-13 05:29:42.205][ INFO](notebook) - mask_generation_diffusion: $@mask_generation_diffusion_def\n", - "[2025-03-13 05:29:42.207][ INFO](notebook) - modality: 1\n", - "[2025-03-13 05:29:42.208][ INFO](root) - `controllable_anatomy_size` is empty.\n", + "[2025-03-13 05:49:57.971][ INFO](notebook) - num_output_samples: 1\n", + "[2025-03-13 05:49:57.972][ INFO](notebook) - body_region: ['abdomen']\n", + "[2025-03-13 05:49:57.972][ INFO](notebook) - anatomy_list: ['liver', 'hepatic tumor']\n", + "[2025-03-13 05:49:57.973][ INFO](notebook) - controllable_anatomy_size: []\n", + "[2025-03-13 05:49:57.974][ INFO](notebook) - num_inference_steps: 30\n", + "[2025-03-13 05:49:57.975][ INFO](notebook) - mask_generation_num_inference_steps: 1000\n", + "[2025-03-13 05:49:57.975][ INFO](notebook) - output_size: [256, 256, 256]\n", + "[2025-03-13 05:49:57.976][ INFO](notebook) - image_output_ext: .nii.gz\n", + "[2025-03-13 05:49:57.976][ INFO](notebook) - label_output_ext: .nii.gz\n", + "[2025-03-13 05:49:57.977][ INFO](notebook) - spacing: [1.5, 1.5, 2.0]\n", + "[2025-03-13 05:49:57.978][ INFO](notebook) - autoencoder_sliding_window_infer_size: [48, 48, 64]\n", + "[2025-03-13 05:49:57.979][ INFO](notebook) - autoencoder_sliding_window_infer_overlap: 0.25\n", + "[2025-03-13 05:49:57.980][ INFO](notebook) - autoencoder_tp_num_splits: 2\n", + "[2025-03-13 05:49:57.980][ INFO](notebook) - modality: 1\n", + "[2025-03-13 05:49:57.981][ INFO](root) - `controllable_anatomy_size` is empty.\n", "We will synthesize based on `body_region`: (['abdomen']) and `anatomy_list`: (['liver', 'hepatic tumor']).\n", - "[2025-03-13 05:29:42.209][ INFO](root) - The generate results will have voxel size to be [1.5, 1.5, 2.0]mm, volume size to be [256, 256, 256].\n", - "[2025-03-13 05:29:42.210][ INFO](notebook) - Network definition and inference inputs have been loaded.\n" + "[2025-03-13 05:49:57.982][ INFO](root) - The generate results will have voxel size to be [1.5, 1.5, 2.0]mm, volume size to be [256, 256, 256].\n", + "[2025-03-13 05:49:57.983][ INFO](notebook) - Network definition and inference inputs have been loaded.\n" ] } ], @@ -388,7 +380,7 @@ " setattr(args, k, v)\n", "\n", "# check the format of inference inputs\n", - "config_infer_file = \"./configs/config_infer.json\"\n", + "config_infer_file = \"./configs/config_infer_16g_256x256x256.json\"\n", "with open(config_infer_file, \"r\") as f:\n", " config_infer_dict = json.load(f)\n", "for k, v in config_infer_dict.items():\n", @@ -448,14 +440,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-13 05:29:50,519 - INFO - 'dst' model updated: 180 of 231 variables.\n" + "2025-03-13 05:50:06,794 - INFO - 'dst' model updated: 180 of 231 variables.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 05:29:52.881][ INFO](notebook) - All the trained model weights have been loaded.\n" + "[2025-03-13 05:50:09.123][ INFO](notebook) - All the trained model weights have been loaded.\n" ] } ], @@ -509,7 +501,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 05:29:52.911][ INFO](root) - LDM sampler initialized.\n" + "[2025-03-13 05:50:09.148][ INFO](root) - LDM sampler initialized.\n" ] } ], @@ -572,28 +564,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 05:29:52.915][ INFO](notebook) - The generated image/mask pairs will be saved in output.\n", - "[2025-03-13 05:29:52.965][ INFO](root) - Resample mask file to get desired output size and spacing\n", - "[2025-03-13 05:29:54.196][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 05:29:54.199][ INFO](root) - Resize Spacing: [tensor(0.7539, dtype=torch.float64), tensor(0.7539, dtype=torch.float64), tensor(1.9141, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 05:29:54.200][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", - "[2025-03-13 05:29:57.100][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 05:29:57.103][ INFO](root) - Resize Spacing: [tensor(0.7578, dtype=torch.float64), tensor(0.7578, dtype=torch.float64), tensor(1.8848, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 05:29:57.103][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", - "[2025-03-13 05:29:59.889][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 05:29:59.893][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 05:29:59.893][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", - "[2025-03-13 05:30:04.161][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 05:30:04.163][ INFO](root) - Resize Spacing: [tensor(0.7754, dtype=torch.float64), tensor(0.7754, dtype=torch.float64), tensor(1.0098, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 05:30:04.164][ INFO](root) - Output size: [512, 512, 512] -> [256, 256, 256]\n", - "[2025-03-13 05:30:08.605][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 05:30:08.607][ INFO](root) - Resize Spacing: [tensor(0.7480, dtype=torch.float64), tensor(0.7480, dtype=torch.float64), tensor(1.0566, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 05:30:08.608][ INFO](root) - Output size: [512, 512, 512] -> [256, 256, 256]\n", - "[2025-03-13 05:30:10.409][ INFO](root) - Images will be generated based on [{'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_78_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}].\n", - "[2025-03-13 05:30:10.410][ INFO](root) - ---- Start preparing masks... ----\n", - "[2025-03-13 05:30:11.596][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 05:30:11.598][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 05:30:11.599][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n" + "[2025-03-13 05:50:09.153][ INFO](notebook) - The generated image/mask pairs will be saved in output.\n", + "[2025-03-13 05:50:09.198][ INFO](root) - Resample mask file to get desired output size and spacing\n", + "[2025-03-13 05:50:10.494][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 05:50:10.498][ INFO](root) - Resize Spacing: [tensor(0.7539, dtype=torch.float64), tensor(0.7539, dtype=torch.float64), tensor(1.9141, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 05:50:10.498][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-13 05:50:13.380][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 05:50:13.383][ INFO](root) - Resize Spacing: [tensor(0.7578, dtype=torch.float64), tensor(0.7578, dtype=torch.float64), tensor(1.8848, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 05:50:13.384][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-13 05:50:16.211][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 05:50:16.213][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 05:50:16.214][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-13 05:50:20.594][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 05:50:20.597][ INFO](root) - Resize Spacing: [tensor(0.7754, dtype=torch.float64), tensor(0.7754, dtype=torch.float64), tensor(1.0098, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 05:50:20.598][ INFO](root) - Output size: [512, 512, 512] -> [256, 256, 256]\n", + "[2025-03-13 05:50:24.939][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 05:50:24.942][ INFO](root) - Resize Spacing: [tensor(0.7480, dtype=torch.float64), tensor(0.7480, dtype=torch.float64), tensor(1.0566, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 05:50:24.942][ INFO](root) - Output size: [512, 512, 512] -> [256, 256, 256]\n", + "[2025-03-13 05:50:26.753][ INFO](root) - Images will be generated based on [{'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_78_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}].\n", + "[2025-03-13 05:50:26.754][ INFO](root) - ---- Start preparing masks... ----\n", + "[2025-03-13 05:50:28.019][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 05:50:28.021][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 05:50:28.022][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n" ] }, { @@ -608,8 +600,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 05:30:14.302][ INFO](root) - ---- Mask preparation time: 3.8906569480895996 seconds ----\n", - "[2025-03-13 05:30:14.310][ INFO](root) - ---- Start generating latent features... ----\n" + "[2025-03-13 05:50:30.565][ INFO](root) - ---- Mask preparation time: 3.810523509979248 seconds ----\n", + "[2025-03-13 05:50:30.575][ INFO](root) - ---- Start generating latent features... ----\n" ] }, { @@ -623,26 +615,26 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|███████████████████████████████████████████| 30/30 [00:02<00:00, 10.26it/s]\n", - "[2025-03-13 05:30:17.278][ INFO](root) - ---- Latent features generation time: 2.967771530151367 seconds ----\n", - "[2025-03-13 05:30:17.302][ INFO](root) - ---- Start decoding latent features into images... ----\n", - "100%|█████████████████████████████████████████████| 8/8 [00:07<00:00, 1.05it/s]\n", - "[2025-03-13 05:30:24.970][ INFO](root) - ---- Image decoding time: 7.6675684452056885 seconds ----\n" + "100%|███████████████████████████████████████████| 30/30 [00:02<00:00, 10.19it/s]\n", + "[2025-03-13 05:50:33.559][ INFO](root) - ---- Latent features generation time: 2.9837806224823 seconds ----\n", + "[2025-03-13 05:50:33.585][ INFO](root) - ---- Start decoding latent features into images... ----\n", + "100%|█████████████████████████████████████████████| 4/4 [00:04<00:00, 1.22s/it]\n", + "[2025-03-13 05:50:38.503][ INFO](root) - ---- Image decoding time: 4.917377710342407 seconds ----\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2025-03-13 05:30:25,361 INFO image_writer.py:197 - writing: output/sample_20250313_053025_342138_image.nii.gz\n", - "2025-03-13 05:30:27,034 INFO image_writer.py:197 - writing: output/sample_20250313_053025_342138_label.nii.gz\n" + "2025-03-13 05:50:38,925 INFO image_writer.py:197 - writing: output/sample_20250313_055038_908689_image.nii.gz\n", + "2025-03-13 05:50:40,572 INFO image_writer.py:197 - writing: output/sample_20250313_055038_908689_label.nii.gz\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 05:30:28.003][ INFO](notebook) - MAISI image/mask generation finished\n" + "[2025-03-13 05:50:41.554][ INFO](notebook) - MAISI image/mask generation finished\n" ] } ], @@ -672,7 +664,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 05:30:28.011][ INFO](notebook) - Visualizing output/sample_20250313_053025_342138_image.nii.gz and output/sample_20250313_053025_342138_label.nii.gz...\n" + "[2025-03-13 05:50:41.563][ INFO](notebook) - Visualizing output/sample_20250313_055038_908689_image.nii.gz and output/sample_20250313_055038_908689_label.nii.gz...\n" ] }, { @@ -687,7 +679,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] From 5831f98c954638baac3e112bd78da0cf2eb7ffa2 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Thu, 13 Mar 2025 05:55:00 +0000 Subject: [PATCH 26/59] add modality as input to config Signed-off-by: Can-Zhao --- generation/maisi/maisi_inference_tutorial.ipynb | 5 ----- 1 file changed, 5 deletions(-) diff --git a/generation/maisi/maisi_inference_tutorial.ipynb b/generation/maisi/maisi_inference_tutorial.ipynb index 6bccf7ca9..39759345e 100644 --- a/generation/maisi/maisi_inference_tutorial.ipynb +++ b/generation/maisi/maisi_inference_tutorial.ipynb @@ -265,11 +265,6 @@ " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n", " \"/tutorials/candidate_masks_flexible_size_and_spacing_4000.json\",\n", " },\n", - " {\n", - " \"path\": \"datasets/all_masks_flexible_size_and_spacing_4000.zip\",\n", - " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n", - " \"/tutorials/all_masks_flexible_size_and_spacing_4000.zip\",\n", - " },\n", " ]\n", "else:\n", " raise ValueError(f\"maisi_version has to be chosen from ['maisi3d-ddpm', 'maisi3d-rflow'], yet got {maisi_version}.\")\n", From 6f1caf9f45b5cd73d45a93b51752c9a37b56f01e Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 13 Mar 2025 05:56:14 +0000 Subject: [PATCH 27/59] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- generation/maisi/maisi_inference_tutorial.ipynb | 1 - 1 file changed, 1 deletion(-) diff --git a/generation/maisi/maisi_inference_tutorial.ipynb b/generation/maisi/maisi_inference_tutorial.ipynb index 39759345e..bfd642ecc 100644 --- a/generation/maisi/maisi_inference_tutorial.ipynb +++ b/generation/maisi/maisi_inference_tutorial.ipynb @@ -228,7 +228,6 @@ " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai\"\n", " \"/tutorials/all_masks_flexible_size_and_spacing_4000.zip\",\n", " },\n", - " \n", "]\n", "\n", "if maisi_version == \"maisi3d-ddpm\":\n", From e2a487411b1f1660a4acf76e043955183313f4c6 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Thu, 13 Mar 2025 06:01:15 +0000 Subject: [PATCH 28/59] update readme Signed-off-by: Can-Zhao --- generation/maisi/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/generation/maisi/README.md b/generation/maisi/README.md index 73dcdfb43..06c7a0efc 100644 --- a/generation/maisi/README.md +++ b/generation/maisi/README.md @@ -165,7 +165,7 @@ For example, |[512, 512, 512] | [1.0, 1.0, 1.0] | #### Execute Inference: -To run the inference script, please run: +To run the inference script with MAISI DDPM, please set `"num_inference_steps": 1000` in `./configs/config_infer.json`, and run: ```bash export MONAI_DATA_DIRECTORY= python -m scripts.inference -c ./configs/config_maisi3d-ddpm.json -i ./configs/config_infer.json -e ./configs/environment.json --random-seed 0 From 56da1e4a0053e176bcb1577bd59da7df01407fc4 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Thu, 13 Mar 2025 20:42:37 +0000 Subject: [PATCH 29/59] bechmark gpu memory Signed-off-by: Can-Zhao --- generation/maisi/README.md | 38 ++-- .../configs/config_infer_16g_256x256x128.json | 5 + .../configs/config_infer_16g_256x256x256.json | 9 +- .../configs/config_infer_16g_512x512x128.json | 9 +- .../configs/config_infer_24g_256x256x256.json | 5 + .../configs/config_infer_24g_512x512x128.json | 9 +- .../configs/config_infer_24g_512x512x512.json | 7 +- .../configs/config_infer_32g_512x512x512.json | 11 +- .../configs/config_infer_80g_512x512x128.json | 7 +- .../configs/config_infer_80g_512x512x512.json | 7 +- .../configs/config_infer_80g_512x512x768.json | 5 + .../maisi/maisi_inference_tutorial.ipynb | 170 +++++++++--------- generation/maisi/scripts/inference.py | 75 ++++++-- generation/maisi/scripts/sample.py | 42 +++-- generation/maisi/scripts/utils.py | 2 +- 15 files changed, 256 insertions(+), 145 deletions(-) diff --git a/generation/maisi/README.md b/generation/maisi/README.md index 06c7a0efc..7a95d27ca 100644 --- a/generation/maisi/README.md +++ b/generation/maisi/README.md @@ -59,21 +59,21 @@ We retrained several state-of-the-art diffusion model-based methods using our da ## Time Cost and GPU Memory Usage ### Inference Time Cost and GPU Memory Usage -| `output_size` | latent size |`autoencoder_sliding_window_infer_size` | `autoencoder_tp_num_splits` | Peak Memory | DM Time | VAE Time | -|---------------|:--------------------------------------:|:--------------------------------------:|:---------------------------:|:-----------:|:-------:|:--------:| -| [256x256x128](./configs/config_infer_16g_256x256x128.json) |4x64x64x32| >=[64,64,32], not used | 2 | 14G | 57s | 1s | -| [256x256x256](./configs/config_infer_16g_256x256x256.json) |4x64x64x64| [48,48,64], 4 patches | 2 | 14G | 81s | 7s | -| [512x512x128](./configs/config_infer_16g_512x512x128.json) |4x128x128x32| [64,64,32], 9 patches | 1 | 14G | 138s | 7s | -| | | | | | | -| [256x256x256](./configs/config_infer_24g_256x256x256.json) |4x64x64x64| >=[64,64,64], not used | 4 | 22G | 81s | 2s | -| [512x512x128](./configs/config_infer_24g_512x512x128.json) |4x128x128x32| [80,80,32], 4 patches | 1 | 18G | 138s | 9s | -| [512x512x512](./configs/config_infer_24g_512x512x512.json) |4x128x128x128| [64,64,48], 36 patches | 2 | 22G | 569s | 29s | -| | | | | | | -| [512x512x512](./configs/config_infer_32g_512x512x512.json) |4x128x128x128| [64,64,64], 27 patches | 2 | 26G | 569s | 40s | -| | | | | | | -| [512x512x128](./configs/config_infer_80g_512x512x128.json) |4x128x128x32| >=[128,128,32], not used | 4 | 37G | 138s | 140s | -| [512x512x512](./configs/config_infer_80g_512x512x512.json) |4x128x128x128| [80,80,80], 8 patches | 2 | 44G | 569s | 30s | -| [512x512x768](./configs/config_infer_24g_512x512x768.json) |4x128x128x192| [80,80,112], 8 patches | 4 | 55G | 904s | 48s | +| `output_size` | latent size |`autoencoder_sliding_window_infer_size` | `autoencoder_tp_num_splits` | Peak Memory | VAE Time | DM Time (DDPM) | DM Time (RFlow) | +|---------------|:--------------------------------------:|:--------------------------------------:|:---------------------------:|:-----------:|:--------:|:-------:|:---------------:| +| [256x256x128](./configs/config_infer_16g_256x256x128.json) |4x64x64x32| >=[64,64,32], not used | 2 | 15.0G | 1s | 57s | 2s | +| [256x256x256](./configs/config_infer_16g_256x256x256.json) |4x64x64x64| [48,48,64], 4 patches | 4 | 15.4G | 5s | 81s | 3s | +| [512x512x128](./configs/config_infer_16g_512x512x128.json) |4x128x128x32| [64,64,32], 9 patches | 2 | 15.7G | 8s | 138s | 5s | +| | | | | | | | | +| [256x256x256](./configs/config_infer_24g_256x256x256.json) |4x64x64x64| >=[64,64,64], not used | 4 | 22.7G | 2s | 81s | 3s | +| [512x512x128](./configs/config_infer_24g_512x512x128.json) |4x128x128x32| [80,80,32], 4 patches | 2 | 21.0G | 6s | 138s | 5s | +| [512x512x512](./configs/config_infer_24g_512x512x512.json) |4x128x128x128| [64,64,48], 36 patches | 2 | 22.8G | 29s | 569s | 19s | +| | | | | | | | | +| [512x512x512](./configs/config_infer_32g_512x512x512.json) |4x128x128x128| [80,80,48], 16 patches | 4 | 28.4G | 30s | 569s | 19s | +| | | | | | | | | +| [512x512x128](./configs/config_infer_80g_512x512x128.json) |4x128x128x32| >=[128,128,32], not used | 4 | 37.7G | 127s | 138s | 5s | +| [512x512x512](./configs/config_infer_80g_512x512x512.json) |4x128x128x128| [80,80,80], 8 patches | 2 | 45.3G | 32s | 569s | 19s | +| [512x512x768](./configs/config_infer_80g_512x512x768.json) |4x128x128x192| [80,80,112], 8 patches | 4 | 56.2G | 50s | 904s | 30s | **Table 3:** Inference Time Cost and GPU Memory Usage. `DM Time` refers to the time required for diffusion model inference. `VAE Time` refers to the time required for VAE decoder inference. The total inference time is the sum of `DM Time` and `VAE Time`. The experiment was conducted on an A100 80G GPU. @@ -168,7 +168,13 @@ For example, To run the inference script with MAISI DDPM, please set `"num_inference_steps": 1000` in `./configs/config_infer.json`, and run: ```bash export MONAI_DATA_DIRECTORY= -python -m scripts.inference -c ./configs/config_maisi3d-ddpm.json -i ./configs/config_infer.json -e ./configs/environment.json --random-seed 0 +python -m scripts.inference -c ./configs/config_maisi3d-ddpm.json -i ./configs/config_infer.json -e ./configs/environment_maisi3d-ddpm.json --random-seed 0 --version maisi3d-ddpm +``` + +To run the inference script with MAISI RFlow, please set `"num_inference_steps": 30` in `./configs/config_infer.json`, and run: +```bash +export MONAI_DATA_DIRECTORY= +python -m scripts.inference -c ./configs/config_maisi3d-rflow.json -i ./configs/config_infer.json -e ./configs/environment_maisi3d-rflow.json --random-seed 0 --version maisi3d-rflow ``` Please refer to [maisi_inference_tutorial.ipynb](maisi_inference_tutorial.ipynb) for the tutorial for MAISI model inference. diff --git a/generation/maisi/configs/config_infer_16g_256x256x128.json b/generation/maisi/configs/config_infer_16g_256x256x128.json index c4d98b146..943f19224 100644 --- a/generation/maisi/configs/config_infer_16g_256x256x128.json +++ b/generation/maisi/configs/config_infer_16g_256x256x128.json @@ -20,5 +20,10 @@ "autoencoder_sliding_window_infer_size": [96,96,96], "autoencoder_sliding_window_infer_overlap": 0.25, "autoencoder_tp_num_splits": 2, + "controlnet": "$@controlnet_def", + "diffusion_unet": "$@diffusion_unet_def", + "autoencoder": "$@autoencoder_def", + "mask_generation_autoencoder": "$@mask_generation_autoencoder_def", + "mask_generation_diffusion": "$@mask_generation_diffusion_def", "modality": 1 } diff --git a/generation/maisi/configs/config_infer_16g_256x256x256.json b/generation/maisi/configs/config_infer_16g_256x256x256.json index 9af24e0b8..dcdf40277 100644 --- a/generation/maisi/configs/config_infer_16g_256x256x256.json +++ b/generation/maisi/configs/config_infer_16g_256x256x256.json @@ -18,7 +18,12 @@ 2.0 ], "autoencoder_sliding_window_infer_size": [48,48,64], - "autoencoder_sliding_window_infer_overlap": 0.25, - "autoencoder_tp_num_splits": 2, + "autoencoder_sliding_window_infer_overlap": 0.6666, + "autoencoder_tp_num_splits": 4, + "controlnet": "$@controlnet_def", + "diffusion_unet": "$@diffusion_unet_def", + "autoencoder": "$@autoencoder_def", + "mask_generation_autoencoder": "$@mask_generation_autoencoder_def", + "mask_generation_diffusion": "$@mask_generation_diffusion_def", "modality": 1 } diff --git a/generation/maisi/configs/config_infer_16g_512x512x128.json b/generation/maisi/configs/config_infer_16g_512x512x128.json index 62df0c047..fa5cdbcc6 100644 --- a/generation/maisi/configs/config_infer_16g_512x512x128.json +++ b/generation/maisi/configs/config_infer_16g_512x512x128.json @@ -18,7 +18,12 @@ 4.0 ], "autoencoder_sliding_window_infer_size": [64,64,32], - "autoencoder_sliding_window_infer_overlap": 0.25, - "autoencoder_tp_num_splits": 1, + "autoencoder_sliding_window_infer_overlap": 0.5, + "autoencoder_tp_num_splits": 2, + "controlnet": "$@controlnet_def", + "diffusion_unet": "$@diffusion_unet_def", + "autoencoder": "$@autoencoder_def", + "mask_generation_autoencoder": "$@mask_generation_autoencoder_def", + "mask_generation_diffusion": "$@mask_generation_diffusion_def", "modality": 1 } diff --git a/generation/maisi/configs/config_infer_24g_256x256x256.json b/generation/maisi/configs/config_infer_24g_256x256x256.json index 80d0d9e6f..529bedd84 100644 --- a/generation/maisi/configs/config_infer_24g_256x256x256.json +++ b/generation/maisi/configs/config_infer_24g_256x256x256.json @@ -20,5 +20,10 @@ "autoencoder_sliding_window_infer_size": [64,64,64], "autoencoder_sliding_window_infer_overlap": 0.25, "autoencoder_tp_num_splits": 4, + "controlnet": "$@controlnet_def", + "diffusion_unet": "$@diffusion_unet_def", + "autoencoder": "$@autoencoder_def", + "mask_generation_autoencoder": "$@mask_generation_autoencoder_def", + "mask_generation_diffusion": "$@mask_generation_diffusion_def", "modality": 1 } diff --git a/generation/maisi/configs/config_infer_24g_512x512x128.json b/generation/maisi/configs/config_infer_24g_512x512x128.json index 012d9ba6e..2dbc8e036 100644 --- a/generation/maisi/configs/config_infer_24g_512x512x128.json +++ b/generation/maisi/configs/config_infer_24g_512x512x128.json @@ -18,7 +18,12 @@ 4.0 ], "autoencoder_sliding_window_infer_size": [80,80,32], - "autoencoder_sliding_window_infer_overlap": 0.25, - "autoencoder_tp_num_splits": 1, + "autoencoder_sliding_window_infer_overlap": 0.4, + "autoencoder_tp_num_splits": 2, + "controlnet": "$@controlnet_def", + "diffusion_unet": "$@diffusion_unet_def", + "autoencoder": "$@autoencoder_def", + "mask_generation_autoencoder": "$@mask_generation_autoencoder_def", + "mask_generation_diffusion": "$@mask_generation_diffusion_def", "modality": 1 } diff --git a/generation/maisi/configs/config_infer_24g_512x512x512.json b/generation/maisi/configs/config_infer_24g_512x512x512.json index 27164e671..6882d605b 100644 --- a/generation/maisi/configs/config_infer_24g_512x512x512.json +++ b/generation/maisi/configs/config_infer_24g_512x512x512.json @@ -18,7 +18,12 @@ 1.0 ], "autoencoder_sliding_window_infer_size": [64,64,48], - "autoencoder_sliding_window_infer_overlap": 0.25, + "autoencoder_sliding_window_infer_overlap": 0.4, "autoencoder_tp_num_splits": 2, + "controlnet": "$@controlnet_def", + "diffusion_unet": "$@diffusion_unet_def", + "autoencoder": "$@autoencoder_def", + "mask_generation_autoencoder": "$@mask_generation_autoencoder_def", + "mask_generation_diffusion": "$@mask_generation_diffusion_def", "modality": 1 } diff --git a/generation/maisi/configs/config_infer_32g_512x512x512.json b/generation/maisi/configs/config_infer_32g_512x512x512.json index a3c569947..2e0682391 100644 --- a/generation/maisi/configs/config_infer_32g_512x512x512.json +++ b/generation/maisi/configs/config_infer_32g_512x512x512.json @@ -17,8 +17,13 @@ 0.75, 1.0 ], - "autoencoder_sliding_window_infer_size": [64,64,64], - "autoencoder_sliding_window_infer_overlap": 0.25, - "autoencoder_tp_num_splits": 2, + "autoencoder_sliding_window_infer_size": [80,80,48], + "autoencoder_sliding_window_infer_overlap": 0.4, + "autoencoder_tp_num_splits": 4, + "controlnet": "$@controlnet_def", + "diffusion_unet": "$@diffusion_unet_def", + "autoencoder": "$@autoencoder_def", + "mask_generation_autoencoder": "$@mask_generation_autoencoder_def", + "mask_generation_diffusion": "$@mask_generation_diffusion_def", "modality": 1 } diff --git a/generation/maisi/configs/config_infer_80g_512x512x128.json b/generation/maisi/configs/config_infer_80g_512x512x128.json index f85ebd513..2b7b025cb 100644 --- a/generation/maisi/configs/config_infer_80g_512x512x128.json +++ b/generation/maisi/configs/config_infer_80g_512x512x128.json @@ -18,7 +18,12 @@ 4.0 ], "autoencoder_sliding_window_infer_size": [128,128,32], - "autoencoder_sliding_window_infer_overlap": 0.25, + "autoencoder_sliding_window_infer_overlap": 0.5, "autoencoder_tp_num_splits": 4, + "controlnet": "$@controlnet_def", + "diffusion_unet": "$@diffusion_unet_def", + "autoencoder": "$@autoencoder_def", + "mask_generation_autoencoder": "$@mask_generation_autoencoder_def", + "mask_generation_diffusion": "$@mask_generation_diffusion_def", "modality": 1 } diff --git a/generation/maisi/configs/config_infer_80g_512x512x512.json b/generation/maisi/configs/config_infer_80g_512x512x512.json index 887beaeb4..cf3d7263d 100644 --- a/generation/maisi/configs/config_infer_80g_512x512x512.json +++ b/generation/maisi/configs/config_infer_80g_512x512x512.json @@ -18,7 +18,12 @@ 1.0 ], "autoencoder_sliding_window_infer_size": [80,80,80], - "autoencoder_sliding_window_infer_overlap": 0.25, + "autoencoder_sliding_window_infer_overlap": 0.4, "autoencoder_tp_num_splits": 2, + "controlnet": "$@controlnet_def", + "diffusion_unet": "$@diffusion_unet_def", + "autoencoder": "$@autoencoder_def", + "mask_generation_autoencoder": "$@mask_generation_autoencoder_def", + "mask_generation_diffusion": "$@mask_generation_diffusion_def", "modality": 1 } diff --git a/generation/maisi/configs/config_infer_80g_512x512x768.json b/generation/maisi/configs/config_infer_80g_512x512x768.json index 9ca4e82d2..1b2e4fd08 100644 --- a/generation/maisi/configs/config_infer_80g_512x512x768.json +++ b/generation/maisi/configs/config_infer_80g_512x512x768.json @@ -20,5 +20,10 @@ "autoencoder_sliding_window_infer_size": [80,80,112], "autoencoder_sliding_window_infer_overlap": 0.25, "autoencoder_tp_num_splits": 4, + "controlnet": "$@controlnet_def", + "diffusion_unet": "$@diffusion_unet_def", + "autoencoder": "$@autoencoder_def", + "mask_generation_autoencoder": "$@mask_generation_autoencoder_def", + "mask_generation_diffusion": "$@mask_generation_diffusion_def", "modality": 1 } diff --git a/generation/maisi/maisi_inference_tutorial.ipynb b/generation/maisi/maisi_inference_tutorial.ipynb index bfd642ecc..3ca4e9824 100644 --- a/generation/maisi/maisi_inference_tutorial.ipynb +++ b/generation/maisi/maisi_inference_tutorial.ipynb @@ -140,7 +140,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 05:49:57.930][ INFO](notebook) - MAISI version is maisi3d-rflow, whether to use body_region is False\n" + "[2025-03-13 20:41:25.227][ INFO](notebook) - MAISI version is maisi3d-rflow, whether to use body_region is False\n" ] } ], @@ -179,22 +179,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-13 05:49:57,939 - INFO - Expected md5 is None, skip md5 check for file models/autoencoder_epoch273.pt.\n", - "2025-03-13 05:49:57,940 - INFO - File exists: models/autoencoder_epoch273.pt, skipped downloading.\n", - "2025-03-13 05:49:57,940 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_autoencoder.pt.\n", - "2025-03-13 05:49:57,941 - INFO - File exists: models/mask_generation_autoencoder.pt, skipped downloading.\n", - "2025-03-13 05:49:57,942 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_diffusion_unet.pt.\n", - "2025-03-13 05:49:57,942 - INFO - File exists: models/mask_generation_diffusion_unet.pt, skipped downloading.\n", - "2025-03-13 05:49:57,943 - INFO - Expected md5 is None, skip md5 check for file configs/all_anatomy_size_condtions.json.\n", - "2025-03-13 05:49:57,943 - INFO - File exists: configs/all_anatomy_size_condtions.json, skipped downloading.\n", - "2025-03-13 05:49:57,944 - INFO - Expected md5 is None, skip md5 check for file temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip.\n", - "2025-03-13 05:49:57,944 - INFO - File exists: temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip, skipped downloading.\n", - "2025-03-13 05:49:57,945 - INFO - Expected md5 is None, skip md5 check for file models/diff_unet_3d_rflow.pt.\n", - "2025-03-13 05:49:57,946 - INFO - File exists: models/diff_unet_3d_rflow.pt, skipped downloading.\n", - "2025-03-13 05:49:57,948 - INFO - Expected md5 is None, skip md5 check for file models/controlnet_3d_rflow.pt.\n", - "2025-03-13 05:49:57,948 - INFO - File exists: models/controlnet_3d_rflow.pt, skipped downloading.\n", - "2025-03-13 05:49:57,949 - INFO - Expected md5 is None, skip md5 check for file configs/candidate_masks_flexible_size_and_spacing_4000.json.\n", - "2025-03-13 05:49:57,950 - INFO - File exists: configs/candidate_masks_flexible_size_and_spacing_4000.json, skipped downloading.\n" + "2025-03-13 20:41:25,236 - INFO - Expected md5 is None, skip md5 check for file models/autoencoder_epoch273.pt.\n", + "2025-03-13 20:41:25,237 - INFO - File exists: models/autoencoder_epoch273.pt, skipped downloading.\n", + "2025-03-13 20:41:25,238 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_autoencoder.pt.\n", + "2025-03-13 20:41:25,238 - INFO - File exists: models/mask_generation_autoencoder.pt, skipped downloading.\n", + "2025-03-13 20:41:25,239 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_diffusion_unet.pt.\n", + "2025-03-13 20:41:25,239 - INFO - File exists: models/mask_generation_diffusion_unet.pt, skipped downloading.\n", + "2025-03-13 20:41:25,240 - INFO - Expected md5 is None, skip md5 check for file configs/all_anatomy_size_condtions.json.\n", + "2025-03-13 20:41:25,240 - INFO - File exists: configs/all_anatomy_size_condtions.json, skipped downloading.\n", + "2025-03-13 20:41:25,241 - INFO - Expected md5 is None, skip md5 check for file temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip.\n", + "2025-03-13 20:41:25,241 - INFO - File exists: temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip, skipped downloading.\n", + "2025-03-13 20:41:25,242 - INFO - Expected md5 is None, skip md5 check for file models/diff_unet_3d_rflow.pt.\n", + "2025-03-13 20:41:25,243 - INFO - File exists: models/diff_unet_3d_rflow.pt, skipped downloading.\n", + "2025-03-13 20:41:25,243 - INFO - Expected md5 is None, skip md5 check for file models/controlnet_3d_rflow.pt.\n", + "2025-03-13 20:41:25,244 - INFO - File exists: models/controlnet_3d_rflow.pt, skipped downloading.\n", + "2025-03-13 20:41:25,244 - INFO - Expected md5 is None, skip md5 check for file configs/candidate_masks_flexible_size_and_spacing_4000.json.\n", + "2025-03-13 20:41:25,245 - INFO - File exists: configs/candidate_masks_flexible_size_and_spacing_4000.json, skipped downloading.\n" ] } ], @@ -295,18 +295,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 05:49:57.956][ INFO](notebook) - output_dir: output\n", - "[2025-03-13 05:49:57.956][ INFO](notebook) - trained_autoencoder_path: models/autoencoder_epoch273.pt\n", - "[2025-03-13 05:49:57.957][ INFO](notebook) - trained_diffusion_path: models/diff_unet_3d_rflow.pt\n", - "[2025-03-13 05:49:57.958][ INFO](notebook) - trained_controlnet_path: models/controlnet_3d_rflow.pt\n", - "[2025-03-13 05:49:57.958][ INFO](notebook) - trained_mask_generation_autoencoder_path: models/mask_generation_autoencoder.pt\n", - "[2025-03-13 05:49:57.959][ INFO](notebook) - trained_mask_generation_diffusion_path: models/mask_generation_diffusion_unet.pt\n", - "[2025-03-13 05:49:57.960][ INFO](notebook) - all_mask_files_base_dir: ./temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000\n", - "[2025-03-13 05:49:57.961][ INFO](notebook) - all_mask_files_json: ./configs/candidate_masks_flexible_size_and_spacing_4000.json\n", - "[2025-03-13 05:49:57.961][ INFO](notebook) - all_anatomy_size_conditions_json: ./configs/all_anatomy_size_condtions.json\n", - "[2025-03-13 05:49:57.962][ INFO](notebook) - label_dict_json: ./configs/label_dict.json\n", - "[2025-03-13 05:49:57.963][ INFO](notebook) - label_dict_remap_json: ./configs/label_dict_124_to_132.json\n", - "[2025-03-13 05:49:57.963][ INFO](notebook) - Global config variables have been loaded.\n" + "[2025-03-13 20:41:25.252][ INFO](notebook) - output_dir: output\n", + "[2025-03-13 20:41:25.253][ INFO](notebook) - trained_autoencoder_path: models/autoencoder_epoch273.pt\n", + "[2025-03-13 20:41:25.254][ INFO](notebook) - trained_diffusion_path: models/diff_unet_3d_rflow.pt\n", + "[2025-03-13 20:41:25.254][ INFO](notebook) - trained_controlnet_path: models/controlnet_3d_rflow.pt\n", + "[2025-03-13 20:41:25.255][ INFO](notebook) - trained_mask_generation_autoencoder_path: models/mask_generation_autoencoder.pt\n", + "[2025-03-13 20:41:25.255][ INFO](notebook) - trained_mask_generation_diffusion_path: models/mask_generation_diffusion_unet.pt\n", + "[2025-03-13 20:41:25.256][ INFO](notebook) - all_mask_files_base_dir: ./temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000\n", + "[2025-03-13 20:41:25.256][ INFO](notebook) - all_mask_files_json: ./configs/candidate_masks_flexible_size_and_spacing_4000.json\n", + "[2025-03-13 20:41:25.258][ INFO](notebook) - all_anatomy_size_conditions_json: ./configs/all_anatomy_size_condtions.json\n", + "[2025-03-13 20:41:25.258][ INFO](notebook) - label_dict_json: ./configs/label_dict.json\n", + "[2025-03-13 20:41:25.259][ INFO](notebook) - label_dict_remap_json: ./configs/label_dict_124_to_132.json\n", + "[2025-03-13 20:41:25.260][ INFO](notebook) - Global config variables have been loaded.\n" ] } ], @@ -350,24 +350,29 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 05:49:57.971][ INFO](notebook) - num_output_samples: 1\n", - "[2025-03-13 05:49:57.972][ INFO](notebook) - body_region: ['abdomen']\n", - "[2025-03-13 05:49:57.972][ INFO](notebook) - anatomy_list: ['liver', 'hepatic tumor']\n", - "[2025-03-13 05:49:57.973][ INFO](notebook) - controllable_anatomy_size: []\n", - "[2025-03-13 05:49:57.974][ INFO](notebook) - num_inference_steps: 30\n", - "[2025-03-13 05:49:57.975][ INFO](notebook) - mask_generation_num_inference_steps: 1000\n", - "[2025-03-13 05:49:57.975][ INFO](notebook) - output_size: [256, 256, 256]\n", - "[2025-03-13 05:49:57.976][ INFO](notebook) - image_output_ext: .nii.gz\n", - "[2025-03-13 05:49:57.976][ INFO](notebook) - label_output_ext: .nii.gz\n", - "[2025-03-13 05:49:57.977][ INFO](notebook) - spacing: [1.5, 1.5, 2.0]\n", - "[2025-03-13 05:49:57.978][ INFO](notebook) - autoencoder_sliding_window_infer_size: [48, 48, 64]\n", - "[2025-03-13 05:49:57.979][ INFO](notebook) - autoencoder_sliding_window_infer_overlap: 0.25\n", - "[2025-03-13 05:49:57.980][ INFO](notebook) - autoencoder_tp_num_splits: 2\n", - "[2025-03-13 05:49:57.980][ INFO](notebook) - modality: 1\n", - "[2025-03-13 05:49:57.981][ INFO](root) - `controllable_anatomy_size` is empty.\n", + "[2025-03-13 20:41:25.267][ INFO](notebook) - num_output_samples: 1\n", + "[2025-03-13 20:41:25.268][ INFO](notebook) - body_region: ['abdomen']\n", + "[2025-03-13 20:41:25.268][ INFO](notebook) - anatomy_list: ['liver', 'hepatic tumor']\n", + "[2025-03-13 20:41:25.269][ INFO](notebook) - controllable_anatomy_size: []\n", + "[2025-03-13 20:41:25.269][ INFO](notebook) - num_inference_steps: 30\n", + "[2025-03-13 20:41:25.270][ INFO](notebook) - mask_generation_num_inference_steps: 1000\n", + "[2025-03-13 20:41:25.271][ INFO](notebook) - output_size: [256, 256, 256]\n", + "[2025-03-13 20:41:25.271][ INFO](notebook) - image_output_ext: .nii.gz\n", + "[2025-03-13 20:41:25.272][ INFO](notebook) - label_output_ext: .nii.gz\n", + "[2025-03-13 20:41:25.272][ INFO](notebook) - spacing: [1.5, 1.5, 2.0]\n", + "[2025-03-13 20:41:25.273][ INFO](notebook) - autoencoder_sliding_window_infer_size: [48, 48, 64]\n", + "[2025-03-13 20:41:25.274][ INFO](notebook) - autoencoder_sliding_window_infer_overlap: 0.6666\n", + "[2025-03-13 20:41:25.274][ INFO](notebook) - autoencoder_tp_num_splits: 4\n", + "[2025-03-13 20:41:25.275][ INFO](notebook) - controlnet: $@controlnet_def\n", + "[2025-03-13 20:41:25.275][ INFO](notebook) - diffusion_unet: $@diffusion_unet_def\n", + "[2025-03-13 20:41:25.276][ INFO](notebook) - autoencoder: $@autoencoder_def\n", + "[2025-03-13 20:41:25.276][ INFO](notebook) - mask_generation_autoencoder: $@mask_generation_autoencoder_def\n", + "[2025-03-13 20:41:25.277][ INFO](notebook) - mask_generation_diffusion: $@mask_generation_diffusion_def\n", + "[2025-03-13 20:41:25.277][ INFO](notebook) - modality: 1\n", + "[2025-03-13 20:41:25.278][ INFO](root) - `controllable_anatomy_size` is empty.\n", "We will synthesize based on `body_region`: (['abdomen']) and `anatomy_list`: (['liver', 'hepatic tumor']).\n", - "[2025-03-13 05:49:57.982][ INFO](root) - The generate results will have voxel size to be [1.5, 1.5, 2.0]mm, volume size to be [256, 256, 256].\n", - "[2025-03-13 05:49:57.983][ INFO](notebook) - Network definition and inference inputs have been loaded.\n" + "[2025-03-13 20:41:25.281][ INFO](root) - The generate results will have voxel size to be [1.5, 1.5, 2.0]mm, volume size to be [256, 256, 256].\n", + "[2025-03-13 20:41:25.282][ INFO](notebook) - Network definition and inference inputs have been loaded.\n" ] } ], @@ -438,14 +443,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-13 05:50:06,794 - INFO - 'dst' model updated: 180 of 231 variables.\n" + "2025-03-13 20:41:33,046 - INFO - 'dst' model updated: 180 of 231 variables.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 05:50:09.123][ INFO](notebook) - All the trained model weights have been loaded.\n" + "[2025-03-13 20:41:35.397][ INFO](notebook) - All the trained model weights have been loaded.\n" ] } ], @@ -499,7 +504,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 05:50:09.148][ INFO](root) - LDM sampler initialized.\n" + "[2025-03-13 20:41:35.423][ INFO](root) - LDM sampler initialized.\n" ] } ], @@ -562,28 +567,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 05:50:09.153][ INFO](notebook) - The generated image/mask pairs will be saved in output.\n", - "[2025-03-13 05:50:09.198][ INFO](root) - Resample mask file to get desired output size and spacing\n", - "[2025-03-13 05:50:10.494][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 05:50:10.498][ INFO](root) - Resize Spacing: [tensor(0.7539, dtype=torch.float64), tensor(0.7539, dtype=torch.float64), tensor(1.9141, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 05:50:10.498][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", - "[2025-03-13 05:50:13.380][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 05:50:13.383][ INFO](root) - Resize Spacing: [tensor(0.7578, dtype=torch.float64), tensor(0.7578, dtype=torch.float64), tensor(1.8848, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 05:50:13.384][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", - "[2025-03-13 05:50:16.211][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 05:50:16.213][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 05:50:16.214][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", - "[2025-03-13 05:50:20.594][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 05:50:20.597][ INFO](root) - Resize Spacing: [tensor(0.7754, dtype=torch.float64), tensor(0.7754, dtype=torch.float64), tensor(1.0098, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 05:50:20.598][ INFO](root) - Output size: [512, 512, 512] -> [256, 256, 256]\n", - "[2025-03-13 05:50:24.939][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 05:50:24.942][ INFO](root) - Resize Spacing: [tensor(0.7480, dtype=torch.float64), tensor(0.7480, dtype=torch.float64), tensor(1.0566, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 05:50:24.942][ INFO](root) - Output size: [512, 512, 512] -> [256, 256, 256]\n", - "[2025-03-13 05:50:26.753][ INFO](root) - Images will be generated based on [{'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_78_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}].\n", - "[2025-03-13 05:50:26.754][ INFO](root) - ---- Start preparing masks... ----\n", - "[2025-03-13 05:50:28.019][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 05:50:28.021][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 05:50:28.022][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n" + "[2025-03-13 20:41:35.427][ INFO](notebook) - The generated image/mask pairs will be saved in output.\n", + "[2025-03-13 20:41:35.472][ INFO](root) - Resample mask file to get desired output size and spacing\n", + "[2025-03-13 20:41:36.697][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 20:41:36.700][ INFO](root) - Resize Spacing: [tensor(0.7539, dtype=torch.float64), tensor(0.7539, dtype=torch.float64), tensor(1.9141, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 20:41:36.701][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-13 20:41:39.588][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 20:41:39.590][ INFO](root) - Resize Spacing: [tensor(0.7578, dtype=torch.float64), tensor(0.7578, dtype=torch.float64), tensor(1.8848, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 20:41:39.591][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-13 20:41:42.445][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 20:41:42.449][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 20:41:42.449][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-13 20:41:46.838][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 20:41:46.842][ INFO](root) - Resize Spacing: [tensor(0.7754, dtype=torch.float64), tensor(0.7754, dtype=torch.float64), tensor(1.0098, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 20:41:46.843][ INFO](root) - Output size: [512, 512, 512] -> [256, 256, 256]\n", + "[2025-03-13 20:41:51.236][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 20:41:51.239][ INFO](root) - Resize Spacing: [tensor(0.7480, dtype=torch.float64), tensor(0.7480, dtype=torch.float64), tensor(1.0566, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 20:41:51.239][ INFO](root) - Output size: [512, 512, 512] -> [256, 256, 256]\n", + "[2025-03-13 20:41:53.019][ INFO](root) - Images will be generated based on [{'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_78_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}, {'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_61_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}, {'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_69_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}].\n", + "[2025-03-13 20:41:53.021][ INFO](root) - ---- Start preparing masks... ----\n", + "[2025-03-13 20:41:54.185][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 20:41:54.187][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 20:41:54.187][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n" ] }, { @@ -598,8 +603,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 05:50:30.565][ INFO](root) - ---- Mask preparation time: 3.810523509979248 seconds ----\n", - "[2025-03-13 05:50:30.575][ INFO](root) - ---- Start generating latent features... ----\n" + "[2025-03-13 20:41:56.320][ INFO](root) - ---- Mask preparation time: 3.298685312271118 seconds ----\n", + "[2025-03-13 20:41:56.328][ INFO](root) - ---- Start generating latent features... ----\n" ] }, { @@ -613,26 +618,27 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|███████████████████████████████████████████| 30/30 [00:02<00:00, 10.19it/s]\n", - "[2025-03-13 05:50:33.559][ INFO](root) - ---- Latent features generation time: 2.9837806224823 seconds ----\n", - "[2025-03-13 05:50:33.585][ INFO](root) - ---- Start decoding latent features into images... ----\n", - "100%|█████████████████████████████████████████████| 4/4 [00:04<00:00, 1.22s/it]\n", - "[2025-03-13 05:50:38.503][ INFO](root) - ---- Image decoding time: 4.917377710342407 seconds ----\n" + "100%|███████████████████████████████████████████| 30/30 [00:02<00:00, 10.27it/s]\n", + "[2025-03-13 20:41:59.286][ INFO](root) - ---- DM/ControlNet Latent features generation time: 2.957245349884033 seconds ----\n", + "[2025-03-13 20:41:59.310][ INFO](root) - ---- Start decoding latent features into images... ----\n", + "100%|█████████████████████████████████████████████| 4/4 [00:04<00:00, 1.24s/it]\n", + "[2025-03-13 20:42:04.301][ INFO](root) - ---- Image VAE decoding time: 4.9903786182403564 seconds ----\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "2025-03-13 05:50:38,925 INFO image_writer.py:197 - writing: output/sample_20250313_055038_908689_image.nii.gz\n", - "2025-03-13 05:50:40,572 INFO image_writer.py:197 - writing: output/sample_20250313_055038_908689_label.nii.gz\n" + "1 3\n", + "2025-03-13 20:42:04,704 INFO image_writer.py:197 - writing: output/sample_20250313_204204_691532_image.nii.gz\n", + "2025-03-13 20:42:06,331 INFO image_writer.py:197 - writing: output/sample_20250313_204204_691532_label.nii.gz\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 05:50:41.554][ INFO](notebook) - MAISI image/mask generation finished\n" + "[2025-03-13 20:42:07.204][ INFO](notebook) - MAISI image/mask generation finished\n" ] } ], @@ -662,7 +668,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 05:50:41.563][ INFO](notebook) - Visualizing output/sample_20250313_055038_908689_image.nii.gz and output/sample_20250313_055038_908689_label.nii.gz...\n" + "[2025-03-13 20:42:07.213][ INFO](notebook) - Visualizing output/sample_20250313_204204_691532_image.nii.gz and output/sample_20250313_204204_691532_label.nii.gz...\n" ] }, { diff --git a/generation/maisi/scripts/inference.py b/generation/maisi/scripts/inference.py index 5d549c5c9..61b486080 100644 --- a/generation/maisi/scripts/inference.py +++ b/generation/maisi/scripts/inference.py @@ -61,10 +61,18 @@ def main(): default=None, help="random seed, can be None or int", ) + parser.add_argument( + "--version", + default='maisi3d-rflow', + type=str, + help="maisi_version, choose from ['maisi3d-ddpm', 'maisi3d-rflow']", + ) args = parser.parse_args() # Step 0: configuration logger = logging.getLogger("maisi.inference") + maisi_version = args.version + # ## Set deterministic training for reproducibility if args.random_seed is not None: set_determinism(seed=args.random_seed) @@ -80,41 +88,71 @@ def main(): root_dir = tempfile.mkdtemp() if directory is None else directory print(root_dir) + # TODO: remove the `files` after the files are uploaded to the NGC files = [ { "path": "models/autoencoder_epoch273.pt", - "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo/model_maisi_autoencoder_epoch273_alternative.pt", - }, - { - "path": "models/input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1.pt", - "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo/model_maisi_input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1_alternative.pt", - }, - { - "path": "models/controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current.pt", - "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo/model_maisi_controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current_alternative.pt", + "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials" + "/model_zoo/model_maisi_autoencoder_epoch273_alternative.pt", }, { "path": "models/mask_generation_autoencoder.pt", - "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/mask_generation_autoencoder.pt", + "url": "https://developer.download.nvidia.com/assets/Clara/monai" "/tutorials/mask_generation_autoencoder.pt", }, { "path": "models/mask_generation_diffusion_unet.pt", - "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo/model_maisi_mask_generation_diffusion_unet_v2.pt", - }, - { - "path": "configs/candidate_masks_flexible_size_and_spacing_3000.json", - "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/candidate_masks_flexible_size_and_spacing_3000.json", + "url": "https://developer.download.nvidia.com/assets/Clara/monai" + "/tutorials/model_zoo/model_maisi_mask_generation_diffusion_unet_v2.pt", }, { "path": "configs/all_anatomy_size_condtions.json", "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/all_anatomy_size_condtions.json", }, { - "path": "datasets/all_masks_flexible_size_and_spacing_3000.zip", - "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo/model_maisi_all_masks_flexible_size_and_spacing_3000.zip", + "path": "datasets/all_masks_flexible_size_and_spacing_4000.zip", + "url": "https://developer.download.nvidia.com/assets/Clara/monai" + "/tutorials/all_masks_flexible_size_and_spacing_4000.zip", }, ] - + + if maisi_version == "maisi3d-ddpm": + files += [ + { + "path": "models/diff_unet_3d_ddpm.pt", + "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo" + "/model_maisi_input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1_alternative.pt", + }, + { + "path": "models/controlnet_3d_ddpm.pt", + "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/model_zoo" + "/model_maisi_controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current_alternative.pt", + }, + { + "path": "configs/candidate_masks_flexible_size_and_spacing_3000.json", + "url": "https://developer.download.nvidia.com/assets/Clara/monai" + "/tutorials/candidate_masks_flexible_size_and_spacing_3000.json", + }, + ] + elif maisi_version == "maisi3d-rflow": + files += [ + { + "path": "models/diff_unet_3d_rflow.pt", + "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/" + "diff_unet_ckpt_rflow_epoch19350.pt", + }, + { + "path": "models/controlnet_3d_rflow.pt", + "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/" "controlnet_rflow_epoch208.pt", + }, + { + "path": "configs/candidate_masks_flexible_size_and_spacing_4000.json", + "url": "https://developer.download.nvidia.com/assets/Clara/monai" + "/tutorials/candidate_masks_flexible_size_and_spacing_4000.json", + }, + ] + else: + raise ValueError(f"maisi_version has to be chosen from ['maisi3d-ddpm', 'maisi3d-rflow'], yet got {maisi_version}.") + for file in files: file["path"] = file["path"] if "datasets/" not in file["path"] else os.path.join(root_dir, file["path"]) download_url(url=file["url"], filepath=file["path"]) @@ -227,6 +265,7 @@ def main(): image_output_ext=args.image_output_ext, label_output_ext=args.label_output_ext, spacing=args.spacing, + modality=args.modality, num_inference_steps=args.num_inference_steps, mask_generation_num_inference_steps=args.mask_generation_num_inference_steps, random_seed=args.random_seed, diff --git a/generation/maisi/scripts/sample.py b/generation/maisi/scripts/sample.py index f992ff719..b19486cee 100644 --- a/generation/maisi/scripts/sample.py +++ b/generation/maisi/scripts/sample.py @@ -132,10 +132,13 @@ def ldm_conditional_sample_one_mask( # synthesize latents if isinstance(noise_scheduler, DDPMScheduler) and num_inference_steps < noise_scheduler.num_train_timesteps: warnings.warn( - "Warning: noise_scheduler is a DDPMScheduler. " - "We expect num_inference_steps = noise_scheduler.num_train_timesteps" - f" = {noise_scheduler.num_train_timesteps}. Yet got num_inference_steps = {num_inference_steps}. " - "The generated image quality is not guaranteed." + "**************************************************************\n" + "* WARNING: Mask noise_scheduler is a DDPMScheduler.\n" + "* We expect num_inference_steps = noise_scheduler.num_train_timesteps" + f" = {noise_scheduler.num_train_timesteps}.\n" + f"* Yet got num_inference_steps = {num_inference_steps}.\n" + "* The generated image quality is not guaranteed.\n" + "**************************************************************" ) noise_scheduler.set_timesteps(num_inference_steps=num_inference_steps) @@ -266,11 +269,14 @@ def ldm_conditional_sample_one_image( noise_scheduler.set_timesteps(num_inference_steps=num_inference_steps) if isinstance(noise_scheduler, DDPMScheduler) and num_inference_steps < noise_scheduler.num_train_timesteps: - warnings.warn( - "Warning: noise_scheduler is a DDPMScheduler. " - "We expect num_inference_steps = noise_scheduler.num_train_timesteps" - f" = {noise_scheduler.num_train_timesteps}. Yet got num_inference_steps = {num_inference_steps}. " - "The generated image quality is not guaranteed." + warnings.warn( + "**************************************************************\n" + "* WARNING: Image noise_scheduler is a DDPMScheduler.\n" + "* We expect num_inference_steps = noise_scheduler.num_train_timesteps" + f" = {noise_scheduler.num_train_timesteps}.\n" + f"* Yet got num_inference_steps = {num_inference_steps}.\n" + "* The generated image quality is not guaranteed.\n" + "**************************************************************" ) all_timesteps = noise_scheduler.timesteps @@ -325,8 +331,8 @@ def ldm_conditional_sample_one_image( else: latents, _ = noise_scheduler.step(model_output, t, latents, next_t) # type: ignore end_time = time.time() - logging.info(f"---- Latent features generation time: {end_time - start_time} seconds ----") - del model_output + logging.info(f"---- DM/ControlNet Latent features generation time: {end_time - start_time} seconds ----") + del model_output, controlnet_cond_vis torch.cuda.empty_cache() # decode latents to synthesized images @@ -345,7 +351,7 @@ def ldm_conditional_sample_one_image( synthetic_images = dynamic_infer(inferer, recon_model, latents) synthetic_images = torch.clip(synthetic_images, b_min, b_max).cpu() end_time = time.time() - logging.info(f"---- Image decoding time: {end_time - start_time} seconds ----") + logging.info(f"---- Image VAE decoding time: {end_time - start_time} seconds ----") ## post processing: # project output to [0, 1] @@ -628,7 +634,7 @@ def __init__( self.autoencoder_sliding_window_infer_overlap = autoencoder_sliding_window_infer_overlap # quality check args - self.max_try_time = 5 # if not pass quality check, will try self.max_try_time times + self.max_try_time = 3 # if not pass quality check, will try self.max_try_time times with open(real_img_median_statistics, "r") as json_file: self.median_statistics = json.load(json_file) self.label_int_dict = { @@ -713,9 +719,12 @@ def sample_multiple_images(self, num_img): selected_mask_files = self.select_mask(candidate_mask_files, num_img) logging.info(f"Images will be generated based on {selected_mask_files}.") - if len(selected_mask_files) != num_img: + if len(selected_mask_files) < num_img: raise ValueError( - f"len(selected_mask_files) ({len(selected_mask_files)}) != num_img ({num_img}). This should not happen. Please revisit function select_mask(self, candidate_mask_files, num_img)." + ( + f"len(selected_mask_files) ({len(selected_mask_files)}) < num_img ({num_img}). " + "This should not happen. Please revisit function select_mask(self, candidate_mask_files, num_img)." + ) ) num_generated_img = 0 @@ -766,6 +775,7 @@ def sample_multiple_images(self, num_img): pass_quality_check = self.quality_check( synthetic_images.cpu().detach().numpy(), combine_label_or.cpu().detach().numpy() ) + print(num_img - num_generated_img, (len(selected_mask_files) - index_s)) if pass_quality_check or (num_img - num_generated_img) >= (len(selected_mask_files) - index_s): if not pass_quality_check: logging.info( @@ -819,7 +829,7 @@ def select_mask(self, candidate_mask_files, num_img): selected_mask_files = [] random.shuffle(candidate_mask_files) - for n in range(num_img): + for n in range(num_img*self.max_try_time): mask_file = candidate_mask_files[n % len(candidate_mask_files)] selected_mask_files.append({"mask_file": mask_file, "if_aug": True}) return selected_mask_files diff --git a/generation/maisi/scripts/utils.py b/generation/maisi/scripts/utils.py index dbcc292bd..5b5efdfda 100644 --- a/generation/maisi/scripts/utils.py +++ b/generation/maisi/scripts/utils.py @@ -706,7 +706,7 @@ def dynamic_infer(inferer, model, images): Returns: torch.Tensor: The output from the model or the inferer, depending on the input size. """ - if torch.numel(images[0:1, 0:1, ...]) < math.prod(inferer.roi_size): + if torch.numel(images[0:1, 0:1, ...]) <= math.prod(inferer.roi_size): return model(images) else: # Extract the spatial dimensions from the images tensor (H, W, D) From 76d85e13d6380c0e57f00e3a06ec6ccf8dbdeba6 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 13 Mar 2025 20:44:08 +0000 Subject: [PATCH 30/59] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- generation/maisi/scripts/inference.py | 16 ++++++++++------ generation/maisi/scripts/sample.py | 4 ++-- 2 files changed, 12 insertions(+), 8 deletions(-) diff --git a/generation/maisi/scripts/inference.py b/generation/maisi/scripts/inference.py index 61b486080..49498396c 100644 --- a/generation/maisi/scripts/inference.py +++ b/generation/maisi/scripts/inference.py @@ -63,7 +63,7 @@ def main(): ) parser.add_argument( "--version", - default='maisi3d-rflow', + default="maisi3d-rflow", type=str, help="maisi_version, choose from ['maisi3d-ddpm', 'maisi3d-rflow']", ) @@ -97,7 +97,8 @@ def main(): }, { "path": "models/mask_generation_autoencoder.pt", - "url": "https://developer.download.nvidia.com/assets/Clara/monai" "/tutorials/mask_generation_autoencoder.pt", + "url": "https://developer.download.nvidia.com/assets/Clara/monai" + "/tutorials/mask_generation_autoencoder.pt", }, { "path": "models/mask_generation_diffusion_unet.pt", @@ -114,7 +115,7 @@ def main(): "/tutorials/all_masks_flexible_size_and_spacing_4000.zip", }, ] - + if maisi_version == "maisi3d-ddpm": files += [ { @@ -142,7 +143,8 @@ def main(): }, { "path": "models/controlnet_3d_rflow.pt", - "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/" "controlnet_rflow_epoch208.pt", + "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/" + "controlnet_rflow_epoch208.pt", }, { "path": "configs/candidate_masks_flexible_size_and_spacing_4000.json", @@ -151,8 +153,10 @@ def main(): }, ] else: - raise ValueError(f"maisi_version has to be chosen from ['maisi3d-ddpm', 'maisi3d-rflow'], yet got {maisi_version}.") - + raise ValueError( + f"maisi_version has to be chosen from ['maisi3d-ddpm', 'maisi3d-rflow'], yet got {maisi_version}." + ) + for file in files: file["path"] = file["path"] if "datasets/" not in file["path"] else os.path.join(root_dir, file["path"]) download_url(url=file["url"], filepath=file["path"]) diff --git a/generation/maisi/scripts/sample.py b/generation/maisi/scripts/sample.py index b19486cee..fcf96f748 100644 --- a/generation/maisi/scripts/sample.py +++ b/generation/maisi/scripts/sample.py @@ -269,7 +269,7 @@ def ldm_conditional_sample_one_image( noise_scheduler.set_timesteps(num_inference_steps=num_inference_steps) if isinstance(noise_scheduler, DDPMScheduler) and num_inference_steps < noise_scheduler.num_train_timesteps: - warnings.warn( + warnings.warn( "**************************************************************\n" "* WARNING: Image noise_scheduler is a DDPMScheduler.\n" "* We expect num_inference_steps = noise_scheduler.num_train_timesteps" @@ -829,7 +829,7 @@ def select_mask(self, candidate_mask_files, num_img): selected_mask_files = [] random.shuffle(candidate_mask_files) - for n in range(num_img*self.max_try_time): + for n in range(num_img * self.max_try_time): mask_file = candidate_mask_files[n % len(candidate_mask_files)] selected_mask_files.append({"mask_file": mask_file, "if_aug": True}) return selected_mask_files From 866d75f55209f044960402ede5e185f976f84559 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Thu, 13 Mar 2025 21:07:54 +0000 Subject: [PATCH 31/59] pass check Signed-off-by: Can-Zhao --- generation/maisi/configs/config_infer.json | 2 +- .../maisi/maisi_inference_tutorial.ipynb | 185 +++++++++--------- generation/maisi/scripts/sample.py | 8 +- 3 files changed, 96 insertions(+), 99 deletions(-) diff --git a/generation/maisi/configs/config_infer.json b/generation/maisi/configs/config_infer.json index 65769f20f..33de4d853 100644 --- a/generation/maisi/configs/config_infer.json +++ b/generation/maisi/configs/config_infer.json @@ -18,7 +18,7 @@ 2.0 ], "autoencoder_sliding_window_infer_size": [48,48,48], - "autoencoder_sliding_window_infer_overlap": 0.25, + "autoencoder_sliding_window_infer_overlap": 0.6666, "controlnet": "$@controlnet_def", "diffusion_unet": "$@diffusion_unet_def", "autoencoder": "$@autoencoder_def", diff --git a/generation/maisi/maisi_inference_tutorial.ipynb b/generation/maisi/maisi_inference_tutorial.ipynb index 3ca4e9824..9b6a87baf 100644 --- a/generation/maisi/maisi_inference_tutorial.ipynb +++ b/generation/maisi/maisi_inference_tutorial.ipynb @@ -140,7 +140,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 20:41:25.227][ INFO](notebook) - MAISI version is maisi3d-rflow, whether to use body_region is False\n" + "[2025-03-13 21:06:36.662][ INFO](notebook) - MAISI version is maisi3d-rflow, whether to use body_region is False\n" ] } ], @@ -179,22 +179,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-13 20:41:25,236 - INFO - Expected md5 is None, skip md5 check for file models/autoencoder_epoch273.pt.\n", - "2025-03-13 20:41:25,237 - INFO - File exists: models/autoencoder_epoch273.pt, skipped downloading.\n", - "2025-03-13 20:41:25,238 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_autoencoder.pt.\n", - "2025-03-13 20:41:25,238 - INFO - File exists: models/mask_generation_autoencoder.pt, skipped downloading.\n", - "2025-03-13 20:41:25,239 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_diffusion_unet.pt.\n", - "2025-03-13 20:41:25,239 - INFO - File exists: models/mask_generation_diffusion_unet.pt, skipped downloading.\n", - "2025-03-13 20:41:25,240 - INFO - Expected md5 is None, skip md5 check for file configs/all_anatomy_size_condtions.json.\n", - "2025-03-13 20:41:25,240 - INFO - File exists: configs/all_anatomy_size_condtions.json, skipped downloading.\n", - "2025-03-13 20:41:25,241 - INFO - Expected md5 is None, skip md5 check for file temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip.\n", - "2025-03-13 20:41:25,241 - INFO - File exists: temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip, skipped downloading.\n", - "2025-03-13 20:41:25,242 - INFO - Expected md5 is None, skip md5 check for file models/diff_unet_3d_rflow.pt.\n", - "2025-03-13 20:41:25,243 - INFO - File exists: models/diff_unet_3d_rflow.pt, skipped downloading.\n", - "2025-03-13 20:41:25,243 - INFO - Expected md5 is None, skip md5 check for file models/controlnet_3d_rflow.pt.\n", - "2025-03-13 20:41:25,244 - INFO - File exists: models/controlnet_3d_rflow.pt, skipped downloading.\n", - "2025-03-13 20:41:25,244 - INFO - Expected md5 is None, skip md5 check for file configs/candidate_masks_flexible_size_and_spacing_4000.json.\n", - "2025-03-13 20:41:25,245 - INFO - File exists: configs/candidate_masks_flexible_size_and_spacing_4000.json, skipped downloading.\n" + "2025-03-13 21:06:36,670 - INFO - Expected md5 is None, skip md5 check for file models/autoencoder_epoch273.pt.\n", + "2025-03-13 21:06:36,672 - INFO - File exists: models/autoencoder_epoch273.pt, skipped downloading.\n", + "2025-03-13 21:06:36,672 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_autoencoder.pt.\n", + "2025-03-13 21:06:36,673 - INFO - File exists: models/mask_generation_autoencoder.pt, skipped downloading.\n", + "2025-03-13 21:06:36,674 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_diffusion_unet.pt.\n", + "2025-03-13 21:06:36,674 - INFO - File exists: models/mask_generation_diffusion_unet.pt, skipped downloading.\n", + "2025-03-13 21:06:36,675 - INFO - Expected md5 is None, skip md5 check for file configs/all_anatomy_size_condtions.json.\n", + "2025-03-13 21:06:36,675 - INFO - File exists: configs/all_anatomy_size_condtions.json, skipped downloading.\n", + "2025-03-13 21:06:36,677 - INFO - Expected md5 is None, skip md5 check for file temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip.\n", + "2025-03-13 21:06:36,677 - INFO - File exists: temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip, skipped downloading.\n", + "2025-03-13 21:06:36,678 - INFO - Expected md5 is None, skip md5 check for file models/diff_unet_3d_rflow.pt.\n", + "2025-03-13 21:06:36,678 - INFO - File exists: models/diff_unet_3d_rflow.pt, skipped downloading.\n", + "2025-03-13 21:06:36,680 - INFO - Expected md5 is None, skip md5 check for file models/controlnet_3d_rflow.pt.\n", + "2025-03-13 21:06:36,680 - INFO - File exists: models/controlnet_3d_rflow.pt, skipped downloading.\n", + "2025-03-13 21:06:36,681 - INFO - Expected md5 is None, skip md5 check for file configs/candidate_masks_flexible_size_and_spacing_4000.json.\n", + "2025-03-13 21:06:36,681 - INFO - File exists: configs/candidate_masks_flexible_size_and_spacing_4000.json, skipped downloading.\n" ] } ], @@ -295,18 +295,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 20:41:25.252][ INFO](notebook) - output_dir: output\n", - "[2025-03-13 20:41:25.253][ INFO](notebook) - trained_autoencoder_path: models/autoencoder_epoch273.pt\n", - "[2025-03-13 20:41:25.254][ INFO](notebook) - trained_diffusion_path: models/diff_unet_3d_rflow.pt\n", - "[2025-03-13 20:41:25.254][ INFO](notebook) - trained_controlnet_path: models/controlnet_3d_rflow.pt\n", - "[2025-03-13 20:41:25.255][ INFO](notebook) - trained_mask_generation_autoencoder_path: models/mask_generation_autoencoder.pt\n", - "[2025-03-13 20:41:25.255][ INFO](notebook) - trained_mask_generation_diffusion_path: models/mask_generation_diffusion_unet.pt\n", - "[2025-03-13 20:41:25.256][ INFO](notebook) - all_mask_files_base_dir: ./temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000\n", - "[2025-03-13 20:41:25.256][ INFO](notebook) - all_mask_files_json: ./configs/candidate_masks_flexible_size_and_spacing_4000.json\n", - "[2025-03-13 20:41:25.258][ INFO](notebook) - all_anatomy_size_conditions_json: ./configs/all_anatomy_size_condtions.json\n", - "[2025-03-13 20:41:25.258][ INFO](notebook) - label_dict_json: ./configs/label_dict.json\n", - "[2025-03-13 20:41:25.259][ INFO](notebook) - label_dict_remap_json: ./configs/label_dict_124_to_132.json\n", - "[2025-03-13 20:41:25.260][ INFO](notebook) - Global config variables have been loaded.\n" + "[2025-03-13 21:06:36.687][ INFO](notebook) - output_dir: output\n", + "[2025-03-13 21:06:36.688][ INFO](notebook) - trained_autoencoder_path: models/autoencoder_epoch273.pt\n", + "[2025-03-13 21:06:36.689][ INFO](notebook) - trained_diffusion_path: models/diff_unet_3d_rflow.pt\n", + "[2025-03-13 21:06:36.689][ INFO](notebook) - trained_controlnet_path: models/controlnet_3d_rflow.pt\n", + "[2025-03-13 21:06:36.690][ INFO](notebook) - trained_mask_generation_autoencoder_path: models/mask_generation_autoencoder.pt\n", + "[2025-03-13 21:06:36.691][ INFO](notebook) - trained_mask_generation_diffusion_path: models/mask_generation_diffusion_unet.pt\n", + "[2025-03-13 21:06:36.692][ INFO](notebook) - all_mask_files_base_dir: ./temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000\n", + "[2025-03-13 21:06:36.692][ INFO](notebook) - all_mask_files_json: ./configs/candidate_masks_flexible_size_and_spacing_4000.json\n", + "[2025-03-13 21:06:36.693][ INFO](notebook) - all_anatomy_size_conditions_json: ./configs/all_anatomy_size_condtions.json\n", + "[2025-03-13 21:06:36.693][ INFO](notebook) - label_dict_json: ./configs/label_dict.json\n", + "[2025-03-13 21:06:36.694][ INFO](notebook) - label_dict_remap_json: ./configs/label_dict_124_to_132.json\n", + "[2025-03-13 21:06:36.694][ INFO](notebook) - Global config variables have been loaded.\n" ] } ], @@ -350,29 +350,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 20:41:25.267][ INFO](notebook) - num_output_samples: 1\n", - "[2025-03-13 20:41:25.268][ INFO](notebook) - body_region: ['abdomen']\n", - "[2025-03-13 20:41:25.268][ INFO](notebook) - anatomy_list: ['liver', 'hepatic tumor']\n", - "[2025-03-13 20:41:25.269][ INFO](notebook) - controllable_anatomy_size: []\n", - "[2025-03-13 20:41:25.269][ INFO](notebook) - num_inference_steps: 30\n", - "[2025-03-13 20:41:25.270][ INFO](notebook) - mask_generation_num_inference_steps: 1000\n", - "[2025-03-13 20:41:25.271][ INFO](notebook) - output_size: [256, 256, 256]\n", - "[2025-03-13 20:41:25.271][ INFO](notebook) - image_output_ext: .nii.gz\n", - "[2025-03-13 20:41:25.272][ INFO](notebook) - label_output_ext: .nii.gz\n", - "[2025-03-13 20:41:25.272][ INFO](notebook) - spacing: [1.5, 1.5, 2.0]\n", - "[2025-03-13 20:41:25.273][ INFO](notebook) - autoencoder_sliding_window_infer_size: [48, 48, 64]\n", - "[2025-03-13 20:41:25.274][ INFO](notebook) - autoencoder_sliding_window_infer_overlap: 0.6666\n", - "[2025-03-13 20:41:25.274][ INFO](notebook) - autoencoder_tp_num_splits: 4\n", - "[2025-03-13 20:41:25.275][ INFO](notebook) - controlnet: $@controlnet_def\n", - "[2025-03-13 20:41:25.275][ INFO](notebook) - diffusion_unet: $@diffusion_unet_def\n", - "[2025-03-13 20:41:25.276][ INFO](notebook) - autoencoder: $@autoencoder_def\n", - "[2025-03-13 20:41:25.276][ INFO](notebook) - mask_generation_autoencoder: $@mask_generation_autoencoder_def\n", - "[2025-03-13 20:41:25.277][ INFO](notebook) - mask_generation_diffusion: $@mask_generation_diffusion_def\n", - "[2025-03-13 20:41:25.277][ INFO](notebook) - modality: 1\n", - "[2025-03-13 20:41:25.278][ INFO](root) - `controllable_anatomy_size` is empty.\n", + "[2025-03-13 21:06:36.702][ INFO](notebook) - num_output_samples: 1\n", + "[2025-03-13 21:06:36.703][ INFO](notebook) - body_region: ['abdomen']\n", + "[2025-03-13 21:06:36.704][ INFO](notebook) - anatomy_list: ['liver', 'hepatic tumor']\n", + "[2025-03-13 21:06:36.705][ INFO](notebook) - controllable_anatomy_size: []\n", + "[2025-03-13 21:06:36.705][ INFO](notebook) - num_inference_steps: 30\n", + "[2025-03-13 21:06:36.706][ INFO](notebook) - mask_generation_num_inference_steps: 1000\n", + "[2025-03-13 21:06:36.707][ INFO](notebook) - output_size: [256, 256, 256]\n", + "[2025-03-13 21:06:36.707][ INFO](notebook) - image_output_ext: .nii.gz\n", + "[2025-03-13 21:06:36.708][ INFO](notebook) - label_output_ext: .nii.gz\n", + "[2025-03-13 21:06:36.708][ INFO](notebook) - spacing: [1.5, 1.5, 2.0]\n", + "[2025-03-13 21:06:36.709][ INFO](notebook) - autoencoder_sliding_window_infer_size: [48, 48, 48]\n", + "[2025-03-13 21:06:36.710][ INFO](notebook) - autoencoder_sliding_window_infer_overlap: 0.6666\n", + "[2025-03-13 21:06:36.710][ INFO](notebook) - controlnet: $@controlnet_def\n", + "[2025-03-13 21:06:36.711][ INFO](notebook) - diffusion_unet: $@diffusion_unet_def\n", + "[2025-03-13 21:06:36.711][ INFO](notebook) - autoencoder: $@autoencoder_def\n", + "[2025-03-13 21:06:36.712][ INFO](notebook) - mask_generation_autoencoder: $@mask_generation_autoencoder_def\n", + "[2025-03-13 21:06:36.712][ INFO](notebook) - mask_generation_diffusion: $@mask_generation_diffusion_def\n", + "[2025-03-13 21:06:36.713][ INFO](notebook) - modality: 1\n", + "[2025-03-13 21:06:36.713][ INFO](root) - `controllable_anatomy_size` is empty.\n", "We will synthesize based on `body_region`: (['abdomen']) and `anatomy_list`: (['liver', 'hepatic tumor']).\n", - "[2025-03-13 20:41:25.281][ INFO](root) - The generate results will have voxel size to be [1.5, 1.5, 2.0]mm, volume size to be [256, 256, 256].\n", - "[2025-03-13 20:41:25.282][ INFO](notebook) - Network definition and inference inputs have been loaded.\n" + "[2025-03-13 21:06:36.716][ INFO](root) - The generate results will have voxel size to be [1.5, 1.5, 2.0]mm, volume size to be [256, 256, 256].\n", + "[2025-03-13 21:06:36.717][ INFO](notebook) - Network definition and inference inputs have been loaded.\n" ] } ], @@ -383,7 +382,7 @@ " setattr(args, k, v)\n", "\n", "# check the format of inference inputs\n", - "config_infer_file = \"./configs/config_infer_16g_256x256x256.json\"\n", + "config_infer_file = \"./configs/config_infer.json\"\n", "with open(config_infer_file, \"r\") as f:\n", " config_infer_dict = json.load(f)\n", "for k, v in config_infer_dict.items():\n", @@ -443,14 +442,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-13 20:41:33,046 - INFO - 'dst' model updated: 180 of 231 variables.\n" + "2025-03-13 21:06:42,723 - INFO - 'dst' model updated: 180 of 231 variables.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 20:41:35.397][ INFO](notebook) - All the trained model weights have been loaded.\n" + "[2025-03-13 21:06:45.213][ INFO](notebook) - All the trained model weights have been loaded.\n" ] } ], @@ -504,7 +503,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 20:41:35.423][ INFO](root) - LDM sampler initialized.\n" + "[2025-03-13 21:06:45.238][ INFO](root) - LDM sampler initialized.\n" ] } ], @@ -567,28 +566,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 20:41:35.427][ INFO](notebook) - The generated image/mask pairs will be saved in output.\n", - "[2025-03-13 20:41:35.472][ INFO](root) - Resample mask file to get desired output size and spacing\n", - "[2025-03-13 20:41:36.697][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 20:41:36.700][ INFO](root) - Resize Spacing: [tensor(0.7539, dtype=torch.float64), tensor(0.7539, dtype=torch.float64), tensor(1.9141, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 20:41:36.701][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", - "[2025-03-13 20:41:39.588][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 20:41:39.590][ INFO](root) - Resize Spacing: [tensor(0.7578, dtype=torch.float64), tensor(0.7578, dtype=torch.float64), tensor(1.8848, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 20:41:39.591][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", - "[2025-03-13 20:41:42.445][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 20:41:42.449][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 20:41:42.449][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", - "[2025-03-13 20:41:46.838][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 20:41:46.842][ INFO](root) - Resize Spacing: [tensor(0.7754, dtype=torch.float64), tensor(0.7754, dtype=torch.float64), tensor(1.0098, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 20:41:46.843][ INFO](root) - Output size: [512, 512, 512] -> [256, 256, 256]\n", - "[2025-03-13 20:41:51.236][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 20:41:51.239][ INFO](root) - Resize Spacing: [tensor(0.7480, dtype=torch.float64), tensor(0.7480, dtype=torch.float64), tensor(1.0566, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 20:41:51.239][ INFO](root) - Output size: [512, 512, 512] -> [256, 256, 256]\n", - "[2025-03-13 20:41:53.019][ INFO](root) - Images will be generated based on [{'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_78_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}, {'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_61_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}, {'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_69_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}].\n", - "[2025-03-13 20:41:53.021][ INFO](root) - ---- Start preparing masks... ----\n", - "[2025-03-13 20:41:54.185][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 20:41:54.187][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 20:41:54.187][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n" + "[2025-03-13 21:06:45.243][ INFO](notebook) - The generated image/mask pairs will be saved in output.\n", + "[2025-03-13 21:06:45.290][ INFO](root) - Resample mask file to get desired output size and spacing\n", + "[2025-03-13 21:06:46.647][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 21:06:46.652][ INFO](root) - Resize Spacing: [tensor(0.7539, dtype=torch.float64), tensor(0.7539, dtype=torch.float64), tensor(1.9141, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 21:06:46.653][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-13 21:06:49.523][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 21:06:49.526][ INFO](root) - Resize Spacing: [tensor(0.7578, dtype=torch.float64), tensor(0.7578, dtype=torch.float64), tensor(1.8848, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 21:06:49.526][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-13 21:06:52.446][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 21:06:52.448][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 21:06:52.448][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-13 21:06:56.827][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 21:06:56.830][ INFO](root) - Resize Spacing: [tensor(0.7754, dtype=torch.float64), tensor(0.7754, dtype=torch.float64), tensor(1.0098, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 21:06:56.831][ INFO](root) - Output size: [512, 512, 512] -> [256, 256, 256]\n", + "[2025-03-13 21:07:01.208][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 21:07:01.210][ INFO](root) - Resize Spacing: [tensor(0.7480, dtype=torch.float64), tensor(0.7480, dtype=torch.float64), tensor(1.0566, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 21:07:01.211][ INFO](root) - Output size: [512, 512, 512] -> [256, 256, 256]\n", + "[2025-03-13 21:07:03.013][ INFO](root) - Images will be generated based on [{'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_78_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}, {'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_61_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}, {'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_69_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}, {'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_7_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}, {'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_2_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}].\n", + "[2025-03-13 21:07:03.014][ INFO](root) - ---- Start preparing masks... ----\n", + "[2025-03-13 21:07:04.235][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-13 21:07:04.237][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-13 21:07:04.237][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n" ] }, { @@ -603,8 +602,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 20:41:56.320][ INFO](root) - ---- Mask preparation time: 3.298685312271118 seconds ----\n", - "[2025-03-13 20:41:56.328][ INFO](root) - ---- Start generating latent features... ----\n" + "[2025-03-13 21:07:05.630][ INFO](root) - ---- Mask preparation time: 2.6154592037200928 seconds ----\n", + "[2025-03-13 21:07:05.641][ INFO](root) - ---- Start generating latent features... ----\n" ] }, { @@ -618,27 +617,27 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|███████████████████████████████████████████| 30/30 [00:02<00:00, 10.27it/s]\n", - "[2025-03-13 20:41:59.286][ INFO](root) - ---- DM/ControlNet Latent features generation time: 2.957245349884033 seconds ----\n", - "[2025-03-13 20:41:59.310][ INFO](root) - ---- Start decoding latent features into images... ----\n", - "100%|█████████████████████████████████████████████| 4/4 [00:04<00:00, 1.24s/it]\n", - "[2025-03-13 20:42:04.301][ INFO](root) - ---- Image VAE decoding time: 4.9903786182403564 seconds ----\n" + "100%|███████████████████████████████████████████| 30/30 [00:02<00:00, 10.15it/s]\n", + "[2025-03-13 21:07:08.634][ INFO](root) - ---- DM/ControlNet Latent features generation time: 2.9929230213165283 seconds ----\n", + "[2025-03-13 21:07:08.657][ INFO](root) - ---- Start decoding latent features into images... ----\n", + "100%|█████████████████████████████████████████████| 8/8 [00:07<00:00, 1.06it/s]\n", + "[2025-03-13 21:07:16.261][ INFO](root) - ---- Image VAE decoding time: 7.603064298629761 seconds ----\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "1 3\n", - "2025-03-13 20:42:04,704 INFO image_writer.py:197 - writing: output/sample_20250313_204204_691532_image.nii.gz\n", - "2025-03-13 20:42:06,331 INFO image_writer.py:197 - writing: output/sample_20250313_204204_691532_label.nii.gz\n" + "1 5\n", + "2025-03-13 21:07:16,694 INFO image_writer.py:197 - writing: output/sample_20250313_210716_671235_image.nii.gz\n", + "2025-03-13 21:07:18,358 INFO image_writer.py:197 - writing: output/sample_20250313_210716_671235_label.nii.gz\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 20:42:07.204][ INFO](notebook) - MAISI image/mask generation finished\n" + "[2025-03-13 21:07:19.322][ INFO](notebook) - MAISI image/mask generation finished\n" ] } ], @@ -659,16 +658,14 @@ { "cell_type": "code", "execution_count": 11, - "id": "e0453d9f-1614-4c84-aef1-77b6339d8c12", - "metadata": { - "scrolled": true - }, + "id": "dfd2ebf9-04f9-498f-982e-9daf44602bee", + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 20:42:07.213][ INFO](notebook) - Visualizing output/sample_20250313_204204_691532_image.nii.gz and output/sample_20250313_204204_691532_label.nii.gz...\n" + "[2025-03-13 21:07:19.330][ INFO](notebook) - Visualizing output/sample_20250313_210716_671235_image.nii.gz and output/sample_20250313_210716_671235_label.nii.gz...\n" ] }, { @@ -683,7 +680,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] diff --git a/generation/maisi/scripts/sample.py b/generation/maisi/scripts/sample.py index fcf96f748..b159232db 100644 --- a/generation/maisi/scripts/sample.py +++ b/generation/maisi/scripts/sample.py @@ -269,7 +269,7 @@ def ldm_conditional_sample_one_image( noise_scheduler.set_timesteps(num_inference_steps=num_inference_steps) if isinstance(noise_scheduler, DDPMScheduler) and num_inference_steps < noise_scheduler.num_train_timesteps: - warnings.warn( + warnings.warn( "**************************************************************\n" "* WARNING: Image noise_scheduler is a DDPMScheduler.\n" "* We expect num_inference_steps = noise_scheduler.num_train_timesteps" @@ -634,7 +634,7 @@ def __init__( self.autoencoder_sliding_window_infer_overlap = autoencoder_sliding_window_infer_overlap # quality check args - self.max_try_time = 3 # if not pass quality check, will try self.max_try_time times + self.max_try_time = 2 # if not pass quality check, will try self.max_try_time times with open(real_img_median_statistics, "r") as json_file: self.median_statistics = json.load(json_file) self.label_int_dict = { @@ -829,7 +829,7 @@ def select_mask(self, candidate_mask_files, num_img): selected_mask_files = [] random.shuffle(candidate_mask_files) - for n in range(num_img * self.max_try_time): + for n in range(len(candidate_mask_files)): mask_file = candidate_mask_files[n % len(candidate_mask_files)] selected_mask_files.append({"mask_file": mask_file, "if_aug": True}) return selected_mask_files @@ -1103,7 +1103,7 @@ def find_closest_masks(self, num_img): new_candidates.append((c, diff)) # choose top-2*num_img candidates (at least 5) - num_candidates = max(2 * num_img, 5) + num_candidates = max(self.max_try_time * num_img, 5) new_candidates = sorted(new_candidates, key=lambda x: x[1]) final_candidates = [] From c8e730ca47c106d94c28077d791ad8b547e04a3f Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 13 Mar 2025 21:10:10 +0000 Subject: [PATCH 32/59] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- generation/maisi/scripts/sample.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/generation/maisi/scripts/sample.py b/generation/maisi/scripts/sample.py index b159232db..e7441b5e0 100644 --- a/generation/maisi/scripts/sample.py +++ b/generation/maisi/scripts/sample.py @@ -269,7 +269,7 @@ def ldm_conditional_sample_one_image( noise_scheduler.set_timesteps(num_inference_steps=num_inference_steps) if isinstance(noise_scheduler, DDPMScheduler) and num_inference_steps < noise_scheduler.num_train_timesteps: - warnings.warn( + warnings.warn( "**************************************************************\n" "* WARNING: Image noise_scheduler is a DDPMScheduler.\n" "* We expect num_inference_steps = noise_scheduler.num_train_timesteps" From 8680ae8738e0fff56abf00aee50b36dccc40c716 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Thu, 13 Mar 2025 21:15:43 +0000 Subject: [PATCH 33/59] readme Signed-off-by: Can-Zhao --- generation/maisi/README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/generation/maisi/README.md b/generation/maisi/README.md index 7a95d27ca..208b408c1 100644 --- a/generation/maisi/README.md +++ b/generation/maisi/README.md @@ -177,6 +177,9 @@ export MONAI_DATA_DIRECTORY= python -m scripts.inference -c ./configs/config_maisi3d-rflow.json -i ./configs/config_infer.json -e ./configs/environment_maisi3d-rflow.json --random-seed 0 --version maisi3d-rflow ``` +If GPU OOM happens, please increase `autoencoder_tp_num_splits` or reduce `autoencoder_sliding_window_infer_size` in `./configs/config_infer.json`. +To reduce time cost, please reduce `autoencoder_sliding_window_infer_overlap` in `./configs/config_infer.json`, while monitoring whether stitching artifact occurs. + Please refer to [maisi_inference_tutorial.ipynb](maisi_inference_tutorial.ipynb) for the tutorial for MAISI model inference. From 719d050b182acdeb67ece7a3f7f7ddb3c526eb27 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Fri, 14 Mar 2025 16:30:45 +0000 Subject: [PATCH 34/59] train for rflow tested Signed-off-by: Can-Zhao --- .../maisi_train_controlnet_tutorial.ipynb | 89 ++++++++------- .../maisi_train_diff_unet_tutorial.ipynb | 106 +++++++++--------- 2 files changed, 101 insertions(+), 94 deletions(-) diff --git a/generation/maisi/maisi_train_controlnet_tutorial.ipynb b/generation/maisi/maisi_train_controlnet_tutorial.ipynb index 62a0a26f9..565adb7cd 100644 --- a/generation/maisi/maisi_train_controlnet_tutorial.ipynb +++ b/generation/maisi/maisi_train_controlnet_tutorial.ipynb @@ -67,11 +67,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "MONAI version: 1.4.1rc1+31.g90de55b1\n", + "MONAI version: 1.4.1rc1+32.g34f37973\n", "Numpy version: 1.26.4\n", "Pytorch version: 2.5.0+cu124\n", "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n", - "MONAI rev id: 90de55b1340ffc8eccf960d111229535bcb2f81d\n", + "MONAI rev id: 34f379735c5e18e7f809453eb1b3606c225c788b\n", "MONAI __file__: /localhome//.local/lib/python3.10/site-packages/monai/__init__.py\n", "\n", "Optional dependencies:\n", @@ -141,12 +141,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-12 22:27:22.838][ INFO](notebook) - MAISI version is maisi3d-ddpm, whether to use body_region is True\n" + "[2025-03-14 16:29:13.938][ INFO](notebook) - MAISI version is maisi3d-rflow, whether to use body_region is False\n" ] } ], "source": [ - "maisi_version = \"maisi3d-ddpm\"\n", + "maisi_version = \"maisi3d-rflow\"\n", "if maisi_version == \"maisi3d-ddpm\":\n", " model_def_path = \"./configs/config_maisi3d-ddpm.json\"\n", "elif maisi_version == \"maisi3d-rflow\":\n", @@ -248,9 +248,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-12 22:27:25.046][ INFO](notebook) - Generated simulated images.\n", - "[2025-03-12 22:27:25.047][ INFO](notebook) - img_emb shape: (64, 64, 32, 4)\n", - "[2025-03-12 22:27:25.048][ INFO](notebook) - label shape: (256, 256, 128)\n" + "[2025-03-14 16:29:13.952][ INFO](notebook) - Save data list json file to ./temp_work_dir_controlnet_train_demo/sim_datalist.json\n", + "[2025-03-14 16:29:16.033][ INFO](notebook) - Generated simulated images.\n", + "[2025-03-14 16:29:16.034][ INFO](notebook) - img_emb shape: (64, 64, 32, 4)\n", + "[2025-03-14 16:29:16.035][ INFO](notebook) - label shape: (256, 256, 128)\n" ] } ], @@ -266,6 +267,7 @@ "datalist_file = os.path.join(work_dir, \"sim_datalist.json\")\n", "with open(datalist_file, \"w\") as f:\n", " json.dump(sim_datalist, f, indent=4)\n", + "logger.info(f\"Save data list json file to {datalist_file}\")\n", "\n", "for d in sim_datalist[\"training\"]:\n", " # The image embedding is downsampled twice by Autoencoder.\n", @@ -322,8 +324,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-12 22:27:25.062][ INFO](notebook) - files and folders under work_dir: ['config_maisi.json', 'models', 'config_maisi_controlnet_train.json', 'outputs', 'sim_dataroot', 'environment_maisi_controlnet_train.json', 'sim_datalist.json'].\n", - "[2025-03-12 22:27:25.063][ INFO](notebook) - number of GPUs: 1.\n" + "[2025-03-14 16:29:16.049][ INFO](notebook) - files and folders under work_dir: ['config_maisi.json', 'models', 'config_maisi_controlnet_train.json', 'outputs', 'sim_dataroot', '.ipynb_checkpoints', 'environment_maisi_controlnet_train.json', 'sim_datalist.json'].\n", + "[2025-03-14 16:29:16.050][ INFO](notebook) - number of GPUs: 1.\n" ] } ], @@ -468,29 +470,30 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-12 22:27:25.074][ INFO](notebook) - Training the model...\n" + "[2025-03-14 16:29:16.061][ INFO](notebook) - Training the model...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[2025-03-12 22:27:33.707][ INFO](maisi.controlnet.training) - Number of GPUs: 8\n", - "[2025-03-12 22:27:33.708][ INFO](maisi.controlnet.training) - World_size: 1\n", - "[2025-03-12 22:27:35.410][ INFO](maisi.controlnet.training) - trained diffusion model is not loaded.\n", - "[2025-03-12 22:27:35.410][ INFO](maisi.controlnet.training) - set scale_factor -> 1.0.\n", - "2025-03-12 22:27:35,902 - INFO - 'dst' model updated: 158 of 206 variables.\n", - "[2025-03-12 22:27:35.907][ INFO](maisi.controlnet.training) - train controlnet model from scratch.\n", - "[2025-03-12 22:27:35.930][ INFO](maisi.controlnet.training) - total number of training steps: 4.0.\n", - "[2025-03-12 22:27:38.006][ INFO](maisi.controlnet.training) -\n", - "[Epoch 1/2] [Batch 1/2] [LR: 0.00000563] [loss: 0.7976] ETA: 0:00:02.073507\n", - "[2025-03-12 22:27:38.147][ INFO](maisi.controlnet.training) -\n", - "[Epoch 1/2] [Batch 2/2] [LR: 0.00000250] [loss: 0.7985] ETA: 0:00:00\n", - "[2025-03-12 22:27:38.683][ INFO](maisi.controlnet.training) - best loss -> 0.7980280518531799.\n", - "[2025-03-12 22:27:39.955][ INFO](maisi.controlnet.training) -\n", - "[Epoch 2/2] [Batch 1/2] [LR: 0.00000063] [loss: 0.7992] ETA: 0:00:01.807460\n", - "[2025-03-12 22:27:40.086][ INFO](maisi.controlnet.training) -\n", - "[Epoch 2/2] [Batch 2/2] [LR: 0.00000000] [loss: 0.7980] ETA: 0:00:00\n", + "[2025-03-14 16:29:23.336][ INFO](maisi.controlnet.training) - Number of GPUs: 8\n", + "[2025-03-14 16:29:23.336][ INFO](maisi.controlnet.training) - World_size: 1\n", + "[2025-03-14 16:29:24.771][ INFO](maisi.controlnet.training) - trained diffusion model is not loaded.\n", + "[2025-03-14 16:29:24.771][ INFO](maisi.controlnet.training) - set scale_factor -> 1.0.\n", + "2025-03-14 16:29:25,271 - INFO - 'dst' model updated: 180 of 231 variables.\n", + "[2025-03-14 16:29:25.277][ INFO](maisi.controlnet.training) - train controlnet model from scratch.\n", + "[2025-03-14 16:29:25.300][ INFO](maisi.controlnet.training) - total number of training steps: 4.0.\n", + "[2025-03-14 16:29:26.826][ INFO](maisi.controlnet.training) -\n", + "[Epoch 1/2] [Batch 1/2] [LR: 0.00000563] [loss: 0.8278] ETA: 0:00:01.523338\n", + "[2025-03-14 16:29:26.974][ INFO](maisi.controlnet.training) -\n", + "[Epoch 1/2] [Batch 2/2] [LR: 0.00000250] [loss: 0.8289] ETA: 0:00:00\n", + "[2025-03-14 16:29:27.585][ INFO](maisi.controlnet.training) - best loss -> 0.8283329606056213.\n", + "[2025-03-14 16:29:28.909][ INFO](maisi.controlnet.training) -\n", + "[Epoch 2/2] [Batch 1/2] [LR: 0.00000063] [loss: 0.8288] ETA: 0:00:01.934548\n", + "[2025-03-14 16:29:29.052][ INFO](maisi.controlnet.training) -\n", + "[Epoch 2/2] [Batch 2/2] [LR: 0.00000000] [loss: 0.8277] ETA: 0:00:00\n", + "[2025-03-14 16:29:29.716][ INFO](maisi.controlnet.training) - best loss -> 0.8282470703125.\n", "\n" ] } @@ -533,32 +536,32 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-12 22:27:42.632][ INFO](notebook) - Inference...\n" + "[2025-03-14 16:29:32.229][ INFO](notebook) - Inference...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[2025-03-12 22:27:53.399][ INFO](maisi.controlnet.infer) - Number of GPUs: 8\n", - "[2025-03-12 22:27:53.400][ INFO](maisi.controlnet.infer) - World_size: 1\n", - "[2025-03-12 22:27:54.101][ INFO](maisi.controlnet.infer) - trained autoencoder model is not loaded.\n", - "[2025-03-12 22:27:55.286][ INFO](maisi.controlnet.infer) - trained diffusion model is not loaded.\n", - "[2025-03-12 22:27:55.286][ INFO](maisi.controlnet.infer) - set scale_factor -> 1.0.\n", - "2025-03-12 22:27:55,756 - INFO - 'dst' model updated: 158 of 206 variables.\n", - "[2025-03-12 22:27:55.761][ INFO](maisi.controlnet.infer) - trained controlnet is not loaded.\n", - "[2025-03-12 22:27:56.340][ INFO](root) - `controllable_anatomy_size` is not provided.\n", - "[2025-03-12 22:27:56.344][ INFO](root) - ---- Start generating latent features... ----\n", - "[2025-03-12 22:27:58.065][ INFO](root) - ---- Latent features generation time: 1.7215001583099365 seconds ----\n", - "[2025-03-12 22:27:58.066][ INFO](root) - ---- Start decoding latent features into images... ----\n", - "[2025-03-12 22:27:58.838][ INFO](root) - ---- Image decoding time: 0.7712326049804688 seconds ----\n", - "2025-03-12 22:27:59,142 INFO image_writer.py:197 - writing: temp_work_dir_controlnet_train_demo/outputs/sample_20250312_222759_124463_image.nii.gz\n", - "2025-03-12 22:27:59,487 INFO image_writer.py:197 - writing: temp_work_dir_controlnet_train_demo/outputs/sample_20250312_222759_124463_label.nii.gz\n", + "[2025-03-14 16:29:39.519][ INFO](maisi.controlnet.infer) - Number of GPUs: 8\n", + "[2025-03-14 16:29:39.519][ INFO](maisi.controlnet.infer) - World_size: 1\n", + "[2025-03-14 16:29:39.990][ INFO](maisi.controlnet.infer) - trained autoencoder model is not loaded.\n", + "[2025-03-14 16:29:41.213][ INFO](maisi.controlnet.infer) - trained diffusion model is not loaded.\n", + "[2025-03-14 16:29:41.213][ INFO](maisi.controlnet.infer) - set scale_factor -> 1.0.\n", + "2025-03-14 16:29:41,716 - INFO - 'dst' model updated: 180 of 231 variables.\n", + "[2025-03-14 16:29:41.721][ INFO](maisi.controlnet.infer) - trained controlnet is not loaded.\n", + "[2025-03-14 16:29:42.102][ INFO](root) - `controllable_anatomy_size` is not provided.\n", + "[2025-03-14 16:29:42.104][ INFO](root) - ---- Start generating latent features... ----\n", + "[2025-03-14 16:29:42.670][ INFO](root) - ---- DM/ControlNet Latent features generation time: 0.565190315246582 seconds ----\n", + "[2025-03-14 16:29:42.672][ INFO](root) - ---- Start decoding latent features into images... ----\n", + "[2025-03-14 16:29:43.314][ INFO](root) - ---- Image VAE decoding time: 0.6416211128234863 seconds ----\n", + "2025-03-14 16:29:43,602 INFO image_writer.py:197 - writing: temp_work_dir_controlnet_train_demo/outputs/sample_20250314_162943_586788_image.nii.gz\n", + "2025-03-14 16:29:43,940 INFO image_writer.py:197 - writing: temp_work_dir_controlnet_train_demo/outputs/sample_20250314_162943_586788_label.nii.gz\n", "\n", "\n", " 0%| | 0/1 [00:00/.local/lib/python3.10/site-packages/monai/__init__.py\n", "\n", "Optional dependencies:\n", @@ -143,12 +143,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-12 22:14:29.106][ INFO](notebook) - MAISI version is maisi3d-ddpm, whether to use body_region is True\n" + "[2025-03-14 16:14:21.679][ INFO](notebook) - MAISI version is maisi3d-rflow, whether to use body_region is False\n" ] } ], "source": [ - "maisi_version = \"maisi3d-ddpm\"\n", + "maisi_version = \"maisi3d-rflow\"\n", "if maisi_version == \"maisi3d-ddpm\":\n", " model_def_path = \"./configs/config_maisi3d-ddpm.json\"\n", "elif maisi_version == \"maisi3d-rflow\":\n", @@ -205,7 +205,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-12 22:14:29.751][ INFO](notebook) - Generated simulated images.\n" + "[2025-03-14 16:14:22.301][ INFO](notebook) - Generated simulated images.\n" ] } ], @@ -252,8 +252,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-12 22:14:29.764][ INFO](notebook) - files and folders under work_dir: ['predictions', 'config_maisi.json', 'models', 'sim_dataroot', 'config_maisi_diff_model.json', 'embeddings', 'environment_maisi_diff_model.json', 'sim_datalist.json'].\n", - "[2025-03-12 22:14:29.765][ INFO](notebook) - number of GPUs: 1.\n" + "[2025-03-14 16:14:22.313][ INFO](notebook) - files and folders under work_dir: ['predictions', 'config_maisi.json', 'models', 'sim_dataroot', 'config_maisi_diff_model.json', 'embeddings', 'environment_maisi_diff_model.json', 'sim_datalist.json'].\n", + "[2025-03-14 16:14:22.314][ INFO](notebook) - number of GPUs: 1.\n" ] } ], @@ -388,7 +388,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-12 22:14:29.776][ INFO](notebook) - Creating training data...\n" + "[2025-03-14 16:14:22.326][ INFO](notebook) - Creating training data...\n" ] }, { @@ -396,8 +396,8 @@ "output_type": "stream", "text": [ "\n", - "[2025-03-12 22:14:37.725][ INFO](creating training data) - Using device cuda:0\n", - "[2025-03-12 22:14:38.727][ INFO](creating training data) - filenames_raw: ['tr_image_001.nii.gz', 'tr_image_002.nii.gz']\n", + "[2025-03-14 16:14:29.646][ INFO](creating training data) - Using device cuda:0\n", + "[2025-03-14 16:14:30.160][ INFO](creating training data) - filenames_raw: ['tr_image_001.nii.gz', 'tr_image_002.nii.gz']\n", "\n" ] } @@ -441,9 +441,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-12 22:14:40.827][ INFO](notebook) - data: {'dim': (64, 64, 32), 'spacing': [0.875, 0.875, 0.75], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}.\n", - "[2025-03-12 22:14:40.830][ INFO](notebook) - data: {'dim': (64, 64, 32), 'spacing': [0.875, 0.875, 0.75], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}.\n", - "[2025-03-12 22:14:40.832][ INFO](notebook) - Completed creating .json files for all embedding files.\n" + "[2025-03-14 16:14:32.560][ INFO](notebook) - data: {'dim': (64, 64, 32), 'spacing': [0.875, 0.875, 0.75]}.\n", + "[2025-03-14 16:14:32.562][ INFO](notebook) - Save json file to ./temp_work_dir/./embeddings/tr_image_001_emb.nii.gz.json\n", + "[2025-03-14 16:14:32.563][ INFO](notebook) - data: {'dim': (64, 64, 32), 'spacing': [0.875, 0.875, 0.75]}.\n", + "[2025-03-14 16:14:32.564][ INFO](notebook) - Save json file to ./temp_work_dir/./embeddings/tr_image_002_emb.nii.gz.json\n", + "[2025-03-14 16:14:32.565][ INFO](notebook) - Completed creating .json files for all embedding files.\n" ] } ], @@ -487,6 +489,7 @@ " # Write the dictionary to the .json file\n", " with open(json_filename, \"w\") as json_file:\n", " json.dump(data, json_file, indent=4)\n", + " logger.info(f\"Save json file to {json_filename}\")\n", "\n", "\n", "folder_path = env_config_out[\"embedding_base_dir\"]\n", @@ -520,7 +523,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-12 22:14:40.838][ INFO](notebook) - Training the model...\n" + "[2025-03-14 16:14:32.570][ INFO](notebook) - Training the model...\n" ] }, { @@ -528,26 +531,26 @@ "output_type": "stream", "text": [ "\n", - "[2025-03-12 22:14:49.047][ INFO](training) - Using cuda:0 of 1\n", - "[2025-03-12 22:14:49.048][ INFO](training) - [config] ckpt_folder -> ./temp_work_dir/./models.\n", - "[2025-03-12 22:14:49.048][ INFO](training) - [config] data_root -> ./temp_work_dir/./embeddings.\n", - "[2025-03-12 22:14:49.048][ INFO](training) - [config] data_list -> ./temp_work_dir/sim_datalist.json.\n", - "[2025-03-12 22:14:49.048][ INFO](training) - [config] lr -> 0.0001.\n", - "[2025-03-12 22:14:49.048][ INFO](training) - [config] num_epochs -> 2.\n", - "[2025-03-12 22:14:49.048][ INFO](training) - [config] num_train_timesteps -> 1000.\n", - "[2025-03-12 22:14:50.943][ INFO](training) - Training from scratch.\n", - "[2025-03-12 22:14:50.961][ INFO](training) - num_files_train: 2\n", - "[2025-03-12 22:14:51.403][ INFO](training) - Scaling factor set to 1.159693956375122.\n", - "[2025-03-12 22:14:51.403][ INFO](training) - scale_factor -> 1.159693956375122.\n", - "[2025-03-12 22:14:51.405][ INFO](training) - torch.set_float32_matmul_precision -> highest.\n", - "[2025-03-12 22:14:51.405][ INFO](training) - Epoch 1, lr 0.0001.\n", - "[2025-03-12 22:14:53.068][ INFO](training) - [2025-03-12 22:14:53] epoch 1, iter 1/2, loss: 0.7966, lr: 0.000100000000.\n", - "[2025-03-12 22:14:53.363][ INFO](training) - [2025-03-12 22:14:53] epoch 1, iter 2/2, loss: 0.7920, lr: 0.000056250000.\n", - "[2025-03-12 22:14:53.414][ INFO](training) - epoch 1 average loss: 0.7943.\n", - "[2025-03-12 22:14:54.826][ INFO](training) - Epoch 2, lr 2.5e-05.\n", - "[2025-03-12 22:14:55.220][ INFO](training) - [2025-03-12 22:14:55] epoch 2, iter 1/2, loss: 0.7901, lr: 0.000025000000.\n", - "[2025-03-12 22:14:55.326][ INFO](training) - [2025-03-12 22:14:55] epoch 2, iter 2/2, loss: 0.7977, lr: 0.000006250000.\n", - "[2025-03-12 22:14:55.385][ INFO](training) - epoch 2 average loss: 0.7939.\n", + "[2025-03-14 16:14:39.869][ INFO](training) - Using cuda:0 of 1\n", + "[2025-03-14 16:14:39.869][ INFO](training) - [config] ckpt_folder -> ./temp_work_dir/./models.\n", + "[2025-03-14 16:14:39.869][ INFO](training) - [config] data_root -> ./temp_work_dir/./embeddings.\n", + "[2025-03-14 16:14:39.869][ INFO](training) - [config] data_list -> ./temp_work_dir/sim_datalist.json.\n", + "[2025-03-14 16:14:39.869][ INFO](training) - [config] lr -> 0.0001.\n", + "[2025-03-14 16:14:39.869][ INFO](training) - [config] num_epochs -> 2.\n", + "[2025-03-14 16:14:39.869][ INFO](training) - [config] num_train_timesteps -> 1000.\n", + "[2025-03-14 16:14:41.316][ INFO](training) - Training from scratch.\n", + "[2025-03-14 16:14:41.337][ INFO](training) - num_files_train: 2\n", + "[2025-03-14 16:14:41.634][ INFO](training) - Scaling factor set to 1.159693956375122.\n", + "[2025-03-14 16:14:41.634][ INFO](training) - scale_factor -> 1.159693956375122.\n", + "[2025-03-14 16:14:41.637][ INFO](training) - torch.set_float32_matmul_precision -> highest.\n", + "[2025-03-14 16:14:41.637][ INFO](training) - Epoch 1, lr 0.0001.\n", + "[2025-03-14 16:14:42.627][ INFO](training) - [2025-03-14 16:14:42] epoch 1, iter 1/2, loss: 1.1344, lr: 0.000100000000.\n", + "[2025-03-14 16:14:42.739][ INFO](training) - [2025-03-14 16:14:42] epoch 1, iter 2/2, loss: 1.1275, lr: 0.000056250000.\n", + "[2025-03-14 16:14:42.783][ INFO](training) - epoch 1 average loss: 1.1310.\n", + "[2025-03-14 16:14:44.540][ INFO](training) - Epoch 2, lr 2.5e-05.\n", + "[2025-03-14 16:14:44.981][ INFO](training) - [2025-03-14 16:14:44] epoch 2, iter 1/2, loss: 1.1254, lr: 0.000025000000.\n", + "[2025-03-14 16:14:45.106][ INFO](training) - [2025-03-14 16:14:45] epoch 2, iter 2/2, loss: 1.1201, lr: 0.000006250000.\n", + "[2025-03-14 16:14:45.177][ INFO](training) - epoch 2 average loss: 1.1227.\n", "\n" ] } @@ -591,8 +594,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-12 22:14:58.601][ INFO](notebook) - Running inference...\n", - "[2025-03-12 22:15:16.609][ INFO](notebook) - Completed all steps.\n" + "[2025-03-14 16:14:49.136][ INFO](notebook) - Running inference...\n", + "[2025-03-14 16:15:02.647][ INFO](notebook) - Completed all steps.\n" ] }, { @@ -600,23 +603,24 @@ "output_type": "stream", "text": [ "\n", - "[2025-03-12 22:15:08.281][ INFO](inference) - Using cuda:0 of 1 with random seed: 22762\n", - "[2025-03-12 22:15:08.281][ INFO](inference) - [config] ckpt_filepath -> ./temp_work_dir/./models/diff_unet_ckpt.pt.\n", - "[2025-03-12 22:15:08.281][ INFO](inference) - [config] random_seed -> 22762.\n", - "[2025-03-12 22:15:08.282][ INFO](inference) - [config] output_prefix -> unet_3d.\n", - "[2025-03-12 22:15:08.282][ INFO](inference) - [config] output_size -> (256, 256, 128).\n", - "[2025-03-12 22:15:08.282][ INFO](inference) - [config] out_spacing -> (1.0, 1.0, 0.75).\n", - "[2025-03-12 22:15:08.282][ INFO](root) - `controllable_anatomy_size` is not provided.\n", - "[2025-03-12 22:15:11.078][ INFO](inference) - checkpoints ./temp_work_dir/./models/diff_unet_ckpt.pt loaded.\n", - "[2025-03-12 22:15:11.080][ INFO](inference) - scale_factor -> 1.159693956375122.\n", - "[2025-03-12 22:15:11.081][ INFO](inference) - num_downsample_level -> 4, divisor -> 4.\n", - "[2025-03-12 22:15:11.083][ INFO](inference) - noise: cuda:0, torch.float32, \n", + "[2025-03-14 16:14:56.275][ INFO](inference) - Using cuda:0 of 1 with random seed: 59473\n", + "[2025-03-14 16:14:56.275][ INFO](inference) - [config] ckpt_filepath -> ./temp_work_dir/./models/diff_unet_ckpt.pt.\n", + "[2025-03-14 16:14:56.275][ INFO](inference) - [config] random_seed -> 59473.\n", + "[2025-03-14 16:14:56.275][ INFO](inference) - [config] output_prefix -> unet_3d.\n", + "[2025-03-14 16:14:56.275][ INFO](inference) - [config] output_size -> (256, 256, 128).\n", + "[2025-03-14 16:14:56.275][ INFO](inference) - [config] out_spacing -> (1.0, 1.0, 0.75).\n", + "[2025-03-14 16:14:56.275][ INFO](root) - `controllable_anatomy_size` is not provided.\n", + "[2025-03-14 16:14:58.525][ INFO](inference) - checkpoints ./temp_work_dir/./models/diff_unet_ckpt.pt loaded.\n", + "[2025-03-14 16:14:58.527][ INFO](inference) - scale_factor -> 1.159693956375122.\n", + "[2025-03-14 16:14:58.528][ INFO](inference) - num_downsample_level -> 4, divisor -> 4.\n", + "[2025-03-14 16:14:58.536][ INFO](inference) - noise: cuda:0, torch.float32, \n", "\n", " 0%| | 0/10 [00:00 Date: Fri, 14 Mar 2025 16:44:08 +0000 Subject: [PATCH 35/59] readme Signed-off-by: Can-Zhao --- generation/maisi/README.md | 27 +++++++++++++++++++++++++-- 1 file changed, 25 insertions(+), 2 deletions(-) diff --git a/generation/maisi/README.md b/generation/maisi/README.md index 208b408c1..38f0570c8 100644 --- a/generation/maisi/README.md +++ b/generation/maisi/README.md @@ -2,14 +2,24 @@ This example demonstrates the applications of training and validating NVIDIA MAISI, a 3D Latent Diffusion Model (LDM) capable of generating large CT images accompanied by corresponding segmentation masks. It supports variable volume size and voxel spacing and allows for the precise control of organ/tumor size. ## MAISI Model Highlight +`[Initial Version (August 2024)]:` First release `'maisi3d-ddpm'`. + - A Foundation Variational Auto-Encoder (VAE) model for latent feature compression that works for both CT and MRI with flexible volume size and voxel size. Tensor parallel is included to reduce GPU memory usage. - A Foundation Diffusion model that can generate large CT volumes up to 512 × 512 × 768 size, with flexible volume size and voxel size - A ControlNet to generate image/mask pairs that can improve downstream tasks, with controllable organ/tumor size More details can be found in our WACV 2025 paper: -[Guo, P., Zhao, C., Yang, D., Xu, Z., Nath, V., Tang, Y., ... & Xu, D. (2024). MAISI: Medical AI for Synthetic Imaging. arXiv preprint arXiv:2409.11169](https://arxiv.org/pdf/2409.11169) -Welcome to try our GUI demo at [https://build.nvidia.com/nvidia/maisi](https://build.nvidia.com/nvidia/maisi). +[1] [Guo, P., Zhao, C., Yang, D., Xu, Z., Nath, V., Tang, Y., ... & Xu, D. (2024). MAISI: Medical AI for Synthetic Imaging. WACV 2025](https://arxiv.org/pdf/2409.11169) + +`[Release Note (March 2025)]:` We are excited to announce the new MAISI Version `'maisi3d-rflow'`. Compared with the previous version `'maisi3d-ddpm'`, it accelerated latent diffusion model inference by 33x. The differences are: +- The maisi version `'maisi3d-ddpm'` uses basic noise scheduler DDPM. `'maisi3d-rflow'` uses Rectified Flow scheduler [2]. The diffusion model inference can be 33 times faster. +- The maisi version `'maisi3d-ddpm'` requires training images to be labeled with body regions (`"top_region_index"` and `"bottom_region_index"`), while `'maisi3d-rflow'` does not have such requirement. In other words, it is easier to prepare training data for `'maisi3d-rflow'`. +- For the released model weights, `'maisi3d-rflow'` can generate images with better quality for head region and small output volumes, and comparable quality for other cases compared with `'maisi3d-ddpm'`. + +[2] [Liu, Xingchao, Chengyue Gong, and Qiang Liu. "Flow straight and fast: Learning to generate and transfer data with rectified flow." ICLR 2023](https://arxiv.org/pdf/2209.03003). + +`[GUI demo]:` Welcome to try our GUI demo at [https://build.nvidia.com/nvidia/maisi](https://build.nvidia.com/nvidia/maisi). The GUI is only a demo for toy examples. This Github repo is the full version. @@ -246,6 +256,10 @@ To train with a single GPU, please run: python -m scripts.train_controlnet -c ./configs/config_maisi3d-ddpm.json -t ./configs/config_maisi_controlnet_train.json -e ./configs/environment_maisi_controlnet_train.json -g 1 ``` +```bash +python -m scripts.train_controlnet -c ./configs/config_maisi3d-rflow.json -t ./configs/config_maisi_controlnet_train.json -e ./configs/environment_maisi_controlnet_train.json -g 1 +``` + The training script also enables multi-GPU training. For instance, if you are using eight GPUs, you can run the training script with the following command: ```bash export NUM_GPUS_PER_NODE=8 @@ -255,6 +269,15 @@ torchrun \ --master_addr=localhost --master_port=1234 \ -m scripts.train_controlnet -c ./configs/config_maisi3d-ddpm.json -t ./configs/config_maisi_controlnet_train.json -e ./configs/environment_maisi_controlnet_train.json -g ${NUM_GPUS_PER_NODE} ``` + +```bash +export NUM_GPUS_PER_NODE=8 +torchrun \ + --nproc_per_node=${NUM_GPUS_PER_NODE} \ + --nnodes=1 \ + --master_addr=localhost --master_port=1234 \ + -m scripts.train_controlnet -c ./configs/config_maisi3d-rflow.json -t ./configs/config_maisi_controlnet_train.json -e ./configs/environment_maisi_controlnet_train.json -g ${NUM_GPUS_PER_NODE} +``` Please also check [maisi_train_controlnet_tutorial.ipynb](./maisi_train_controlnet_tutorial.ipynb) for more details about data preparation and training parameters. ### 4. License From 5cff0a4168f2402b8602b8a84f873bc90235b53f Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Fri, 14 Mar 2025 16:54:06 +0000 Subject: [PATCH 36/59] readme Signed-off-by: Can-Zhao --- generation/maisi/README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/generation/maisi/README.md b/generation/maisi/README.md index 38f0570c8..e2e1935a1 100644 --- a/generation/maisi/README.md +++ b/generation/maisi/README.md @@ -2,7 +2,7 @@ This example demonstrates the applications of training and validating NVIDIA MAISI, a 3D Latent Diffusion Model (LDM) capable of generating large CT images accompanied by corresponding segmentation masks. It supports variable volume size and voxel spacing and allows for the precise control of organ/tumor size. ## MAISI Model Highlight -`[Initial Version (August 2024)]:` First release `'maisi3d-ddpm'`. +**Initial Version (August 2024):** First release `'maisi3d-ddpm'`. - A Foundation Variational Auto-Encoder (VAE) model for latent feature compression that works for both CT and MRI with flexible volume size and voxel size. Tensor parallel is included to reduce GPU memory usage. - A Foundation Diffusion model that can generate large CT volumes up to 512 × 512 × 768 size, with flexible volume size and voxel size @@ -10,16 +10,14 @@ This example demonstrates the applications of training and validating NVIDIA MAI More details can be found in our WACV 2025 paper: -[1] [Guo, P., Zhao, C., Yang, D., Xu, Z., Nath, V., Tang, Y., ... & Xu, D. (2024). MAISI: Medical AI for Synthetic Imaging. WACV 2025](https://arxiv.org/pdf/2409.11169) +[Guo, P., Zhao, C., Yang, D., Xu, Z., Nath, V., Tang, Y., ... & Xu, D. (2024). MAISI: Medical AI for Synthetic Imaging. WACV 2025](https://arxiv.org/pdf/2409.11169) -`[Release Note (March 2025)]:` We are excited to announce the new MAISI Version `'maisi3d-rflow'`. Compared with the previous version `'maisi3d-ddpm'`, it accelerated latent diffusion model inference by 33x. The differences are: -- The maisi version `'maisi3d-ddpm'` uses basic noise scheduler DDPM. `'maisi3d-rflow'` uses Rectified Flow scheduler [2]. The diffusion model inference can be 33 times faster. +**Release Note (March 2025):** We are excited to announce the new MAISI Version `'maisi3d-rflow'`. Compared with the previous version `'maisi3d-ddpm'`, it accelerated latent diffusion model inference by 33x. The differences are: +- The maisi version `'maisi3d-ddpm'` uses basic noise scheduler DDPM. `'maisi3d-rflow'` uses Rectified Flow scheduler. The diffusion model inference can be 33 times faster. - The maisi version `'maisi3d-ddpm'` requires training images to be labeled with body regions (`"top_region_index"` and `"bottom_region_index"`), while `'maisi3d-rflow'` does not have such requirement. In other words, it is easier to prepare training data for `'maisi3d-rflow'`. - For the released model weights, `'maisi3d-rflow'` can generate images with better quality for head region and small output volumes, and comparable quality for other cases compared with `'maisi3d-ddpm'`. -[2] [Liu, Xingchao, Chengyue Gong, and Qiang Liu. "Flow straight and fast: Learning to generate and transfer data with rectified flow." ICLR 2023](https://arxiv.org/pdf/2209.03003). - -`[GUI demo]:` Welcome to try our GUI demo at [https://build.nvidia.com/nvidia/maisi](https://build.nvidia.com/nvidia/maisi). +**GUI demo:** Welcome to try our GUI demo at [https://build.nvidia.com/nvidia/maisi](https://build.nvidia.com/nvidia/maisi). The GUI is only a demo for toy examples. This Github repo is the full version. @@ -131,6 +129,8 @@ MAISI is based on the following papers: [**ControlNet:** Lvmin Zhang, Anyi Rao, Maneesh Agrawala; “Adding Conditional Control to Text-to-Image Diffusion Models.” ICCV 2023.](https://openaccess.thecvf.com/content/ICCV2023/papers/Zhang_Adding_Conditional_Control_to_Text-to-Image_Diffusion_Models_ICCV_2023_paper.pdf) +[**Rectified Flow:** Liu, Xingchao, Chengyue Gong, and Qiang Liu. "Flow straight and fast: Learning to generate and transfer data with rectified flow." ICLR 2023](https://arxiv.org/pdf/2209.03003). + ### 1. Network Definition Network definition is stored in [./configs/config_maisi.json](./configs/config_maisi.json). Training and inference should use the same [./configs/config_maisi.json](./configs/config_maisi.json). @@ -142,7 +142,7 @@ The information for the inference input, such as the body region and anatomy to - `"spacing"`: The voxel size of the generated images. For example, if set to `[1.5, 1.5, 2.0]`, it generates images with a resolution of 1.5x1.5x2.0 mm. - `"output_size"`: The volume size of the generated images. For example, if set to `[512, 512, 256]`, it generates images of size 512x512x256. The values must be divisible by 16. If GPU memory is limited, adjust these to smaller numbers. Note that `"spacing"` and `"output_size"` together determine the output field of view (FOV). For example, if set to `[1.5, 1.5, 2.0]` mm and `[512, 512, 256]`, the FOV is 768x768x512 mm. We recommend the FOV in the x and y axes to be at least 256 mm for the head and at least 384 mm for other body regions like the abdomen. There is no restriction for the z-axis. - `"controllable_anatomy_size"`: A list specifying controllable anatomy and their size scale (0–1). For example, if set to `[["liver", 0.5], ["hepatic tumor", 0.3]]`, the generated image will contain a liver of median size (around the 50th percentile) and a relatively small hepatic tumor (around the 30th percentile). The output will include paired images and segmentation masks for the controllable anatomy. -- `"body_region"`: If `"controllable_anatomy_size"` is not specified, `"body_region"` will constrain the region of the generated images. It must be chosen from `"head"`, `"chest"`, `"thorax"`, `"abdomen"`, `"pelvis"`, or `"lower"`. Please set a reasonable `"body_region"` for the given FOV determined by `"spacing"` and `"output_size"`. For example, if FOV is only 128mm in z-axis, we should not expect `"body_region"` to contain all of [`"head"`, `"chest"`, `"thorax"`, `"abdomen"`, `"pelvis"`, `"lower"`]. +- `"body_region"`: For `maisi3d_rflow`, it is deprecated and can be set as `[]`. The output body region will be determined by `"anatomy_list"`. For `maisi3d_ddpm`, if `"controllable_anatomy_size"` is not specified, `"body_region"` will constrain the region of the generated images. It must be chosen from `"head"`, `"chest"`, `"thorax"`, `"abdomen"`, `"pelvis"`, or `"lower"`. Please set a reasonable `"body_region"` for the given FOV determined by `"spacing"` and `"output_size"`. For example, if FOV is only 128mm in z-axis, we should not expect `"body_region"` to contain all of [`"head"`, `"chest"`, `"thorax"`, `"abdomen"`, `"pelvis"`, `"lower"`]. - `"anatomy_list"`: If `"controllable_anatomy_size"` is not specified, the output will include paired images and segmentation masks for the anatomy listed in `"./configs/label_dict.json"`. - `"autoencoder_sliding_window_infer_size"`: To save GPU memory, sliding window inference is used when decoding latents into images if `"output_size"` is large. This parameter specifies the patch size of the sliding window. Smaller values reduce GPU memory usage but increase the time cost. The values must be divisible by 16. If GPU memory is sufficient, select a larger value for this parameter. - `"autoencoder_sliding_window_infer_overlap"`: A float between 0 and 1. Larger values reduce stitching artifacts when patches are stitched during sliding window inference but increase the time cost. If you do not observe seam lines in the generated image, you can use a smaller value to save inference time. From b265a9188eb01088ef903bd999b2d429d5121ce0 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Fri, 14 Mar 2025 16:55:55 +0000 Subject: [PATCH 37/59] train rflow Signed-off-by: Can-Zhao --- generation/maisi/scripts/diff_model_infer.py | 3 +-- generation/maisi/scripts/infer_controlnet.py | 2 ++ generation/maisi/scripts/utils.py | 6 +++--- 3 files changed, 6 insertions(+), 5 deletions(-) diff --git a/generation/maisi/scripts/diff_model_infer.py b/generation/maisi/scripts/diff_model_infer.py index f63ecd36b..c3e66612d 100644 --- a/generation/maisi/scripts/diff_model_infer.py +++ b/generation/maisi/scripts/diff_model_infer.py @@ -91,12 +91,11 @@ def prepare_tensors(args: argparse.Namespace, device: torch.device) -> tuple: top_region_index_tensor = np.array(args.diffusion_unet_inference["top_region_index"]).astype(float) * 1e2 bottom_region_index_tensor = np.array(args.diffusion_unet_inference["bottom_region_index"]).astype(float) * 1e2 spacing_tensor = np.array(args.diffusion_unet_inference["spacing"]).astype(float) * 1e2 - modality_tensor = np.array([args.diffusion_unet_inference["modality"]]).astype(int) top_region_index_tensor = torch.from_numpy(top_region_index_tensor[np.newaxis, :]).half().to(device) bottom_region_index_tensor = torch.from_numpy(bottom_region_index_tensor[np.newaxis, :]).half().to(device) spacing_tensor = torch.from_numpy(spacing_tensor[np.newaxis, :]).half().to(device) - modality_tensor = torch.from_numpy(modality_tensor[np.newaxis, :]).long().to(device) + modality_tensor = args.diffusion_unet_inference["modality"]*torch.ones((len(spacing_tensor)),dtype=torch.long).to(device) return top_region_index_tensor, bottom_region_index_tensor, spacing_tensor, modality_tensor diff --git a/generation/maisi/scripts/infer_controlnet.py b/generation/maisi/scripts/infer_controlnet.py index fde17ae3f..d19069cfa 100644 --- a/generation/maisi/scripts/infer_controlnet.py +++ b/generation/maisi/scripts/infer_controlnet.py @@ -161,6 +161,7 @@ def main(): top_region_index_tensor = None bottom_region_index_tensor = None spacing_tensor = batch["spacing"].to(device) + modality_tensor = args.controlnet_infer["modality"]*torch.ones((len(labels),),dtype=torch.long).to(device) out_spacing = tuple((batch["spacing"].squeeze().numpy() / 100).tolist()) # get target dimension dim = batch["dim"] @@ -180,6 +181,7 @@ def main(): top_region_index_tensor=top_region_index_tensor, bottom_region_index_tensor=bottom_region_index_tensor, spacing_tensor=spacing_tensor, + modality_tensor=modality_tensor, latent_shape=latent_shape, output_size=output_size, noise_factor=1.0, diff --git a/generation/maisi/scripts/utils.py b/generation/maisi/scripts/utils.py index 5b5efdfda..d9d9ad002 100644 --- a/generation/maisi/scripts/utils.py +++ b/generation/maisi/scripts/utils.py @@ -306,10 +306,10 @@ def prepare_maisi_controlnet_json_dataloader( LoadImaged(keys=["image", "label"], image_only=True, ensure_channel_first=True), Orientationd(keys=["label"], axcodes="RAS"), EnsureTyped(keys=["label"], dtype=torch.uint8, track_meta=True), - Lambdad(keys="top_region_index", func=lambda x: torch.FloatTensor(x)), - Lambdad(keys="bottom_region_index", func=lambda x: torch.FloatTensor(x)), + Lambdad(keys="top_region_index", func=lambda x: torch.FloatTensor(x),allow_missing_keys=True), + Lambdad(keys="bottom_region_index", func=lambda x: torch.FloatTensor(x),allow_missing_keys=True), Lambdad(keys="spacing", func=lambda x: torch.FloatTensor(x)), - Lambdad(keys=["top_region_index", "bottom_region_index", "spacing"], func=lambda x: x * 1e2), + Lambdad(keys=["top_region_index", "bottom_region_index", "spacing"], func=lambda x: x * 1e2,allow_missing_keys=True), ] train_transforms, val_transforms = Compose(common_transform), Compose(common_transform) From babaf9032680dc6353eb189bdb288d45ede7a9db Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Fri, 14 Mar 2025 16:56:48 +0000 Subject: [PATCH 38/59] train rflow Signed-off-by: Can-Zhao --- generation/maisi/configs/environment.json | 13 ------------- 1 file changed, 13 deletions(-) delete mode 100644 generation/maisi/configs/environment.json diff --git a/generation/maisi/configs/environment.json b/generation/maisi/configs/environment.json deleted file mode 100644 index 5e017645f..000000000 --- a/generation/maisi/configs/environment.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "output_dir": "output", - "trained_autoencoder_path": "models/autoencoder_epoch273.pt", - "trained_diffusion_path": "models/input_unet3d_data-all_steps1000size512ddpm_random_current_inputx_v1.pt", - "trained_controlnet_path": "models/controlnet-20datasets-e20wl100fold0bc_noi_dia_fsize_current.pt", - "trained_mask_generation_autoencoder_path": "models/mask_generation_autoencoder.pt", - "trained_mask_generation_diffusion_path": "models/mask_generation_diffusion_unet.pt", - "all_mask_files_base_dir": "datasets/all_masks_flexible_size_and_spacing_3000", - "all_mask_files_json": "./configs/candidate_masks_flexible_size_and_spacing_3000.json", - "all_anatomy_size_conditions_json": "./configs/all_anatomy_size_condtions.json", - "label_dict_json": "./configs/label_dict.json", - "label_dict_remap_json": "./configs/label_dict_124_to_132.json" -} From 32048e686751239c9a34ae48260291dbdbad01e2 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 14 Mar 2025 16:57:07 +0000 Subject: [PATCH 39/59] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- generation/maisi/scripts/diff_model_infer.py | 4 +++- generation/maisi/scripts/infer_controlnet.py | 2 +- generation/maisi/scripts/utils.py | 8 +++++--- 3 files changed, 9 insertions(+), 5 deletions(-) diff --git a/generation/maisi/scripts/diff_model_infer.py b/generation/maisi/scripts/diff_model_infer.py index c3e66612d..8b01e1cc9 100644 --- a/generation/maisi/scripts/diff_model_infer.py +++ b/generation/maisi/scripts/diff_model_infer.py @@ -95,7 +95,9 @@ def prepare_tensors(args: argparse.Namespace, device: torch.device) -> tuple: top_region_index_tensor = torch.from_numpy(top_region_index_tensor[np.newaxis, :]).half().to(device) bottom_region_index_tensor = torch.from_numpy(bottom_region_index_tensor[np.newaxis, :]).half().to(device) spacing_tensor = torch.from_numpy(spacing_tensor[np.newaxis, :]).half().to(device) - modality_tensor = args.diffusion_unet_inference["modality"]*torch.ones((len(spacing_tensor)),dtype=torch.long).to(device) + modality_tensor = args.diffusion_unet_inference["modality"] * torch.ones( + (len(spacing_tensor)), dtype=torch.long + ).to(device) return top_region_index_tensor, bottom_region_index_tensor, spacing_tensor, modality_tensor diff --git a/generation/maisi/scripts/infer_controlnet.py b/generation/maisi/scripts/infer_controlnet.py index d19069cfa..b74d6672c 100644 --- a/generation/maisi/scripts/infer_controlnet.py +++ b/generation/maisi/scripts/infer_controlnet.py @@ -161,7 +161,7 @@ def main(): top_region_index_tensor = None bottom_region_index_tensor = None spacing_tensor = batch["spacing"].to(device) - modality_tensor = args.controlnet_infer["modality"]*torch.ones((len(labels),),dtype=torch.long).to(device) + modality_tensor = args.controlnet_infer["modality"] * torch.ones((len(labels),), dtype=torch.long).to(device) out_spacing = tuple((batch["spacing"].squeeze().numpy() / 100).tolist()) # get target dimension dim = batch["dim"] diff --git a/generation/maisi/scripts/utils.py b/generation/maisi/scripts/utils.py index d9d9ad002..9b1df2892 100644 --- a/generation/maisi/scripts/utils.py +++ b/generation/maisi/scripts/utils.py @@ -306,10 +306,12 @@ def prepare_maisi_controlnet_json_dataloader( LoadImaged(keys=["image", "label"], image_only=True, ensure_channel_first=True), Orientationd(keys=["label"], axcodes="RAS"), EnsureTyped(keys=["label"], dtype=torch.uint8, track_meta=True), - Lambdad(keys="top_region_index", func=lambda x: torch.FloatTensor(x),allow_missing_keys=True), - Lambdad(keys="bottom_region_index", func=lambda x: torch.FloatTensor(x),allow_missing_keys=True), + Lambdad(keys="top_region_index", func=lambda x: torch.FloatTensor(x), allow_missing_keys=True), + Lambdad(keys="bottom_region_index", func=lambda x: torch.FloatTensor(x), allow_missing_keys=True), Lambdad(keys="spacing", func=lambda x: torch.FloatTensor(x)), - Lambdad(keys=["top_region_index", "bottom_region_index", "spacing"], func=lambda x: x * 1e2,allow_missing_keys=True), + Lambdad( + keys=["top_region_index", "bottom_region_index", "spacing"], func=lambda x: x * 1e2, allow_missing_keys=True + ), ] train_transforms, val_transforms = Compose(common_transform), Compose(common_transform) From 7cd0d5a03e6384a5e0371095a6dcb7e92241895e Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Fri, 14 Mar 2025 17:22:09 +0000 Subject: [PATCH 40/59] update fig Signed-off-by: Can-Zhao --- generation/maisi/figures/maisi_infer.jpg | Bin 145693 -> 0 bytes generation/maisi/figures/maisi_train.jpg | Bin 187025 -> 0 bytes 2 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 generation/maisi/figures/maisi_infer.jpg delete mode 100644 generation/maisi/figures/maisi_train.jpg diff --git a/generation/maisi/figures/maisi_infer.jpg b/generation/maisi/figures/maisi_infer.jpg deleted file mode 100644 index 9210da5fddb312c82e22b3bb1177e562ccee8f5c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 145693 zcmeFZXH-*Pzb+aCL`0-_At)#yO(~+Zs7MnLL8%cC=|n)35^7MScMwoekuEjTJCP0o z(tAxn>5w3VB?;j!_dWOCF<#$u_y6p1_J=cu3+9)VWUl#}I zsHh;1z(2^*G(-(TeeBrpfAFFK|7lOq($dh-o}{BYe&W>0Q>PeCGB7ZnW@TnP&2pN7 zftiz;g^itqgX0tv7dIz6H!C{_`|nPusKMXR(9+Y=(z7!%FtYzIUq@dcEGLc)&_bxG z&Owf`P*Jl`9koHA5C|15INIL>{vQ|BF>s8>=}yp}WB|WV$qYG0MNNH-hWhuY!EgJ6 zpF?O^Xj#u*zHyvQ--_;>8@tSl_>2=mH%ps23qPshPEnt)0DtqtkPD4^JR;@Q8%O*Gb7K zZ{DV6W@YE(=6%X9C@Zh1tg5c5t!rs*Ywzgn>i#-3JTf{qJ~26k#Vsy<|FOKXx<=Uf zxw}W)2Ywy=mWv9M^Z&X2Z^8aWE*4O(V>C3>G<3h^qB`aUHfk0c+OwCBv)<6BvvOlQ zC-dS2`_1@_(q?)gSpz)B<7a~>Ifdo0=Lx?>`wz+fnP4yfOOpM+g8hGTO+ijkQ-O;| z%>scz4)141h(i9h|MDDA_rn~D4SuQ}rU!^PDSFHhd4tn~_jDo=^`^YntdbU_Ue^_xm;+7SdB z38h4CcQTPBFeF_lzCHE`qQ1WvO5wCT%x#LxjCHUsnTkw6_tW6r?vWk--r zIaDhr?2v0d1`3#>h^YrRBUCEyVmB5ieqsK}2tgi@<_z0QhhR2}R-hz{rmW&$?=m=hGK-etf1oP%$!V7D=_K|_ z*F&LXFuJot{kj4MJTRizD(pa$!y#aJe#aTbiP|t2#q7qI^&h@Hf-Fr_*pDC+dD%yh z)iZ`1u+X3MM-a9pA6+CS8s((R*Q%;Gs(dyBfOSBbEBnq0`J2J$9ZP34^LV)Y9OcS- zcJn&;jkWQniPNKz&TwTekf`eXXd#Sg|x9F{EwJ$yP zkf^0Ar|uFQ&uo?Ry3{FmS2#lbbX4OBA;AcMda*hz9k;!aM!+XMD^w%U z*{St6INrW5c1BB|Hw^Mt@BEg1v5W18m7&!HNh;#k^92YHi$z5nn_bDu1nO_&X(>_r z;)q2w`@}j|)&5}bM?SBZHOF3QzR!=Af^8nMyn{;M>4N+mae5Q3xpzofBq?qSO$Jg`MssjsogP6%=_*vf=7)$nYQSOGi-1K43Oilvv^G_2g_ zTIMKw59Y?1wj8ql}C#d}v%N)PsIJI)xUZ8*2!qoIG1k;juUW+0eh?x+y76 zvhJBp|9sfQ1XJ>!uHLo=o@-OtqMYYX_hnUwEdbtxbBkZrLacEsWF<=AqYgiO$c?9i z*6(qwm;0?P2-tKs#{d~TF|A}JWnj%mHOnQ%C?$Eb_ColVi(g1V57BKX7G!nn9CX<7 zivYtA*&%_PFg=Ti%xOKvr)p%hV|$wE4&=Dfc()f+z=IxT6Z>i%Ki@oVajc5q6&V{M zx!Zr*>8Z8^XOH|~oayYtuq@LH2+tF}5CJQpxLVH|7ym_dzjVCaJkB_%>Do#%(??)l z8-JlV&%E=K8TmTFKcrN`>oN_}u0q^PzPW#!Q~mq#-K*S--{jIyA?w?YAf?+;6wx`d z8DUBF?7(Bfm5i)*>!Jo`_+bQbxAV+b8J8QEHdP%Tu{nv}Ye;5*`VLz1k!^v-MN4)# zrN4=5sJP*Vn@Ehi<6fg3?8F-9lwF4B*^278X4a%$_32CVk<>uEI<}n24x4TTMy0%-LGeL(m4M9kvL;gLQvhBx;6CO^!xWwQOGap zU+OwASm=}*b~|K)3zwUMMYF9QWGlY(yE&TtOKK@%_Iw1wz{NIs&QK|+WMX`o+d}lS z$-(k1f?YIDr_XHs_Q<9O!}|-%8^#297k{z+6kksqQ{A-bU8cw4W=;HuZz)i?pDZC| zlLa^JkLuuUqp%Wa*IM)D5&H_v^TESbi5|UA+VrHavZ|U`c`q3AU}q7F#$ip24(V)i z=F&Lz`55U<=e>$CX+PWSc5FVmrHmV{=Q|KT&2gS?Ys_#Vfty9|wI}RmrL!7Ii>doc2mZjLFTHxA91&TUQm zgzWr?6|sqYC$qE=WICrfB6SY1#qq6}ZfuQf*1YHC32;d7wxc!o4H}5O1F9AMab+u< zYY?W4wlX$)uaWLP;nHxT;h1E}scMDWQ|v=uZGOs>)yr%#3O~|3{+)`1S8ivoo?#r_Jc5*;NoU@*gd#gz=Y)rq)vd5p2!wA| z>mrodWUa2aqODGkCxIb6+d+$^tRhcvo99|)OX43*+2w0(y~q&2FF6pL2X~fH{=a>*VK#vDXNO*q%+&Iu?1dbL;~*csv(%CDzPN z=OGFw=(DRyy6;}z`YH4*;ew`z?M2PUCyJfO*ljg^O23c`doZj0eeLMX{$>-H_t# z$z~FsO%GUC)=ITOf{H>xdW`y#)o)*2r)uASh+Z{a?k087bEI??-K&Pvt zqo*<(fmbYhv?5pXoZR4S*Y@)C*9X?DS{!i;(G!`?)~f~L&C32bU5kF=S3oBdzE=I& zVSG+tT%>L|tHz`HgH~U4@{Y#oG2595VOE7R9tBNYgew>i65P*j@IL&Vi3wn-@u6jC z$IDk-dx>PUdJA4va=Qd~NlQPmU7TuH^7)i>6s)Sj9H&c1Vl(|rX(uU=?`%6O*NY!P z6lK5!f7#NnCW|W=Y6SB&bht8CG0=~tl;JDIh4X3VLOR1X!ygr-%evFo0m|w~k;O}IAQq%!sB!4%N*FJEb&jv;(nF`>!!%`MnaLKsu9K>1 z`I%++GMD&y3HUoEEr38^xK(QiH3%zi`d@J{ta2IRR)>G{I0(u(u3dr z0j4g|a741#EClUJtPL)n9b4eZLHd=~1`0E~^?BQO1QW)R2tv5ig}AiX@C4+2K>*PP zWvskO^x>6YXs~c3Xq9QL3fkMbzhR!C<;k3yknk^8_k$CZaGjCLR^E7p=DOoKXyj09 zn+1Qm`KY`e^&EU9C3QI=?jm|?)|V-m0$rcyQx*5~C>;&BfE-sJs7PmNwa;ChgO1L1O_dH83){1nSh`rA6TIau z)msFA*=)VeEB>=z6O1`tEHX`4Y%ku;As{+ffbAQfF|}oi{wtNAk094;lOg5sa~m~3 zX-n-Lt66PTAjcYwi9ta4A``3MBfuL^(ikS|d*@mrpDni4y-yF4NBCbL?j>twY6Xw{ zY%vg)k(Fr|d|nHCb(!{M=h&`s`sOqIBX${^M@Wg&@#L!a4gLXW<_A^<>QyvapzpEY=S?&c8Mvj zTG;OWqVCOQ9JFmatfi+7%BgHKrHfmwO15Myft3}>@fZltvvr^EWtn?Or!*sb^sB)#v{5JE<>{UCh`!Me=jkB6Cv4TXPwVSYs4hG## z`Eb?2yzWn^C!LP(6;%>0dNW-?zO8nlk1;e^jsV1Qj4>I6>4n&h$VELiWLlg4dfjq_ zoc|S~uK0uGuNNd4v54##84jsu@9uoObn45aGseS;I!*%YhlS*`3ls92PNW-Ez;l#X zkim#;tpc0oCzFoEUenmOmozKoRqywD1|_?2iM&t;H#@s=bCv2jfX!zFUccF zWJyV}gv|@~cD`oB`JT7hkFJZcQB^4PYCOHh@9d`os<_II_$*4V04!t+6_H}}d7TTl ztzN!!evC(suc0gM%1K#f9TCG{fd<#jh})C89#A$vl}H?C<)SN~E05wTnD25Y)$(Z# zFD{+Wd3VZP%U7}XE*&&C?ZJ@ zOKykZ{9?7yoP@j$bOw;vagY*O!NiHI$Bxw((ojayT6m*832Ub!D+L^KB20RMzs#{K zoJl37iodo`bijaB_!n}P!2zOpmdeJFih1_%>4zio;+-`k@;o`_Xyx+iUYjr6LR6kn zRE8H0Q+8F1u{p6pd-Lo-d7E>tWo}*$GV7EzNA^tfoqgaTD9{Oq*n#(0^& z`b57J;7Woy!kEZb-Z)Y}GcvTJNvfud_t6+x*Mq(Mh65B8&)UkapUFiP!zOWEIQ7>u z(d{sD!%v+g1Mq1Q>`E86Pn(#pewx~T5t2#t^kPjuKjvToQxt4F))f<>EgV%Uvgso> z8{G9|V_|qxu(B##a`U5lglN{if`}}t>--SN$t7iTe2Nw!z0Hye`HlF4D0#m*VXiN) z)n!HS^(>svTY2GLIV9@#rfu_je7k3b0ZSWDh}Dga3=l#pb;z!peNS2rQ5`1wt9AhG zwjRqLHAuHiRP7*NcP{xO^2RG9mD+hFiLr%~YM)lU1{y0zb6V(P9445%zU+bDY_(}oalz{jBH?9*k;o4 zEpn_@Ws2b#Am7NDWAMM-ofXrC&H37l#lC0#P8yO}NmrpEeWvWYURtiB%yZe0F*&i~AkE864ASZ#yh4hdn9`hNe z!ffE#yk6w(QOT`WX3<4ewSYpxnTIf zBZ$-4!J5S0ovJqt+af4c~Rs+sUM-UrX9f4ouLXmRI3aB(Asg_>pnBNxxI*!%X z_kd};HWD}FGMkg<6m|CEjXXDPH_H)emhMxEN~p#Sx}@A=gD#_+;6 zt3x_#MYM_K{3=DYDOP{Zf|(l*{6vP?`%qE=)YYRC{~9cgV7sVS$!pd z4t%`9c~OtciNZOOHR9gW`21don!rSMRGYz7mXnv_WUtI$j&e--A_y3VDYG4>5F&8_ zd5cQYQ9kF1=wgQG%S~MGbx1OlKFN#-dK;3!cPiwk!f z3HKMqgT4b<`N&8u!d$SwuSX_=xF6qKvPvA%T0Pl+PW;x6JSJJ#$jIo+4E^y&9jxn@ zSt!<}6phF)SXUJxXw)y2WTBBE-$m+DuLhmSq+$_@k)N3`8PqUIOx<-7dw5zLOtjIv zg9cp>uNkwouIS>_ZJn2ea;1=LMCSoA6X9BbU=;=@ z&4R3~@UGB_R(seGW9@b-QSFMUk^F;;1EPje!jEL%zkQ4t`;@t19}hr@_W0$%WrBZY zE*fo#G;W(mkn~>3GkjVaD()LjsktN=rIpqLJAM=5`Gupcl|&C{H%QxK>%lk_*f$+; zpLDhzk~`Ei)wUWUhcBke2m8fwQ!&TxH(8lXzDyDkTD>S*&+smVEUqg{;p`+!KvlO% z_9QkEUDq7Oen?CF>k*^eu0y0Iq~ z7d`nFr}4R+-R+~jUCT$|cL};ahM@Y80Xp$?>OeON+h^o&Z;l7!SruV)E`q{a4@Y!y z8ZyY4y1UyvEljdiIexkC9^gy54BW@ghbik7tNRus_9+*J9}UZg%dZc5x^l{1{>dK^ zGS}j1Ap7}ZX2Bn+}Ai)0wvs_38m){0|9rcL^kr_6PH@^c>Dw&~mr zK^o347MHQJ8B>0X>Qu`C!&Qt1De=b_>G23mX3wr!zP5IxUafH6uU~$v2S>n z8th3*eGC1sEV*2iFODBUn45%<<+qZ*A?CRrX79`ww5;-1u{@5DYfFGce9B4tv4_qS z&D@akW5@;U&4(&LyJ6~gp$^!mi*`oK0T$3RD!`!RX0lfMKpoe0c-e_tfeg>!p0n?a zCz}`m`y7C)%|dqUiAY20db?SwQ+58cmF_czt{gQcf%k511mEQ@?N(ldcvRefBIPX* z3wk1!>WA<5ph5R6*S4{d>j;vm)KaRD59gMn-Rr`z78|)bZfEdTcbTd7gO} z-UzoC+mnWxbmt-Fgh!8n7ER}03R4TGE%>k0E?81=4wmn&qt_o z4etrZ-f5~yJbeCnr?>lu^)k!X7xfWDaOd*i#x2WGueMa_0X1y4Pi~{56;^C>Th82R zY)x*YSn84F_itt1z6ya`Oj~$0mWVY!7~WoQP}%JxQ0MPzHs-M@(pm;Mm^=CB^tE^Udpu83#IrE!!$-m*1CalZBa&8ce@HDsEf`~od)nQ+>KzD?z7gX|=e zC8e85&D6p%OkV0-)>l7KSXWud4(Yh3wT+ayv{nM(T)dw*8PJ;|{q&4f-<;jA6W=x- zF{LPv#`5~f4v{@@(#H`BKj+juu;#jZaN#QV2MI>i$umVC^Ur(U={)&h>9kv%&y*-j zyEf>2CNAEBvPJi86L(diiLD6NM%5moeXY*R@18C{6*`W4S<{efe|+APp6qGi{Six0 zxIJp{^ZszaS#yR)iz)BG2X3yXAUE|Nd^~ny!lsll@!caP{mek+i_l4^M%{?A7zzz6 zcFajN;+yhZ3moqwQeVq>GoF4LY(Qho7{@cgqs9FbBBH1&eE4R!&$xX~kZ?M9QI8wR zx!iUq0dZxH4-=x~yUHmab=$zfc4tYnAO|bj2}z7kCEf-X_lKh1#x@qp1VVw)v05?P zcgg`zdqw$otU-L_W^gn^2V=@sR~n6f!8a&K6U{{mEX?fdJh!MKmKkYu~Z1?;C1 zzMQW3J=XAl&}F$Y8_C!q;38ox8mnU23}%-(^Gk|~Gpi%zaw;S5?+kRUvkAvmwlGc$ zPfTon5En5!EZvX*txJD%ok$Cap%0K9Q9dts$tgvZe|Y0ZwMO#Ui?lN4S?}^GThADO zuf^Iwn6YA55DMBLLg<(`HmBO&sPyNwtsbr5clxR$sd+a(B+Gw9S*;r%L8eiTN09KG=)AYKR!bew!;24N#e2VvENnf}}8gQ}O?MDn6;6oJSNE`Q5=&YH{GnO4b8yaW>&1 z@9oVzO8ElUVbXh0+aShAJa(4<^-clhOn}`3ikN^sNe|UsNd|LBr8-7OkfCb`7!k|_ zB)S6kL0ZU%M35HZW)!*!YOD+61ba%mBpjrLd@nkJOr}$GF<8CB2p>I4eEaP@JmqmZ zK~*z*P~C6VgY?u<5~s(y%B#>48}R|GE4@M))egE!lU1;74V9!jOjYWpoUU1r>sM`s zPk|+GF|w#ZLd^1zoN?a~1V055LT)jDJP{VmVO=cXtN?0G+TR?G6Z4c=Ghg2K;l%hY z|Fi0T^G6WIT#$cqy$R%>@PT_9)LJ3Kr~^SukVz5^GD$|jF9%I&*6s##{6vwjq=Dp* zW53BCufQj`WKsBZ!D`nFzu$Bg@EO(m8%~}~jsnX=?Em>NG)wa*RI+{3we&5)pSC(; z4w(2@7(?|A>t@8A9K_q_ep4}bOVUwiS_9#dxj zOa4PCt{)KPj9!2-&8{~I1M-B(>x0f$(xNpD@CD5@@HgkSyxxiS$b9Nv`pmz+#;4Tf z!EjZoINP*%Sv^^dc}ekem(WCz=~vd!wuq?LRTnZNE}lFo3T6!UiFZMl&lCWS^kX~F zZ_2(jUND{%&^^C{fE_=AY}^^d?1u2}nXM}xixB3+*oS!PEw3;bU($;J6sEzbYq;H3rNci414={}r z$oiP<%&%--DA}v%TgEP<5#P@3cqO69CT;pd?T@lO^aQSDAEyvSw$1dr2ZS#4skN7@ zspdOlXK#Mv^K!^!Ijes@tEMG(+g^Bp?pmcW<>bTDacqqEuJ_NoSL;dF8C(^r$j7J2 z@aI!?OjA{hZC(PBfJg??t35R@Xc|selPqW)$6QeX7VbDj>qFN( z9~i&}Ku>=@IOR27*HNiBvvxqNMDEwcWSuJ+bx9~0XWu8yE^_dOT1I z?-h(pD&F=MS=dnb$f~nP^_pGHxuq1#El2eY>58K?64=_IWdV)nN57>&BP(=u^he#N zP2WrA#jTcVQGdMZVQgnJ`_5h{b!R)$_=7SY9j1DI{lk?p)eDYBj-MXR0M?xXbR&#g zL79`H$60)H)h@t$1b=mCWzWc{iFT-d8Bt|g;hK}gb7Gj&^%9%!&B9lWuGq3pXeu*M z3*{R;{gmZkP_z9ZX}NBD!ykKS0K&0X=VS?aAL?6g{povd!5NXNDqIF6@cUV6CFPo?p58RHuVs%tAMFNQf&JdjIa zVZ%%D+6R87g6lD|c~ z02lBjfoX{%fEHvm*n?@asl7dZzuG_VIbcGeZ=+!mnr|upDTyWHAB46B`DECeJhEQ= zs{GH+4Fw8%aUKktQAZHx^%b9Ai$pM}G$w3=*bU4Pq-syDiHBjc^M}8@I=dnNjsKa@ z|A*eE>$Suitf-EXSje}-z(S!TxI#&MPD6*m4c^jidXs9{v5aRXPMl^>hopw|n##>& z;UKGs82g%!Li+%-kd4~0#jwD(_>UlYd}cqu)kOmzDN+FgAaf=Ca%VP>@26?S=gwL8 z`sSl>%2yed4YP>IeaByzf#t;gsU6GwJzaMFhk?Jdz24g!@4whX?d%jJ+XD=;t#cayqrHYsuX^x)YNUc zJa>0K$Q6HQJwrpDLtDq8;|LO=LvW&KlvEN(Uj(3ICGPb{5EgaD*@`CJX1MnUQQ>bV zyDw(k62z-rYIj18xGx6Pn=Lc|B{*d-U_j_mhSW=3-Svvm2vz*%hPi zTdw++*VZj0fM_f%l)^!n-H&wMwr)t&<5E21W23{+e(fI0H=2b1l5nZ^8~bVT&*So7 zS`4{}%Pqx1Pp=nZ;f%+>&f#__N>@wrJ~)=?LsBjlaA<#{@4MNlzS^~&lbqsJZ|+>? zTUnZ2HrZ+EC}8N@_@vhC?t!IemEQBA22GqKti_CftF-W;f1++#RG;!R4^ec@^OG>; zSc#drx<7)km$8W_-bSOD$3W&ZQz@FE0Z7L+a(#huxUZ8|YzTTmaoiqA$-!HM9_C~o z-g77Ero8zT-HZdokjw36Ldct1XxCj!M)S$H9xA>%Lt_+&+!hX_s4N<_WTltT>sRxU zfbg=tO>*+6QcZpRv_|Dhb*+v>z6ke)lIgLnzGwO3;+79~*R~Nt(+=qOo$b=>CcyFO z_EO+OYn#u7M6`tQz9(}V@AjQ%pUfPHKX~HboRONlCd9E~6d*C$aD&jYtuefP1?hTg zC@cApQ`kynX8E$z4n37MUz34sjlF!1vWH@_wo*OZrFz`I0_B+^lb<}Ao#t$6LE5cK zv)EdjDsiIbXswm>i_?~# z+ZL`nq+N6U9uB7m-*21M)T-=0oGDc2+g=~HDWQaCHYnKrYAI)gGEIwDda3Ypp$CE?n3wWL@-|OJC@$UpKH4qq+GqPs%ICK0nB6*5R zm_MBgYze=J{TU^2y?euhGLX~9JxLnd9CwyRwRFHHJ!=>wI72In^y(DYkOqgx7QgjF?E> z5T^d@_hEDr*@NQ>lS5DX-XVCDWPC@4I4fw^B>e1-`u-zf=C&ut)!%FKm)!)v*!@cZlQi5B;=yQ)ji?2jIuc_rh zcjKUWKTRO`IUcSzny57*cjzsD);_;CRW0pRg~4QEp z9rg|_3mzfEb(VWrO~&}!C5r58ibgJ$QZuvL)o^&P`?IN}{EOsn&E?D=-#48PEQl-q z9H$0nZk4s4>)|_9m_fs`cH)y%C0v}ZPzRpYND&z= zIyWeH1*89@YNINlckXW2n3idP`>D1X>u*%l5GXw5bDp7^X=U#F0DsKF%P+0#@(y*W zH+0~tY3|!Lpk-e1ZRg;q(4}qAZ|Hxz^1{sVSjaQFIi>47DoBVAm4zkRPlNJp3|)-E zIs()>J9aiCj>D4muuE=bYQ){1)1lujKbF*0CJDd8$YHL;@XDRHxO;hZX@2SLTIGA3 zG_xaxZD89Y+Gxqlu}YsO?|o9*CZaPbCau+FF%bxBy--Qf-E~WcV6w3Zzjt{D;pA8aiL*0 zMhSCW)5w5nFY&NpKhQt&FC$Ro`eV#gQUWRbz};<)s)#{l=_1FD4oPy@ewdkZVr`I6 zoVW0vimZjZ+tuLPhTrWu0%C}F$7i<^$xLP@cf`@BcW6#^{7;;>FF{lg48I73f(eXI1St8vLTf*XO&Kig%j&&C ztw{f_yq|`Jp?)dG0(R*qjvxb;V72-Tiunk#1rQ1o;TYSLCadvmhrP ztmLHn2^~RhpupO^8(9quTt?R_aWH0OXF`w}@ZJ{-4QYZ7$`vc=3@3{^S!uC!q+^nig3hBvLTI4I?A7KJCT1-8reyB;43 zZG(~DTL>+>PAjxipofu^vwj9atp}3T(PX}2Oz4)gjiXCNePmdrsPp+`PDZ|lJ7t$G zZk)RGJjTbBk9^}Wi^2c|?6*_S4U(xA!ISN%&sTh34_~BE=b6V}_fz6?`1jOnw=cl0 zMDNc0j@#n@$#~3{gZ#{M08)#hbhx`tym!_*=hOMgZ&ZBPcnBBqWZQte8uKCQ&^-L6Z@8Pa*uRXfw7eqM|F z+O@^wfgn2ZA3QF*@-R=8Yi){{n7LvhGwf%Yzh(@xjhJr*32;tt;1?P0I`4-ub3Say zF^=zZK|uMM_>q1Z9(quur7o*QPoBuMe40bG`7P4T`f_sGQfh|^QOOz?{QJF)#ax5m3KHmIxCUKfN6#|Bof1JjVKb1g1Y`gSSE4IIR zK75W9uNmH;p0ipP(?|@G*f&e6U~zOjbNoTVdT;z4gw$-9)~~b|<}N|FMonWNt;n&v z2x+n38ozqQxXLJ*OZP$%T3n~jaaf7AzCil*SJlU5&z_1>Q~!f0s6zp83Jq9i?xCOZ*ar*Gt4mfBicZ=(^F^LMi$GtPHO)TGMU<+ zdr!AXy-52wSF0QW!J__&;Dun4GlncrVAGkAegvTf^?AJ7 z!uH|oJ}8Jtni+CUx>6_d>&(R4tAnqTiPv;t#JIyuve1h{q)u}Htm@ZAqc{(7~`SviK{@$%QQ%!7bqjb?r~f>Q`WqKExtjL<;dJ9ITu%^_cA6ZLS)V)aY6-&l!` zwCKl^J|;l5PRKqgcl|{;?Bbf-_Eh=ec<|FEA;21o=4ok=?+o7aD)U46_S7&YZd|q~ zR_>&OFBCNS1bC|OdVk>ct@lZH(zg^_#o+a&X(q7B&R8a1fVT6XCt6R_u~?H-)?)H` ze@c9m%tkWw69mFb-JxM~0(BqsYMtfRroYW=3}BRZADdubFsK`& zo+|Hd>u!}~dyU2?=x+9t|6MpgzkYBRfJQq}5&)rMPqz=RPK_%pG67Yf1khtn2Cje4TQ+_I0x;PBvMX-jMWQp0viQ8f8bVG;zc>h$cHq>f?Oz7(d*ng z`2F?+m(r&zGP-GB1wX*D=B@)x6BKGvRUw;U^_g)Z$`Yg1dsl zg>hD3I*{%I<&Rp*uOOoRVXi6@;6((nHaSihjs})or({*OA!nBjS7l9mcH+VcjS z@Bn;ED`5(6?T?xud_kMNf}B@%3Mj`+6wOG>9`HO9s1`FVSn^q zxb!vXj#W6kWweiLK(2RATA)VNS%4`#+40SNUCvriYEjjh|KmmkZV{gogGk&mdb| zIW=>>Na1{IY)<>E<_to|4_Rbnngj z()f4wSG&-vAjGH?0KG%?L~v(NI5=Ts&sYEfeF@z)RRvWJR^J)~%Oj9htHV)Q98LSq z)jxgS?Oir>`YMwMc$R5P-%7B2k%0q2?fBPz(t{1q!(yWA#`fnUNbX@8>MikEdUJ!d z=lu2=`$na9C_ zI*>?b0?|{SN{=9m6{uDdGN=kOyFt?m0zttS@ha&{Nb@tpI)_YA3;N5&zHAKZq@P=9Qgf3qDeA7myakS#U+xBaKb zy+%+LZ3?~L{SU;Q5~ju~6O1O^MJSn4CNKp35C4H4vBOFZ5us^g{8!nGMZ9=B)DF zrKiCTBXD)Ol#yR0gi_x0A)X#Lh$Rkd!#z;cL8rCfr~d4ybQ<*#D9 zqP=nblzA#6Jz57v-Mp~vb7K|$cMzA>jrAFV&X1XtE8hyf=oQD#5_p{H;oJa)eVr4` zd&wO^xDg*DoLvsazWGX*7YA0qk_bMrGjeS@I`7&HT;)2%@Slw>8gN~HW?PVt)dPvkxgCO!6W7&B&idn_12RUMbT%v}=nPjpm% zG9A1fwJRp&mgmj$Yat9C8+~|Yf=OdJYbIOIcNKE+XNT@K9a(lnE|zZ&sJ0v*HXmF5 zY<}fRp<8Wje#xGNb7_dYmOuk9chhMqeP0d9zn<#B~ZX76r{h?z*frqU|@bE z6Fl%SBSq)AD}}3mdJcaj{z{VY@Yc|O2B7^p_D0tcIZGAte%|qMHP@~Ybp&cpSP z|BJo%4r_Az)S(f4khhTx*~8d(J-RIp^MI|Dk9SGQV$rbB;O2JKph*&V#&{`YgX@ zay_!fhoWY%N7Wd#lT1r|6Ska`_^YawzFbHx%6~-?p4fyjv<1;U0HA`Z@pe8pexP51slk{K;gN|`^)yNvsZDd$e39CzD`jJ)ebU!dAFwh>9up1R`R%i3KDkSYBbBtv7#TWjDb0hx zE)4CkDmxVfna7nyOAF#l>{A3AYfxiN(TouL(KdfND*~_eFx`f(dyD_LMo(#!?RLfcNwt}dmUXkX z@VV6K14M_d){kAQrRMhR8@?rqESGd=VkSP*bxm`g8$n~oVNDD0lS_QZt7>s)5KMjZ z;>;vHj(T~`^;nHkk0ae!@zRQ`4{qJFTIkClR$d#df`3I3vch!nBgu1->LXQOQTIM? z9~p4JfihZWYAaE97BZ*|iMc%eB-ds#li@w`Gc%_*M+^K!6>6=ImFTC9mq>jN{jw8z zzHD|vQLd9UiZs`-F>mrgp}`(!?akk$l}prPFdNrdb&`D)WbegrxcTNt(h2pNi#k=s zZ>faGz9*R3iK6qm9P_AkP;ScZ?K{{Uc(Ml97lzs%FJ_W#-8aq)DBRnijrOlUmV00d zBKSonn78-w^HO6X3>VfEC_ubQ(p)jE33Z>;F&Of#;+?8dW_21pUFr9958&ov-GI1Yc*)VAiSI_;Lc z;{Dw<{SMQ8iH_zvp(LxfckeC{*b{w&2hFFJ0*>u=@4m)|fy zm%#84A9PcXVR0R;)niP-eEbnG`jz71jsq0b`jZt;M}hW7E)v~h&o3{1S)wVcpF5IT zwgRs)i8xb>_X_bBR?c7@1P&lqd>aNr9g3M7|CG8O{>bzPEl-d^-iL`#dD2yqP|KR9 zWLe>m(gvw1-T|`~&t|sgnsS%86@n#DNAGy9-HLkA{cJPA;Nq3H-~lW)BTmW?OZ+Wn zqq*DU^r6YT)#!pYA1?XbppPZAyKk6-J#?9GfpK4mJY6XwM~Wujo|O z<7G>D@yHM+aes?J?%U(6$q(PScBYZjV3Fe%=e*W>sqDCgq}gS0vfU%Ac-^WSJXZ)D zO7miGPL@{L3a7cjbvkF@OuDm1xK9~;1DP!jMb9-I&qu#Dy4RJ>TYr)B$U&YHOs2yP z(mAWUrcS*Dd3RRixokSd)*jYZYgrCB<@tY!JfKggWa@q%%HK%i--rv-#+8MU+{u15 z4VC@dq`ft=v}53k3Fyhgu<{KQ}SgTB6Me2 zwmxwH{n2)~LDR>R>rqwQv7C#O9Jf~T1WuF5QxiNa{!5H@6OjnpOmEd8Xhapsf3LTA#jy2E@B`rU8^c* zKYIN*Z-!-Z;WFNQT&O8{{8XWSmHUUevSlh;zf82Yu11IE;2Qdvcy@(fy`N!>aZRLG z!m-Cv`tsGoy&SF|PRr2l?I-@Ep`#<8y8Jywg_YPsWf?i50iyhB#H?`BhrWH#oh6`# zdKgJ?9Nj@3Zkb1o-6kN=#E@JwN3L|#Q?zJzx7ku_EW>tyjIs5R$d1axKCSYq5n1&Rmz|^Hu@Vv^XN=E^J#~Kk z?6t(4XQ)EEqu$eB#8vve!Di?w>?{Kjjc@OuvOrlKr)(pg9+LFJ^C|gkePP?r-c`?r za=Rfl_|V%HJ|<1rz4KUJ?3&xZWYbEDG@e9fi9#ECU0)?+ueE|Zr~ITboCk>^kRT%2 zKyX;>O4_e7&L$AqKSCL2*9OG!Qv*S2=@i(`biV_=D!E|pP4RZe8D=4 zkJYTM!8{BHjoR-10<^BS4EnE`wYCs*1$#M0F!w1rs*2`h8=On?xXvWc@@O5RRfTU( z=VLfYY~p4;Ylg8J_VLZMqVhhr{evY-gupWcKins;9j>dWg-O`mdC3d2irCX3PzKhj z;I#gn6@%+%%c~;iEThVsTsW`mf1lVs7_s2(Z(6+0>*CqFGQP3vo5q=<5S%A(GFd$` z5gH-N-8LolnS3~_uY?$BSlz$<{syy~#~Jm`rJSLR4^JGoWYW^(hKS$N(qk*teMUo| zcCaw2%;=f^s=gC%hnwPwD?iq++($g_Ymz;zm$qE5D?{!$uYG*L7;KO? zFU!%rhuADz3ERtJ=?=OEz@A^n?59PDh@W>9yQt zRXe%N#OJ!DNWlt9nmdx=qcsi|p=>d8O!~cK*iL6or0UZ5XMDqh_ETpl+5T+EHJ6Aw zc%;+dkGsSgb$y}?j)gx!*E1o~Z(bathc`~1t&i-JG#z@AvM?~(<+h;7K7FZ%w#v#E zcSBhDVWaP5j4IcKTHj)-Z@M8~b{40VRJAeA4aANfW5kHEwy0~^KvJ`57m~Cvb-&>! z&9u?5Jrt+!65@DHZLS-ntW@4x)wqPqT#ohfld-(lQQLNJP#|0=IwzL>#+UmEp;!5W zO$uQziX`U-VEt&SZGiGvL!hYi68sQ3farwh?uIiGH(K^T4Y=1-4?U3H`qFCVO;-0B z#?gfb(gLv&&-p^4l2)7sY49pd%}a`aXN zldkDjy<7Li2Py^#Ma$;5OC-bkum>g*yVJVOYS<*pxTG;SzvMQplH zGeg&I;^nDHcCsr~f1N~wzfOc6@E0!t$ClJKBI1(x9x*bT=&J(P!t+meIahtPO3?0b z31witA~6~9h9~YxYxypO+n)p1(G2Aa>@`Y^wN5mbxgq*d^6`;>MYGZ&m2g$e11zJh%U)(z_ z;It)I_9NXBnddFPhPKZqa?E!`_@ANDVTd|q6W)Q-h6uXlu_%)%-r8_yE`EgFN7jxD z4_WG1k!LP6U3S3reHB`zvddxtACT5xuQy@F56U>cl5wg(mwV0MBrqgJOE{i}qxyrW z-G^_~tpyr-8j48vUo<#GiTtXg5#*S!p~QIpgP^m5{tiC^e1>eYCy2>=$A8l3uX6!N z;}6?eB&MeHF45~o!?5gWOrMqgDP86IyVd8S;zJH)elT!Sw^$PzFtJ$??Ly2Js&XPe zgTOV815S285*=WnkZoziw~$f5+Af2vGeXEzB=r#X)3^NvZfao1hGwz8%ps2E%givNG z0zCc$@CAr@3!?7}WogIZ@9r*#q(%oW4LI1(B)6E$?qe6%0=Iq9f0g(heM4*^RN#zL zXS?e&nH|bk_8a^dgs)_w!~sb zJh#UC+KTiiY;wKz2_1~`iZLA-37Le@lTY05>tB{31U7nN6BmW}Plu%mYD?>)C)QV+ z1J8}M%|XWsX7l5V&!=ja4|p({_D<p9YD6+KLa+dSn;XtRbwEIe(W6Q;ay7Em{>l|_SepDm7 z>IHAL<|&DOG3=~JhLWh0>ORA`+2VUM&zkp|d}d-{S;A3Xy`nFM{jbh5heJ4&Z_h%Q zh^f>QJ)+GcBM*NNrkc5nQ;SoB?XMvfq{JLK;j^9Mqv)H{2Z0=44_qsCjt-gGW~!Iu z!Mn|+k{HjId1$cRjk{N^UaM+m{jkXyYh!FU_2f$5eDO2~QK?OI*2&7CDx%LIW>L>< z)eQZjCgCjdQ*`3><6WF`7aw0Od!Y!*eeCO%?8fv~b1YDU{&-+?83AP5sAy<6^E3s=- z+f0J-Ya!3F9>CnA%uaUOvJLvH;OU~tT5~R^S4|5d`D<{Q zp*xgo{W8S?ib5$1oUcw)XkQU2Iy%PcEWq2V$u{hWphsU0lrZ&Py^0rE8ql&ml3ujD zwY=({RE_^+P0LgF+C0o-LjL&G@z~zcrx*w-$O^BhGBb4BY{VJ;&^YYg3*DMaEbo7` zPdB!^eKs6I9n;vCx2`7b(|cH*pVs**j-iG{*=Gn4QFE~8n`Dz}yhnPZN^o6IO>j&S zVC3F#XkudM$fOPA_D(e) zZSeCeUA5#I^x$4iAU;D~7kr;Ga#`SUxNlMILo4)^g93rkfd>4+97ZzXCF1`sG(qW_it8s9X%&4%Bl^0)x5!lnmbPuPEd^uIg~M^H+C5jT_O5U5 zzW_SRADfE(smoYd2w8#LS%AN>1^pWd>ME%H#-1ED67(E%nBLbEx21jp57-GXQ4njl z2N2saB1WLa7%Nbs+Efw3cvFDqI865YNmEgY0w4sLo>Vkd6BYVi^E&7Q^JHw=2#PFI1K8p$zu4kT9bl~=`Q@jBtggMwQ9v2z2go2d z^4D5rg5UA-+h z(A46%zX}ip)j*(SfSZhHmVc{RWDm#0s`k0@7+j017@DRFn75mk($^ zzg_tihPlkIUbDz(WqZH5MzfX!PL8+k4Bql~DZBSv|4#c5hcW(Rx%oN|vn=E(-BlAI zN*a~N++XJTEE&U5lhkay`(%22c|#_5d_qe(zN%jv{wbQ=X??HjL)n_dk;4XHg8qp~ zWkP@jcc9@EF7?)s2Q$;Mw0e=l`B;k;)0ix`w^KS?Ck>=kKE-LhJNdZ2eBJN|))o@x zg=LhH46E?=<754t8|ox6qO|&Yp(2ibk~P-6B*@z1^4{~sLs2uaM$mt10SwJYsdK%V zxc2nOf*9hY4>VfwfB94XKfD@$r~Lw$`}f6Ngsr4(t=8?f^_>t(EnXgTuAKW=R&pGj zJ82&eJ84&EQGerXZOr8Zd+)T9MQG2v#`e(oQvO5T$YEC~H}qR95ld}~2TEQ?>K*0+ z7O+Bx?L!&=+7ol+%EMxgb~yf zOM$3dUEH0`9T5h(ZUGHaeWuQ8+z$Tw_u}>=l2OXS1;o{sBwxO_6|x~CeX)e%Vwgx5(U6J5;ZQmnHflgDgt za52DCv%JIWP<63;yawS@#)D5K^sM|!&z7UyQX;iR#AMgyrf&^OI_>bP&U&?Y*o$`$Rp$(zej}{wo0hE?RdG`BSeo`GJFFj9pBI#)87yX&> z5zt_01+ux&Kv$7h^G*z}>PUd48&i7i*~x|EFeNra++~5coV}X2n>xu{lf^#yso2@Q zbcnIb?5L58%m!6+Xvm}w%Z#zXG=Ifm8t=t1uWsVj3k9~ zXNB$iPny%%Z!SdcUk~dVcvwhFj6Rj3Gql`-;NmM-3SrfU7eKQAM=<2yy$1oyvFQdl zKDqq*Z2a(k+IR{nW&oChw{;DeoDJNa_SH~5#Et*d+ z)f^d7wv}f=rP5K=ujf({z*jbpQqlr>z4LOn$|S4IT%YXqI`hLtT*xnwmc?)pY7-o< z{*`P-G??!OP#mP)tNcz2rAKjzlKo}A+Utg6W>x@m5jb*VfkJ~YqO*hyrlMRL%+dg- z3^>9pn>tq`6&AblH?HOF-A^24B^^LNhBfZm%;EUdnX_n@Xr#oTRy7YCGC z@`Z9rrvgGd3u2~wFV(;GawM@<$T_gW@@eAJvy#_yxMfnt|KdE)BnB3!$ zOTr_}(Rk00EMiR)ayGV1CiaVYN1>*7={cdzIFX`P3j0yJ8+SkZ+vA-Z=TM+Q95;>$ zQ_c~#q1qwaTDLMT6CD-AlBLi`nvS;i-cU>Z06neDG)Fh$qI|s$Q!0_kmsAW*rerP_ zvnP()-HLc4lHL?E!8knq6uW7FkwSd&fo>nP!0-czfUK z+MU;funE1<1XN!abyRN36VqXjo1aVLQ?4DBsc)28ZNUf*BpZ`7F*f%6j8ZXC1;>LC zC#Y{S78=Z2c5gX?6V|g4kykfyXBop0sf$hFVdZdoVr?j|Q!;uoyO}LxF;m)piR~~it&VhnMyd@f%rfUV^X?C?xpTZ{uY@;Jpai1^1 zT=4P({=+|n`5xBP)O{PB&70~gT$3>g^7d*O$z-4^c}HsE(!{r+NDg%qs zPcKSXGjeGZc4R-=!I{ajWr4j(kP|!Hg_-TJ0&@r5Iau|L8WxKSo<_IpX^zm!(e~Z6 z9#VGS(Pl_?lP}9yul5`b{Mc_(wH^O<4=!T)qbAO;%t_pNqZ6XN!WC2CRJP1sU8gxN zGoGomUKciIq}qYLirlq+sJou97x+WU(YQg~L#M~39YX8Dd!21aQl`5*@}Jq~tG!F343*l!_K3MWXFaXTvpTiW8M{7`LaMIy zQX?k#%)!G=7sF&IEY<^6nm1g~K9UD6CeKZ=oc)@}OZ%V&BW)uT z?a-l14+RZ`m)f6*lkif1)g6I+YNTi(9_SYldA*hAD;xU8RSMrVOHYcCnJz ztLjPT%9ZWsndCpHp2m#sg!A#rbsFV+f3BywbN&NQ#vPL=;+AF={wGa4uS2J@q-0Ef z|C-ZWO1I$<`=DOAyTz93F|7D zVd{2$@eWBS0te;uR=SR3j=?b7kFf=t8mGy2Cv92@I}@Jsc7B^N-pjZQbv)lMVC+q1 zJL6$|s8GKD<M@3JWd~s%By)eT;um;XH0Yf6=KO! zF&M~4Z0V4z9+)JSHP4$Vx>sm_IwP`mpa14MclhObff~!#^Ww@3#VxoRO?>G)Tn5tL zsv668G4pPtV@E=|*4ERfp<=CB$d^)Amd%k;DZw^tyXNEDq#9CE@hu($u6k5=7pqqY_i*=Rdf=Vf&gBwprfH$A8MRUuY-&^1DqiPv zkd2P_2um$04ZY5+hac{16%T~agM5$4;l}ShH3ULqiFnGF*Dpl{i;8LuuSs#PPdLw#;PQ*rQ{74n5Ucf%(p6Sm_svJT{qhSQ{Q# zUmRG>gI&l?v$$KY!W&oTUk2~_J}mrwe3ff~e0F|ax`V@X&D11S%v1$GxKMZ2ztHAr zNwXB^HwoPFD~nsjIyp-i4qQ~qldmmDp4r;ytk?+hzic(b z%K#7H)_&fal4G%=e^dN{*^4Y6?e*yDA4lKf%jgaUDvf3tpw%tYWfBW(P@q(B`qSp} z=R7eL(*YNaSMD2LWsOPcsG(g`QOQ;uCFg1*axH0hJRV{@@B`EHgdB4cE~Qd1?Mrb4 zr|LWG$_*kQV}~Sxf^kP}w2kiACG7OR`s@B2`f_K38tTFQPQFg_%*Y1aIVRD&12WUh zM0<};N%p(Kb#?WQI*#hP@2Ag1+^l4uz0%hOyJA1hyhiOPn3N=FzK{tRSnS3hDJJYe zo^{EOfr9t(FqQft2xo(yGB*y@X4*a%XJ)_Ua}&B)d;xeFc!DWt*!2AEv^bj7XtHD^ z;n zR=9pPsWCZ?vu)vT0ujCcgF2rc{zhWDQ3uS)mwVlgsOIMn84)^d;$(t(nW2!fgvde5E}?$aN7rAOJ?0{bDGYGiOa-|IvqxKAAb1%@0g zSOOE^5cTVr_ou^3wNnE(L>xD44GRbN(;V9TCVJ!%0zYYP`$n8EoA0H^Zv+FxzyPBN z;*9SA19?k`wru%b8mmaSTSP;aX@z94N>kM_qz6W#*8hUBa-NXNb1cro`#}3yyEf07sPkLiIG*cs4cE z2-78zC4gJ1zNkYS3OEoUx`++M&|-+OR;4R7RAqentMXM2&(0&6B{Hre{Ny(mj62}w z#*mq3?*4W zaBmPRIlp8BngBlIm^1zWx;RR``TXBTU})t#Kj1Iw;-ZQ|97#Xiy6VC+%7kVNKFL}> z^SB`XS(`s7ygAGi3%E{AH5$jG@sb)MT~Cf)n%ERABo>mSSH`H%ZS8;ceKs9h_YS>5 zRxjEc>DNSW34KnUI8M_J`{dXPisJi%op716HBBV>(T$z3_&Lkyx(jAbRhr$k&^lx~ zW>J}Be4_P1p0WF)_UFnGDm=W!vih9%<8&pB@#+2AF@1Hmy=VAmmVTrw{whO(qW*k% z$q zU>{Y}!5@wa+SM4Qo2sgV)qSNQM|Ztz`Y0T9rA!h=JXrR{|1{Afdwivu-BK23UT*Pq zD#Yi}w^+5@SSbC1yrzu+jV4_+bA7*=+9`t*w_w*dC_X-1?G5v6tlN5v>E4>^#PEQ5 z!I1`wx66v2sV)yka7&7e1AVNO(g$o4@9G~rbAast{(YRTEg3>Y%%MW-ro&~r3uXNJ zj#{qB*OexGlG2tO(ONj^W6*xPd+g0ec~R@xK>c{6QFeT(P}-vsQ0uN(nqQhjf1%<= zxkm3A3#)Y{4~=LrUbq7pmccRwa+}MSZe;p$Iv?6Z7HQUEwIke%jQy+T#y3r*v>Iaw z5u52s^R1YRA8?W_ecaxY@E;cT`!cbne(Wi_Syy*K4fE6iN}jSk@*R=6nR@WqzRsM} zy~&Rpqc-mpR6Fn}(+r)MLc-+$Q#e4uo{hrjEDKuLJ%;}x*i9%1Y{jyW)kE<~9Sq}l z%r@>2b+jY;Y|F+&#TN0V8_Eo%!{iG%c(@FfWumFf8`WxR@yRREx8Maj{D)wK-4{jG zVUgg_TRyi3Fwbe@U<{UQD?^qW>Rx+ab}sH&c=t*tBp|avc)6dmdhHK%i2p=p^gsE` zUofHe*)hI9X@X)w3L=qw{U^d}4d=kG~)NH*mf#%K_rpJQNWY3$r^ z{cb;flFZFE@*~JX4HnW!$7V2I5{UC0? zP~pKB?JBG3?8yH-OkRWp1?pU1cC10oF@OITyFf3dqwN%=$5aQEs1=g=c!0Kh$Y2ryNOk$-5IO@u0@s z*=qQ820+bV2l9s+r1MwC+GC3*$9-z{bvgxKO<7648qlzEd;0jRG5-SH>G_nc%)c1( z&y(oE=j1LKs zf)THHc-|g-={nBV<&a71f2_p8#H+@XeOXdadZLAQ0zck&_OckR_{Ep_Q4zPPa>VZo z#_mrSll9kM3(0XWQ*9JB5auY zy$;HC4V`3s{Jh~3o%5R2q0#blVs>vM*bMK9MA4{-c7oc6-!UCeHj%1Xt*;vLq^S+#9ZyS3%{NOJmH~g2B1f*oOnZXgq@_&;0e)$`$HU^yY&b)Q z4ULJU6OQhKoNumIzqhY!+y&|&FLq2wu2aIT!7OR>S)a1F72T=xw$Nw6xu+r57;W-m@dVm(n{vo+0~ zxA;{x@!$-%ih42NZ^oz!zqG!B+YpO14_J#!*XgaCz{{%B2H=v#CHm$Y{CDT?Qh?ne!jE!k`|`UJo$l0FMV46 zgmKrtWNf1kPWr=sDB7o}FRR#MN|z8BVwRkEI`1bqgTI0p0vw9skt zv^-Ti5M0L;(^qM_w;B2(-;?d798LqKRN3h@KE9{@d=p>!Oy*(g_jgqsB0y`YKU?fS zTTe@yiT0(PqD5s7-0oFkLQYx2oT!$|_UxHg<9S1$MK3PbcY93Gne#+#jlBP>GvH6( z=pf8#7#TcZzy0vlO4qAb%UwN>1}jz6bO!JBZ;v~2yU4E*@Go+<@_jPxI8S>Cz6MDY zQaddf#X>g=Nhh#c-u1LVdWN`B6 zum;=h9?vnF`GW_aSqA(w!`ZAYyODGxoGF1@E+vM9!0z--0w&XS)zIWuL$JR)!A-X$ zfG1mOg7gRZq__n9D78b>g`!#Ikol)0rRG5# zQgiOmfcxsTUZ?z&OUdFkACBwM2r$lN>)uJ0wd%wE4I7};1UJ7W^?wx`0417ANW>X> zjS@ba5$kW0n7+3Kk8;7D?bNTjs2;OC9cvrZGanGAv7}vo+CaKZ_`j4z|392x{r?>+ z;lJnecdo>LjpYI6v!@C&cB+~0EM6%7O&dpe)ufJb!upymqS5mbgkpLk&4W&MHOl2d} zXCOB(wjz!MA~$7!S<#QrXERAh%n(a?WkI!oQuqg4rZ|VJuzzlu%(6|tnfbMks;=Tk zZD#;-k({E##a#$n=pO6N28mM=^z*t`=78NWY!uw*G^DCPwF1M60eUdy0kWZ)Qop*N3Ly()8#9#5l;AJ+Z*KKnXx8LvbN|#zpgDk`H_&a%`%2Z65G9`4 z7VWpx0;~k+Orth_twEL#J@2@qAeX^CU|Z);pjoF!95fqqpXMPuB8eK9V^hnXJp-h+ zx->a%=+yIM=2>|1?XFA1p1GrCnd!)oP%bnqRBXepq*R_@LZr+!Ku(agaoC{y&ZZE? zWl4k(v>r24#{7PHEEz=ARHh+S5oS8#bnBEHeqJt;f)Ypxts5bP?Nar_oo3ZVn9cVx zzHC^&dcqZ3Q}Z^*%Lx=?b_NPNbUWJj&8Fx5Nn%?s z=c#+BO2;1PLy9TMoG3XvzOKDFf2%QRozJFcs8kFYnS7=DlE4g&>QfW{i|-ZLth8B; z#+)}SQZ@n?%^xc`c<{p#*Yl~Xh~#f$Cgf8Ev;J`c7>ObOyZGaey&%LJJDqo>*Iw@3 zW41~mKo3#-5R4d}J*QdF}?5H!l0z&eIqrda>{_6jGj?^dNKWW_Ee$u#k?1%^6 zs)+^@4si3J4B^nRT^Jv~qa;z&#F+XCexB&Heq?u@Te{}c7&CNwPjtt(OuOwOpgte_ zXQo-zg;MpCX2c&r_Z5|I5bM7CF&Ohdy;RQu{26)#D^i+Lfc-W`6$5a6UNK^s2c1ch z2Ai*jd;vUm7_b-v3dL%K48VzYBvyh#{KQhwGUv$%VyzG8$4%o3sBJ31ExR$7cbkZ~ z1S4kh4>WSSA1>0V{-C=fKZ#fTuowJ$-BN&#OJrHV|le0 zOL9+qm-++FH4EWwy z&*H=#d?=?{@3OG$Qs#*U+F78Tg`o2o@7~}c->MrdlhqZpZbY^YTCUuxpH^eEXH1f~ zc6~!6+>oVCWm4}jO#p|w_iih6% zUFBhWoWU`g4~|@sq1_H(`(9VD&Og(rG%Fbvc(%#ar`T>vfF$E9o}cb8FtsAxdn{t= zytmNp(5|$@U)o;K>nhFmb$~o1MzR(M(t4p}%^3unnk^-V4>{*|Cp&Rr)LSEkBlPZr zSfhwg`aOfym(o}Gvef-|?~@)86{)=)=6u}vN2)X!tcBco#}oU0gjJ_#&o_Q+ZQDC3 z=4TSJj>V}yI^jsCLn%>)d63KA=wpLEX_6OIuK65a4m$GepLbDxIWINFAUs%Cdb#W68SQ4)~|vMyK)a?q}kGZbe5 z3{m@XpcJB0op=iLFG=qM-D!G!OWlT}?Pb?;lZspb#ZN<)-v~R|DQScl=DYpNcfhSu zpUXpt62RY?cSp3q$RNx2rl*6-P=+8*`l8vwDuEBx_YsucG6RDl^&O;Az$ZSyfp_Ht zBg+7uFKVB00x?quay!wZm+(+RFit*Pm6wRr8Qjqc-$04kgmU%-uhxFOaZr#3679Fn zmaeT(^zyL>zimPDPnta`VI3BQ_qC;nI`ALKOvzPpqtaQ^LIG~tEs7Fotojyq&_6f8@A z26_b9^}WEXpZ~`sm%!MF{4E%z%IYe-P)I&8Xkcu{Ld~VfRMU;e?Xb;Bk@sgVnt)E^ z??)!R`_^?y*R@D;-tsMDYnDJI<;lwa8rQiyEuP|OGUYc{y4pBTy$)JeAWdqB_9nz^KX@)CEc+ zp!NlW?Bo=7yU27~7txvm4|$|IUIHIquJ&sU4CoHl?NB%kRkOTJx2*o?t>Olx3X_*^s(9dFq@WLe#>Mu5uOIK17i&+AukXq zKWSvaLxA5%F10`R3T%t<;gT3mhLN zKG?X$WY_VB6$^aJUW>W=;O#YSsg(LH)pl@CX@8qz{-1S`&S%$Zs~X{uWHUJNd{Rsm za>o?W>T>MvYCplap~CIaQVwZKUjkveUxYEDl!FY>b0V6g>~8>@?8x;lA$n5vo;i7( zj}ilop;+m@paYBy+nXcK7oPY#Nn`LnZC}k(f7K~f7+1OkGg14LuHu!Ua?*6g>FXHd z0Qo4Y$7g-@i{Qb${5#(0ulN7IRpJh(2QH)n!WJ8np+JcW;i zMtTPXBRJ2&PB_rVW}l}uGNfT;q@iJ3WBU%TgpuP*;nWT&J7Puc?Z0WK{RblWFD*5? z9ym^aI+8L53E2 z=MWIXGV9#B4Z}NhBQlqb2ioZ}jBo5jUKY3)aqO)A>F^Hv+zeWj6X?pQ1H(}ZA?wAc z)#&^wiEzo5b-6B;8z1Jr{@^gccm``l=QY<|GXHkub`;B9Y^89>juYJ+UB@0;S7=wS zYFOEM*Hg;UOj&=j`B7<7&x?1opDW%?)Sfm_GQsv=fzV2Rz_L2P%RAQQWv4@wuOfVA z{N74>V2^$6QB5xDbm-S*oYMPxF2OeH2G1EAv4h8ns?j)Tlkf2?{8(SBDhttOUYGyo zz_!kd@ixc_w78A1S$Hx{RW%x${<@H%_S+ZMN4Jj2?liK}?oC5O{nfJVFCs$&4-yk_ zeRFAJCAwZS#5eC7aponX+O~084~?r%RB!~~rEVPKy!AwM5^XQ*5(ckf>wDp%EIKD{ z(t;7gVN$ck9L9u_1=rDeLtd3GX4O~xgBhftuje05q~X*-Q#P7Fzh&q#_vzp*|0{qo z9<9pbe;aRHBUe-CV9J*@%XHh6+B4$x!^S@Qu4=W^gB zJoXv+wx6pHd)6#NadG|88F!W&TVtJ%=586r9nfxd65XOri#bXc<}I{nPMjr~fc)5r zH#nCUWXLQtJLzBPWZ%!1MBU8%)b7tU?pk4pea25 z`tS~t!9Gx~dPKG|+kOzfvFqS*o|LWEelaL*=Nf)&m%#ci> zyH0rIyYdWmA1kEa(eCw`%ep0or>e@$tED{b{+hX?Ts$vKeg%I9C0qv1<%&uPXatHR z`wc-W{O{vTNo%7VSR1^}x1Tf*E0Ld4EHFGHsn(9ffn}-EL$1HjVoX-_Z>`iCkab z$le|ix~cLoV$8P3|{gwct z_``QY{x~uvJ%S2rL%jZv6TNqeO0N%`(~VgZ$}1-j^?|x?=iRlZF6DYH?0OIt}Q_hN+ zVeaFF+$E)tiiIfz8kdneU(xNa8q&sbwZu=hBF;EZ+rznJK^Pqd@SBL0ZGm+6ypC?G zJhYn=rsO_o3PZd5LZdM9MCsN}(Ce*zUosMXV;$2T=Us4vo~pM#&UJ&n(v7USun3Z; zXmuZt%`|p?;;Y24QXPd3{TgxO^=bI0^(vR~?zxB0(aL7Hz-f~g0i+EEhgBe)JKj}~ zr0iHMfMt953{lfV;4v}5Uu#A*RLLL9VmFZe<0p-;S~eYkEvp;*n?}wFal(8N|rMJTRSev7=LIf)3uY4Y-L%e`nY#A zH*1^^U8FnY`4;TEzTkL&30y#ngR6*RU2t2OXWH6zL8~i%Q8w6_z+qfsEzYrtPhi5O z;ZlKg%~Twxe-hMKw_qo?pG;TgEeh7}NoAW^S$M6zOHsqf^Hs)V(3UVA)okD~AO2iV zUsp17hgdn9*TyM=&M|G2KP~9EK|wS@Z=Xl?PIAxhuJg`JW+@xZB6vSwTTsgy0|uC9 zr4do2%=Y?HZYuq;xk+vZV&4oV*9zX~GwY-+x=zizcdtY>q&TMRc2rKI{N~WU@97^F zFNh9#t658#XyG;nBm*(NIpxtlw@P(pk?HC?ONKOtSeASJPG2|EYRoe(y7u^O1fwHS zCy2JXLmTc}dFGbaD$%K03!KlN&^|iu8Wr_ePsyZLcwE3G)&JVuW44#VKDvc++i82_fE97iC?L!FXgSur$g%;jv~_B+U|Va@W~@l6?r z@tIL+lTKx?;X|TzHSR+SBi)TNR3iNK)^zi78MZ?G18?*L8Sx?NDDQJBAo~dCNZ z#IsFyqbH~3Q`2Vm=jGZ}3FLdX8s94Fl4M^tx4e;eO*OlYk&NW_8zk;gW)o8LB(rOG zOI_}lT)VX$dI9ZvoZ~Z8Phzx~!ZvC)-{G5k>it-PTIEz_x{lk)^>0(v)GS;_g>(|i zT_w)w<^9y$bD`R<7@&3o2(r1nJk>O2P1O&7ybJz0nV1_`Fb^T=W=SkIjbH;*&yt1E z`x;Us?cCeeB3s`28W=NW(+TiG(U484H%S!v&oMbv{lfn6e&2(F7brQ_x|Wq^o*sgY zRbY1GIjraGnAG_;@`)VE$a4BoVz{bUSvZ|mkEEW{WobSG zo=sfeTto;t%CQfpdny`E&<$r~jB&6%&yz^{8c)n*8pfc(hWG@$U}AhypKbe=qHlC% z72k~BCc$ELK$T9BJ3lv;J^gHfrjk0J5e27eD$BTak(~dJW?zd!8Rut>f7Wq#qjclnW=pEh%o^LRx6j zay?Jl^S56fwH>{A9}K~egw(Dz)7+;szFO+X41)gZll|*^62WBSAZ274^%}`N;XTTz z*N7Cb*^e$M%0PBsQ!w>BP(5Pl2~zWOnOny6LjValB}!_c zkD<3+%isM`)yJ^H1Od2)L3?udOOQx$DI-T0)Jdak{(L4IgqJ~}j{?RNGW_GXk$ zs!8&R3waFV3cGXAHSuH*8W$2M4EF$qp`o!PX}OjzOG!yVcu_$|0cDH#=z3tv-acEV z6A`?DKuO9X$~6sdY`f+|hCaVJv!Ktr&hPt)dNny`O(y=-*@X?{)O=E%M*y7}-5b z+#?+rT5$n~_`$&L10P$aXyAmq&$!ZzSWZbeJ8cy7$0GCl)P$@gG9L4iqYfR|U8z?g zZO}=D1GofQGm}C|8bfm0Rh{fzl8IlGH!tYqWIxg=%bRi^VRrnU@zH+uZ&Yii6Vn*i zKa+M9X&Zplh=f@J;u55bKNzktuG{~rzVOHG^hg7dg$QiCYb|!htLT`n#q{leVwt-% z%-a`yw2}vBfQcod@7NT*2D*BW-;?R;0!?OK{MWs)*flyE1K9og*i7-k2 zsPBgkC9IyH&WGSB63LXP(TB=r!1uRdQ{3Z_k9s!D$z?wLv+=tQi&y!BB9P~C)Hc;( zXH1=s8@vVfr5e}-kK&mZ>CNq~JNqr8E<1$$U2ljoXzJsK7V=6r?JeOglJ zgM|L6c)N74@BCzlvzsqc>WhYO&#p%5u&3X1b$k2VXz!8B)q8o006vlmAmvL{3g}T@ zkQrpbt@ZO_3o$OUj1p6zahfCGQagV^XGkv^nBm5T>Of4eE80Q^A$HZZBLdqJR6 zr7ih14*X!WkVp^WYGkDfp?BOMq<-)lSDD5G>1=pf8OB=wqqpX!>WdFm;O*yK=P4Z!?JFT$csehDaAuzPIDu#h+)4>RX8wG` z=^L_22mYtu@3&3h`AWUdMHM41z_(taMV7l|z&sLegEVo}*ZeB*8%Kwo2T|+;C@)x2pbY6m+uh6w#8OTDosUzn~ zZJk_(qZ`LpEndwWp7O=UxMTj8_uW2nPG8(5q+Cru)LrB4F9y)RIYOJ^MjNLr{(Cq= z!^Hv8Ed`yZqjRGNVZawA)@|L;&mK6okU;K4YNGE4GI)^FF{0^5+SZmoqHFYNS+t}d z43JF$Wc-BRG7b5r1W8(K1AR_9GbJYBl-A{KfLc>?!6Cv8_J6HP%A=#-|dt;m>os#BUX!zdW*3y`GcfMQAhZmR?-b(bw*zJ}kBk^&*hX zhs*}v>|y9wGCCJ^c-M)0)9E@$-7O(DQLIS4qT9_jexkx;RPvC)=ar32 zwt}}_iA7^p&Kxs3g)~BnqfA+-`|xJxj_I&X#~soqA9a~HpQeWGc^hFI!YtNILW%fg ztz$>au6X+%PjS-7@^L#wzPxN=f3KpIO8&LaM;5FizvvyD=9nvcn1aZ*+vp|eH@exl z@U$|MPy}uM2m}L*17(iWKW!x7L=pee9SJ1MfxHOb_E^J!1!LJDo)L8*s z@3*#Al;?5z59cR5mrIEJ&WU}CA;aOOnaN_^vFBfzfi{SEmYWXhJm-`2a!#*&($erw z^F!7@qKoF~TC$E3neEYRR8O+NFwm`73U{rwDjD5&bd<@toI|as=4G$A7cl+oxOt2) zANgr2RgJ8gN0J_`fDFjXZR3Mw+ZMDF951aV1=u7oNeJxCevy8Fzi&N*-hO@QmOEhp z@gQld$O`D?2D6iN<(G&WX)@+j?pj1r=~~e}Tg#L5!B0JR>0i5Dfhxj67Wc*%IVt&# zLG_-fsE>^TOQV{lt}`N?Dn!W`30Li^D;q9NqC{!OD`iRAlZGlDMk2MW-M3;7Jm)`a zUuJI!tj5{M?)>b=k+NTWbYxTGC5PH|nXQ!ctis=pp6Ai2d@5(3crLXow5*b_qY)2- ze|0sG`(@#)B3bO2;KAAMkb-jmV}#RkOz7Eavv6Zr#Q_8X;1`rHGh= z=VTMwtvI@(+@r^j@6NzLP*3$J#%ZWybG>I#+d3$e<=FPcKJ(0mo#C0YN+0hqEG^yc zKGUFF;=H_S-|tnvo|n{Y^EHXv9R#!91=srviE1-Bh}%UZdN?}M5+8lv8Y%L1Iczcf zSOMeI2hM^FRC2v zytd9U3TKs~nvBBvEC=S?x2xhugI!CduA`DHit%y7r~y?gj_Blb9N$Jzdu@bLJgYLs zau7NczeK4n;0`d?pFgOf(0d;#r2gi{%)uRrFHqNJ_b`pOBhwu0UtgVUJ*`?|2D(YE zFT}csroC!TxvAN0##y`Gh(Ffi;47jfa~JAN%B>!!!%vBZSQ zmiKdo#$V<9&XD(lJIMTZ=h zBCAY}WaCZ-`Frg7ER;TLZWFi;V?$Za*TTla;q}{xD^Ddcn$N`rG~wIbI9%vCKE-03 z zrFh`xB(fUcRmkNO=*6&~F0l?^KJuth_>PCLaiCpURo*C?(+3*&4G9r8jY;7Tpbm?_ zRRO|=W6u_YQzY3ZJs8-o0k^-l^6r+9z_w3rv$dk8W0to4xp6#OR-^9s}wBX)y z;q)Yp`Bxw3kTKUK%Nkwxg3^K$&A91>-ap3(_S~iA- zV%b>ZeyR=TO?X^us27-&Lwe^tAB`94yQWhUpUWH^qnPb$uMIfD#73$>h+fPPW z4}Vjhx()-JO8mQ_LH+N-1}#hKbZ(9U}*{s?<$Tn8;W6zDKt)7EO|>26Sx z0e*`O{mWG{8y9v`>`mcM-5`q=a^u%a%X*e_3H`I~Wl0|;u&e!dl0k2 z8C?>YaCZ5?8h@b1JoB%&_*bt1C-w*&K(02R{v;Z>?PvX9xCqdeG5{5nHg9MCJ!hjW zmkm%SVgfwOo4?_~<6mM<@z=M+@S`mgF5Q%*Rn}k3oPKq8nt|71zKZ>KFn9WmFT`bL zfecclk@^~2E4>32*KFs~W7SLm2<~adKfrE@n&N2@eqboN19fqRxP&Tp>g`TC_l)9Z zAocP)$}r1yX--tsOek~@!uVr_{=9L?kuNVV+mgTFj^%w4ue)3MNTLv+A-{Mxw;#yt zurvg)$uF+he_<;vkfrWv{98RQ=6B<61|(=`s~SJ9W8Y6>dH?>EEeLBpU81RpT9R&dte+s|P`g+W1Jx#cH8lq4|p=PWuO0uebev z&mF(tZmw}alGw#hS@>Sqm&J$+Y{yTFl}mRF`L>xk7{R}n=wP*0F3E6&UJ2StsxgyVOD-Lq|zc3oOv;Fv;_elsHf z-GW-y;Nx0)c~|%wjy%J>L*n^m@ZMqfL>`h3CZGs?WKJ$>P)oJcU0TZMfnNHU?2S{$ z?%dVT^ISHk{cMMHSdB#Jtv)Zi*zm?q$--=~qWaeZC4syhDo~Jk1E!5U@m@sczYNC^W zw)S8T+c8c)&T3Ht){W#*yZ!v>Sh(QwqA_RU$!;^l(xKDFA&-O@>Y8_1qzVgNyx1n} zeEV;WiA)Sn2u4~ej;;v(oEBDh(Pg!KmZFs9UeMhK4&)kM6-ZsN;vyZNONqVJH5Mgt ziXf#+=+oR?E|_c=BYxH--`QNW7RMwXR;4_TXA^FB(>VZp-mGM%;O1>p95Xih>P*3w z&X=0g7?t^?zco79_n)Q?N`45PT6fR)SOIcG+wr-E3st$gllc4Ig;8Db)j2Ov#LI-{ zL?Mkjq=s)V%1UTuPoXpqX9ym%O7`DX%Xbfw;<7zmlfVThxnoA>-$)B#G>-5x?RZv{ zmwO7{$`Jo_oF$^-&IH+f`S0u%*Kgs9u~WunC0{1eEOR6tT_<>K#S1_v^2v6J5lQKD ziqEsg!mn?Cna#JD_h1sr$>J;2WYaagz2bD<7m*mhVb{K_tQfbC7(~_a{_y#T>FqY3 zaQ;-nK6h5>x4R0FLyu9_)tu5!1Du$S3AuYfA_vO9~`q-cr08Dg3={pTQloLe0qMKX#Zr<1JFzjT)l0hM5PR!2E+!|An_nFVA3r)YKvX6G{b0qO z6zY}Ek%DfWx}&DwN{nKQ^LPUQk7(lswa)Ku<}x=fMT9D-8&xsSKAm<(Cbx#{};d7tc_B%ZxSs zQb0>AMA)viTwMRbz-F(=8873~Z|f18z7hHQ%eh?Tj|-WXY6>Fj<$p8t|GhxHU^``h zlrz0zhuW%I8LU$zkDDzj_lcNqNIcTz<2@Qm+hY_y(Y1W*jK6%}I5W2i%!>Q6z}P2> z-#)ued#_VT87NGh>EZ^)J=WdD-Z=PFO=h#cZJ3zRAQRt15w5BEIQ-ull$0AP;`!5W zAA;D+aMSgCT1d_{0Z!E`r=I=f)Z@m2bEhzTq8lL9sBl3ZK_L7A-Pp2A+Ej)n_oNbh*_wZje=Su-NrdgeU2J-Jsh#?z+QoaUpQ85sK@*dF8c9MMMI7gPQM&G6_v$j&tY)z;YSAk zeZIbML3H(X=tKe)pyv7EDd?H_%*7h59WVQB3juf4bK6BGW|l^t9Kf$V58L*$g5$R@ zOEdB~-Hu3$Q1qa8+!`3z^Hr_cqg)OdIea_3kfmL?QZ>g!Ss2w$Mrvt8!2Q~Z`UW-HSwTI0ph zlad%b=Buq0eCkt055bPxQA+Ue*Wz~ODowU9>$mILV-H9a-KAH?(db$kiUu8M7=XFj zI0fW9fApiW1N~F>0bje(#-IIDL=9rRazF(Ssa2H?B>#9|ou@!Q3397B=xCge5Ky9` zfn?2@ss_@A?p*){{Ciiu_0J%bxeK&rVftxuSBv>)|DrU|m4)l4I4uzJvwIe(OZpjv zUVwoXGSWY-?+!zMwo3v%k+44>DsUiK<3k?-i@}ZI|C$^1L&0`RJE%-%RvX8 zBm$cSbm8;&s-sC;1K+Sy55Ex${pKGmm#K~6P5y9%drjq^+QRo&sji+YK~v8lk4*DB zV@q7qz`*@BiT}|F=a-oTTj1B>{%7?&_b+3v@!N3!vt!S1V-ER!UHr3S&u@$4FILWOwnVt{4 zxOHd!%K^M`3pUSYyA6ZiFmeNKE$`vFy}!Jp0~R`5C^4-b^7=m`Sg+9e{O-cWBB#)W zo8W+`$L<^}Lehj{3P5E8`X>|+06Ip^Lbo+IZ2h{3D~@PR^n%_WXH6UJ4cN&8KMkz< z(*8JS3^LKIQ)1v_OnB+|kat*qFl13S0pD)}2SN_7ZGqq0O6f>Hzp~@Jh1CH%8I4=O z*OR1;**XV(4&MYoOIOMekeqOzre2ZRX z=kqih=MVMFtET2Hh>Pl2-3Xlzx9>6Ji=D7JE|?9HhzTH+J%RtCgk?Wy9%*cj+id=Ibq*7%tL03h=iE#ks4eSJq<_d2eh(`Z< zL0~oZ5-4V`_yNVN$4_rh%PjSOUlG8e{~pKx6RT$y&xebz2drcfNN_EsNKeT7CgOY} z-e>6~w?6DWgiz@SuWCWdguc#6J+aVWu=|?2R|+a^5ke~mn@GMIO1XvyJ0C;_qZi^A z0D@a(^Bq62EBhgFl1lV&KS$)x(xywN(e#Pb#N09kG(zAZ3TJHHR%fk>6M@)>p#v=b zQRWASZKvV4R5Ymnd|~FRO;4;{zKAcx&(_1J2k3`>!r|!pHBd4KcGJu5e|h_;uR!dT_Hn0nN1OiE0C+f{M-u<2X8iSk*XYL2 zE~t{|o#59wpd<)I^dF^oTwU~xWjYKz+y3L#y7aa;YX$Uw`(6G;bUg2O9^YU~=bc;3 zkbg#KKkmW5cQ|xt*wtG(XVAu-SgSBqW8ijQ^1 zoMjyyz%To3HQKLz{O%$6T(rNw->7Ka+mYzf&|st5=~BIOD63`@p2H?NB?MjtO#Qn!d(Gfq?^ch!0ojpmaU z6iY&v8zJ{d^f$6y*wkZtwtDX-q147>!;7)3R6cx{BsOI_1UcHZfv!BHYBOo+Yu}&yp(p8;bsnS^JS?QYc6})G<=qgc5XT&I#-xK1kCR*_3DT@~`S33XQfHePd>asD z9r%&>ssr%|N@Ih-d2%@xkMzv+jcca~J`;m%4ta8*0-0E!Toj-kW^d>wBw+6=C6k#irf-InRf_3eAiu!+Mf zv+s!b@KU@divvl-T2iZkoVkT5Q)#b2RBL79v}GRixq+HUb)Nd8ESW(Jh60ZdM^6}z zUC*cU9bH%T4?NHwQIsh^`+loF36u=JtFSAag)?PSFF5>Qa9^!gtgr)ZCN6W{gG9R; zV@WV*h$S=-7GIC%aFskd4J*goDlBSWJ5?SaCG@R9`b=cr9#Q{;7blE~-NqO*`39p- z{rnZxK-L%cDZbV>m)rDLS50WqAa2JTT0-z*_oG&Nyu_QN65M|@Df;ShQe$zskT(MHJ_C=g4aA-7vm2e8o#^?~`u&iQXC3P*nkn zSz`=cBiKha8TFjcx~1ogRVjlnzYfX+;L#(A_&wCiN<$_nOEi-`OXq5B*|Uu>{8eUa z$Y#Cwu}_fz3g|~WudY)Rp(|s!VNBv_oH_w);i{9J^78&wGKiR&iX2?XnY-Ojy~JKQ z+}{q6twBHwaG>OoR_`i(2(>-rx4fa6Ops?*ssDVCwY?Xoty|LGNng`d_q&9?^oKb3&q-yaX+_!y zE43TF!?!L?OEc%d4~wA(h>?x@lr=g!a;{rf-6P@2Uu4V&z5~YS}2$fzxUuI5W8&YxN`#y9JP!9OaKGlIxiwR5x>zv=BmOs zsyJ=^zCuAHcnH6s_ebZ@oYA%|MPv|q;}i-|H`+yW1-}tNnI(bHiPj%sy_PlIxQb(nRT1)$b4rQ>KPGZxTn3RtaEpxkP zR@zHdBiwNcUFL*8zsPvj%Q!qJ$0)&IT&cd?Ok3K0W7&JYe~1;$bS2#M@P#xx;e_?_ z%_!uSqoZ8q_L?H(Fx81n46YEpKZcMFz&ms-C9TFTc&$A$KepH3=v1(W04sOXqGkLD zoX-xEgQQ0OF zF-D9*?}@@MKXYJrq%lP(>-zregDolcbn$NFTBp8!QNj6V;!4a}t|g$u1nM;Q9M5QT%jzJIr;q#Ar~AM z)}qebSbCw^%17}q6!N@v(3~V+!d=ZLG1rd?@Y{_dUhk5A=ukKh;U2Wra?Q{4W>Fpz zDm|^eaJK=39a`^X%(bkrllsFxv0X4<(aQCr9eZE6iK$;w z#^8KeWey>7Lq%?&X@~RuMl`zkq24`OA2-JOt{<}UZ3tPK2X&y9cz~)!TGb`plT5ZZ z4GfYj9C&@_lRFn7|2aE1$Lr;O2FD>xx8cAPoT9C&lr=9gIw}%@J>GMC#Ei4h?J&&Y z=taJW!JZZ_g_aoS0+^YKDwkxxMMb4!g~pyk8i{XloP}j}IKOk><%w;XMeCOH>OKXd z5N2F0{y6H0J)IkVc+h;179xFw0#4gS&axI|n~Bnh8w!Va-Kk^8UVr#L(}KcPqk5jA zW+SALF=XFi2h8zSCjII^ZG@|T7A-z-z5aoYMqIKa-ncS1CIg8ZN?Cfi5+Ih|nc7CB=N`JJ?fuvf^*&aR1cxVGT1 zFl#T-DAeZJGjm4{wlyoXxNT%mbw(UH=$sZPD{tY_4oBDZ0LhfuVvhaF<9z-hejlnF zx?F85q<7Auq#XD6^Q%tRD<(vz7P#6(?TZs!3DXwbe^2kq`}^FrJlQwJa~Sj7Hx10} zX|5I(R6LVH^~lVat(NF=X@l6n{RhkBhl}4g=G`_#I9A)9`Y>hqs8A|L!;6PGNuWZ6 zW$?nRqFj>O1$*(xhs;7K!#&rkwH*A_1S%!k+-L&5bx;;%lW}ipX043u91l;&aY=-! z8KPVKE8Dwp!{W@cMa`9zPki?bTF$)^97xNoiuD%8t`IYaM5c?D67?BhY8h@?WG|FB zX89P+Y&dQqky&oY<921zL2LYn=QJ$T%fvj=im_awOJ3svQ|v z%MF*L)TR$22(i%qN7`=(x7>GixwAf)4l}kGF}p28?ALGPbP}v(HWi3_QKuyMTo-fq zbB@@!xMIZlkb#Vg2ES zd>_B$*9{ByFqWMa*N8rFBBYWmU1LOKr5~M%M0uJHquK4_9CM4%9^T@zUms>`#oghK zGzro(aN>J>u3q~qL?Z5aFdPj8h=K%T0XLoQo|b64iFofVDsglz;s58LKkinRQkEYL zZ98VK_y|Nt@(ml(!2$x^u#Hy|8<+~RR`>vHsX1fdNtbeW%el8g9xw#aI3>y#*PE&3 z=AL(}Iiou+%zkFkk+a0nY^>yFcu9waOjZ4dk3M#}rnXfF2}Ov|CWnQ^1(Af@oX2)T zx1KFs@+x_^Uhk8WLGh)Y9L4TM0rSzQ8Lp@~FK>-8rAh~_jA74+@`^tHBRkzu235!0 z$2qz}8hlM=d1pPUh{8V@c%?7A+!sr8mYZzJiRmx*ETUL!L5x_B+vSX3Bc+1?qL>TD z^9FNwV1k?Z&eCkcXR>ioj#XQ2zs^)z`psg3QKU@Q^$CW|B+(p{N@ES~&>W=be&?WV z%*u1pf>+a^S$wV6NNex%9oC16#=1KOv>5nP#guHqM~k6h7}Cb7picO^a=!Qh^QX@l zzg_0rHt@C2wk2fARJIIQ5`{FLCY@1dJo|EU3;CgwR;0pPqbl;W!IG=9XyCS#WR9D} zy|U*@DTXhE9=CiF6B0gx63s#M%8y|8QxMhekG`Y4b-Ek`PV#X%TJLpk63X0N<$URo zV=nL1^mLootVDZC+n35znuDidsY-HXY;7kEXee@ST=|JCSNw%3OtjBEpi>DYiP9UD zur!ULH~^*nDt!327UB;rgg7k@J&^#EJ{uQ*+J)-zU#m2F%t(Q6{9q^`HN4-^_WNo? zKTiTEdw7K$2&rDua96ryzPmAnzBL(mHGn)KwPP;y*7cy)2+z)ksynpw3NG?)WIyi+ zR~>90MPR(qNR^pRq<0k*c4se4!i)OV_bSMrf2X{xY_sdIbL0zFMOxGZ>D!x~+o%E^ z(&5k?>RFO8Rd5#nnJygReF(Qq@62{%LU4bZ8PmkXeEY=Q={tPhSDMZETyf9jTRESeN_}Yt{~{}4Cv;?3*yi-&PG!T{=H~A=td#(kSbz!hW3+=Z$YOWd zktp+q6h!LaOkCoNP3P}g8T~_QJT1aUue%E+=N&M7w9Dv3$@Kw>$0#zyQk3>_960VT z?=_>S)j~*5VDHVYlx1_po*k5>RScWc&8$5S39+6zzfU*zjQiumfqJy;g`XUS1ldfj z-ORL#tTej;x--$gPb!xMe4XD_biu*l_{91BcLHhp&i&=f7&r&4n)RvW8S*Fq8H#T{ zwdzfSaS3Q({r3|uSUb-5+HD&*6IrTw<}UaR#7#d=iekb&uiD+cq4JyfQvL@yEs~;C?PF@&(h&pnA8@x zp_iN0J_akw(~W2Xm21R^%oU=_H*D*nn2SRuQOwk04W8iKOlfe=r&hLLYTc7gVX+O>+RZyr6I*5;S8Fx{9^XE=B-pJnn&h;Wk>_vWsT%r)^ zt?DBD=}St9F3B%v4DTQ6={dH%U?$CD+Gp469_l92e?89oyJr)mIX~2ErMOW>@6|u-!sFF_{{3ue8bTQIB`$maUSitl4*Nh9*h%amK{Ztnc zdSM>=*;|^SZ_b#_P9OECu@O5U*I|dXaw3b=6;FoOH0PcDir^J<-XtM99sCa153^Dd zc2qQy%>YsuKs_*Op?|reJ2^)7p5%TkNAa!VDJjWh=;;%V)7;z)GpdX}9zAqHR9A!Q zAu{qQdh{R}^W3_i^ix5gfyMRyvEfPc`ZN03&eN~Io0#lBd@gQ7oP=khu-2*a(lS&f zC`-#XH7jH@Q65^wA1kBgL`+gkcC?~32TgV_!`{3)jQYmn3N4VXck~#ysjlR$rwg{9 zAPuMaN;dNg0$c4qEL^mDcS%*p;N42C4>MYg-RnI80AZ zf8xQ%g4mbPdA&gV+>k`e!>)n5mrpo~GPn#p;hd5u<Bq#qwhS2LepToO@u;>vO zZ?a%9f92nQwQpd_(jfZA)%Ta#^F$d0MAb;+gCBpkXK-v>%*)Uj<<}lN{F1aHsXyu%sw07bkRq!&)0}OX z&c-b9?h)WQ!S+V&MsD+Yu1KSWbu%3gIwQwBdn5Loo`C5Z?j^}-%u*BY9N}xWO3(?q z?6@B{ExKs=Ihtk3kH4SZN@pN91}*X+bRc8bvXIG@*&mf7?tZF@xVobiBYdAZDC&UN zW-0Db6~$~Evd^yzd$Aal0-p18anbz1=u zGQ{6e(g!+faiGy1iwv7rJ>E-Di3^V)92J%pUFy6uz^zTKV0)n{)abXL%1>r}TycNA z5&(gAt|xyvW49*Y#xiYhFeNk}b9 zF>xR6CsEDj1-z31Ou$4owDHvJ&z|54|JtP$j7< zlr(;W{52-uR?h28hDdmx+3`!GXNkhI!G{cotG{0(aLn|>gQ2#V6rssvphW_Td=%b3kLkASL{+?ir8 zi{Xq6w5=xKSo-p!G>u#C7_mwoOEp$1c=;ul1?`ADW=fSMVe1x+M;51|NnJ)59Ym{@ zqdU8Yt<+cDV7+#YvfEb+!~-RH6-R3r)ZL5G%s$#&b;y2*fulU9szjITjxP^fReCKg zq~`X$<3q1%KXN<0n-&s^8>64{IYPz-K}Ql3xhXbxKhTmIHoPFu9i}_!J-ZiTI=#6{ z;WD4X7H;3a$nvygQdH=KK>Ro^eE4KOB}KG$c{fTN!bS0KyB=!No!VW5UMN@66p0=W zxa!aSX42)@v(uLt%wzb)@cE!TVfh%L(sE1|{_!RNrw}|8m_0s^s%+3$@FnDr_2pi$^+0a!t$HZYmM=AtBdc zi7m-BtQd0{-m&`pA(F28%~#yhxB<3x8owL8;8F^7)7~aH!#}%s{Hp4yx(djt zSLh}siXou6d9Q=w0(ryrAy4$;z8w_`dgBEtH*yrHY~XWczA^Ec^;z|&Fp6sDViYFB zuWelWT$fDR_2sx=$$MS>EbgkX$$EXa^qX%z>>KM9Q%h54oVLnh-Xqe0R!K!F^>*y4 zl|*PMWqqWmg42lSZQRm3;-fB&qNq8IZ3jKpX*dF~|S{URZ}Ddir?+g1-z+Jk5a1&8my~!4PXeUmdY^a^)h= zmCRsTX*?}s)8QZ`s*xZW*J0Rc0~odn(<|0HeXl1lX{Jks$nUHhkI6=a9;(*J^6mk7 zQeU-HiYxZkj$~^0o^Jf3U#aV=s({gX>!m)`OUBhLbEDn^ z+2!!JF%`KEdfv5cv?%Tt3BI5~Qu-Vg1vrz#GTx=VSg7%}ZoZPu0;jg8YxUmYPE}_q ztE9HkrTl)45I;c_tkyLDL$iZh_{08Ar|?a)mdM&W8wyhuaj!#qET&JjxyKw0*k^xB zd%|t|!OQy}zNe%n*dXuPW8Il<$H>4xq%#U7I$z20@R=gjibT2nyL z8fVENq5kW)6e0}uuFDaO`uh~_c=2@nbGbIWY)BLctdB&Q>Ui9MvA5W5P}Ynp!YnI` z$EutH-B$%B?-RrBu_cQ6n2byA`Rv8l-xgfdDqmZsd?rf@^xJDhAh{nfzE6G;us4dK zX(EoH59}C*+zSoZebhZC1$8*o%uQnmLMgMIYl}*L?6}tQ1kv>dGs2QZh^y9-m7d z@x^0U5bRf6<~Y>^G4F0kJRv~uz=L=M;MJf!o@ZNRCq`AQQxA}8mCoEiTN2Uxz5fS8 zkPM!2rq`>gB*SE8TPKeDA+qRD=fRVgdlEHH8hF1nwt6EPED}u69NcfL|Kjt?L6v&9 z&IYT~V0AhA081B(XLV@KD74UO)@_Q|jymOna|Ma>>9{8QGHf*A&K2i5p-+roeFL1z z8ta*``zBBWpXt)1TkuNfEZa^Rb}&2r;N2yO_3JMlfA&8w(VK#`7NhEDcatPY#c<=1 zo6=3#*fOqq^a0dM+pY@j)HWg4;9Sm&tkhi}i76UmQ!HfGk>CR-sOs%sgy`k6zO-@V z_kvM)9sFE{I%%i2|JCwlZagep6jTGI@1Yk>%Hc^janItU!zwP0Nwelwj?TJD8V%eo z?pk!yi0er8*A;xO^(=6&zpBxJy(1w@S=elKZoRi>`e!@~6%0q6&*HA9bXzQRtkHn# zlF#oMu-sz+Wl;?1sU+|eoX$WmD*iMh`d7iDlhF?bRLTzqq@M;qa3k8HYy8pq$^EC< z`LAD)3qYy^9%vISeuC&hC18*sSF|SSvkv@Ng#g7bNdcsw(=tt=zqh?G(}!&x{k}zd z4642jC@}MseZSWk;k0mav{WdN7)hY}(*EfC;zWr&PceD_pVLd3Z3{pabgGvWPp_8* z~Qs`B7`SEisrzBCD-2(ffv!tin&O=E8h1T~SCaP&%Ca=|}D zyuZGW`HR2)r+)2k{b~U@t(JJ0s#f4XP{(u6>zVtC=SS7e2u-sd;VazUu0jeN`n6C- zAF&adgkD!+xS*1muhwO9l{hM&@ruo#}b_@mPk!a zq928YgVHpZDg^x0)At8MvweL>^7JB~vAj28Gl7ZCRE8E6Ke_0E{B#?AAIM5xXdF|k z&DTP5HxIOC47D6-q+PJPj%}_~{PvA_v+y~g^WdW6-OGHpYM!tiW8P=T$8g}T~2pt=Kul`YG3eZ926Bn`?f-8HSl#o|?h!~IFo z<;6c3F0?!NTu7ZQ+sMc<8}Rf>OnuSl0e#*0_4d<)z@O+Wf7!0Lt8qzoV9yXtjfzq= zQtCE%db&5u0xG_CCa>Ob5js*gF6?@I&qt01bd44nPY<33%Y!N{jo;GIpbBGoL0Mm- z4-`D?R(5_cyz24$25By%E752-XhpCoU&W1qrP;n=Qe!o8KZq~_^*h&)QuKZI{1KrU zxy+1SL3Oz`XT5wrjTN`~yUJC7_G7RJ6SuzcP`y=?1Sz&7A2P07e^MCvnY-;`F7IEA zH}tnzNe^edWuE7+-QneD%B8I16*cvCz}ueaP50W4_k&P`M(|8?gt!=NQvT5Q>4ns> z74K&yU#eF+HSXU$ta?i#rmKQ22h}p3QxYxcS)k!7loVKFd9dejO0wNGNwww`qp7Jm z#G9kgC86sJy1ucy3wSrYDQKbL7}Cn?<-Jvo`2Mt&3xd(^w@VhToxr}iB$R%lHx^y1 z;RA$50bu4CL+Ko`Pk-(bFa$RAqxd3pjkDVZG@q8WP%e+Yx4i1H8XoWdgMkx;1G3{^ z_io;H4%6u!+4~a_m|QE*x3uis?V$wY;3`gvb%UZ*e2RU)O}|6o5Z;#9Woe{&1z#|VTi zx9(k@D0AFo=Q9#aC>57Q3>f#K@fls;siLGr?5MAGZA_W6`}V2qMUlz3dS9|m-n*($ zdw+jl`(4dF5Yz7f*-M_U5{SLh?O;{eDI9#)+vNG=J#$CC)rMUMaxckFgDw2q)&(l6 z-?zAF(oOMZ@d3xKyrVcH*qhc8@1@P<`45V)L)4R`Sbtfy3_Bj(&|51N(WRwjaZ^_@ zJ3*c0$KdxrXK(p2P#7IAFAZFR55qQMi_9_zJ;~n|8wR1TJg1ik@V~QcOlU@d;+O&> zOU>T-*NHYzghW+|O&a|6z+8+xr-xHYYf7CXbNiR``9iz7HJ1(w8}G6MZP%drno%nI z-rPkEvq`RhP1Mpj{6VFYKeW_f(R@$X5%pWY&--5-@0oMDDTZl!V%u47y6TKR#HGj| z+mVaF)d3l`@`Z7J7Z^d-QK2oPk3n+jLum|`AOYX%QMYfcZ>TPI5p-pE| z%QbkFs;0oqyr&&*Fsso1+i4h7E5TS60PuZ*rAnmilfkEoOCfm3&## zIZ(SHz};1!n1GzP{OQX;Lb4~{x`(?+0><4+YdweOZ$9n{wbV@{nNvhhO^UkzLqGrb zR>gUk;fIQ>zjY7-kvUGB(gf_Bxkg~L2oLBs1|jp?`CwuUYT#1Fu@I_qx3cyq{vgWx z8tGesDo&=~IC!auH@L(umVn@?+jeDCMbuk0+}tg2*mh5U)?`~>0nMh7xFZ2tK+)TA z>GQw518~Ja>Bu(yG0a!!f6{&TJ^C>8?0<*myFZ4Hcv3JOY7x>OPAA}Z3GihzI+73n1iQbZsE0*XRJdX02x3DTuVZ=r;Q-V>Su zlDId|?Cj2{pYwR$GdnwH-}!?NdUMxb{kH46QUt>Af(9EpYnMeh?>_1h{-+iEJ1f0- zQeBzINpYbqlY>(IGlz1FHpT|1%l;9XkWT&)m2in0NgGJ(Ej^W*6mVy1f@AR^dORR+ z&Ae#7CUws0@Ptr*P1_6z)WO4eMD!CJC^`@OM*<+Egh){cCY_CwxQzU)FQQlXgnXMf zoWwet-^x)waxE(s1}8zCcV6F#L2+QhWFtiePH6=lHl_lB6hWdLke{(K|3x^&&18m^>mEUT@Mm~_fx+S`Y_eEf56T+Um6Z2pnjL}-6#;{PU^=rEc; z@GF^;@CGY1yj^%|NyX!~rNf|1fW@YMRpM!(g2(zUVg{#zb!lgwriy+b_~C{d`Dy*t zNY61Ud<^UcPN{P`nDH)|uIP=n6@8s~Dv74z`uWJ2UtqERYvbz|@WkW=wbJ1bq8ip! z&?d%~b;%+zUV7Xt1>C%c$fjqhmAcd?CNF$&M4Q_aBGe{x%ec091X$ zwxBGK&sS7MweuHEEHWN>%WK~s+L<^*b2aW>Q2o^KQ28cPWFRxq4u@V}sM< zoV>X4O_Va*bMUqj>m`0A;$tA|qt73tR!V;HNV4(u*4u6lf+tEvwL`NyW>VYButW_1 zHn`-#Z8yjWdH*l+y8C57j6k z&P=?-TLbU_ahnntj2Dg@M;!lPfSw0B>SKBsUDx%T0<88e4NH9SlW zL_SG?*bqkH%aam)tALO*CEbbg!7@4rDuYj9U4@NeQo*z>{&h8PUOYQpkyt-BLwXk| zNZ7|xHwL)348fSTYSyWjJ}$)*Y8oN;UF}Ra((TWe)QX&pxm(|ccsS)&G+?j* z*nYQa`KA;dE0q4-&P|IJG4c1N9`;ne9TX2KZeOzT5pXA;A_(G>rrE<~Bq@kGL(07D z!4KeKMIP4L>Mq#>vIFG_r(@*8u9&TceBqK#h`aXM>iUsyFA!q>CWC7CnOOW~aeQwu zw<`LdFsPZ5QFv+;r)#Q@+l|-{&l?SDz8K_Xo&WT*6qaD9xi3Y$Jxmt&c4>Y|C~CY` zjMsivtM7w})-7`v9$9mEY6np~c2XjzL(0EE_Yj|p5#W~7dl-oByud@DnV(8s`ON{C zL8fJW%pAtPB6)Gf@n+5C*oUuAK5u*Q6hKRKBWbpwq3kfW(}Qad+}N9K&8I z3u6Wh)u;5U*jcFP?nz*RRqX&WL_1PeEj3n>l{^l|8XihgRl?aex=1qLuUKjl-)zmB z$QW{?zM(8B7TDSDXZnr|2r;m}CzYsD5?Y)2coXL&!vYWYu7Y1zEOf0QANPgH58&RX=9vSv=p3Fa!DF_k$SdO z_U*o2U^fz1igW&%jG_y~v&r}^>*1F;gH8^zKn+QH^9f(&Nxp$6@MAK?qf2sY)H!UBY9Ohw)&<|#4A4{p zoY_P}Y#rU_p;R{fkt8P%Qf#KaJ1IM(QLI*F@nKtj}*D29wgy?+* zaxaVp;!p!1B$5DRL;zcq+=)GZu9>@=s}>m#6(6vsMHLpGi!hJx63PWhtahxxSOGn~ zzOiX77~T|9I2VRmzK_O1m-d7sW>UEgE)P0xro}phT7O8Ea{}3M2bUacISlgO#Y^wR z+CpK);8oV_@reb+)N+iahNF8f!32}tw~kGeN#vYzIGrR=EyXfsA;%pZ@EK#fdqM`X zw+X}#FR@{K=j&}F)Q%&L5{+@_CMFIuMBViLdy!v<^>n$f_Dml;6e;rJsnGk9b%5}+ zIfm__=rH00ijdWaAwPGSHroOyJ;2oQo-&;FiJhx=b9&2ipzVi**f1|BUQ!6~9zZ7l zE@1oL9CNVMzNz*j0hRSl!kb#v<3Lzj_s%ci5fp!OjJh4U`3k+CuD1W~4S&E)!tDAXHgx3~^-2XS(&GNF#Jqnx22WDE+r16rhpZGVe+TVfMx_6E zm^=z?uaCRFl*Ct{n0OfJApb zAflXdPvzwfbl{MNMmLGktl-g1IZ`=&UE$w&ZK)lMVEVv%-?B5 z7H->0-ksh70{*6WKrQ%yBCcW^p#Jy?uAXd5AG$NWshSJ`lwtslgdgK42U5g8>N+h@ zE#-;)TF3LR5T}2+-+!O)`)^8c@)m2z;;%7GmRb&E#)Va4U`!+T_l~^D*O#k&Z{_@W zT-WPS&K2XUUPGVR4o9YHu-sFQa?kwpEaLwJsfCb_bswb2u9FW^WYj>4tRqV0E)XVXE}L?UwZo!$p}5DsS}7?XY%4za#teFOu5@qku$sec5n8^Y#zQOr zaKsZ*UPuLers!?OjtS%u>{oBV|MQy;=k;1U9t9X-e(xK63mphhfSU7}mqy(H`Lkax zDN$N@WNXBK`S~N_g)FVPovyRGnsV(?R>_eQsXo8^64@xLfV?42V%5pbW40Y{Jga9M z>6MO+-x_Ex{!hHxV;f~cyxkOM*$eY0ByF>Sa;H>oaV5Rbd#N{HRDI=4xSY44t4#Yf zQ>Py)GZCAq=w5i5Mzk~u_!@U0YkXYoAZx6ShHT713EF?;mNgD?%g=a$+_HqrK-$YL z`p3ubzYw_#jqZr;nXXeUlTyzA#aQ~)NGw=oYO(-77ti|Cw|)l^%$Yi=d(!puJ&iD< zH3kWLKiivCme+LeLj}R1FgDBPWo-I1e;6=uV#S~LAISt-@$k2l9V3C4%{BXuYwHxn z%a*_H%B%6eJ>CykDSvZn{QjwQ`8f|<9jZRX-VHwPBorG%nZqfx4?4#nXj(E{*5Rss zttw`tW%kg^@ym~6veT&hlBDY3xmHEvLp__|pWFQY&plJ!&E>tpf7BprMBP!Yu*f-1 z#zfJzinh;{)5}3w$eBj0HB0-@JCm?%>ROExofe{o%= zcZEeKBF3Cz-c5)~i0bn+$7uvVeqlh_zzb7n^0~~_mj=<#TcdY6atZB;mg77x5AqfW zwHLZ~JH#w+Yv#j1S>gm@^Mjdl@#Cm;N8VxM4K%U(T_sWQk2HMRA9C@GKr%K7$n#tT za`ln?2f2EIb>yx)K&Im6{_A$IANLNyLZ8+K;Jz8+B_oJ}SYG}nROjB6o<&v8>i(jW zqFIxZVlTv!qZ?q!oo8>p3l|pT**Ey9=Ix zrBqQg=Oum4$)_B`AuBHP{G&?7_{4H7^f>oER8U8M6fR3+F~OFJd==F3&i>KIIO?ku zleJL>_Vo5Nk1PGiwrO+2`5n(Q=~UbT4y4hFeAWNPpZu$D`QI0Uk5`MF- z82q$Lap5C?Ob5Vod;ghy?=0#LZv%^I3;p+ZczTU=xaXhLr$xm zumGiRnwd`J zaIojuMYJ|JcCo;LOFBK`rdL+x8ftaYJ+1~d6j!s|GfF7;dz3{yhfkTv*g#E;EmW2? zDofm$_ITP}@^Oawl0eLBI6Gc8re?~J#+$X}?6e{Cl4AK}T2`|c*u&T0Rj)zx;tbQP zhmUF!r-kCX0;sUAl<4ME?!hws%0wjty*`(VsVr1^I-wl70znU3IjI-TY)%twGrjX0 zP;yVb9{KR5Ca4rPG10w`O{RZ9ulG{DRe@>@K+rZ`D}9w-S2kSuRg6(0mG45-&fKv6 ze#Ho8U2I!262jqcon}P{H|r@gWUw?BGC`f5w$Obfw$R6U&to?NER2B}mxc$xji!eGNl{ z)2|m=_2yGg)VwOJnH$>A4m5U4>|g75FX0%Mu{$iDbS81MW&A72r+@@5YCVbHPqSX0`?ujKAsr|-1wa?!{pe0;m`f{qIVp(7L%EPANq@=#~XBS?*=eT)X zVhUshwfLIFmG^F5*xq%$B66tGp~7oJB1Y;(_O*A$vmTj+0oLmQve$-90)|4tjD+C% zRv3@0XtbJ93Xp$Xl@L+R_Y2wa%kO|ry4h1Rl!&Ltes&w|@OQw8S`!8*#s=oJ6~Da-U@-@=d7Fpaii1_0J;Rkp?CfITVaZ|1cK(~I`8^%O_H zgYK}gFJ)CvxYzHf3g8{XYBe5-H`pvNJlrX6Y8P~uu`yadtgB%2S`cQFP~F&lEEHO9 zvz5syJ2rU@EwbQVYpg6GWz%A_VkdH@dcV+iUfhswK-QL(FpZUeu_9imYz@is4!r+l z+3X3gCxcqYDS{VX#c}W7qpm1t@(@pWLMr zOPyC9mC?7yzZ$FBZ0R!;lU@YUENXX(yp!i`i-)$m>6a>Fh2-P!SL!oY-{LU5V9mpl z9#l_B?VIn8V{bIX7_e)U#$WG?fj$O5*cKH{seG<b%|SQZvXS93&xo7IAJVkX4${ZP2sL z#L)F>Y92&EM?fuhuV|%^xj*7PxHO2 z#H*@T{Vdm3g3S}S^)C3TYHdw4*N~P_rOyZ{SOZH#(mUemT#Kco3RDz&gzP#a&seHk z+lq7#iLVj9s5~=QR_m0plzN`9P8~jtS1nDuzl=dv7#@j#(o5g8?qv<4c0Koc)B45dclsNfoWVc9oG^$?_tlx3i`gv8EM1TiwTq z{2zyv4cy@)%f9nr9+qu!++WHpGb60e(F?yWiaC0t{x$_wow|HIj2>7 zpP897UouHj0t=>8f?u_%k9U2Ve<-l`@B(yTdD07Pxvlrj%UV%l@5yKiKZ?W6!ZXrj zc3<>x7=HfLqJ@t;M)Z1=jWxYMKVIDxrn*1W4nZCN1>7WXr$ZQCu17WWd*P3o#Ah;V{q{ro8e_Qx0)p%rtr5n z@oM3LVg&c=NW5_vEK`$V@Re`*nkf4%b4V?ISA7m87Ivxn#)(ex}K+ zv|Q_obUXUO!>eOm$2q8HLz#}{b_@72=`H&6Nco z?A;NDM8>8W*aM147zWvBX&a_^ui7K5IDbhI(q6HHN@-|MjHqhRe0GyPb!Ll=-LC?7iy_u zI|OxR{NkupgJ{1H-e6RefLKbI>C>_=i1C*0Yc+#z#Aq!nZ_b6|rzv$1<}4RZ#~+%a z)G0)>k9Oepv0arhLz7Nh;@XDf_92T&X+et;Rf3ydR9uKy)TLc5rb4%6B>fWz7aKBX5X#Ff;na;B;V%I$J~ zT%XIEs>zfl>XtrlOEO!%nzvgmH8os-|BkhBU9D#Y{xhxE^_H|_$8Vo|8bn=jPH*S* zk-i(ATa`;tN?8`ycIDx9NN@VPUS*@)rOIWn#}vs$D}+&JrrYhB%E%?k_PL8M71vDYv#CGbrr8qUO3incD?yv-1x-p=Gx?NWnP)u z#MbTBcGidK6L07CkyA&%=;=0#?Afj_bxV?f5YqHCHk=ew2?VkvXbbTbi)G*lG|je> zNAs8qI6IN*VwiViTlQE`KymV-KNT)fExbB4)5{SX?Madq{UW(84(`g=lv(@as3c%w z)bLJ}Vk724^X-WiRWYIoZhQ5Wt9)p!$fi}@KIN&}I{T65N6E5LZg;0eTwfxF=a5DW6S!o9M$x-9{@nlaUfqV2zd9s9Q*NluaY z&j!SIy|6<`@=1jP&R;SQGGT9!hG zI9g@5Zlzx6NyFc=;fyychBnH>Ra zPsbn3(~NZo;y7bBF7X%=&l$**!)yT-+J_$NWCK8l=*-*iAPva_er!b`{C8&9RTK2+ zANGEa_)AS!H8KNxO?*3DGIcQ%VEvRt139PKbvd-(DJ!jgR^JoaMo%|NUiz_)dQ)S= ziNbtxc%YC3;M-6IjBJ{$G=^}LYVTY?erKg_c7|Hk7R1S8{b?MP|Wo9p>>GmF2}fBj>+UT#ud-^^tL~Ag7yz>RfLKJVIe>E0RInN zS>uccq|#a=J)m6P@s|h?+yVh1$#|_IIW^s{P&2DZLQgD=Lqls-@UX|1lcvIOU(o7L zz53%llQ5Fabl?|!Cz6bz`iP!&SIIN|9t@Hl&?sCj z^3J|CY}PflpL?fhy$V*XIPCM!pBn$9savPNa|2O_AV||72Eg0qt{$s_4N8UAHY{sG z=()O9&gXZDN+U+5H4ndjC-S90aex$mPbZMxN>}jQDMEHLeP=6(0=~p4)I(j`YrG;$ z=W5b|+G(HT?ofI?bYDVzT={WJJ)6g!#|9zpy-{j%>|Gc)_Cg18z0-oZ z3lc}dl!gtjM#3K4en0Ona%_7&HO1xZV5r=yYI5?M_Ke$o0atB*76JGLA^kR;`RU{# z_GHP@&b9))8$)r#>|@0#d~&$Sz*O&(ny$+W2^D3b^9ir+Ju?;?;Uyd?tJYho$+u2* z`T5OrfRwd8nrd_xXOr0dmE%a|ViRu6djo;zZ-|Eua+*dQKt{$E9IVMvh9X+P5 z(2X%eG7!+1x^r~%So8?z;=;Lr44*-zr9rYjna2u0H?v#? z7{tHXXBH=ZuSD9n(4GLfKCsR0mpv#0TX}9*6(^Fuuh%`-#PmhNbZw1iRXH;CUh-u` zh4TJS)%-sfi`SV%rltPoXwd|DGexNKju6EOX}G`uDoeZ<(V$k}JqvBQGbR zWX0g-!)S)V&RD#7tpvB5x_Zd4D#^weA5YektcUIMtUM7ww7K3&`t$75VD@PuSK)**SDs zEAcI)84&5%KH%Ul;%7NyODCh|zz8oDO*SAMedats=O{eSBhr9x41yn~TZt zXIr9{`dzFIdx?dt9f#*W_G@)Hfkm{8KLufMIxp>%hj09p?9TtIG#~T;?C^uk8<0@` zQQ_^c>Fvx~L7-X<3fL}g${OE6Z8DHXeQ#~@Q+~i~8utdavzI`t0JxIhy=f4T)a0@R zWIJdMOsueJK(-^~XB8u$dqKdE{~ax;ZHqq=i2o10XFKw??d~V^zStzt^nj2;-oM-I z|NI!Vf=2`jnh|dTON1}_Qm-RX+k7rwdyc;Xw$~kGyJPZ%i3I;c z$>Z-2^dQ6eUmFoJd|<082aW-tQqe4Kc_71C@EtS?BO4$whNMW}0|ibC{y&rOI0tLo zjf6Dq^2OUK2R1^M$iOW}&4&RZ#7RUrJXP%u5|9)Jv@rgt-vS%yV_qW010DNs-O#)- zz=nqhA*Wzuae^&IiJ#uvXeS!q68_$77v}J>yu9LAGeRpCN3*upwS5NB{vk;97BHDr z34l|tt$pDBPyhm{K+y~oAq;@%dk=i4dKX*r8ASfSPyO%J0&3#O`w+sJ@1U0M=V)Gk zaXA=X{wAI`H0B*tIEtt%r7rOf>q8TED16?5YMujRUu6%6;IxBe0h|S=ESkxMEH{V{ zaP~yd;t27ULoBk%hFcRZv%5$1q8?hO>|S(lQEi96J#b}&4FDpweO6^)jAo)HKo0=r z?*CPO`#=9pfkQW_fqE&wc?KJ^x?)@s92HN-UXDuJuzorhBTqq~G99qwrbg-7+f zdFxLDCFLro)KD+5l209cgc9x!$m~`?rH*h}`P{7~;+aRO6+|=x+^4{l3lN6d3oe*- zMzilA$NM{Qv6o{$CHm!=39~QPJRL6j@N9U0J#yr*qqihR_*o!t7xt{O(g%$!NQ1iP zFzVI)HGH=;C1-^++x5qR#vu2;Al2KzGE&)KO9R|4KpG1Uwz-GjE^$ioOcjXSF_$RM zolRu!?bL^fE|-eZb93hJy@Ns`IOlA)u#J!Y1^IW;ddiwR;^12^4f+RR^B=002R~Tv>{YK zaiMMAqeN#z$WOX5JmKr3_?kmg6qoo}76XqOhE`LD67{j-CwOo6dbsGn28ed#%{IZ{ zLzU->-?n8nD&O>pjLGYhINGpP)X~hxK8%nG1ndM>nK3lLo!W9DkjFp~eXZZ$G=IXP z)#=dCA3+BBEPlHx0>6OV_OuW~<( zQ<4T!^YQ`(cGR&JWJLo16#O|?GXh?;uzm&(K3Zv@)v|+anQs?@iDw31@w^(pQ7jnW zDnrpp?l`em^cFX?t~cmWCvD{_X^tqVaFy6|8Cez3y*6f&dcSw({$smwZA}}OSSSV& zjTda4l*h|AAR~O&gKDjvax30oXt&2%!K#f_RBn@Q0GuDlx05&YV-p zdkuSYElvaUhhqz8Gz6%iZP{;MYS}*&lRdCZJpJ%XK zNt~Jg=y2TKfD)P!s?Wk43o2lJf<7*P3fXLUB!yBRN=*nc*|#pQh|SU!)hIk1?hkXi z42OwHz7{%7b6cua$uQjsd9;>0!<&J~=7cF!l%&<=);tqGb0qjutC$(jn$U*X%>28# z5@o7=H3A(bru+;Xvr4^!O26znw6U~<-O%|Ud`1D~!_)HY=|&bcWstJcXMRe6iB55N zS|SZwsA~OKr`Eba+l8UIGno4_8`GQP&Yw*#U%UExq9JOE!YXwBxxdl0B!HaRap^rB z$7?ax%qm`yODfLkays(9*HWj)(#nuU_l(fJL($njQAeiYZSC#{Quiv$gA3KOMgx+? z9d=wV@A?me6@7wO)H++;9x7N-jLV#a#ni!q>~TbPK94+;ZK!?ykM;i}%VH+WGRr^|Gi zJ7gZ*iXKY})^hn;KxQK-H6e@7)Zx{PYLeGmePLme+jQvTx`E5?r~9=9T<(rl?AjwA z%}jGBPzlR!R|W+#5Hv8+6RY##TUjC3>ojAqurup-aFG z0eb7EsnZS2)k_F3RH5c<`^dh#eo1=a9FJbsZ|Oecs;IPI8P z;SU6`WzW#MZm&OlWzh1?hvfc_OA&S{y!UP%r+7K4FfS|oluQS#SctqmSlE`rR>!Vb zVuN{~lXYgOy^j{D(+jc7^M+f(J-7)3qHeR{A1Z&rG{Q8s8uoIThikIjDZ^Px*XMM` z-66{_jY9EF1$e>gjH~TODK%{3r})G6h8xgO8@lt0h{FWGJ9%TamXr5Y`G(*U+-A3) zDYjLtyYSq>KFs*?Qm=;6kg~2+pTIE1%iowPfFo6gt$q!GUv(Ui`7#sd;>45ERN&TZ z$BlR(aHA%*ws*?*sVX%AT%Zb1yWh>1*Awst42Bm;W<~OZ%xZ16&B?U!pJP_w0o1qx zJCSt$DgjtSZUSm&67ay8N4@8pu8$ZzQY!Nlahua-ZcT}_QgVB6;^Joo5L3o7cLSNR zm&gvz(MUHZ#fV=fPv5Gtl|^?}rD_OuZ?Z;r>eknDPElCO{J8UgOdHiWk@jd59l%%` zefe9xs(ic|4&X9DoJLP^q&%>k9hPkT_>#HO^7Vp_0fk;f8=u9$*LP&<_vZx()zZa33JfwtI@aYJl^$4FxofwEr<@-f1J{ z$jt4iF|B-r;P=!pSIL0>+anu(AT)jo4Pdz7c{%Ty5+FYvPbVMmUIk25Z8i*0zjsO; zD0i&}NC!ZGl=;tp`2E}Fb3s0@qY`;9qx6v|R`sefUE{1!e>O@^=3`YF;AufY*=@Gt zKsc(ziHSi?gT771KMf>`R6QRQjLabl-y<&$d0m_>0 zMjPtdPc^+|pIyBkBCL{F421fm=9Rp3Ixn^HP{kbfj%&P@b;jt1{^Sd!HA`LDa_Ml@ ztRhQ_k6y=<_2DZO{Y7J`vQuD&F((KE@y@iql9OAlp`8SLhcnqh&7o08&GQXPF;V$m zD(S*=@KlPvF3#pDw&n&VnsHvPQWq;X=r&W8@~+6lcthtULhscKeQEwH zbKr~SJ7OhPFQZ&P3=dcLecMlcVI=%CN$h6D3;cTJuJ5}Lf~_7Q0iWM;f=?v6oZFQP z)#g;~Unf%`1 zqEfx*>d?&)I)BM&*UQ`_JN5Og^$4-scxaO(CBf9vF}|_nzM;QZWl5{g!?atXu7XOY;!u3L4Nmgu3Q-ads0P~^ zJQjNpmL>s|5$>S;8;;V0IR(QWxQTuu_141;@WPE~Chr=9(vO2>12vVUt)pMR>7J}_ z?Kt(aF-SV(>k%h^*(oL_g0_Bje_4u@l8faBgN9OoPF9fGuS#tE(U(F*=Gkjjy)O*& zC&w!)#%#N10}KN5g@rrgkv26J=>A_v@^FnYYVtEJ!9QpM#av)wO87?(wh= zXZorp;gTABSaGPQ!z4>+T{`^PE~H7M8QFtJ$_m zog4gz3G!F)=pbdyX{KYkLI~k~1ANkTza(8T`Ujr{ZC{@Yy`Ig{!pOw7fGlk##)4@f z!?{344Fz|$J?zh(?;@|5ITX&5lw>&9{e0GJ7P2mLCzus|+IQ1hGTdJ!n_#F}*7qbI zQnI*{U&7&4y2>fcc~^HPHLJ2=YW{NGySQFij~cIQ>xL35!%Sfb=OPWnSxqhLOyC+# zPx!-psiVZmLQ7m-Wy#NamaF5&GU7kQP#))9cptj|W)g}no zzOY3~+Q7X{PJ{Yr%l6RM%nxTMhToYYoXBi_TWM*^ScydCuV9 zF&+V}ny=%=8gF`EMgx@^H#*WRpU$A#)>@QV)J=sS3dFZvoGT@F$JwkV#*hOyd>6~( zZKYJRh3dv!;iD{Rl3iD{c~s@o?c)Gwol4-RPT+O+LL*f5EUUKG>{e z#xA?=wtm)^LgGby%v(k{IK#@uduW3ra8OX2t^e)27oPUJLH>4;J4{CbMDlvDn}mc+ zfYQo0Q(6|wH(7B$=GrH{+B(MqN@LDC5La;!$ebGn-IgW*j@m+<;zRH;!n!-G0$f9a28A<+u#%a(y->?^8{bs%Qs{P5U1+~ z-KOix6L_mm-aYAgEGTFyNX2CVO-Gc*p+m}#N^aGuq$A=ag~rFNx*iEY1y&hEi>U-2 zs)#D?PeFq{#P$5fqK|S}KKI}KU_PsJI{xA~F8o+=ciyJeMn_&dtpv*5so&C^sFeRi zKVW}aj7|G^S>K-TSjUDw1euMS=p0Vv?JJUSj9kK6>SV-uQygn+rVx7{^w3aHz+*vF zFFCR}FE7XVMo`{)&{no-PqR#%V_BJr^6$uYY+Izk0PNa7m`IV8RDck;pUl(%WA>I> z!aCx$qh)yQ3~=Nm7Z>rSZ{I}iPhR-YQsBNRX+{5$S0_Kb>3LGa*X|v_507^t7yvtF zVmz>8J~GWRZ9!8*T=Ze#NE#v&##MVtyX8$e1CvH^zr?+AoX`DKGuJ+O5sT}3aoLB0 z9>pB?0B|Ma&jHFo*6DEaF*FHLV}H~-M)vlHgzl^y_!|hOij^E8+{#20MD7Rh|NW^+ zrLlo1nEPU$D4^)%2Bb?r9K|;Rw){F7txc3a1?fFR08EQ#UI0I3`e>UMX0`whSe zBJ6>X*Noww0E|Zs7Xt`HZ9)F-GL?5BV@7KMvYP%KfQ8lu+fr9mfM9?O!Pvr&7y4bK zuE-wWbB^3iB6G*8Ed={N1gtG3KxzCSGXHm%?mT94HLThiWZRVdu}OZsbY+GQXic@9 z!{0$~0sE(<3#&$|1mMq`;X5j%yrg=dr)u1BP;6EmsTi+jIrtrf)yBZfNH4lJAiYIu zzRESv#x`5OPBg|R@3H=H^S+sQ?)<~)K(O#nJHT+X&F0^|`Va2a#k?QIe$I9OFdp)d zA4x?YC;15Qf+aH7u(BwS0iu}$bLY?1JU3Iz?!B8Km=*|573-t2msBg$&U*sf`RfP4Pe0|p8$wk zbKn^Z@0s4JVVDSj6LvWqSQm-n0H5g%ZQ@BlISU{(z5WMPZRWS{pmU;&ivNFnU$%B& z)zk#=0w=PGqR^EC#3k;{caR;VLE}KY{HR0rcMu;RBzE2hIWlRxhfZ=^oxsPqETaGy z_3_*9peK&sLGG_8;J@nBpBvs*xAzji+)rH!{dts__C2{y-39xpOVfWbM%cDej#F$p z{-RSwelbh}gf&6_K=keBLHPgd`_@VC1mO58acE2wmwW`GUiOWwfu+$W0}0HU`1HO5 z;JX%QKzSbQn|R!8WquX*q@Eu7#o z=p6D53+mAoJJH*;%ISK;EbX2U%lm0gs!UJVzvbb*{l0?|%l}f|(=dbgA~C~&8UbF+ zI@toSS?J=M(OZ{+Qp+i?aOAEFfa3%KWg-%W?2z802YLU0wmf|3QgU{b>S>}5!BPlD z=MP{<WG(qCPEWY#itP?9#M;@O+? z5%gC_?C(-C8+Ou=WQ&aIA#}CUxUYuC)WT)j%7*Yq^5x2Nd>|XBrR!khuze!+E z8Q3cjXlX2_L=@)*gzW>ASidvSupZ!0IYnf13SH}l3)hVNp5`-@zpoX0v&>?mQM}Mc zySYarGoEb<9Jf6OOykDLU3;?dX8?FDP>q#PxkA2JSB0plb~P(+N$m%5ijBk9V*Mz& zd*T$}S~z$(2A34N6~H(-x3q!2S{l}_^kO$^n5E!8vvLWab(>Y9itd>pSzzE?I?!wQ zvmbx-zSDP50Riyw>>YtB3)J>DuYc9g$2=u5t#il8XZalW4Iy93iRJ)Njy}-(fbO^2 z4zS3@g9%1}*Pr@_*KZR3r^P3F**NlD7+zI7W4N3DBYF=Bh3uFBzCYk#rY+AyN5CZ8 zR^(r3md6JA2=^o_`QK~}9=~zR{-#*3?E*bek^Z9lwM~$4iwq zE2hVyz6j%kspD*Fa|WjtOlDd%Qnr9Mbsw{IVv&IaimM8n7tQgt=X0ojb$R1vA^RN! zl=3hkft}fL8FFGd7s^V!G~iZin_JA2SQF3-GE~^Oa)og$IzZUCtV?cKtKU^-U&I8} z-gi8h#*V{m+L$&^B?x4k%mMV0j{2WAB%7(xpjeqDC)k7Tlb9Ixa~yWbI7a3R$o_TE zMS(yWie{n^q4$Ho17Oki@1GdHUJm@jsHqYG0Kfh3r`it|bzi`atiJAkEtiODr9LN$ zfX59fFs;wi`V7cE~KB>|W!XKkxIW;eqLFvV)AiLcq|7ip$7x_c% zH^Y(X8R5kjU^2tJiW8)5&>eDRezOKRrcCL4oEY{=B$JmT`w?AsC z-_3s6^oB{0C&+i3?YF6MK!4$a|2>MIf1mufq)q?X)(F(;0s?}3NZc)Tj6J@teh}5Y zFYB*Mg4)Gmz-p@y_qL5ns#oYruVlHkR<=E0CH$E|<{qR0PACN2;oEpM;u$ptAZ~H; zgs=fu5>bGC(rYv-*ih7Kr+Z>j-fk#JrQ|>g=O?-%0P6H3sZo%M$-htiC$@l#?UYwk zIh@z~a5)om`LpEaqS68 zzj+SDrW)}$lzg=kD@KPTDhdU&xB!K6W1Bu2!3cE;%_&<2Y=oC zcw@D!o~~V<@{6@O_8oL+ALmLA-&Yy)hFjYba*2=}D+1E!WsLb6`iWKQqgk4-N+)E$ zR!F986#^!oU`GZiPnEW11&W8%>nuGqt~IzvqZsRSF<>mI`DlVgMuIhX<9f4BS|0e1(Vo z^vUTzJ;`M>Cw(;h?6lfXpRE1UlZH!gXqPhnqHRB_3i!K|V1Z}6`K@tTHF(E*c}3My zNAZMC2rhnP-Y)l0?xy#hcOfG|ex^NTMl*~?8+PQQl~b@#wbE};#3}FW7F#xdDpIba zk3>kCfpEmiD+A{OF8!t>wlyLf4XN+NLh1s8e(oor{S?M|+N)r$1D4XZOcU?{SfRdm~)>W(D?2_Jz?18V9rK^Tx z#YOc7v-SOWU9JNi1CMYz=oU?3?%iB&4g`k8a*la@(lC z5qw04E%yTunnQcPAUB&qwtRcLwG*ajmq{VFI(uuz72?njRF&aEb{rLu``Mc7|BRCzW3R26wUcG=yj&Al`^H zHh$+Nzr<@h0r#d|e6u$;dv;VHw&c&`uK=%?XIc)aVn<1hko0+OmwR)6)Q9`7m4DZm zVB{b?I!;E+Y!iZ&}{QLN1DLQa--W!r(}HUlovX7v;HYZEFo|R9(mt zv|7o#IG4Bcj;?asTbn(5jFG$hjbw>InG_8SI8qpYW%5*y?3iZ{c&_8bA=wpYa)3=rz5G?t??f9DVMI}?67te*R zM`8^bJXKR;wFQaQS{)xYfqh$r)BZ`G$o|EgBD0A)**I4>Vk&A^df7R0X^ETLyvR++ z?0qPgu8>_&-Vt5ZdRp8%pLa4qcMJ|k1?=P(SlHSweh0Y?$f(hz40}n~UH$k;i}q!D zA}1r2WAGz08$MV4CNeLf6w?LNeKnhK1tuJqH@UGi{Au70(}T6%FH%bE(Qgz_sEOYG zR`lGi4<=-{xZKeNcU28E8JG<-n~=TfUb4K{)sY?G=j2w|v&XM9xKh3YiVH)S5mGV3Vq1PLG_j4IDUL!Ae&oB9SCe(`)n#c zn}@mPWtwceg9TO`K|qv~z2}LmVKb7a_Q|l#*`d|4LfZXnabvorHTS1H3b6>C9uQ*( zMM%&+Ub%R(s!gGrt@c8sM;%nurtS%HoA7S?JYy>V*rxD=xNBkMcaV}(sbh{wU=7@9 zlCy7apxm;pKp-*orJ|y0L5x>?+J#8b6XT2G3LC6fqj5cjXB_LpS#}tJ_%nOqz zwL6YV2u}tja-iN;(_?F=TcUHlJ9h33yKNVmTJGC~6d_<-R#v-@yEu?xvXU5_5Wl{{?d1z8T$2_&UP3LpFQaBW*7*aV z%_a!WJ~3|dVWB9iZjPk(u&?w}x-zd_s{}%?)A8_u=E`eDF=e<^C|?R+TCTHmRM$?l z$!d|~#fzUkXP%Dr)dEu0uG-D~Y#z@?zW4|~pY4Ca*E814@jNg{DbQo=X2$w8I&JYi z6p#SMF)2o$^US}o-%fWe$#v9rQ{}{Tv!XTp`2(Wb=^`S(OmB(07p5 zle18JW1dp}OGon%$MLWD6mbUdh%F_rIMER<7c(%;v#LX(j9RPjV+W{$d_D8|#EL6E z^@5A+DQ&)6#HN(bA^ob%`XsQJV?zmM7PClfwo>a*dYHC5=7;cWWU# z7jNZ~42DYo2Yc@w)Kt6n|DuQm1f)w>Ku~%|YHT!l5J7s2igY2;0|Y{m-UI{`gh;Oe zsiF5GARt|8=sl271B7^%d!PNz?CqYjpWm4?=l6TxdH9E!BrEIATKBrkb$!2|Yeh$+ zr%#=j+MnCvdpEG7OS>{|2=L?#EgM4ir{XA@zlld)f3wz>@M6U4+P(F{E;o(xOyX#= zpgYb_Duz;%VeHcEZq979PnSUaMCrPDhh9iNf4Fuc&cU}3Yrmo*9BEtqnB`7~ygMM8 zqQ@)&Aw9QGH_i1<0UB7h9I%<(ase!oS#nWcNKb>U;h!Wtn+8B=d!Pex@@_-&+(yNp zh@tJ4ZB??*djSVaTk`uC#3BWq3WD@Fnn{41 z4>`O8NH7KfEIBh!<9A=WHSM)T2Lk70(&zf^+|iembX4usU7K>NJ`p|v;kWZ?R-68m zB7WTH`#*l)KmL1;x9j6l$3umLT%%r2wGgj|4;G8S9!kFXgXr`o|9n<11yG5%SeAqh zXG)pWA;OT`3JyEUH3-qI#ZH$$%z?(uPD*Jsv2lUywuI^a=`{p2?JSL=Bbz1 zAD-yd-^sL1+|_Ur?4`XE`QnX|qxci0$_e6t@1;h(4>nhI=-||ze}Qt+1o@rETNN^#}EDRmZJA=d9#}_HgIxKd@7so#u$`EoxnyVf{X_d*1xyoKDTk3aD ze|}@%MQ~cMg)!w2u8ccGRo7MxSw{Iz(GQ$cE%y`Ux)AqtT9`bYP1{I~wW+ zBYSfbbPlyY?byn@xB~uj;jy)Z?U`D`BM>FwH5MEpapOHOZ?F z$&d8nqkVPk5M79{UTn$ySZ=|CQ#T_nn=R6NyXh%xjFVX;7q$BQoFezkRju=&CLN78 zuu@eii-16`OFKlrs?O}ze z{76$F+nf^mt?}&B246TgUqHGlYe>$*@vG5cv^Si8czI6%FR(Bckx7spu7qF1H!d1_ z=SK>-xnx(?@Ag$NhRZ`2uC{nC-C3+D9&UwSBPcF&iMjBO&H5

@_#s ztlzE<&#o?jLt!$)4T}J7?-3&gUZ75AydlmnFGy4LrqeXJ}Uag$Bo#omQ|BIT!^Dx7h#<5>T43MSZvIm9Mr`#I@~ z&#Y527USI^YM(}mbwid!_LnYgu)IjQKkOR%xnj6f(!!!tb4@qZkEz$o;_e|QEi_3J z3&tqg`ygj1Cy?POS{+0cytZ{Sx}ByjrvI$>@*Xyr~|iC@tan^GRACuFi?? zipy>qwk@o_h0jwDE9gwNs(!Y6hODgIfA{h;@~F>P<{&cy$kG;WkLjvcPSb)9!D8#FyD%T)tI z>sg|7g+Q%N6~g-n;#TP16hGx1F6+yY!)y>EU3;W4TbWX3P*sN56p;-wG3sJ_y zn!}YP;@f*36OYEqo0IM`E9u5@6@7xcCT|xm0~ChfLAQaB6N3H_bQ3k?i-)spw3Im5} zf3p;n|FRSSNvAC5G58j+j^agQbBP^_K;&C(0ZBdSP1Gz|7&r?zMiFCs0c}D#2ms@> zB2MY|5og@t30m`n4$uNDM*TNv1^D5=>JRd>v0uTCAc^&Y+ZlNq|MUz0Os$^X5-NZj z2zv-DA|={DCuSuE1>-@RX%ba<`%^V&x!#y7oNge;u95CLusM~~qS!F5&=O=yZqsCy z%C1jm)?XkEY2msbxp$9aT^W$PNuu7u*JyqbF(|u2;l~$aerMVMIY6B;S8tJP>oUn! znT|>BOtF%5EqpVsF9JX!kN4oeAHj(ongwzopH%^XpbW=N08+@Jc}GAjXJH#fAu=(p z9Bd{hzjFWg9Z29O0X4HT}Q zJSdpuz_D^WA>&y!X)Uj!&nbB}&}N}|&XWr;Kv&Y{w7^Nc4}|;w1>Iu`f0o|c+Y87n z+`Hh1!2&4=go()xTC;M)5zjPF6)|UKyki)>sxx-5nb$61cQQIyQ)AP4`QDFn1~=Y( zcI61DBsk5baB|vSvz>g4y4FHD%@jWNtzc^5om^mz)&>7GIS+;o;Pn5(9Qu{d_!oxJ z*kJl&$(dW6Zu_E%ycGonL-8wSKGO1ve+GQrXU|FM+V>DZG*g1iaF;?rl~dnFgj~Hl z!da7=$$HAf^lv;bW8lsEQw z5w8ylai?ev1Kwq_v-i^dps`1We#`zmwC!ncbL@r7R6231ry8 z;<&rpkA+9A45F|t(CKQaggxR{FknJ%uZAbDrh&EQp1oEatmBA{AN2k?b zxV3C8`_AFE5vj-J9@(7VxK#h9kBn+is@M(1cpU8<9dKR$rYr;P+e(^2%?y{8E{Ftr zsZ{3KoCF0C^XF3^J_bB^sG31*!>O(5myA@=MW6jtnFqX5T7Ut6U+dyDG}y$mq-4t6 z5a^wUkwmBW8RCgTeiP5Po~R!-UoYbPQ%@~iKO?t41JIkFN550By4f~SQwMKpTFtl6 z!jq;O;hA)>KK=PfyIRW0&M#AFGXd6Xkwyx^x@2$Oc+MQDzFwBx@$)h(Zphd)nB~Ip zHsr6<9UqRfRPKJe_oL@&l%Ioq*&9;Yi|QRfnCw44%Gw7CN@*)ouu8DUlTO6s^%8Hi z@4?QVKHLl4^b9o!W3~JYNJ)%m>QU4l#l06$cew975_?c8+)Q$V;pr3g)!}R>uaK>S z40GvxvZ|T|Nsc8}0Q`_r5rf^IxXWYiMLn49@HxWuZ7QNgbLJ&!#F+LMW(h-kbZ5b>xAUE3< zN;kiqiX~2-ypIIcH?xxRoZ$TMnQ9&kQp= z-@*r&T_oh!Wf9q|j!@x;XqYVj>cF%3XKe%XJ}in#nqZxAX66)3az!BzCjJwnp0@u~ z_#RS7wUsk$mfPXJxd;WcU|s}sK>AG@HRZ0smdkk~p1h}vG(aM`^!FeC`J=-n6Zi+bOqcjD-&nRIoSZ#Ku=vQmrU z)P=j@2ENt>gXVgFrllsIx(db(S`Ba&|Gx(AYt;3gnnWjxme}LDU z9D%AI2iF}AH0~5_D-Ady(ORVS68zm9_^haAw@X*W$w<7i|36f7asqOPfAz#}5&x4g zMC|tfojmXo$%rsad#>pMfn%80rc&!~;Wta0ehx)ICb@Hqqo=^{R5s)9T9pBO$)Ilz zfE!Gza4g6#lL+ah4TvKh4m^p7-Ogp)%^IZX-RXY2iC{Fc` zLwB`*g6!%Vb(seLL)LEYBB#%S{axqoZK&@TC+jWhRTt)<{#Zr^r!p{0CnxsFBV~?@ z+t!NRl(?Z!GztZDaVn8VG6f~}l#hbQ;`=RHd|E5YCOC@ zRVZ)YyJ>F{(IsZ`P@yMA-ajtyN-vv>zuo|2@kilL!{rgcXeg|04#u@Do`*@44`Ku% z`-W08Iz(1ovzLpn-M=@Ujf>y5ga|!;UUjdVZCro$?qCIoYF&nS1IHU?BOu70T#V6f@Eb3r`d@(cVR&^b_EoDou-^qaNoxJ#iTLOSi5>=~17 zl(nUA!me8>l=8&2kH3Hv8qV4l=9$cz3_Sn(ib=Z%NppV`6&U%(v(Ivk2*NgnW~C_# zv?r_N@{pUqDsK#b#uA+!P}!Gbs&dT}4EG+Wm+Vfr7PC|`JL?8&X?0oXg!hcp z>t)y!lG?F!1ceXK_0gHm@Vr#}etx?yv?tH9n~kzOk_}yHcb62Ckk)FRSdnuj4F^gg6R(f)_j(&xCqslUailWPtq{qY&jZ=eRX-~v! zqxWs7)2Y2k@09{PywmtngaUd?(?xR(hB!b>961e?E7e`;TGUFDd3;jpEj!VhFyZ)G zTlc`>W!wGIxT9x{Rl`M#r$u*3r8VDK^rHqtwG|ZJFHDO+0nxL1B0bao7|mS z4;`*v*|@GJ62{4@9>yWON4vXoia>=vEk#~)ygls? zejKeqO&jZDuM&}3W4^VC^^xAq;lU}4+a`mjh;GvyJ77pK>-z93@4>K$2Go5^ z*)TU?O0G!W-4uALb0lu61|-HV^0HSG zykhWobbBGEbOg18#tq@LF6@Yd9ct^`+|(Jxp?3xDNi72R41Ypg?KsJL>pb9ox(H12 zvA-tya73FqKypUoSa-qs`%(1TrhdP!=KtcX?x|V*;#9}{W6u))fk?Rp$ORL#VqN9o z&@b;$qF)T^;JB2ci$etwv~mX+Di7m!Ti%ofMntF^P=2BI{$Qn}tW}IxeSlLglCJHK$hABCF1?oCSP|_Sxybyq z+0UtqVRf{5v|yQcbh2t{Be^L<|G0cK^P_g=^%fQVCnt5gvqDmw`rBtr`AeJ?AyNBU z3^2u7tbS0w@uvAL+|@6WMnBf~1pBFzqx(Grd(r^%N z)~Zew_+w4d=0;X*Rt#_0JVrn@wv&1P^!8^##h%-JuX|ZES-ZFdW2^bUw%zpKxV&FH z>1sV7SIJTzXZ$*@={U{JR2gJVE*ZZZ0Wq6$KTU{J(i~IZOkYQr@)#ql;;k7ATsKWh zP?vQFRz5Bs6Ix}g+1%!whQs$`*j2ODPgS7~FhimKkTZYKq4CBIRtxwlwIn54_$P^g zqhb^J!AAv=jK0rNRGM6^n)Ej!ZIQ2or)+Kn?cz`*03>DP%%!c?AgqYkU1Xc+h0O8r zWExu+w;s1YFJWpg;uNl370xc{DAeHN=+P_rEZ$kmKzsHiTZ$gJ4oP-bpnk~Wh2L4c zf&JnK1)owX4IZ-jq+p1;DOC=~9ZC#=-Np5NNxO`fLNbPeV=KJ{6hoX}#vL_~NBeOMA8){Xr=!mK`iv=ha$fL<^ zk`p7SVmva}kBlGj=W`&W3KT9Eh!&K_R*>8!p(5jan#3JpZ*o*swlXwXVt#Ub=`hPD zBTh@hKGW-Al1ZWbfmubytL`*&Ze;*EPJaXB4Ae`D9B;lc z-dw8`k_&d!4;V9ys!Vt2x!x3)wd&~PKSDK{fQ8I|Lr}uF4h`blJc__(eR*~=`kPg< zjy$hRpWc(dM}E;qfFCN`ioDy9FkL4vz0mN?;dr~Rz+!zQU0=h)lHJW9{F!eQPh4YC znJjpcPp!1R5pONjkx;+ey-308;mM_M6`Hk_&f}c57oG4GpQ2tJe;7K7xiwY+rsA@x z%Fm>4;J!qiVsHl^prl|y(={gCdF9Z$*t2Wm@v_=+>;fZ zNGg46TUlnGSvl#UzCJV?(nsl*35Co-Vvv&uFr&;FsGl)eC5qVlCE6oSXrBF5JmdVA z_WYJ^?Z|lJcRN}!;Hwy6VFhlUaMV`^n3&C)MOabR&0EgPMpvHdxktZUXbc5Yt!re$ zUkQJ-l1xnl{~)XNWL575i?BMS{U|Wj@*_xmt}M3&5J|2pmF{sDD@wz;>B*dXt*eUA&qE8mLMzc8*daOr9?8r`pSn-9nXR$#;QjCv3 z?YoFr_cErwH}#S?e`27%zC^U{t2zONu+B9_1IS(``87Id)U#79t)UCj(kPBskKRT; z-Z&&aH{GQZ^sW4kXS9`Uikf6dEP>3=sOnV@IWkTeVvC~4k1y*c)C9(McaB|RUdL=> z*W@f4<0SJcHr4jO1dF|TcRR#(qfQLP@uMJ(6RQ;hGSCg)5(^0 za=$Dr^R~KFcHO?il>X`35!k<|T&ZT5URA*PIg;N+l^sBFkH^*L#l?q09 zczWh(DrQZNwyEYe<5W|JfY`fXyAqAKxOy>U{md?Ssh0EIfjE-PP^fCP zjiMrP^LVp;j^xW-jNe0pdiylCA()o+p{BJ)`6yDxaaRO6^lG4Z?1EKF4iv01XccWH zuU7gc@h(|`BC<@S_wvgHQQfP&D^^p(JZT3NSm!kEDQToDz%wu*QtXT7et3{Z_k(B0 zs%h6A-8<-&rg6$!9L3a{DlOnvs_85Xp~Z1u7S^Tct_mYL_UoLsuHuKVY%(@s2h&(t z5~lTyJ%a1yJ&_9K(xcz{h4!`|1QX6SM=H=)*1VMRJ@O50CViqaJ%A*oOyXER+pr5F zwVABuK(aO|$koh3r(#58-&tL24NfAp5O{G>(QNyj12s$)LB2=!AT9TtlaEv!s{zt z7^@$jIXu%@b@(=YE{XF+{3R6Hk)%W`qt0$fFHVP$wDECOyM&+CIR@DF4=y*d;XTfh zij4r(ZfgmaF?Q)ID`wJRY%>%-8R_FMj>g_P8UKur(2Cc`5_2smj$KSAd&!lYDnDMn zv?rCR%t1bGv?uFr}o08h6M!3wt$qNhbf9tA ztd5jc6d3fqZ73c80&D&F4W~8=$AP&1!*hzgKH%AY7dWrvDfj}^$!!=JMXJHlEh`sV z7K33v*puEqvlr0AJ~~-#?_+8=NTR7q$(a13Nf7o|5(gAw3 z$M{WvHj{uNW=={yI^;Fs@c!M1{qM}#HURYhV-zS{^&@c@z3TvHgm38p%++Y)fnV5~ zr70p{ocfUq=ylfF^wR%?e_JP?0Q!(i(28A`+H)J<0%etiDQFa~p&5w$&}T$iO5;`I z`Y+g{(4qcuj6~x)vxT8=_!XXcU0Nrw+Mp@!Q#!WvF~11{kan~&g3g%G00+$oewYlJ zkKiOm>;UXG!KPD9pPS0Im;w*JiLTN;I6=$;@J9Lk!m|Y>{Uou?BwhzX$!m=SK+oR_ zn2IXKhv>C|Pg7fSboBum&5jtr@^l0c;uyhaIWhQ`U4R<$^cU-mLWDRBR?}9k%AZ#J zHcByTD zHTSQp`&Vc9>rwk_WcX{`{cD=|YZCsGYlhvf2+$m&RwhOPWZKY#$`aL#idD-!iGI+$JHC(@3I^n1 zl-Q%6BtpPq==aY?yR&`Ui@}>U0*Uwxre6{@QkddTl3wHA4!0j%%S;WCP*@}~LBgBX zg8o*{2~7>(9s75O?|a=|eerGJ?r-PJ{O#}tj&Ru@E`K#x=&vgb6bSoUkNQ8nOB4Wd zg4b^2W4^rjKvOKB!UzJ*bKwNNf)Y~;wCshx`b(zDeJ@Y3&55+qFnU88?{|gtF0hY| zkN3LLUB2?4>2=uvy-xi9t#B4BL!dk>0nIbx`LL&IK-=!%0`-Uk*h7Tv;h98hnj3N; z$${ngOZQ3?u1ZGW`KxQTJ;J*C+sf*c0ec&`+%FUCc61gFv>~(`piES zWFcKaPd)p4IT_=>m6Iu4{?``@w^Uc0Knog6zgImk0F{gUvVz9^-&W8P-+#`KW}b=z ziB}ZiHXz&7QU`d}ck{LIaIxXP-LgGMIA;Z}iP|j_9u9DZcFB$nSmB*;kQOwPf;jO8HY8N=pn+GGg*i7U`~%NZ3RQkl zN;<#${BzeX-(9J#&Q@Pg=XQ&eIhQc9B&rZV?F449o8>9TCtz0(L&}F?@`W%Od^uI3 z2*J#f|2`moGLe0&B1?rgm!;v5;IS)dQG7G1+6JOgus&5Fw!^RNq~r8{7>I#6 z%~RTqtwW>$9Uds=5buWyCHZj{y31b3vc>MuYuY~~fBc`P?gz~zvRFeO>o6|6yYE-HF#$9Oo- z`@@TY#P|T6ryo}x4_;d805?kE-Ev@2Jkq<9Cjx(WMy1w|5`kVMY%qN}+yZh|-8J0lh!GXEZ%)^IxgDks4Q9EO^Bmeyr?-cpGwhl>$7uPH; z6|UVcNou4gxQ-L4eA|85QG;34wIjj`_D*vMYV(HolUeVK&&uA!711njUwztf;}N)C zn<lncG@E6r78 zg_wd4CQh-aF7Wknw9`|V9oytniuhT49Xl)TohS5pY{0J>nrMtphWkrk2iB zURH6MwY%F#W0oFF9~)pO>;qAT;c)U@=xb5{AJ+UbUVR>E)!X8=lKI&b8Ur3jI_MK7 zJLozthY#{SjWo8sjOJqF4@B{nxr#)k3E^cIMZ(g^@gYSWi!Spqmw-@}Q_g1TgsP>B z+pC;ciOh?ko-fAK)by22gO{|Q*X+zH;Ko9*y)7rGaF~YAS<2`2r>rTxP5kPxye|C- zmU0<$KROeAl=kdon-c{j_>~dO7Zu&ez(5T69KpRD-?xDH0Rd_hz?5<1ZBJ1+>fp?g z5``PJf}#@>CZ_yfxlz?xXl8B$tz;FqmqnuD*S)MeA<@Je--%pMzWHMRJbYjq78Eqx z&XR+ihf}DRY;YL815iKhHxsyYdIcn7Sbavd112(BTsDoviqlPkP~x3@mb0adtl`mF zRMjPy&k}ua;m13Z%%ulmX0{6yb(*v*6BRLZ_QdOm`$>AJWNAUvHbvJP zo$3M?2#bh%iME=Y1A`8appx??9)zv+6tKzgsoBEXb{ z8km@bO34q*Oi_^Bv+h2gBajNvdnDL_-ntv z#vGgp%ST)n#H?u4@HAB!zOss?xD(|6hTgB^OLHW-Ak)z5Fb56JJasXK%5bw*!?Y^Z zD`?)nR~C8e2Cb4!&ZAh_OFOq0)86WLz(Q~?3rA6~OE|h#&=m_@OxQNW;`^)dgC@o` z4oWkyZjFA8&GRs!&nW1P3q>heHlfGYt}^yH5^mxLapZd~AjWvJaNIJsvUbLXLkKtK z4+?fTxW0t);IviezSsDu^h@mGQ)zLwJCeWy1=3b^yBe=S{y0X`aF1J`EM8Gu-l~!7 zJIE%6evLy2ue**j-dg|>Zpn=nCluNaK^2QCT%+e-T{L0cue>*F8eVOX{9rS_!xnZG z%NaR;R;?u4C(=HnvOuW_Q&dWKoYV|2&cB*?q;i`rw49~&+|$7XtA4{$px0o?1y*DH zn;7b!Epqw;#XbSZ=LW)lbl=CLUEf`X(>hEc&%r9eVcafHf09APkk+YNstpaZ!XpZO z`#H<4%0J`7d&WD>Wk+3m*|l^=1i9m`B>Kp08!=}nKNZDsV`6%FKAZJiAADYOaK?ZG zfQ6t4UpYK{apww*2f~6+KGoKwf?V576Tuz@BQ;UvBJZ*}%M_Mf)u96!}2<{C}t%?()3I3Rjyj=_M~> zQDSmqq(JD67%)AZ=vvVdeAD%=F6((FpP#t>k_bQ&W>ZjJ5DA-Q7|h1!^EP>SUs!GD zpu#nUWMYYhuUfgj9^U@aX+^$xH9PxQ++$hO7T%&-4?=DL*@XQFq(p~-F=c9FQ+Ah= z1zBo+w+BzsVei$*p_`VXUxd%UyIysj#$P$$#Pp;!{5ZFZg9{&@iSL?kYVo1#ijoPd zj8IfH7=wAhvnDIQ>A#VCYv`b@Pc4@GD#l5N!D3o3f@Ku1w;02U*E;kY^}U8iw`)Ux zsLxm6lwudCzbIQezhvT?gbH&<&?FkzOc*gZ7v1lsr&|3kY4Dc#{ntR3NqB?@&-t*n zm*QD^JK{EY_uBg$t`4<2q^omRy?uhb9H+D8ynz-J@lVE{az?VCTeYZ(gH6{&2-*_%G_b!?W)D%hFofQi1_VB(vpV{|@21+X#jiOjYCnW5IFGhia zWdn4r@y3MRNfwJ^!wV!#tY#4Nnk$7La-)WE@Ytz+nL@CS_i5?$;oRdX@I0l0?;A6JsrxA;M$ojJGzH4RRUku1D0W)2iUoqv*?st}5Ji<+!J92R+h{%MaYDhPOT9}Tw;&U4ZcWy$nD?a7zao?U3 zJy#ypcDK`h<;-Of)qWfPijxW2t2LIcBvy_qs#T0y?x^6B@`FYR#_?)1n>9Z4w5&>O znMs`xpen`n&ci$4AuKoWDcPHcL{LrjP)_t|+n0`uQIc;wiEag*t?Cy? z5*}!B?;7yznIT%?bWmU=0q3Z<#4dK43v03en8LdcwAWhYJLOLOFnv%q;B?`g+6}#^ zBv;lzhYO_rHeZT;t|QSX2vxV~`G1}FE^a*R06xjpHeZZbjKvPCf+-N!;PwE3RB9|!w$?_o-fBaZ zOD{~%44XZWoO;m)=u|A>c)I)e%>@VvjMl+-%6wEodr^y-aK8qdWq!-VKGm4n0kc|j zp4j^&GJ&)^D4^}~q+z}+$#_1D4qv*siXX^lDn`|IqUXGQ}#@qM+B~JXYR2Qlo3kt7SNK9jC ztYnhczB&5Gt(ml|qVfHlvfRDReLnN|hN}S&bnyQK{~2uR)DHglLQD1l5C{6CEk>OQMB;*gpH*p$9v;Lv1H1OPkc*J3iYaUX8Uv7I!JlT4Aa zea{oGbv^l+LW;`8#|2C8K7Sg2OE`yrxVXatlfrr7Ef)G#1NY4=I?}j;PqlnFSXV>g zwx*pJYYDj&=bnqsJm<=q&p+{<%7_T>lEO~S6XUUk%^aLq-{9zEl`u<&C{*%PY63mw zH}6teo|Aa6f(@b!(gD6o(857tBzK~`{s3&!SL=+A^ss7mX=C5nI;<&tDjz&R%;=eQ zt-MY{=xg8G@^sI}E-qXw&B2rL;?oYj5czoyf3d{i?4+|Lz-< zkGWG09zF}~HiA;Is7@uSQ^+BNz#}KM5_^ai=sN`=NIOV;aoKF-kH#g@EDBsJiGiKA*vL>oxIX!kylxyE%><5^@#4ZLqya@O8M6gFX}M3 zCHk@*f^;7eYI<7uRFhE2K6#BHm3S+p_}e(!MY_k<@ixufdZuyobjb_CO%}jf z&9;$!yq;!WD}pJF84pGdxZUas8GjV)U1wOFng+nCTNis&gz+=139~;GFP}Jv3p1)l z;D-+#gEtnNCrkVY_xbz?8gqS$6+Zm?M#zKm^{vq0v4xAT_UMkZ-#si85Wb&_Cp~o4 zfnrT)nHB>d0IY)jsz1JV~Ug5>~>u}xp?vhs-5kavl=u1Sp z4LCJKK?4bm=on9vz^R57b0wb~s*xd!G-zLkv)VuZc2oI6{GNo^OQR?Yf&reX2;aNl z5#)OVs?n)2w?n&D<-uZF;*u?#NjCFofVIL(g2(c@Be=_Qs!mfNI(Sa>TM|A=^p za37a+h?s1e7bF5OvFuW|rb6H|JVZ zm@q-pzR<8)s916{Uzj*bp_=%{n2*F!LG;oM((t2`+Y}rE^ALZcR8rS02Tmr2$fv$A zKR=RkDx*2H?=~bfYxJzwwm88me&9{bq|zfD53-B3pcn+uiRYmNkQPuZz#je*GqNSc zSqT^lIdK>lwo~aHaKkdDAKL%MDSY4S4q41CJQ6E?)M+mFUBPo+gdP=8?~*yGnN$3E z(hEFgZuIyo2I532&iHhV`El(vm+n57a^D{00wM%NTS3hRMeE{}$`{v$6gBh%Zdy|o zboxKn(0JZo%4NaB_;M-rn~V9evS{gE$LiMTVhp1ho)OLrGhi9QtD2OLVjLuQsCf|% zPD|%6e`ItQ;wJT*duv+7#S2y2=s4=%6jQ94MC87876&7&6|*e*>O=`@9J|PWw|TP=)V+wUa}+ z^~Y1>tnbz`4s~7j>k;{FU})69nanxtMAo}~hVHeR-+_=pS5+%gG|lc;>^D`_mnFnb z&H_(~VET9Q1lJp@@yXwS2#Phuas5r)`Q8rBYi@nk?Xyz4Otpu}Pf`wP*t~C;T-W<> zk8a0jMNnPPL<&>!lu?26$yplAtQdOQV$O{Z!f{YDDbH<&l}HwvP*d}!@Wj^(QGax~ zovevh<$G<Wwvb;9_PY>0v#}J@-+(D1-9-dIUgK9JY+G}BpcB& z;R#}%x%K`PS?n{iAETW9wPZ~l7?B0Oma4HWU<{y(e-JDWrdyYzY>Z`NimUg>d_I-5 zg#b`xmn+;_vEjipU^{K)4hJo=Ul$zBZocVD2Vo(Y)#6k`2x6Y>XK4t_%$yX(CEOiD z<3rbkdTdR%uG;8_^UeFkNAm+fc-Vj_gbxFthPG!-L|TlMBKYemDCtnM>Yd54H;Vmu zSH`jRY|A1t?J)e3#VV5e5Hwr_x9a1Z*W3vc&Z!+dkd;Epv{y+NLY%C<&(ejqBve@? zBi`G(UsJy-<2uuI|9^Q-EgMvB{kls{N$gK#fwX|G6QoC_JS*Ad+s9eHB{8`V`XsAm zj_@VxE3+cr(>Yv)I@Iu=>v)rdD1%4AROfam@D}ZGCM9$x-s<#^+F>LhF{SEyRfhb8 zQ;TqAbXlqTT6MnB_>RMhG=KGp9WVhXc()_YALJ92eh{8unu`II>I3CT)L|$``lU>;c);Nq%m!~)wCl&EFC{RIcp>QfnPC* zh@EMoqe@q&0Mit{d1vlSn$_M%oK=ge3j#{G5IT5kIzncrwcy|$G+Gd`@9+Gsk**3a-GEz^cEnbXoBpBlHjx*=T(R`uO>))P=D2caRnkd6?H$Sg@lkk zNxLDo_eNkHx@c&j_Wgw}8MEx%v$d>qfSXSAe?YqIe`AX4e@|Aj+--->mV>ceCV8yT zBFmVn96ilLgCh=K6X*cKz-|O;?5EC|dN*67dHmbT?=O;`+EQm!@OK}7DbR-KhQ43q z4Eu3ZE6}HOp5P16hcPtssbDkga$^1q+Z?;Hbvo6;sUkY&%xFG~FA~pRK2>_n7WhtJ zX#MrwZgsO?1xSBs=KnjUS^s9eyyzlZ?&mi z{h=zw5;`QApVyqZQqND%ckW#G0Hr|O3`#LLd`}Dis1ew~4n6|PyhLeA*5NPv~@B|s+7n=EVSk~y?iBKdFL`mI&b`fUFVoBK_P9UtEr zOL&U24pgwnUz3|!PnJ$7oC4c;Jvy=Jm_B(65K@FjJ26c!^sh3*$7rG@%n{E@ZP8CN z!tY%Duc~7?-CS*~@?{yfdnMRN6g53PqCj?#w_s{m$G9&S)OXB=cvc}(=p=XcGQx$J z@L1S{%kp-YkvvDPGVGTigX|*K9RMS!2G-jzj)69|C1=>vD?vLNVmg^<2bXbl#XuLD z@fFm2J}J1KiNa?phTd++>lEIwJQdV(C|knGoU&ZwMyv@7l9}k{I|A(N{+0ZCPMj^d zcf5GV(WnKVD7~En3N!vOjl!u9uQ8d-p2a+uBMx}x4BD9{!CfLO)(09Jp;)F{ThgV1ZdUA0L*y;gVsLK^M@-jf~ zU&C}fkaVCJZp2|>Kr{n{#bG7GA5JVf!BrkDpz}8Fb*FXDsRIu> zJTXU@J&VF+1xhTci$lobNI0j=Os?-tOfnagJf;;46kIZC>B)S6T}jHKA?Uk1b#`qu zze3~}2RE`ZFRW~m1|Ii=PhaZ*3F8>^B#ORGfQx;;NguuB?1AP4*`S$T+4|CB!ELA3 z8*cU!Zt_{2TgN6Ad>dk>2S)*og3tjFHi_VZV-6x7lmsE?hZt0kJUS_8wmnNUGa8wg z7Yt}#7Uz#f-8%bP(r*ed;9e$CJ-mc|s=Jy_vQ$;)QZTUw=Nu=`X%KOFyuqtk~K@Xm*@5umI> z3W2p0^Sx#M3%WV?`Uy`suy@x&A2TY6{Un+AV+_7eVRw?zXz(j=^poUkDK3WCV+AA` zKbshgGMp_3{3LPBBwhwg@3#q-1f2u;FJ^fi$$$0C3_u8QDZE}Q1B|Co#-`53=i2%5 zjUyi0W306jPgtSDnvL~>H>05xV$ybqO%m4bmd@LZT^WPsz)!fG)QLX1opugb>rtKl zBuSElx8A~`h~a!dY0bdKpCrqDU;*w_jwEHzTml!E6~2}M1$N1o<#&jtz+U0Eqn*F3 zs%HPmFJ>!}9b9)cNdE1NoY*iJpgqb6o8u(h*l9X;Q5B$a^)h2vLHzc{Z*A&n`>U~i z|1+-aYZwy_iEc*(HeSHFr(cyGKOqLbD42fu`f|7|A1Yd3_p18;)!utXHPNW;!cjy- zM5Ies0Z{=_={5Q&ARr($QbJTZAw;A@AU29r>C&Z3mo6=lt`zA+LI>#t5=saV;u-fj z`~29xYn^@8`u4ZJ_gl}8S+mHL%rKL?UDthIc&2x^v^w1kht#i%m5&XmbAHQvX~P$-^yUQR&F>Y%Tjq0GH&#Eac%JEDMdY1e*N`hDatI@ zjFR5nIs|ndmAVkK%7m2sPU(Nq`#%`Vy}X9}v1T*#chr1FH%l`n`+@K~qp*cszo zEj@EJ7sy&~_ayquwt~~+dETvj29NHZdS!)e@#S4ul)Eo8=XPY$9Lw8uQHtAK2Irmm zNA7?M7(7jLeKah$Sq#rMIq@bwl+;4t11a!`#I~2*?LeQC!7_R)&z+!&R424Psvqj@ z41O{+|A=d9B@R@{-`&Vy;dI@KIxfQ8>Unc{mi ziC24*)d-)tXZEjzl7 zgAc)bs?fg`XoUa;TA+JRry9S5@32T?{Q>y%n3m8hA6TNQ%f^}JV4;m>b4M(*xK{s* zB`C#`$z>xr=E5jTtO$9tI^>n?*^{Shr@(#!cS-{e_Q*k`Jai34+%bT?7Kyc=mE_d( zPw@E`?Yb&1JOiVK1BHL|dLBIEfFGFnNgZmR@K0OVFBO(*)|Q#o9h2pl>h`*Bi@q0C z)Ad~CvN)4ItJT(XNcB3FNl?|}@YZs`<##~d!0z2JcOq#5(#AV6sF`8&*9R7-L5j{S zEYLS7;;3ZCpYA@_fTfNDPCVboi%~+)U?BXX$cY=O0ZnJ&Lu*TYr`EVK=mX@bhW)!I zxzYtjoTDP0$6uT)-1mL4BjzjeejXIN2}?!8?Z$R_No`M8^<1^JRr=zKaz(qUFa<^K z`d<$?U$;EAXV8l?^p!_=4#DCv4rC~Ir@2r_qfXf}ug50d14^H%tHBv``;$xoMs^Bf z&DQuXC%VHmIe!Kms>Vf0t*;|?lnEBfq~L}&JvCMq^qL?`2A_RzE$AzVac+;ko*T!$ z23JVErHQO6)R16dxfzy%PZnH|mZF7ZWkYFp>u`FfZxp_H{otu%0RKUV*;~Awi+3?& zj)gfdHb7z;OuA7)KbJ<~@8J=eQd@9GF5H)}c;>e)d5 zURKF9Em5wlDTlxZ$GS~Ybq}>rYQ(58S$$?FrKU~tRMLWf#b}=frhW_cwJmxfD=;zs zvG2j&$@r5v@w}*Sl7{-Y6BUxVi}dy^}7?aZPqVF>^-bx z7;?G(yzNR3He5jmYrp zmvS9-l9p2wTMo3!Ldkx7#s=!B%oSi@)a~CBy*UAi(hTgD1PZ{|Dw2wUsYqNSyXq*Q z)%C>DFHp4gywFixFHm*7T~k&le#F>}t~UTSp?Bf}cxhLCvAw`~p#K;-`Wr;&TomHM z{>-7C{yn7@xHAKSU6Wd4>l}_6m+=xtRgq?zRy0~f+Hif|5mVS=JRr%p49w|2L9_DQ>4lUL{ zyLD9)nl$77q)(w3EQqoIa)@dbfe#d4f4dfDO-R&|r;6}bVf@b?W zC%J>P-N2@)V8IlQXZvn5Y!&l8Q^)v;(iw4QuK|q;SGTsGH<)iCWNXn~w0meBUcNJA*WIwO+B>${RV+Hk3wX3^o(qBp+gJBkYnKR$Oe zy6-Quh$~%AeUn&ZCD!7Id}={<;k@?R^ui{&#?PW~toElry}EaP#4eH>NR|d9Wnm#o z{!JaZAMd+V+Ye>4-#r)3N}pR1w5>N2V0Y%Wi(9)9#dRKe4!~VWQEv!Tn3o&b0&Y>n za?aAB2<~x~{N$1h+r5zapI;E4f9?`sR1v88#b5SSkv zU^lY_q>N{7c<$YVwp8A^rpxX>Cj7W7;Ei$oR(=bX9i9Qa&9;7Mj?#*QEFDno;Lcm; zq3-IWjbxReI~cu3HRs7+O7mL<)kxJ*0F8+WVBoT^;Bd!r22n=D*p+V%=lS(winWgy zJ?4DwEtWoi+9y@?yjy#|NQSOdEv(zZC3*lNR={KYGuicZ45pCUuv4+$0JH1+P{h}Q zks3rHa?^Cw8x9fglJB3vSafb@bGinDh<5;Ua${guUjX%x#2w~Rh2&_SjS#y#Vh2#s zg3<)~@HTIlw;uFu(@Xp~c1nR^6?S+zNx#LihtrNWf|)uOli5iAu3Bkv_x$Q;xG_u zpHe{)Ri3u{;|vYfp_OH*N5qUEs@Rh`IN|Bd%<22l7FHJRqAgwD86Tc->J)P%G@n-) zwGUMhFZQ-mwKk^s=PYln;oGJ>bMWmwKT?Zrja+WsQaJX!D14`kZbW1#k&L7u?ek?j z4_LxVN=maEZpUao7fZ@DJa&P($Mu!>r8?L1JsJYNGBpT?16c&JG|Ixf(~kRCV7o?Y z)%4=Qo4d^}X1t$XwI#s*LRBTjxxo>kIn$Jx)aKPIwC*EEy9a!J9}e`9xm-#-WY# zYStRLSVK4_-aH*|>@-#tTW>nG2FFbH$u;p>wJ3zjYtySi~#(Rk_}WB&d8?rI&S!;aTu7T{I=Ks zZ!AJ`GYOc;Qf9XI#ouwh{GRj26#O-X~J zB=qm0farhD1oZnuWNk!fERm**oz*-F_0^|Ew`-$Y_aie%0h+l6>YP2E|JFVF)HZ|n zuLuc%*WGG=frkAw%CySlC}O`rqIC25N0FYtKnGC{Bd!zQ{RAu3IZX48S$ef)1Yey$ zgx-22d$mdzu2s*S?3sT8e(}|Vb9aTGt=?%i!iBCo6t5aZJo>Eb(CcWD##0zKnXEG2 z3TE6@M)R3d%Gv|2A~~E!<+dyyNtiO)(^q_>mA?|dX?MX|WQSn68AM~C3^h9zS$L!8 z?(upAWq$1aSfx8lS&SAmJ@JBYQvk#t)zSn5BqJiSU}k2kN=*@g3u_@Ih>cQ1MX~44 zI5|Tkv@Sfhk2R0)+*MIYi{HRDZkBoZ?O3D%f-3FmP<*c>zcpMO|E-iUI)mr z$|e7}%)=BKbR691n!dXnXNoq-?*7)DTY|IJFU2h_Esj#hC4qd#To7J@x;6sTz#ltV zT_fT`6YtcGNM_bxg64H~zO+cZarowO<(V()OZxp%x;f3(B#aPMXk;gGq^w#8YTp(; z1#{{<7QLPsbMKapjP)g3nAGA4dS%)&9p{cNXM`p5wdnXNmm?T-r>S}kE+FG}ZFqq} z(ikytdEavsHdrn4D>I+S5kZPOnWQZjlrS+VA_>S36lrv2N z!x|;2=})j1wIO&kczQNK%5$9ubC&wFM5{NlHb&NI9HnJ8gH84ERpY0sn$$+SKrKuT z3Fr52T36qu6WLYZvLZmFg{kZXOi^ z16nB#jl5H6oVXSk#T@2Dw0lkd1)>y4x<`E?U`A2c-BncYVa_4et9xnlbTEyVoeZ=Q zX6yU{^+e~PN%dViM&3;<1s}^2(6gCf$BY9REcF{N5CHN4N=yH;ZwjZ|s+0Ia2E{=i zZ%>Pm`USeB0i3L3Z4pcn>m$59yidJ9Tp}Z>(~J{hM~SkB*ppO;n65ga z_UZLS+O9IgEmiS9@*$wj2dLxxw5QAi-d7q39Q)_X9+k}r0&|;UV)wI8DyMek1=4b@x;VoA0s*kvrnBpKqyTZt&efHK1&g&C(XL9?g ztwEdpyY*^y`QAZ&1YgrkcmdugiaW#pR#(bEX#IwvhRMqlo)Ukv%dDRUrR9yYQa}T~1#1B@RhPz{6CNkL3a%ouf8(Rav4D;dO+6|cO*WFvI698Ud@TDuJr*{X-ywD4 z8|iuL7cd=o)@Hek#9*4N3!|~Esfu^M54n5yXHRY>mM?6_^{zz?6SLTFR-=krW40>yjp@N}gl3%I2EemyT)1SA~Bn)G4@?kO0 zb_bb^Gr;E%s~cYMa)-pzd!KJUPk<6QyFR%)kEAr!BAVnz8j5_=qJ%=~9dqH!*&Nja z%kgQWdzwiWRH3HN{f!8&$~nhs1hSn=>WL(P%iMct-toktUmfK3LOv$Ix#pz4Z^OJ) zAefjy5pKl@(uNPk$m+fEefWSrZ116(8nhv3^2b_il<&^zZ^7;L0v#Do&|H=I-PY(^ z<71Pe!*=3Hj2A?l&kDRX1o5Z{X$$hcc{($?)_~vJoZOHNYwOZ>_09LNsq(B?K_pCR zip@m@?(L<^s7VkzL$I^Fqj}X9;(c=M$P4um7FW!euAZqAe&&*DiUM+E!R^_N^4Y^O zR!b|VH8x5(8=jhCF?G-Y2S)j0Jqm7i zKE0F`hvBnO_m>;RIePl?3$;lF)uv03xPu$jv)3|yC~bs_-M35VfwqvkGNI$k!4c=KatZdysCV)^< zf#0hp`0q|^_0r8S{XnUXpco6iBdTGHeXOA!V-g9B>sVh~=EV)QC#KCj27BrbD?rOS zRm-Eq0b(Ua8pabMBjoXjb;8bWrL^|3Q`_|oJ}GxpKp)a|eL0lc1Hb3G<+u7FC66kK z`kZ145=4>pX2-f-zYdRdTCnFA6cFvI8S)xfPOl$ZPw%MgxvATov?dvFe%HJ@FQNoL z?baGHL(BcLtwdgrRp19vEcPpA= zVs#eEaHzG5sFk+#&%57Pqh_7Z%$Fi!U+S~g?pbB}u4Nz}?8sT#1cO53*D)F+2N z$VR@71JE%uD1M{6L8WGIRuG#@3APwU@y**4dok6|kWv!%L&8l_1p2?K$~SC(^nkuU zXB1F2^{hTr9G^f*4^YK&;4zLvD+h&e=0d0iQ{v~(^hrtmx6Yb1d4taS;>rii8qW$s z^pvNI){1KCVO<^NJ>>=qP8Jqf%f)4RHP8(ou1um)o0?1ve!NpqCAN+CjA8Mh16j$& zlhra_kx87v)Ap;-b#-6-rH;^$OcRacAHUEIrl}*f1C=B{OTtXY(S8kzMNge@TDr?M zS|XUAF+hE^{w#p8|4#zTf0d4`d(3B4WKfego2L)i-O)!EgDR6sRQrs8dXDtte;)WB zUjKJss{9wp$cpUZ^a&uS`G5j2f%5OuzYAo%rOHWt)}#O1PVX62{t2o8Bhvt#qM z{qyNkYNPfJ>jC&L>-#i|be8XGR;i}yID&7s-D|Z_!KJ%N@h__)T}tb)6~paIqc)8rpD({nL3e#jzz|c_Lpx| zaG7z4OHEz4A>eAEc8j7r$Asnd)`tz*4WcyFBJyg|M9bcWzZCdBM9#H=wB;ietPKs&l8k5Bz<%b%opdo7t|%vqnggTSygegg;E(c&=kD#?y`v4NY@__^ArPpVI{Q zinh!9cpew@FPc6#g*WXV?Ctq;$L}ggF;X>1wJ*{3{&a2J6)U_M>39^E#{DHnRDsRd z#kBAY>6viOFKh4MBbW=`dk=~E5i0aK=64aIHBD&AlA#C1P|l~#2|cDvpYS93|h9Ns!@XHHhH|Ip(IIM5Miz# zY3^A$X#QNl{Icoet?N4WF5IAF>00gf`fD;P9gI2-sN$nFDwa6{?ypy3W*nE@q@V0+ znXrG<=ce7)L6gVPBq&j>c$4(ETrV@BVCg=^R?&PH-o|Fs7$%5G=5@a_J<6XNqtw@i0?Vu zB~!_lpY{}f%(WDk=KN-*FPrP|w8Z<{>cSFkvZ8))I=5$8|HOi$hSC)a3&}*)$@JPI zM1e*7bQOKi4GJ&x;!X*Z9!WB~k%lMnejWWJ#}RV21D3I5WG({P&MP@w zPe@CZKL=I0G36MupO83k29bP8>w=&Lqrx?xVo7wfnsjZ{J*b%JP@^USk$)8YaH#RY z`MZ7>t_2L$adv%s=Y1iZ-y{kkd@v)`1kBcYQSOw>Bh@FHcs<`0*O)&32#r1o+76F> zq4fBS>2N?rx!@6gZNRz72sdz$osJ!zVm(y8>b^QvHc+BGsg_`$4b(W+Gt~Ye)5R6_ z3nZpXqNBC-F{4*J8>~{SY=No|pzwYhun^L%umfl*E^qYK#ByY_I)`xrr;+xcv%93+ zwEmDYVds#^hP4?EXKUxsR=QUBf`jGuwMmX+MDQ2vJ`W8@l*S(5z&i?SqvGFa^ZNk znVs}=v=8R>?1x>l)EyI~d6-TYJWENk`F3JH98v>$`-mz>2?KbN?NbuS6Vu*KFlI`k zkv|k?&K?$_v}$aUl>MkNSafX=Oo@@N|t zZuTO8v}3N$!s#e^treO8R(!y1N01J5WVnHjS&LyTg3+{XzBI zT1-;O&U^N!pIw;(cB!4L5#b!~^jt%iiUZiG4=L#EH7j0$hMyZcbsHK?`Zz7%z zs5n{#+j~T?YU`AzB{ACk+_p#tU}a$5!3r<-HA;6@z)s+98%=01MZ2i_&PI3enN^n>;zbDJMEDRwZX&Z(O_XcF|UBP#?o6>LM}r(Xfii zTof0Dyrf?@>d`AHBQvz~iU7BLnmd+lCUPvt-iIfQ)6)I;XTHr>Xzm4yB$`;k?AC(* z;kr_mX9j1J>>R&bd)o9$Zq$?e0F!R;>T{6DbsqumYe}~JmQKw>hK4x{ep@+-n~Gq< z8t`0Q{AzEN@ZSTF1-PuZIgUo4!H6ERDvqR;06rk(WkE?f0y3I^4ST5aNZE}v>WfR} z7PIC!fz)jrMLeCa1zWMcRWY{WKFYuD(MLx$Nf5`LT>~D#tDU#9_hp%xeUA?&2a( z;%m|yL!|-)D8R20Lr;l!oP#2DmGa;2BvnnyYnA()P0Mqsjk$s9xFeise}?es@s*ZQ z)^vkw=N7{)5YRe!FF|(C#|2kO+ls(qvj>U|Qz|`|OLFS~$#tYSH8N%PdgVNl&0*;g z$)hwZ-(vdCi39(j_ck#OCUJ?|Kdr0ZFPu7c1AAkF%Db^9-KykVFBPInk6dw(y-AjW_)Ym?est3l-}TF2}eH}lDR73!DL3%kC&QjPHG zY2NkvoDt0=%OHXciug3)wJm$1A)93Iw6SjI_}QYV1o;GnOwtM1`n`uS8g z7}u(=e+j0JyfnyM@IJQ;-`2}Dwhh-X8_^e12+T5~ zFHidl=xFt{-C3LgwcQ&Ehce#W*yCBtL)15 zaz-rX_e>3}EN#L}5GqZwN;zA*ryC29YBd$L4G2Ep{jyDmCjfw|;y2$NVQ)&TN%~M5 zMe^+Tecl1ETBx!0FTe}|$nEsWZx zk*{lvaSMriek*OgLvbU~eSw&d#?Jcd+=ekbaSvHFy9UI{P6j7GUyXiQal0g@;l1C1 zaR47hvSj)9*d=~%7kG(kS)+W(osW~>_#>Zs51Ltqe0z}4_WC2;>t4HpIZ@H8Vm(g3wB-ighVYbU+j}g!=HTl z(H55_K%PJ4zcaWvOnEbOq_dK+A=TIumJUW^l$Ud#>&tOxNc-$r&bKrMf+o&`wBIQ% z!^PXcCmMANy`@@4hv1_$QJhTL!&RK?8bNr>s==kln=i>g{)xf`>wJaMAktNREjKuR zd_u8bhI`0~Bqm$jkaa*{NJqj31G&zFhxce-K6oX&ZVcOJCrIwKnISlh#$GE;nI40?(!5noPpeQs7| z(O#@Zzpfw6Je1KSZYdWXE4iLQUyzoAu5{62eLGp{c_W|VXgM!G_W1CL@QTv(}fRUmRQqPhrSEi5<4^Wul+#|x>E>h_N)hi!OZ z7>m`^%~3>eS)jY^Fs40gJfy@Wdhjou{lFZy<1awxUw86vfAe1ZvNy#`aU^EXP9Bky zxU!_u(id0F*!3jOJXc`8_I|-sxuZ8wOOdB&if+(!ye)aURrRmF);*$k)cs_?N&VKL z34NK{ERq5JIJWzfLO+#1t1`&86Ux~N&i7W~q+nI^l{$yL%Cz9jU6h2Qp7EgLwmU9f z52yPJ%Oa?wIZArn4-* zYmJVi6w4vnWhV@0IhKeGrjI-K4I%cHI(wA!JtgFA{DAS*eLXLJauF~3mD;t-FLS)D z5NP#^L1**xIiowKH&&)D zn=$-2e;LQR9?@DPl)~UhNXCovtnbFvLRHcZYT@H1xn$G2H@Pp!^%}}*7^l8jZ89dx z!eyv@(p%hYSmo}luC&33d&j^oU&!6Qr{9GDuEa@!I~{j+F4e5XQ1oY#F#J^I0To5n zrSZj2Z?eCC2oz0vU-ae=@5?Z@WC{Jb^ZMAj`@eNs%^QAlegd5H8Gn4CCiPZEGx$Il^SeXh zX(xcZ81xACrgy4)Fa5dY&x>+n`0sYPHh3^KO}>rgkX=Rg7bq7`@I^eIVxT}~%7Vnb zF=vSZuV-bOuDApR=iRXp8*8xp1-c6Hoa_zXnQ$GgPws7~yk8^^kShMsnMgM~-uD8_ zPvf9;cj5u*PB0T?ycGyYfs8c7x{^0#Y`T;DR(c^J`rh5sg-Ypey|&XR8DR=9 zsy!>;ML`oU*c`I70R2$2c7DMPtzDduxH=e3FAK}%$MHFZQSw^H!+j|FXD#~Wl!2AH z|KT02S^g+~suHR8)hqwQhDm|NN+GT6ugEW^OF?J{mSDjfKV+~xE4P43j3>_qePjM| z3<5cbVdrbvpMrY5ZPB~xv4B9&Xs?k}lNIwASaX#Ikk#w7rGjM{BjM=F5jGJc13@Nn zKUr6q)i95Dj{}_L-wVK9`?s5#Xm4h)lUINY6nvl!W=3-~!4_LWuatiI-$r$tB@DJ@ z7(kEyvEcT*aiP*Gic6;HjPJrCgX^8`ggbFZ&}ZdkBcmJeb!lxZhvk8iZ4dLDM9zVfF8~VkA8tjg^I7o zDt|1O0S)+nZ2IHEA|I2T$x8p$6=%O!{r~v|n~ch+%D0(H+7_;m>(f&BtDAl@$=Dpk zKURnIs|aU$>nREk$UwoGk$O<~!$&jByQfaVd$9lKXZ>+cv96>b;2`Q!LR!7xziGow zP;LJoaSnf=VSm2OWm%}g5eZ?a8x$(%2V~zAt4{=O+X;f7traq z+6x*gxrJA3`?X|%#B4@nqIHeF1lU_^tJn6u{`hG1kaa&*pAxhG|H#mC zBbrbTkferfsa^w&i}k1f^gaGQzxYd1{ugl2G^>0Dn>Y9Ek;Q;oZeElIy5*sK)EAt; zx(t4VZ7w%kv&TGx%04t3Zm2TFh6Uy(j{Vq}oNan;^Wd-;^SJXCxu=%yJsR^cP@kkOzgLZJ8;o0hNINs(&j)+e zPw_qIBL@+!&^0uUm2`<(7}jT2(ou8nlirDlozj3ca!P-r^o6jFDP9t?b>FQjO)*iJaqBO66UFz%rA6`xFqrF7Hj3JS<9vH|4 zZ6C!*iPdZ*NmDA8%y1jBW3r4A?UjkiS4`sLl#hGevk<0RT)t`54#PeKCY%Lc-t6YU z{EQ~Ot%Fv>Yxe;XoCU{k1m(Zu@*f!02g#;nxB7a8H_8@yqaW2A+C>+c>pt;8HpTV^ z8DG8RV#xEn7$9HSzY>lY}#NfRJYT_HcC+3%s7S^oaEzyFheza@YF-u|{Ff7{8wJ#T*5C9F8bHl F{{Ufm^M?Qc diff --git a/generation/maisi/figures/maisi_train.jpg b/generation/maisi/figures/maisi_train.jpg deleted file mode 100644 index 8c4936456d897f20a6880cefb3d413b0ae89f9eb..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 187025 zcmeFZXIPV47d99KM0)QfN)eT&RH;#sE<^+YsYelM0wOIyOcbPd5l~PfU8Dr0w?w)~ z?+|K0dP}H*gfMZwHs5>RAMboqu9@pP581z-guT{YYu|gdz0M}i<^gPmx(2!cDk>_# zUz9(<*%Uwsl zPDRa5b=C#|0svIB6l(tv{9hlca}*lS(_NrvV5EGZiVbj%ikkWy4fP+YDc^oYc@Lmr zr{%bGzoui?lrlI@87uC4{%AjVap}lnDJjZQQy2sv}qVjJp zaNSADDsQG2Q+R;me)4jVfk#{sD}n#R+W*ekf5uqI{}N~aow5JH7Y4vYO+^tNH9G(V zI5jI3Y(og61PChDb%Uhac-EGB9;8Q2|L}o~CuSB>bH#5)u-56<_p+bA*@DM?9E1t1 zSVSmU42E&2tRYEcat)BchaiG3toO8{Ks(^K*Kh``|Avj{R?H_qjKaXfb}8i(c1fu` z5%llL9UyLajt;b1Ob|Q+Fc^Wz>Ut!_qvrk-DXsHofR=atXMp%oAmN!;=ow(&P=oyp z@OBAt1`y04^JmPT0Zus*Xy8~?=QBXZdSKyk%I5kpFB)5bVu24n!l{tCQsE^31cK{M zC}Q6N%6tY0lc4-q_!|hs@SbYx>I|I;ngUBj+Bn6e`Gr)<2$OX!@FDcW;4fcY2 zG(S6t9{r7=!7~7}CeDW(-TwxS%dI#AELQ3Ljh@vrK=?BP=nQZ-rx{2vd2|NA*Q*vD zM^qI3wx_(xB&ePNbew<85gz<`g#rBw)nCs5tqM@!86ewqK7k}DeFh-R0RK+566pG& zA2ep}|E^VBT`pJ_=|d(3lLdn3S5CPR)Nrf;WqCVY{teAJX8@*N9Go1b7Xre4`m+wc z@V^l)G(u{f9Nm?Jw}PQU6m}}+y+~L8u(S0y(VN4uA5pAlfWa#`Ju)XncAg3UM)K*1 z55a0Eot?jlHRoSxc1!p-QvEy4K4yEY&Xzx${wCGRzte2`Z=?$PSE}RPV2}UN#Q%%? z`5#UEKj6zhn)nCF|G~t6F!7($`JdDIzt~*=VB$ZR_>Z0VkH_+l$MQe?(0?%TA58oQ z6aT@)e=zYMO#Gjtp#S*J|M<@T_|E??`OX1af_UAyMH!wcD;&ARv2=}By)jPPxXvh2 z0@E(|Jkqe5|B(qR4LhcB@o#y9jW=h25KnklDoOneP@6#hQl~a)r^8Uby5BeYsnKib-lBz+WTF8`Sj{2r69UHMXfTh(NE zRZXf{gOjx#4IqrQcsO@dqq$*xb5^#~b{p?p532((=H*vhewCDt*2RNAzbH?oVeuX- z*xVvMgE%Z6MQc3zVq-JwdURr65#an6(4@&~dlMUfnU}*r{6eC>QQ7qdI>FnWq91He z1fqAD;L<39XHPQA))uL6iI`l=Bx)dP1&M*_JU>2Pyx0dvfi z9cze-DFrM!`ojXeo%>(L}(S(8rHH0ZgqPYv2!Se9+n2j+SS4_U>af{%ryO*lJ zeOL6lR&QLc&`PQMtj%FK4*1${iZy~PhHr3#sZ|kq5Z;2WL#N+%7p&eF&ziG8r}{2D zNtY&qul>@l&ahHCXs?chjU(ta6iV0CEV32`-mTSuo{Y#{8s1(mD@WYC(GN*dWGmBP z+m&UtwbWiX1F#E%q7f{0WTr{ypQN1c?;C~V%30ICQk5aEg_VjoASor1_NB3m{ zOz;+Aj>RrV1M33zS)OUiY1O47s@Z-_@da5lY&sVeuOJyu-^0WSY3=o(xJIeP&w50& z4l+Gncs3yzGN3~}GsUx4ynWC6T6RRZ4mE?q(x=$pneh3v&Ka{dAS+pxWRKS^24c5T zi(pVyxAMC&A|IdRD}S`T=+^>Mo>^%edW#i@yiG}(7U&UT5j@D}lOjjWN3+6}4w%(DRH9xjA{g9xGlj=JIcQ&lH8^0~-U6?` zeQ&}2RE|Tdb{gVA69I>%_=Gmb4^RcX^#XsD16#(aQH%Ajb|J<>>nXc#4{tXMMRU$O z1F1jwU1khc4Wy+IE`#<`IM9M*B=j*Jz8D`>v<`G1)=(*(oLsvfc%xtv8_bvIH~o!& zce_*8V1>VvdugsRVoM*YO4No(VdK*lWz3o_8e~1IPW8aJHcVV=S^W9xnuu4GN$($D zl@oU(zTkSVn%xX5pvdDrN&B==iw`P|7st>=l1yX{Vjx5AMK5Z?qoH~Y)wF)f&?_NuDXazXx*yx?#~`q|fE5A?gMxr539 zocaOkK~hOLhjuR}=v~}%loaliRC7WD^Ez>uD2Rb1?8u9K39w}fzpu^YA*r;7-0UKP zYdS%(bL`8t_|mNN5xH|SkJd!^;4Dogh>0wH`lifX8$Z7fm2t#~B!;hD+KJkX2wPKo z1FQq40qYzG)hSp_*NcX3^cq@3fYV=TtM562_;j^1b?cLN`Y<7kc$Ywp z4TuySs5gdeaDovV!&PbK+*d*d(#hoVOQFI{cKbq;fUxA(Z@+xXzPZ!qj_L>x~1nw9L*bgkm+0|BWn4Jo1}MZU<$%2Q5#Jos~9q2a*V9=Kq} z2IEjmlTuxmDiQ7zc5`>8B3{8huW;QWT{a`&s~hk2Nh|BSd(*blH}x0d%q!Gy%wGY# z1K+xGu9y3_J^g7hnG1Rsuag$BFUKR(QShqPs-!(^wAgaWyqu^pe7C%RH)O8i*;`i*)GrVy>>B<}dl}x@47ooG5f|ldJrPeE=I(C(x1jlq4{s#7Gp! z_vSUVe>%4s)9oJPR2x~J-Bi&fQ1Vmy7u`DtiEstVxmamx%iDsC&!i)aixfkJOU?kU zy}7><=P>=Qnt8oNYN=p({z38kKRAjlnL|TfxJM-%&JrXf@>3uv)rd`jce;27VXaM@ zJ3v9&wpokrC)!u1ZfJ+o2}Pe zCVs)xil+w`25wyXalcbZuN_ob(As}~f&g1FA1k#m4r=$4(P|ktXsk!2j(n^6G?sK{ zSoh2IE?-*_mJ6b3BKxDR&V;c+EJ5tA)4yl%cZ!;g$FD?2n z#yUqQU^_d})KIHYIYl&!JEu|6p1XOqxcNK4G5w@rVQ~3RM>$_n1(Pr zVaJFr%V>4?K1CeHO+k^>$8hJlhNaGrpYUV#36Tg94LV$g`S)R{&B7I5n~kYQUNVo< z`K|heyo5YC-4gpzE+e{H{xZ9T%|Ce6*QFu|mUv_{T%dA;*iYvU!bBDHAG-f^s!AVP zz}$N(o!Ap4`t+sBm7i~HCKSDN=cbHc zLP`{kPy7|?ob1LUN@YwXT`T3tBRI6fL?g#dGuvI%Sm&z0CE4H?2VZ9JxfwtBB? z#z!BUVb1_t<@r!o3JY30w~b18+hHPQW82S1E2ofBT~*VG)BMlgJeh6l=s0I2t1s1R zX^cZ3b`X4uTr$rMR`m-stHd0{NC^h!6NOsF>~yW}SDs%IC93ty^3b;mb=i%?1>Vjb07+^LJ4kxC zaHTTNB$#L9uPVspq?ItIc+RD_+D)Xc`xnM#U+O=g5{t*fxA?yhx(}Zd3KzY?VWKvl zwOiA93Z;ECGuk!dHhJC|hS&s0wqB)?@<_U__k?1Tyv%My7!c;N2y*j!Z%9sfI*0J) z0}nZ{tE`CVkiEUJXX%40PagDb2CyX+Y6vkg%n{u270n3Vr=yo}s1k|M?bM$&m{&_F z?Qt{JI75D!ZLxSA3(Ulm+M72exk6Tabf5?v>)|ZU96>_{+C%m6Js@1_x;0d;a86k3 zwg=NTgqQO+@8nh#Q-u3_?3+%dojG>Yb;>!Qg(#RIAgp+_A7uP2uc@g?-Co``ZisHB zxw30`XV?GgkKega{wK~MvGZAVrpJ2SBnBARwPmMMhiwrhPw~RvWcX_FaNAgjJm{W(w zrz{{uKpqKHYj8%GXu%tjpQnan-dX^9ay)6$Kq9LtTJol>81@dLp04nobnOr|A_V*44OY`D&7r~_UU z$gy@3c69g>QoU&4p{!oGR1e9vcw@=-ty)ZW!uImvMWG?;Nf#vV@EPDaA~YyvInO0i zrMjlREX>nhaKk9WGF{nQps6-hNrmduw@?<+JHWbBF~I@f|Kkk6@7wf%;?P^Kud}WB z1oGmiZH%2YbGD{w%KD;2&#BVBKS$Mk^NVc2d&>k!@L?Unc%cMtJfzsqeZ1$E=J0rU z2WZj|-Ez1ne&I_aB&@Mm{~}G|JKgifUeG&)(|L!m`qApR1!jA{G0D&^_X5{?kFlH= ziJnp`2Eumd%Mbqzlxpi7P$UmNg0MJ5wP>xBoNBAkN8cum)i0#q+*GjH6LS$cV! z>cJfYK7OT*eSoPB7t@vOXu$uMhSUn2aCwFF(W@0rj64IZb*!Tzk)O{1^BIU;Cp60$ zV4LF77YKe^Ex;L#!U`lD7W4+m5yRZOXMkqlv8Eg*hP(tnRWWe5Z`pkYpn?D1N4v=p zQz%uDGZGI75^E>fz)n_&L4>|#G;E0Cs1-L-xnpY^AI*Pge27C^)TZ~h9qnSO*4GS{ zmg%2Xv7N7NWSA_O?+mGwK{9MqCU|b-0A}Z%z=be7^+{tLI$mr7Wa?fgN#+_In z({(vNg8L?atfsXpn_x~n?=t|sI+b_${TOV@p28~$85nIA{L0c&CAb;oSz%&7d001f={{H z9rq;L&|KhX&@+eVAiw_D{tyi#iP8p%xfTZ|i04#K72jvXR-DEI!-r{{1}{tae!o|L z-9K~^K}tbSYLlA$pt8L2y+9<-D&H+VEiL^>fX5;jwPYc~ohJ@4m627f1nq-|!=#&v z$zG1ITLw?Py{|wPYva#%oL33x7jcm2`~iVe)K!ek3yegUmBtt5!=E7!Pstg}k`tCa zH>*>q=eL$BV(}y6YcgE6s$hB?EFuSbi=dJ5<)`2Kz*GsNS zR0?e3iJw-)sBSadumyL*&knl78m5rgOGyM+1!t#==eY z*A(uAYh1e`&6Zgwx&G@4Ban_f2p80##Sa@H$lP*mazJ+=m*=ML#~eaHb5LnsK|9Un zPW8ee?L^Xrp0^F%Ar{wF+r0?HRxKq$Ii85^Y)xPx{1R3pV)Y`h<5dVYH|xMrM}&E% zT!l?>l45ykayKt0olFv1Bb}%lEz6^Io8X*JsF??DMQMFp^y0Q6%p1;;~`n#(l)0Scj7R5 z?ZWpUUN&Qmj((*Sx$`Y@0BO_x1PMGvK}_6AJ_Q#*E_M6WtA@N0j&$`QakHcYOSOeKy(Bn8~n--^psR9 zN^sh*7u+c#T1Bzrm`cZ#Lx0hgd%r&eys?~me_tK=xjG2>h6KktL}&?wE+n+=@Dh^z zjKx_ay!8rfW?YWg3XvkD8N%Cy0h3Z$_+ts;%DJ~SOkanbYWw`Oiz7XHP$Q!zYx(Mq-3rPEYJphKQ zX(5X->mp(m(FPt{)(b60kaXjs6tJR)v@X?#!(&sOmd-9X6HL?v&5iaF2DfDjqz2DW zp#-#@y$TIH#BbPD1{KHd(7#|xN>G%=DZdk>Stuv-5qQn;X(Uc`ry6hAU;2&Or9kd> zzPB{Ng&Ah3STPlU--ij3)q?|;lkZJmlpjwye{qQTBojP5ir0MT79oHu z$ggQE!J8%twUHw5QnS%wN#6E*NYQR``bom!1P<1;I5+9IAQFKsaz$3f#Bf12xAvCp z^n?bxq#5H4-iapxgg%dM?NjF16NQa{n6)I`(JYN}^GFsO=$)~?5)ZMpcaxRISpA&1 zn%UmvJF|BaW3vl>_Vhfodg-1?faBF$<|@289O4cshN+(-t|My!%${GByN-ElB08$L z=g#G=P4k*Oym7r)nL7>f@vXTJK$QS1E8G{@Ig~BI2ET(vrBMukttx`IW?na> z+Z6K~aP7DT3k7ZNeZ?!ev%mA9>Kr+nhea%lRw1&92=& zuTwl`ocS{;zUP(udqNWvh!yRQcS=?hy+e2ru#F z*iDaxNzVjlapR2t$QdJOFbj;r@i0Zsa;qa*5#n~IM+(-gW3uc^$p0b5Y1 z*)d@W{a?vakjruts;CRq2z97ACCF;msB{LnKsS-AmUdXE4a`{bAZEgMNON@w8GgYM z=m=TGIZWuhOgQS&h*F6Z_V8P>?a!>A^}543?{vgpnAkq`3D>GRVCJ%whR)$0-XUWU zmDT4}9zjgw)e+zAV#&8)ck1M(eA?rf-#GBu@IwaNNqIn*`)-c<3!XM(_j=y(1QCeAKJ3tCKI*4*Hl-%b;?Tzv!pyUJzai6H!_a-gEF}I8hnL*GUf8o=6GQKdXR1s zmN5F<4+L~ef7Bqt*HBZY-(!dj3l9{|1j^rfsEoSZ>j$cUmDN)s9AS%gcYwTZvuYKH zW`STE_xk({ucxwN#8(riKSKCYt<1ws#jZ+g1A4NIU&ZL~G6FN^7-5J2Hvh%S1vp9SAxoqGK;olZ}=AUcDvG3nZk_>U-6}Zk0I8{}@ zFl4w*gc(Opnb*aQEEy zS?x;%FG*@>9~_D1ZVWDhuy&#p3CUko-YgG{cckZ5J516mR`7;nltadV$}STM+f2V= z#I7#Y?w&d?MznUr36i>bxbN<+!=34%w>I}ipkk*V#q1_#TizP^T;pzY`7UVbWDT~+K&pVT_$N^LRB1(MTw zXn&nxgsUFDN64HNcXwcecx}O0adQze9*UniJr2G{<~@7c@98MnZSJaFPUdJRUqkR~ zDY^mA!Jh;9W>(8xzjv2F91M89H~fR%FZ#ZmiTwShl4z95Kz;q`d%fD%594nV;~UNF z2)qluDhtlhP-omx=NO8&4sVF5MmiR+iamU@T=Q02sB9~fF80_h+PvcG)TeXC6ql`A z02xu)4GaNMRiRl3N2a5V{8(15bu&Uvn>|N4qj%nnT})aaxmQE}!#n!YM}}SOe(|?n zu=Z%G&u2iu9l%QNqsT_NMX&(L(F4@tGcipFKXGT>OnlcDUrqI;GNSimG%xUmxnM#3HEao!R5e^7b*Z1X%qvBE}tR@=Z! z=t1_jM?7CD5XiAx$*g|7O>pE`kJ4C`O+NQvsarif^1vZdK(`KFkh8<{($2li z@Z!vLw)|Df9#WQ9Ek!PNlwiU1+J3_jzy(La#C$#BOhM+dGR8%{G+89`AH* zwVs)^KmAHljVq4RXz8%&H1`lTVy!%3N0Unau7cwg2{w z%xe*?K#U+I+}t7aVDBETx_;cc3oqa_?!O-7(UAAc_>f;vKvFQy zvZFsPwP;?Gp}T`8$Up8z6CsoaVyw|v_`OBdTKebEUq649o{Mb0nA}pf{d@v8JjbE| zt?-=s;9(9X&t64JF!3p43RA8?h7n;6%SuJMN(d-{ zbaoyd;UMuHbDOZs^=0mS2YT+>-Yb>2{ik-~_zzJ7T06E3d*I2`zS;3Zsqv-o3)pwd zaBd^@jyLy8m8ABW+FmVIvA+zyA`B?iGb*nh(c*)(l+D<*`>?%|y)E=pI6yL@!1uYx z`mbWvCsbSaES;;Y$dd>ob3?3$rH>pdY-k3>0*Q}ix q%G zi|g-XWq|4tI2VEZ1cgr1HS1>|9iftVwqfy1V-XioIk9eMQ93!c)(?+vKaSUWY|A_o zRsnC;uk22(jw+>VDGHIe!Wg3Da_i!Oka|Ln0M_rTSPj9M=$ETsUg%-`)AUVUqdN7C zlXnIr7vaf$kvjYaB`nJ13#Zpv1oAaA&(`Eu&7^F#=O!`Vy<2wt{o1$0*p3BhoujD4 zYazF(jj)!TAR6fN2=nA-%=qD_blXDagW|*F$!SbQ;nTfKpE-1xsID2#&oACYa@Cbr zHxc%T*Ob~D&8pCE{U)1$OSAS5E4N~S0*Sje>mWMkXIq}*GBi^!m?Dz>sgb)bL)_`BlzTZ+ti6mHak+A z?MK439DWORU^dv42nVizi(k`d~qmAT<7C*$MRe5Yo;1X|QUC3`E&VJ9bpErH3@xD+F z|H*y8+J@wJnPWHuc(F1NlH1d|vR2j5uRVPkg>{!~x-#yoec~u7`-?^Bi!@b-w@FLg zI-Knc;06u{GHMtozgYlr#=LqmtC+q%*4MIrP`voK&VIg`Gm;D0!xW~gy4LG!ebnJa zLva7$FQSab3#UU&PK;c(sv-^2XSKaWJL#GEyiz`nc!I9<82~sPZjxjOC0M!1n$9`F z%7kdO54hQy5=h5}-SGC#d}1lGpr1>0Qs%=|j>^Z@VjNEI<3GC1=iuxP6NoIU%GTRq zmy-(If$Yzf80A>kuhY64tF5%LcQJ9?h1hl+UkRdX7S`cmM7WV|5)fEq3=H_nrzI%H zt=_Qjuw`d+_|7}|*9PDlyg`q{g$P88M{u4LDl%x6bm0sQ}S?DkJ~lJ6Spkz4E2$nTpRuYIZ#GnpzN*pT`n2bXiy zY6#X2ug(BJ57eaEP^ypz{&>mIgN@H_CkwQA-DK|kloTU(XkXSGz-fAZw{89gz_Nsp zM%J0JBsWQ%0S1_z8T!mV*?M#n(!s2JEuR*p_5wX>=IZivAd;`4uEj2BMidt$pml8Q z<%acADBsG6N=7gwD^@Z7*INbP$6J7Su=VN!;qyYAFzIpjI!33oVBwXZv7JSi@B5@n zBbWaA0f>E+5}NgZN_DIEF_WLFj&gbvis)mrt>)%5>UdRGMqTAM=D`v=k3_jM4z|i} z^ueaJmw2_GdcM!I}1IbRPu^-y< z{Eb^pUwDvKe=e#-NmKJhr#Gk8Wy~!a(a=5}_RhgH8RU)!0gf;tGfAY5CunYMub%;c z4W88`(`{cN(tzNv#I z)VTt8u+wqMuVIA!tle%Q)>3c(xNPp^r#vO&YHK(FcZP5cD`Fz*(i-ZS<7Cl+Zg(_ z_<(Dv-&xAnV$8N)%e}g$Dm7~Tm8A}J1@G3`sxk+xCSLA|?8)N`&-CDg9b-S6y)eE9x* zyR&N0-M5c}9iHtJ_<6{!&n4!hwobOZEbI>Ud}ncfd3mcT-2AiltKNGXn8k@#8i_CS@UtmYkI=Qf^-MvkAG z-se{*EtDCnqMpET3lZ_zIXycbTCSU?=f7jNq0!@`n*Q#-q*FP9!sJ&ZKR$qB?Xj(D z3E*DsHRrl4S{R__ifrlS_KC{3l$i&v|4bGylzC0n=WRFF3gVn2)U=8cP1T-O(EpB; ztqe|5s_OZR^u0hl|28r9#;rBkd-#;lPUj=T=@%QVP zy#||dw(X8v`Ich9OC{tD=+1g~LFjU;hIyV#{@Bd(lB2mL&(fcV*Q9#LwD6yx4#c7I z`rOHVm(v{d@5FV)!Epg4Mdr)xK#_+tB<6WWO%~`OC3t3K?}4#A*wvJe-t(~Ou$LOw zpty|gEvBx@g*i6OG6#p{Lf?xFuFD8{=Xw@w7fM8J-z;@ECoGjt5d^008_SNhn$?Wu z4f6$Ug<-g6ZykL6|;L11$_(ba%wFI7A~bhs(3Wws!Q-V;2Js@1N(te8I$f z1@v*a+I6AawDsi-rL{=NI{9{Jb2oqQuPvu3|3e3b^?IpZN{Ixdz=9AJjP;_|&bw1@ z_sP;a>=4QIU3SeV&|9>lyUSd>92CL5JjV$2C?;slo~$1Z^{ESMcbK}%HqeCJucaz2nKw{) zy?6~vCoYS{TZ2!df2oIA>^*CkL_Kb}iFuRcO{vXv@2hm+*TbDS#KA75&Xl&tBLz$9 zh4_V<8M0TZAHD8%)vy(PU$1M@k!_ssp5|WQtY?sFBNd_R(#q@4arK&c8Dl(am5)DKu+hkSVWUK;Gosk@(n$KC zhU(Yj>YBYD5*Ug2K%u+8k$qfOf6fanWLvG94jB|wmF$u0)AeLa)N2(-k-=+IC#cMz zoY^Z#W_(%)f}N76AFG7q;>gj}PPAdAsN}ntvMYQaN>z#tQYFscZWbD^e$;cs^TJ5l z{_Z>7;z3X5vZQzNH*X$rS4rQMGBk~tR4sb!_IQ!M-_;Eb+Sr~>kCtL=v)90lzx7fi zY6i5a*~j>O^&$$Xg>Amj?CWi4dpyBA)ua%+_~M|n>G0;1k&l1$Qw2S51|zN*X%iKx zELy9ekgNOQ6wem$pNzj*Ee>}r`_2G3RlKLaC@pk6-xWeR_D@Y&|6A>O^pg7js;kW!1$Q;p?i8rm zig=7gGLi0%L%CgnZ^TCL2Ki<>HS{UbXHVl)C)$btCJMV>`)JiV7wEQR=BF<-8qCi{ zT67jq`Bn!-M9FGMe$A|gi47N2_?^_5@tpxsMVAZL(>25E+&roT1wHOe zAB3-SC$Y}|h~6uM$vzQtifit$f03Mkm4){TKZH1tp%(DNUGVObRvZr5#>xa)pX{xq|8DZFT11krpu{DH+C%@HS31Qc-sKY5jpFE8`+EWcK=i zck8svGnpqdnv(-r)HtOhE3HdidyKCvc}mnq4QdHD(wt4Jv?r#7K7513ptTZ-n~ zV32BtN)mlZta#s+8I3@voLTCst*X`BVT{76wbMh=hw;i)nAi{${7g>K<3YDYrKYU3 zCx=TviQa&F-$cf3u#3OLfw3+}hs!+mKhgXD>^sH*R-+$3T>IMf>?R;+fch))I=)~_ z6PJPA`9KNew#l(hf`PNuM!z$?52EG!Pb^a$D0LclG}ym7X>ej41faKkTEMw3Na32% z+z(l%JC!3N?_F9k58ZBT&!o-nUow%0mIu5_MQ&^5W~0 zODM8(ukI|2p1x|fX;2z;(z#d@M>%`wfgOR0HqVG-S{y@9s-_Uz)5=dt8;kf)POTGy z-zI*lU{ny~#B}21+Jo-$(KA4Vt^?G!*ks8{I4WsH|ol#XE zw{5g)nZA%k8|Ijy$~Z*moio5=h?n8eBluEY-Y4(6lQMo#6f(R6f_!gU-*W%6-inJH zdKP|sb@KtX(F43-c!Nk$G#x^Qh&3) ztK{9{Uf7HiYeq5SXd4bZ+{kt&OLRmnlM%`F8+LZwxY~Xm1;ZLEL$>_J9Z5nZde>|s zua%3Z>f0~-RHFlU)Lz7v@HeO@Zlwjp1}UOc^%WWUJ-fKOsR42pNnton?&POk5Y+{! zY9zYvqtF_ZGOV3rWi%gr3PDc2?U0pnvIB6o+B~{a7~Lb;uVi6r()H+ynXC0xnJF!Y zo6Uni7GI^%I(6|j1kKt_ZBjG~mKaE-!*!p^{`MD=D8A-xR%7J=`I7ad>)tFFQf7ob zNJ;Fz^iqZYxLGTt2^QUwcfrE-*O6U@L$aQ+kg*|s2T#T9nr4%ksslgaGML}W<+{<= zK#sNalFYBWkt%^5bS!3q;WHB(eVWX^0o8?YXU)p6w?4Xq)w8u?bimJG9qR(F>35?a z)hfJAy>x1Nc8x*v9*1dIG?TRr?Tj!Y?VKtmX!wkJvTU-mI$&(TU7_une|LKYtlK zjK&th=qc^Qp5R!?T!)}Pt>E0|Lg4#WjntHEsAdAC6GK^upSI`E}55yovb=mBZVi z;~MplkbsV{6KU(|L<6N<%(WSAmoNI^Lcf~)SH5iA`!{O=`d8u#v5&9lax_%eG=Py0 zkq>y}y$i1;8u&;lJsquF9kyeq6dRu>k9PzGL{%AkC~4lA(@i$W=&oRs?%B&lxlr{@ z(=gU#*7dug@nX#gTv;RUR|0$s@fXoKjhwU;n4zqXJbJ0{xprf=PVVo^Ko+Q(N1XD= z0nPweup_14o7?GIRS6S}(W_G?a)lX2x8xumBO|?2-Dv|1A)%uzMP!K2pfjSHQkyVK z<75rgGt$vqT z>Gj-e7tpO*nB#@F-1Q9zESB-89Qht6;dIRow`m(DNqx~`E=GgVdx4b?V%eq~^kulz zSLLI%WHqNun?m3db7FT{5?`xN?7mVjyw!rlN$E$|J`W^a-Gu*XM>mS5p)>_7htN?% zZoxpZ`u(zz5nGQ2a6oX8jm=!PVyb*`eu1BF`lN(&@M=m`!|KE2dCx+x>~$^vVGSo) z#C-Rfgj_x&ZE$3EN{kVcPnw`b3db7;g$1J z{+;i-PwnC=w2kTPBNT++P3)DmwSZz!VYkU=`UMIO5U$eeHy4ElP%fJPg z;Y#0Nr&yT?s1<_@gLueXUo|IYCUA(Kuc_AezBXrN=x%7dyS!KUtOO1mzPV3vY21dC zk%f>XB~9H!M*XZB_A-%|AEMO7Fricd96wkWE?53dS^vJ1a&hJ*F8P(!ackd)t0SvE zSTy|}9@}1XZx}afvfK1Sb7=5WW@2m(N{Ap&9 zrm&i&&0;@w_hLU_8WOZ5%ZRdX?G4__>SP7cX znW+u%QiPa+qr0K^SIlxuy?=Co-DcJCbWI?x(=MWob%B|)-Fr=K=HL@geMMJ3{nn0% zM9Z&oeyX@|8=wM{kWcLJb28iPsy%SHWM7VZb`8_pw2z4!%Mrh0bwIz&y!>%~`rD7+ ze?9fUTu@yu`VW@MX#IWZ3}W`wcU*5d`s5&3!tY2nx=T~Zz$JOW4m96%8kgDdGE=&? z0xv>|3B0qnXg^#$8KZXN}{=UKV{cbh5L`qr1PPxz9X0Z9nA<;SO=pp3k4EL%HU zF=o@RA~qu|l-W7UB_R2>2ATV}3s>L(SH z+$54=cEe{~(!Qw{?Quu{juF=ox6@0iR{46mpHs;bX7GPK4gS8tipLnDiJB*x1`VbizDy zeyE^;8}ag$&ex*t_iLt_$kwG{8NjfhnZxodJ4jJ`XPZTkING(MG68I3xA*qiyB_RR z@RcXJ=VCt*E&Qu+U@JU#vP*qh?C%i`#c_li&ry0%FEq4w4_a04{1ZU>vMDad-*N5l zTWqu!c=(>6vmj&LZ#0CuN{})e*^#i4DJ~{yJ4+A$>|}_Hxg_0|2%wf!N^UlFZ7)D( zjE~z{CVlIX>M%6O^Qer^jK*jNSSLbiuv$$Ea$CeJ(5GFLW4c!hQRvzoG~juw_+nD7 zh&4VjPyfM7_3puB}>am${SnQY6 zTgXbO-*QvDWE1e=%QHY-@)p-0}hIv_Q;C66@eTI+9=L<*;6yKeBd?_o;k!S^V!=ZTFK^GL!94+V#IjBU*1^jlI z31iK$(vT|}cfUV;Y%>YcYJm4@os41tNBXMX4Q|Q=Du~UU`;zPXh&^dUzO31TpC-sM zi)r0qcQ+jthuov2tTb7V{T?FC5lha5%*qhwsEM!b7`_v=Mub|lpvpP5LbZP?Ot&{(gbntpN}SlbhbNGCWPQm5qRoW zEwwjl+6%AvFj{k9`H2v4U1@ad;ZH&awoxt?c45Z81s(L~Vnt;@FnXrug%C`}{_PFE zAT;e=vOj4v(C>QfH&9!i#4ehg?6ZN9S}x%wGqB@A)*)Msw*yNEJjVncl;q$_&GQqU zTdN=*qzlfgy`P$-{ZNY!4~syi@=q4HvEN&q_*Q76dtP1GHylt4&WDXLew)7~4t4%L222!g45e&>XNKs*Dms6@h#)pBrYq|dx7CFK5Re*r3B3doY9Jxreg5}8@3`-Ko)6EsAMS?? z$G}b4<+s;wt-0o$%bMVIcEJy5U4^oBKq(pKXmdSc(Nbb}yAZssnVA4o6+80qrP$jF zEyD+PrNhG_{Ij%!^2gUMMMy9N$r;8kT;R!1D+3IJ{W$*6JcWUgi(N!Uz{=spzt5}; z0;UcdhlbgZLcfvnT6IY(U2~Uz#OgK>ON@q{Ywa@uM<pC zPw(W``2!H~n9A-9w3_!ke41Qftj*lWHo#~AsPzDga9boC*#`ZtT>vc6J-xD%ai7Z17V69tm8oAmn8%;+>!S;uQ_3L(#|z?!6_1 z#h!X-+ON4K*9GBUaX(9^-Z#kq`_%ruK8nV+XmNBAn8gc#*jZlf2v&o%+^CU_o%9K4eJ?HAx#seTXArQ2!ka)r+n{j3CD4(BlN5f1|Xheo4nD&_4xnsbeUR$+)hDLGt3*VP?d=)RCOlmrGV#WTp^el7%Atr zL1Mtr+#@1mJNmClQq8k^yAp9T-SIV}YQvkb%8kx8tsvLAzBfV;(0 z%DMhTr2eR*qN0n!$5fsSR;?6^ue>Ds2sAfjN82OVX{-Y-B<`fK{Xp(b=c!-sJoDLIu%p=7F>;q#Om6Dy4a=$M*WJVe+oOt+fAlhiC z0Ny#Ri2-(UfuXzqXb?}jk8ICUsMiq*M=m72B8T}aezyu8YN)q{OG|gresZ?r$F~&H zpYN&SS!_MusJ&6+j)xB;7ZaK$_xNAB+QGHX#R(X>LNBMaDQCU z7d%A`zQB0d*ra=9b6CmI<-uWlywQ`-ajf|?uIG5aapm3cCc1S-lEq2r4jdJXFA?6; zFcET?oBZ3L>_OZ889}Eji-}*{yIDYMFT0pxIrBIKM*Pk#3Bbb4>4zvPR?(p5Y^RD#>C0U{qPJ#3&f88^=Z3%H?cu9! zfSEWnYo;Imh>23jvB_nd+B}5}Y(TX_LBRWwK~SWI&A0!dp*#S9qA@U`XZ}2NJ(>aq z(nNCry^7ZFN?7WbQ~XUaD278GQb$HAXWziRZK#8G0WDJZOR7O?$ri8Wf3fcz<-a-m z+RUk$uVD*37KKXJZnj?TR2i-irKd@cdwXwcL2J7e3eFrNYUGEV8(^y<1vu_`Zua<1 zpePU#lcKW7Erg!kzyRq8N#h?xwGjHRRZ^PQiUi4v5U*?=VV{mcupTy`yqfsSuj6hX zIL7nGH+s-S^fUqrUp5Y9Uzx@y-@c-@M@)r^cO7rnb#~jmGh3feV!l0hKgxD{CXpNZ zZM>I6{X$;&|`;KlGSb?_&}Ko z20&5~03_`y8nY{5_-~Ib$5_uw4l0q1yuWXZe=@WmhX@jtN=h<{-$*+y#=7hX+b-nInUCO%cw2rkqoKg4~;Izvn&%#~9c<8!+@;Wnq2j-8br5{jkL$-qCjGGf-{kU{)}s`I#M;Rk#ebLP z-1>!g7>?4$7kPdUh{E3%`UcAo@ol4XQ#GFwF3V?e;3hVo&PDWXB{AQ}+%LeD%CVR; z`(0zr5VlENT@f&w+L^&9fWIB|g?!Vx=CRqUKR%=u;6?e}hu1rbAbv+yL-^L`7(YHtfM=%Z*?j$5$HO5^P zp2QMyQsZ^;dczeJRy7KA7M)VkF=LccnHPDauJ^9|SP+MnoUsfrAFNTLcZv<-6=QM$V%Twv{1 zWGvJ--TrkW_D#9#dLsZ^@%=v^dF>;BK#%|@hMoa6--Z?F zlm(WFtoEX$!J?q}K_HF1i~ik9Ehpqr{{}&~0O0u`LS*f&&v#BQpeUaiV^BvjfCPF|A={uBo8Wir4^8|B;s~`{4tb_h>2R1j`_{G36X?G^ zzHr-sjyTdWl^hgs6Om>-v=h)>Fgfl0NqePe+i_E@>H}S%PegBtl5%>XhE*ClI)6CP zr!1Fad6;}4*DJ6Fy90;a2;lG&FDFhTNbpw6s!Yv0WcBKB;lFdwMRPtdnB6DFsvl`H z`>7Ik-%kxIz;(#t$5tnIezyvB3@;TJ;`HBK^p@^))9ZR>IY11YzP)p9pae+TJ=zgi7svoB@fZ@=5Kl70TX+rDLtkN)>u zB3rwUN{EoWl3P)WxLjWNz2;c=xVpH&JbuRW=~e_e9BQ0zP9wv&VYBjZTci+M39Il- zno{tW%Xk3lAOYIoAbD#v($b!FYSuw8TF^zJvF!3^54*|Vg72>d=pt6Xjt9sh!0o41 za~b}nmr4bfug}UF4{}THAVbs=8SK2NJOJTf?3y{!uB2)w@-&kdHA#Nx<_wpcuA3V& zS6f0)2|Oi0JA~-lYZ&&X&6LDzurH=&G?ve?%0moQe}0DSc-lJ2bh@Wvy3iNxj4SND zrhbR&ay}*VRj1_Ke{C!Z(uu#Ms|NOpp7@z>_)E?;R`%E|v&j*;wKh@nzLT%Zynjl4 zOQi~b=2NpayCM|=2cO77yNSTzSHcJ=z)_i|h0Tw5+K#29eR6&U-e6Z&@p zO*)4*hj>G8%Si1DbYxqFNG`TGH|MIbr(4Q^FjaZ*ec>iw=VOm;H(%D88m{%~%_H)u zm#l+82w}Ht_X4RDw>qym{=7z#pfJ)ZkwZ`&;_Cy9N^)C(^pmxfq(^?bj=FhRVymGo zI<+&clgTDQ7HToVXTP!#s8GHmDdQX$W)YJ<4mNGySLV0hcPw4g9(`(nO?=fJ)Q{wl`nbjN-FXu01QbRBZ7TTteQP(v< zje8pJh8+!GJ&;zSJzr+7@%p9UOKUa#k0#nx4bx2}Yi>rb^RDa!tMOH$OHOmh=BL|n z8{+>VDOJlGfm`OoQfrQirKUndcOO)aG%7hevUdAK{FEQ!ymFKN5x2FrQ)1P%NTNBI zZW0lzPTYfIOKg%c6ZJ{aPI0Ezv|m1c3by9`3f0PhXIL%?xIWX?*l+|*?Uv4pEW09T?8ak~J+ zai{y(dYelDNLnthP`>c=<9eNP>Ax7wFvqyy#V6{JvV|D@y{b1H#x! zQ0c_5k*p1Nj0W@loBJP+TG#rTr-+caf2y2(7WD?x|M zr2`QM-_qVJHr6%iB4dC=arxy>f*8rNZ+(g)Nns#^r+qt{!EJz&$17au1pvkAZNThc<}cUaz^tv2{3%Vl;mzS$6O84K z17B8CLMz0aB({j*aGN#m{@{LRSH9F2Qb#O=kbCBY=xJAyhQ+>3@gB5&E@vw^E@O7j2Z#?(DzOSW; zdN0rtW2P%UYANJx>{HW}nLG`bGa*1SjjA4wxqWtDjY zx;QQ>j>Rm3(Yn*7rUgvXFn5P=`emo1*%339w+ITujK3*`Ou^XUp>C!A<*MY<@2MjtNqmH0!b-Q zecQ8($k(HzrX*`lu5P2PR&ZO zCD?d`230E($9opu%nH2ac+V!M8WZ;68G#65aF=&YOFs{$9^W`^3+U;#IdPG}(Y3TZ6Y*q$o(+hW|JA`O2h!8`t`3!L|O zF_XvWz7ri^yY%fI2#f$pa3P?8QmuPFG?}%kJ9-sEunOQw?o37RO1wzSoE0oxF;#AT z<<)hA>zq-uuA0Oq>_^jKbut-55o&CzjTj!Df1+kno6)Ai`d3HA;{_VI)B{GKF0Ghv z-wmF+Sd~q(ZFjLBPn{~?bgk+bnKcj0Mf$pMRifYjx@fw@%Z!!>fQrBX3f&0aQ4rhe zC1vbUa8XE-+`*bLmb&znEMFtU!k&30?g=xJpk2G%L}2XLz4^4RQ3e+qc6wCvRjsnH zC$C3T&?&;_q1`eUyPHuNg_(fv(w?SYGDAnb%^k0dG^1~a5qXmq$9uh8kl{Sxsy9W? z-;=Tu`9iS@gcOa4_bD)GRgD~MsR{}Ty)kseJ6amzLL``9D(U={56t##`K!DVb8g*O zB&uo`WMp_EOKrGNQl0I!raI^6Ip_STfgWVm?B1AverHXZnTDjd2;N>=7z~5b28bIY zGKDl?5PG6i*a4WKC*Q&AX2zYV{+Vh2&rEHicX*M4@4m(t7K%4>k%E92f1~*hwVTSC zC)pH{m=)lnAr&nDAe)3z zA9hEV8zPc*8pk{g1BL|7zOQbt;+|K;rwtOm(I78@6pcxY3nDitqC-@$LjZeCGa)AS zLRE8yg?PM}u~6e1X;zsU@5uXnEuSQ*7%86j5ID(TQTvV`Jt>d)*PwPK0U9inOOT%e z40Qb-mY4$#neUws4V`saN-Sc|zcc5#e4KI}FU{@k)I?BOq<#bRQEQ`A!I`-kZ6ci! zA!su-Hi~$AF+|Zep9ummUjx(`8;H$FieAqlD*<(PBs^j zMA}dN`yGYtyytc3xc<;cY#jmJq%tvp4nOSA-?+B0+r}7*0z+;f=5a{ihRd#X(HFzx zHf5Kw<++0)+10g4;vthkZy0&FRoD#TC+}6jUIXAx82|sM3BBa!i;En*4iC5*vi71m(WcqZS=bSe&S7ph zo$kEfF0)|uGdkTZm1YkdiJfC2~7-e^wT zX7z86=r<=B)9wfwgBygdsogdL6+6dB?HG{<=WGNpRW<~*)pG(UT1cAs5fe`D7W13E zs6b|;>`OG#TqXW4M6Lzi4-mnt45Wl-`=#Qd^OIhw{wrN54=a{u^_>cpaX%w^Y53Xb zXt_RU5ky*?X(-~gSRMj41b>Z>i$7(+gb=&-*i&LilW`q57{IhBLIm4(-uAg`BPOate#dntVjkifY?7k4`aSv5 z8Sa0|{sF=Q{79v2AbgJaJ^AM${ojGG6gyZy*fDs?PwuRs5$HKwhD97`Gx%4uW^$0F zjs~)?=Ap{Trt=lQEE=3v24tOBHu1^O|I-yzqLL$3h-*lm3z#kE+4tVrQc zeMF=@C-{Z=h;EI41XIEd(=vmU-hm0fVf2{w9FpQ68oFu>#kEAFYd7@yx8&|Q=DT~B z#vL)FdK$c4>Ei$iKLr~a#C|(>CJk#R<_`^%K5`LG;-!Xi(Eg#h_z6jLKR5Qmv=Mo` zkwyFi7tu3_D>I`0GdGIj(>T4vi%%icENIm%nt(y@J`QvkD^9vf;A)MIaizJzDUn6^ zTjNmR?by$2|M-)C@&nSlgj0Z@RBnSg_3+si7-;my^)q#Yt9O!15Wf25Sn)`72;Xm* z{V~0~C?noreN$Cpy^0czi=})Ppf#4pi1awA;6^dD5)b6s#H`_*>f~j}*I|XKiq~fgLT`lGc#|pc zZDWCjcQTbfq>U#$=<6uL0Cmg0OH;VKj#fIfaEh0iKFiilT9`7Yhs16rHoiQjDnt{2vY z46ZMkGTRKeoGERQ!u2@3*oh~?*ZZ%Vp0TM)({=J&3c4v zWkd_(&I=p%pX8wbuC0(5&@;aWhXOO0x1SFI3PaXIpopx)a%<*`ZTN~-2h zFYsl3edgp{|FR1?H!b{E=I1-1uCjBf30gs)p z$B5hzv`f1<2IE^eH{n-wa^)yhfH9w3DnGqW zQ%A%!+2vLt*T_ZhVN9X4yrs~oLAavb%G$Non8 zx#Os`v*4*3n+6|9*O#}$;_Rjcb2Ci_lNdzP?r^`m?XUUk zjrP!XO!(Zjv2E&4VIBf{GKV$FfbkY@YLp}!A~(*EGpBU;5=+08gXd+ObD(V1YHU%o z57AFQmD=Q-HQlKDq~xwWSIol%WbZdaCdG`83kwl|Q`Sw2*qCtRJE{Ny8-{%{CM+1= zh8Hfruq~z>V|2-E{nZD{p&y8)@!fU+;)*9J>SC*+H4OZ{VB^cdXc5RjH4z^!sT|Il zP)Vb$t2W3}J9ni@z5HV{9Wrrz%WO6&M&WRGu7W{9_E9=SdT*n&+N2m(m&;{KLFlq1EUtu(tJfMCE?7L+6}#f+*Hgr_<@ZL z7=Z3+AEE$A-RKWZ!L@HMgre`&ZHm88vT_1ADBZCTUWDGF$M+O396q4Cz1k;n&%V0G z_+u8I`C{CxrEtKj2X9>IuS<8iKDo>J0C3O)=nDyABw&+#w|gc%{Yn;y_ZRM@2t(h@ zgZpk3$JKBADh<8&VboFe?lDLoli3KPa>BNNG{yyN-S&s3v%^Pm|BJ3@sBSUsL*~0N zSA{^7xq$b6dJD7H_M9VFRazbj<+3t;^wV&|g#>tVJbX52d4qR1w$c<2aIe30I~<5y zw{PYnwUX5w49r194s@tv0C-Y3J=rT2cJ$HJe75wYO(cz(p-fO+OH1m;T=VCw=7E}< zTDjWJSJbrQx=qyF`sZ?{T|bxZ$4gj9X`fqkzS2#)a_w|N)A!Mnc}3P z1=x+vqtjo__lMSq)`|G4aXVTpO1@j&9k!KZ2d-0*XChmhZ zP8ELxeHU_n)Z#K9_foG~pokMqr~i_YBPUOjOxL?~YlsT3mP5wO{iX6Cef#gjn`o@6 zw^w7TeXj3Al{8OOK+wGu;d^S9j>P&9eU4U^izp=!p}YLQ*hE($`~3cp?%AN%0#FxS)5obAFmvX)_o{HaxX+5k#mK^^pU(3 zV4DsUtosj=TJ1IW&|Vt$9{%odcl@3Tntje`pwKnosAS9DL{o3FsN~q8yBEvJ2D{_O zOR5w3Xhoz8gI4BN3lumwf+?B|atzasbKSUhx1&EWRx>!R$io>q06A>sK zsZR+uM=RIDBJ$kv&7wE5*=ZiB{XB0_F=W7Sdti3U@i!ahWJ>u$K7wBoUD781| zOz1AkC1tL)vu1S8PR}6(%*#z0{EhX={Wsq;3(nOZKDnaX;C8TZz2Ip`uqFA-XeOHa~L` zWH*4%DY%RDY%a?JIzbi7yG;MNoi>^ETX0VmgQJ``quJVXz#R=RtljvYjPbJUtwcr! zFp+|ZOo|nR@ie;dxjD9QCyAA?{Nc~}=Rm3QJD(QS0~;CA4@$*aQ$GvNv-n*~e$&^* z+KeuowLfwVF^HFO{G`HPEs6grzgFdcL&;fCNUiE&zxi%%rNg)}RR~kN8Y7t~zND2_ zm53JD%-A`oYK({ZPm{*yoUb{+t{k8kW=QBR13=!F>}W&1R85xGQ<9ba>nodS7T49* zo^?7Iv@Vi)9dm^WA8 zpCHCe+Jc3mpNge!Wirx%HeVXNd^D2xR5$7n`#lD~0Y5pGF0_0&)=|G7RH0#SRK%U2 zHN7#0OxaV|0WToCyxr|~z7g2tmEAAxCd(Na{vAx8h7`tUGvbKfZ@z*Bl5I4 zlXlo)L_&wl2BGz0?DuI6@p3J$SC`(^(t7EkOl_LRBi8ah6e-OC&e9jK9W}S{Rd5>v5laH4#uG~ocJ6Do#=F3n&+}~DD{3r=X23#7PPlZ zk!nt@@Ybc=AWhQJ2CX9apVn-iPdR%Cq~RBUxh^Ty+QAs6d$haanb*?j58G{iRLBa) z7W|v8T}pQp1;ANZPZywc8^(Eo@*tej=*6PjUM>jC)6A}MxE_1%(1Y2%zS?`{O>BXp z0wK+3rHu+?Q@=N7sKX4VzsD*Cnq(TEf(}c+U49hK*~=AXOZ)b#qs)4HRQ}Ha54|G^ z@Ub;O`Kd4#^7YXm1GJvuxC0MhPfA0T{4aN!|8lAM=U;CMX3rZ{F?jY1cIt$Ff`%qj zwZ)FS0ZMHoK&c(uwg&IU9VAoZ2kC&8&^uHy@}L&w22?snwk<#pHJ*gi)b4^YSI(dB zE^%PJ6MHS3r_7BfQKqK0&h1gK-Y@n-^qh3Jxv%AwG5^ncRnwj@u{=hV>7JrV?&$#4 z!$N^}aeqq#0xGQeK1t%RNzt%kZw-6|J__CblxIcpN)%pbm>BmU^jQ>JDCk!Q}O6+-}&Bwd9lK3`ia)j z&j-U(3Qna>;xh-md|3^HkN$siSqJV^uNSBNqR5G?NEqsfhXf!N088?)e(&E1@A#Hw zstJ%kJpWXf+WhS-A!lyhKNWYp{R6o3HWjhIQq2lYtl*UAF7M^y`DfiwH-*2K0g92x zH2a*I+EDfS_=j03>98r~4 zst>QO^mA(UFiBhYw4T>*@-wawQ-`u*PFhp> zVp{LLeC>5EFq^*xZ1)0o4?)|VI{zLC(3)b$ebzeCivA`@g-dYUQX|HOaXBh)4oplN z_tC0D4u9nzD;dZLKV#rb?6#xcC5p78!Z8=`%p^*BC41m&x~Ij4AWqw>A~?2C4JuPa>-?>s5i`TY7_%R+3v1OeKK2|ML1SwhB4 zsdPXisC)$H2>0;R!8T6L-x5WU7m~&*NtrD70{?|&$lFL`dCO&jR z0HYewO(g+`4&@FqCLE#8LTWNWX*jwtK@ZH)9B0eK zqHOI3pTZQeadrTg+I{{ybo)60OHGDXiB8n+=p-B|k~jo|rx)oyv_fYIXN9ppRU#|{ zfLS>lM%r!Q3pC2oze_$)+6>S`Mr@OuA^;`DgaD?7gFhk(dQ7C3dejcgIj2W#iz2}n zp=+6`;gt7i$E?U7=wX6FT_vzyKg$c5{LdjYMOitQLf3Lk;QOa4(F`%}Vq_O@AY z+=2+1mOAMAP>+!JlqSkAe}QUI^ph%vY<(DT+fRMGk>5||EsD|I_hPNTu>z~4Z^jd; z;_$npUbNdX{&aKc$aF0!qbNz5s6B?;$N zkZ5n^$(?lXwkH7T)4XYFuKgEf8F``WE?_#xO(zJ7cWs(7~x3nA2)^wSv=>9I2VB3nlwK+a) zYW}1g^BJW2#E_#X$@0U8gw*{s?}fA@$Fub5ju>z&Y|2xuBrWKRq@Zi03k0$Ech(~> zzeZtS11nc{1tYTTs|Sc4WdjY9HofCl$H%6+zSY$e-CI@n>vBPbj((}eXUBVxN&+UP zQEDm7Capd5-f$J^+g&xDWM5G4HwD_;mNB8|Tt%r4=gZTGXg{b_gt%SR>RsWa(~tMp z*S_BuYB)g41?i9iAi@fHLGFD<6n8&`ck@%SGTP-P(acxi2krBE>X7db)D&`>8^d67oQXu$IC(GnzSQaA8~4M%e39@`cSTDB zl{V+InJk>jD{wG7N=yjJ#1A_ik7(F$p)+F961z9-%|RnpqaGz%gDN9SExrxpkAV*c zpErD>TQf&~qnJauN$YdBhj+xAwA-&P8dgnsuu1KktEKL!Ub-pp`C%2^)AY5>u@#?H zwr*4ffs}ri45o1Hvn0Rlp2So)hKa52oJcM8Pd1P$VjN&}04xZw#IAZTiIASeqyqTy zM-KV&Y>0!xDUg|SbzgZP;2SX}Bek2my9W0VOA>Z1 zxLs-5V}fpXn>WF!e9R{DQn*TR(ICI4wIW9^M(;5hDbOW0cp|Tql_KWbGnwP9#y!qN z)DW-9FC+cE*&o~!s`%yj>rq;>S$7l>c7x=rSThQ>u1Pi_eOUBo9e?0b(9RqDYig`C z>w(-p?=)y?tAAPYcX!}LX|#7*n=C8>W8cq!2x;SRG6B-KV1RdY{YvWl0BZuhIA+oQTpoz{CdxmU z%qfuam)p#rdZ?41Vd`q3RJCm;64UBn*FNL%<{uiI1ppCY)9;z0+F>85*GO`U69*y} z5N_Ipxuh>VY|PJ6m>Hbns0Y#1k$C^DVt@y?^*=;Z*Jb_!>}Rjke=o4kGSu|XpEH>- zde)FzSD$DZ^LRw?*Av<;a|>Yd;UTt@?W%@B(`cgtD55g@T8iaEsiZi;m=8N@t``^5 zLT)o$cZHAc3gQpx?S*y#ipSj3&mJuDM;U>X4a!|GQ(tmNg7` zyZX-`j$em(&c}cjOy0b^Kn2t7JCi$AgKhWz^l>t?#lGs zyb?on<`@0fnh3MzNVp?WU~#3FG_N=+lIjiVbY4U;cGLGeuYez0xz{@W!0)_#uL6qm z$e(l=qG*r4T_hyMI-F*VWo!^xd%W{ae@5I|d9rRv2X1(pkU19li(gr`^C1kQ0n7G0 z!9cmPWXv9F8f$j-WgFL-6l&s!obyruz~wqs6ydg5H6H9-2%9RNDp}U=jrL$=e)A51 zc!XI(?q7Ih@?EtwO`E6{Su|tPYFRX4KIr0jG+|j&r^c!(<+%Tnw%6&d6T8)t#J>JF zM++B%!2(Zqp^_t?rmaQ@Zio01U$gg}jbl2?eIebwbal)4H${f5ZkMd>ZJB3xY13BWC2wpHTIF4Y;y&sXp*x{CuN@{bkwyiU-&2VZDIHa(ff3Zyb9$ zO5R>eQ$|{-+BG5plZ$uC8)9XQk(a?0tOO}x51P#Fl&xag_GBh6y2OKGWVp~JVmid* z5b+Wt_lEf!83D ziR05O&1Psnd-GKKK|{c<>jJxD@yeH1QEV;-+VxY|NvYH3YKa(wXC628Jnh}#1_DmQ z`0?IT)6;>7PJ-sB!5qVHr`I8H1kVV5IfgAn=>EfKRRi84ns?eIjs^`ZZb}+;`);w^ z=`cXj!5?;o1laGFt55{j{fuzVBr`BmBk$2J3kgN#rZN$=Co0MQsVfJ_`p{m-hH57# zEu$~T=$fK#j4k+t_jK?qCIIN|jE3suCv|X@6 zkc&W{V@hDtK|rp;glh%$zaK`7oyw+OM&kett-#Y^zk5U&Fo@LI#3X#DgnA#5hfj4( ztPOmJz5n>SCEuGc$q?y8QY&{r$dJ;6YxaH~4!fNV-7W#L9V%dVuDvwt)phe%z0(%F zN78;gl&m}&=Xg%g@>nirrxvs`=0?2&2u^rxiS*RP`BRtbLJ!LR5*=ah4~;P}$%+iQ zGhE6w3{G#05E#VRaTSTQ>~dr<_OqgoB(QPIw!a3G+$z|2x?EyN=(Q*za4|OT+0m-K z*+E0qU{e9q_OceXyl#+U2aLzST=bJC-YHlVa&A9e3v&<0-%r*i8=EqJtohVJwQ~8% zp?Z_uD#j)K0If=tifV)|q=`2W?=*a0myDO}Ko(lrNT*_tI%>RW0>cvx#PH7y5&c@QpFzvU-J+`mn>5cs1_a+-YA3%-7eH z7*O|0b9?6-Tmf{_FkQY?nlYGDdG@AVe}rrFt#b)l{N(!2WY2xUIMJA5cJ>>sYYIgI z?U)<#50~isA{FR5P9W_C`{ExXZWzg{+c!+|?0<@E_a{gvFPJ|KI z^eL%tCrrg3!bwZM!9tx4`^D zA(C$P;z`(sIedvw!2I{n98wUDHyE%rGnv@Rqq)}UC4-fxT@yi!k?pxnX}=g@l8F!EDAy{#UC#LdI2*Kb$#AwB-3c1AtD;Z_Rkj_43`gg4>|4UMo% z>XtX>wJKQ&wnS;N_>}#=6o1wIv=_RQ+-Pg07d++6wb)vEgZJ0zM$Vhls`=*BUCV7D zlMWxPpa+#cnFY3aYHq9E^j`1cSUm5=9ffdtj_O;+d{C3WlB_x|DF5{P^ro!YVRdfa z3~$QS+4LeoUG#C{=qHt0iG41F?Q^0KYP&nfVP6f`#IIZj@hq{kfd3rPNWs}#*f@RD zNqplfyT;S}t)il41aDbg6(|Qsbd84Gz7(NA!+s7X^ku2hN?m8>2+zv=&tA$eNhyWHlpnaYdTzO!S&cldlTp5$w#=0O*Jd z{h=W%AqM~5smY~0{6iCWLHqO!dUikVWbMY*7ZHLA;B1z%H~7E*Hki0FgN+_w`kPF8TlaKW-!2FZ`hy>-77lzdjF4Ix`p@ec^fM|Knkx{dqjl;X7Xk zjJEzm!;X-heo8{&FgMoUwZHQavc(D4dZ-DC4&gNn$3Wq)nd90rf&}HLi7)<%$7|?` z*u}S~i{ii{ZDvg9s7FV#sT0i_>0XX=&&yB#uFUJ%dM~y8s%I)JG9e0;``@;X0L}W` zqS9@>>;FLs6!QHP38e|O^N2K+3VJzXR3)sFlgHF4Hi^8%B`|7bplD9J0HY-#O@^r= zFb7tJjKk%rqXMUqnZLPRSo8S{P2`>kcSpIpnHok^J*@3YHSn76;S9MG%wP6-v&P!7 zakf6Q2O8Zd2QQl5(1V|v8s~P+^K@za9QXF}pUpRM8Bl&C)31$2WGfR&3TL*At72l` z_E*_p>Qt|zo|t|}J@FK|v|dDJ-Z-t_@GL62R8p!$3iJP}DmRTiD|^F84tS)c>ads3|xvg+V<$u1|hC@?R5le`CP^J-YdWdslSVU<}8$s8e>n; zP57!twA&c9FB{MPayDx3KDma*s52 zEeLl3OOtuAj{0*Xpxc(-0y28{F9U|>PjbI5y`Aun zB#YUmIv$ter@g0-#W2ls;}T_u*2<>Uk?Q1=s@%#AQjWXDUhiWxC%i|-#CfN{7h6Hr z->9(tEh$_5O)92JK~`gySBg9K~)YJ?jWTg*spol|RgHZmbqfC5i1Lh3{q4f`c=Lmy9g8xS`gk@{hGUmY$|dJyl&OA5 zC{9SA;|S_FbSS4_Jm{ibX*k0@v;&y+bKAysHz7hJ8|fB1&vN}PJJsLGOd7G5iWaIj zx!-v&Du~63mf>Z*^3!h4z;9l|esqNt|2Xk(>d%ng0ekT?`4hRC$y)zlm7m(WEiOH4 zpn$k*%0*_r*gl}89B4-uaK2<*94^5#4`R9TtvYsOZdF2GD)0v2B zlfLPE(YXDrE>|HpCir%|d5+CXeIW=1bnnM9`d4=;JPRN|yAPhHcoB;njFpqoX|s?{ zE;GFsmE~l}jSAMYf>C+LPaWOqoR@fhP>18ASL4S7w#KB&v3Z;BNUNsnu`^&1p0ih#$<;B`Bu)MeHP6T9k)FKjx^dXDthZ|~}V;|bnHoGzhn#he%KEAds+NNC@FkLEODxA=sGs%Xi^9q36p6qz4p$bA z=4a1tvKK&Q(;v6eO>doUx4y{@*Um613V??8EBM7Qw9_$g#a1}x`BN~U_3_MqAdj5? z$I9{rSrgeKIgHtMC7`G|vVbXvw+de34-NOUI>6rSYeSxX+8Dj+3?MTX^GN^ciK#=obE(qE4OJ{4 zoY@aL{FL?IOiuVeJ7DXeHK2y+Sq&J39zRBI)Hg+CpY1{7fySEA!WwGl)?q9)`4*rE z$=?yaUlpkKJQL_0>y;c(rvJbOnSaz7EXlCo$4!pKm{vVD7*QDa#8ranx4- zq9mJrw#;25@UUB%Zo1(AV(&eJnripGVH6dWCcOy}K~ZVad!iyu1XQG#s7Q@~^Z0fkozAkR{~ z?hLvL=}CG|>Xs1vBc;0ePdsR&JJT9F%bIUg}eAR=ZvKo=lJ3|PL@YZCtukQ{>mx&U7h5a5C5bONfDKTcqc*7oqz+&Dbvb=#F# zG?Qy{PwswV$K6%(sq(%K5rO!(3(W>uimXN{)gly~+)mP0Wc2aH!^2>ARvmmn^)|;E z+Ce|HmW9V*Qx5UUG59(|PbQ3k#@`9(0B{>c06G8<0ig;K_RmZHczRcQBDJL+7fKVQ z*ctO2geS~R+w3+yP;opvXwADmc$XKFQKr6{UORq=yT@7fs0#6b@PtGbP?r)v{T806 zL}JXT=K$e}ZeWT4sA$FjDw;PyGElh4p#UMG?QoNC0s;Hs^V+JdV#axy_-zF_!BZV5 z5;yh?>lSG^nB!mIi3SH;;oaQ?F6|fqkc-CA(?H!My9eXm@lgRMYLrxwk>0Cavt#l* zi8|(EdTf?r38mEh*B*9=K!S$aq=5V%x8*DTbfREWi3?<-(%J^=uT)1yKL3xnkGxC zxbnR0RhcJ!(Z)Xz_hewyUatDPbNwGbtT8lMWcQKfUvv|4&?D6*U#?CnFp>CAi1vMf zcF_J6C`EW*ZYPc(05u{BZze=6a-aIJ#43LM(sg0?gegNrupq^(WiOQ{fHmAZh<#-V zj5zRlWx6S4Xt=0#vTnC5Jer!L;BrffElF`{o!~EC(D^LpnFwt*mboFQJbE}M zwW^3ZfbM&CzI^Qo9NGdFBx+Sw)6V~pZ#q8XfD^T}`QY2NrpnHCq( z#{0y|{cfU18n#TS4acX>IJ-V)L2^s5yS5v)F_`}s9r{tqkvKwL=3N#UA^R3Zw)3Ni zs3zv>?y7iEx2Z(d6X0{ih=M3;c+^+t*;-B>jTgPw2y}D!Ow+cM8pJWgghebcB zp$%_=d&sz}h)pryP3d4I*XjPLIrZ^mENxg-I_g8`Y!)M>p>xa8$(8>0d&0n>A^wKY zF52$#;cfUZ=qLJYW!4!h^i(6st+F)BH=OP%G#gdhO4Q+8 z30=CoDUloa@Bwn#fOgf2q;cxKME40-(YW%CT#4T2bi0?olpvp?V}q|?;HWvtvQYY< zL(mzg9w4Mn-Svwcrkz-7Ce3=#eKddH3-WCn_acq_p1?Xed@ogh#8u%+HpPwjot#wi zIg$B+?EUDZaLm<~r4s?;lcVia2nA@ug;}My3b770s>yZEcY8d3n$UOgsUYftz|W`j zAx1ZC@{yVg{s`F;=JO|2RIYj4Zx{k}4G7{U$pICm3XWpWfz)I~?jH#He>ooNhn}Fi z&c%l#Pa#g?nT=-SUvKz;SbuTz8}yF(9nfovezDTwsI;$AcoO#J&7IKLr!2`>Wa}Eq z?C2ai5=0TeZ_+Hs_H-n(?8cl>mw|n6f`B>@MxKKZl`uGJRW6OkF&h0Vj0!MrzTsGr zTVpXFE7k(~?ho&1sOb)$O1CS-PB?$(Wz>h8Du)v+ns3N-KQTI=7zFEs;C(}6?+rxQPuS-XDZit9D(J$YF!dWq@1*wh&ws1eahT&%&k|j&!QTWiK zHt{g($>&I7gSrVI?;=fLpq-)w{zaEk`Omj=_<6yeF4vv%iRL4}jQ|ByxOX5|y_M_b zj%l|!b2o1Br}yPu8caa+i}&(AF}zSNHa&37&&=X_#r*~InDf@@b-P#zJzjxZC+Uyf zm=l!w&75PbXov3HqFq4Fx<{b*VLDW)0WRbqBjqJvE#55j7u`bXgZozJ7F66OW_|^q zBI3f`^6Lkwko;vO*$~mRj_=AgTb`aKHx-2PiHEJ&u>u82O^l@mEk`D%A^mh-YFw7u zRY>V`+<$#(BY~>E3t!8F$7+-ya_GQ{e{;=L)yw=|G+gul##an_a|gv+{HbeG^2cp% zrgIAD2qkVj9rupf5!60}j{DdZOzSgM+~k@;`T3k}&=gJB+$xM07JMNZ`{AM9FXs~* zzpD+4^xw!g5U|0uXankny)xE-99IyaVa>&y%DV^P&&p zt$%nAa5^jT+g21>x?t_U9fzs?;&!s93!xV|&T^U|VdN}vlM_I!oIn^ncbg+x+k(*Z z`M8J;hc8Y8pG%%`0J$XUC1HcTu-*HUw#oF>2a7cgy;(|92dD+Ak+Ed&XZvI^k}DqDDwOk!&3ATs+l`@Xok64A94b5 zs|s2#v0*>#4q6cvx<_RW7LFwrs^t2G6&Zfs^+4{+>H~8aaD6JrWBYvbI}mYH3yvrO zXz+guGjxZMM-hQ)EBKP4O>wU}SOT_TdIeo6Lz`(2!R_!v` z-kk3&*!H-|w^l^q8xCPhLWXXsyAxp@Ga%rVu<$FiEGu#bH5i=ffSgVGU7amqSlRXG zF@!?_@i)Ge`~|CGrnKelTiZLRxX$M|jz>>FKY6|zgsVh}qSt>-OQzaJ6zjDkLzsZ^o#!ew%`s<+F1iS=xAyotsQExKdt(S5`v%qAmTb!Bn&25~Gt zdukeqN;mitJ-x65Q33Y-rL^(kU2y};2{(zd?8X}R2Fb>kKaNf-7b|SBlsb9c>@MCY z+uhyV)(GRh(|4($Q-i=75zI+)$aW7ZgWKthxdXGVk*O7agmE`F3jlr-^;)*9PlRjQB4SU^0&XQeA8CtxQC*%8v=YmqCUPjuZ%(3d5RGR z_2|GXT;5e?XZvaeOapx7bxj&wY7aKof%sW~xBrw^v;r;y%t6vFaxa2`c?PS3v~iK{ z(v=j4G=j!42sF$47 zbG9;Nt?Pd5Gi{*zo)`O+UDf3Kl`=mu@ylfSuR2k*3EY-z@O6M0204-lL2HU{5B#aV zGxmE*(G^m76r4p%(4}@pqUV8<#|^0D?z{sKjE8*NK%xH+THCST)VB0Lg>{GqJeGjq zFDNpL(+11~&uX{yi&Quavg~{?C@mirmcy@|;qtx5D~KtYU}XWHh;_a2VO;BvKxcKi z#H(`S-wYhX^_K_ASLMx;{y9(p6u5s1pjtmH$Qlks*k9P7SU-=+pOaRde&T(e3Aa99 zPM{su(l0t}7+r4c`D5>kr`TiywQ3FziT*D-dW6PbbUl`~t5f;C)fzlBHdmg*Gu}pP zF|2F$_i_ynWrIG@O}=28b34YjJf7y;fG-tk=gt|o7%Hg>w0CV4SoMB?Y!~}2cKpMC zeZs~)jF#Wqy+(}QD+LYc4|{7UX%A~>{eCGF&z|P4{vqK(7w|foZfV(q8^=TiIh?nm z7;C;zFIMvp@D{JvQZZAuH5aL`fwcHUGfvbfKLx{T9pB|%NR2W^OZAp=W4rG5Pc05lyQbX zZvDU5yOek}hAtWAxjP&p;{KvDCmKZ{v;vtIi$6LCWXy}OS=(B4$|vmYTf9x6m?RT7 z606NpVEGdy0I;{#uNjke+(x?jlf8{+Ao{U=L%By)QVR`p!nz&XKhK z*Fg#4^5K&8HHmDSax2TW@=o5>FcZ3CJDH4fzTPMa(!Fs^Vd>wv^0BwShVl}lCpE8)uA08hS3Fwso7_-KheR}U5AWK2 z#!_H9)uT#ohtCw*L?@g4!ltu2>d$TgP>erb>OUSJOt<6Q&{dJEck&G!CN&(Q|GmF* z*@#^`6spkjSNbz!1O0hRZB7DNIP4F#pWl7J|D3RoUnPmP3&~OnbAMq^qFY&cej!N@ zeyP5~#uW$U1f|U|8dwf(IC!w+ibm``2Ok$@Tr17E$WIc@?m+ySm>(lz_YHI9flR_=8hl%=qKR2$R zM9PnHC8*}U6oD37S<>PL`<%|8JvYuY#UNX!j_b!yN+3Co^bs_HWTrx9-+?x);l-BQ zlR^dJk1UMJQFr)kr2<=Ls-c;M3C615hOr17-hd`<)V8sr7W3{I-oG&ULf9!wJ>F|C zHw^55(mK&x2>o!9Uq|)u1$Xpgyrht_3~jyb6`H1IqnH3pV0S6{=Le@K$?7-fR@;8t zRqYlnsR8Wk`$j)P#D9bX9qWaK+f5wEjCk3?bEthUJ+x`+La)DB6vPP^p2;CmLz5fA zn#{I4UafxvGAKWFVa%}=fRNczpBxo~St&6X8V5-gkT$`XE7dR15+s{aH`gpgCPi5E z>^oS%#MQ~Q%~J?IA`j*ZU`RN=)%X_!P?sI|^YFtqOHj1futXU`8t=+KhE+1qZySD7V>t2scFe`cW^~+Q zsGBy@t1R8*be}>3-HbB}o8VvaZR7Yqm>$@E?{gk69-D5S`&9^KhVqyi zxd)8<8O>Nhwen|bcADa>Iv{KU1f_35Ae)A`nK2&{^9P<&GX_NNXyR4xXIrnBzh&7T z!D@zsrEFo`GZ#q@xxd^b9_m!TCp5knSXcd!5MqJf4=J$_xOpK(L&iKM>x9B2GVD6# z2{7|@y{UeqzSyWPMJdw8zwMD2iy7rc2fSaU)CwA^q$)Yq^l`buss_^+JodD9Xeqg^ z-n+kquQYr}QPHb1p)xjJ^IV*yoD`&GvwFS0&nk;AhhJ4FroTRNxh94eDpA(vaQ2w%QW2pkyy45WdLwK#dD_ah3A*pA zzSfSPjQ^tNUeTPPD(Xk zax8+Dc4BJRg=>aNW>)+!xf(j0Wz)$~5{tVQ2;!q!k_sSrHVPj>nB!;?#bu4jyB5F6 zJAWyXe13d8UXWPkm9bP-M$fXqqALaa)VZ0Ydnl1A-!l6pj%&rzdO%;1HvfLP(oD>b z2=KK?!7RlD+({~=CO9U~LceJ0Bj7OGH^~RljQ&-Ed}ISL#ZZ<2J6?6`&P}WqqCP0; zV39W*c^L&_z=L6?13b2z+66ZkD;cy`1>Dkf^XT5Q?-j3dy9u za^Q%`;I>Fj02i{MlOQwGDxR#hs7K|)>)s9K>m}>imDRD2g+s1K=Kf?k88pV)8e8x> zQ>t+VB7QSyW}9ks5yfujf@G12$B7*T>$GvJrglR3?PzT1kl|(SzCHIwtIzF@HN!r& zy;;fLijWHF&H?Vnjano%OfXYe9Sq6%YdwHM+kF50VbLBRs{9kGzJ=({`sms6qgkB4 zE%Lw$wxJVmCfjL7yX1^$!W~`i?D2&Vjp9$PC?f^n4IPPizu#q`pSz5Fo?6G`i>^=qnaD)U=`A6J@HgyJfrBAsq1B%R<{G*Ol9yj?Zp_zU?kM@`VY0o<{nM(Tqr7 z0ph?qGjXzgQv3a!!KHck2CiO!e)i^w-KGS2ZHLz(jsVoZ7Iu%5y)T4l+(fwm;84H=t(Na6A`M5-hm>T0ofZJ{jK>Z5quqPCy9r| zjvdT1?n$Uy<%%P5W^95>wEZ&QLxZCn1W(p_R#-hGW)_O}!%I3F3#~GLW5L~oGRG1d zAeiYN^PkZT;*fd-x@heI!5XM{BlB5JkVlu7t{yrfyQ?S|+7uu(`}OP*6YBdWHfv6J zEci4f6_eH(f_OZ8_`F~5RPVmN`~JHU#Dt`MVu-pS2Z@rjqx@QGO=l!mrn(#w1xy5PUK&yYkh82+a0tV zC--p0_(S!BnBZu=@)0wI$A`+aAI*|2W7I%A=(tr4wSq`{e>qncl`WTSGcy}v(l-i{kC`#}#s!`r@T+@@<#@A1&9BT2i;*k;LkHqQ*H7kmy9!V*_?BV)Wqf*X* z$98~yNPI_gArK;>h>pM@HpSaLg8ys}Xgc#?a=k*jHqmp9wHY?%x}II}@Y79VyzSw*r}Hd`W>6~Q*cfSqk-7Axv3I(GLjI$p%Bq+*>in5P6EAh0StQ5pDj zyc8}?cBfbnw|m@K;q;gT@8vx=(^eHR5P7r(Io5PBdTlYeBr}4c;`M%7!{*41<1gsG z<^LxKpnpO!pa21j+4QU}do;^mbQ>an(G?1OKDhWVe0OrPTBJ=5J|rWfKspDYyJo=L z$?-2=#vgD1_Lp=2+LKMCLm3?1d5@fyTE@*dfUDuR9jFu7~1w;HF__Oe!< z8cb{@Z8{3TI}C!xp;A;XU;qo)Dk1-WTcts*D&L0SPxEb}BgUHtvLK2dWDPjvZD61y zZV5mnMDGKZNJ~eVkYAZVjRXV!Hy?d)Cwel|rr+I>rE9;2COB}YjvNGfW#7kg0pJXP zparxY9ODnf(EFAn$o-4Wi8*opeyo3g`0u;%_uBaTP5C<%{2dto-X{LuH2=;D{?3v9 z&KUpBpZ_ip{)fsXu56&Z2YOsdk`%w+VxdRM(`G+Rwl}F9UbMS4c-&DNa%axx{sY5% zqr1?eQr=G^gWkyOopwY6(d@U)F7W67pUtk>h{@Jw3v^#Y8XG}dKprXSSqvQl#=8nA zauCoI;In;9>X~z<+;qkFXsBUQZKX)iC=?*?=k3Zq244?rYgJ6>P14)yh>mP4?_PcZ z8(zgAorCD{xHe|vZ$hU@DW%2mz_zsJTm=AGp26)b>E;Fqw3s zOjfd1NpR5IUQ_|)eF2=6AclEQWh_}$teAG@NwNIwQXll|g}4FeLwUzw*XNe%uXHz* z-Ci6>zod!&g0SV2Bm%gGr_d2TQI(Rn)g?j6e(c&Cq0a?guow2qvILNzYGtJ)&}>$R z5gk>P6cHy0zDAMmTn{SfP|!cUt`NwQdvJTI;7-IkwB-q*Alt{*_y>jl?4Fp z>k4y_QKxd5K^Id;E~bD z8tuYt_?Pq}m1Y6+o}=(_15Tx%BdE9U?H(|Fe%ZYIJ!CfFO`clB)m{<_wHj45LX8YVRs&BD| z=)4uV+!)5lWSeqTyl8B0@tIS{PBjTFZfFlJ)DO?a(TvX|&d6MO_-_6CYsv-s&c4ub z_3=oA+)>7wJSa3+2Jm<{azwd+mFjE95Rb2YCtAFBFk$92sIw4`Vq>$qKuxa|i?=5h zMC8MPuLi&V*dkaRQ8%0*%U9HeD=vT*H`Z42eZEB3xCnQO#+NOx~rCGsih{w@s>>{#GRur%`z*&m#VT% zXqZ&Yo+7ubbys!y6yvQPwcB|TiC1q{Hm^6X(^17$kb^ImECLTL2{&5(CFr{%~X8)BXV#nsnlC8|#$0oOcj9aQ-ePWc9SlMqRsb zW!zzB7D!VTFjeOuSJ8kF=Ary!ut1)b_E`jS;0m8H!41O3v2yF*}4th++*{ zaBFV`n%|#r{5c*^;m~vEr*&qr@4Qy(Fa@*Kj)5<%jFAKK7kb)VJvTouNT!)bK2LhN z;2>8UrQHAAru};m9zYwt+${~X9bBp<@kBuIJZSx%NRh9H-hP=+&xlLNZQh{UJ}17m z((uKfYYY7?gQW*BXyVKHr5cYZ0-sy0?}&iEai;N&+BFe38o$Wj-IiU~i6cnPF%5>8 z{N#2MkE$C}V1!J>>kXFf3S5rzsE3lPvbNT97u<|GPK2zx7Ep@GS){C%^dlC)4N#l$=>6xW@%KU{Jxs<%{HT;>tBxe? zNlmbs$K3h$!{e(w2TN-ivr)U^ldrWGc}AY45LPrr!>=d^yt{ZZUhJ7i4lp$KZS-j7 zSb=bYF9Z+CQ5>As5J7k}?{eBwxO<+OiFD4e@I|o;@zZzn3C|4`yAxSX^894CEC@t) z#A6!_C_1Ecd<`bj+d^R)+CEOiPW1`4glk;mvq`)Pop48M?V}9&Re(5XEtHOz=Us#9S^iBBcmA1neA9292 z&U|QvFd^=>H3*}AWpWJd*aJZ!V$K@6lICTC?fanR_puKK(77FlMp@ z5ddFDwq{fjAL1rJSOCJ82uB$ZJ8+fzAjd=NeD%NRYA4BdYRUt9VF)CFRRH#TlHn+g zRl{WRk%aOx`H3|#rInImHcIaD2L`WuQ(*p_UNTZz=q%YH2@6@3eHfA)bCgLFpz_vH zIQ(XdjVL-BWwgtbfUA$fX*xFd4Zhh)eA+-v@VL~Z?}aZeF?p7quU`oou=OGh69Asx zRV1?$U>avc?j)x)6*>hQVG;4t>&7LvZoZ1X8~syEb9{oZMOGdPq%BJj?GhhjX_!5W zW1tW^TL+hYv9`%`8g5%dYU2LuCkhs4uDCjAJ71CwRn$bKoU=PmphV-$hLH9+CZ~ge zNcs44f*`_J#$%TQaIOZ`)O7i?u)b1lrt*wwsH6}9?ZcC)?8gH1;>G)sPC(2d)&-xU zjJJHcHSLo2EXr9J7=iieMM>gl^w+d;VF>*>WRzv$I&%TVoj^;v)MG&OYdrw+FUd#W zBStUR>5P<41n&E$7#9gPl7_<9E(v6PoKYP0$zW}@Y|h57Gt$_DJh2lC6@#>&DOu0n zvZhF@w3zP+DP8|o(q~2ReT^UBgiB*odzvC55fGIz{EMgksd4A%NRN&V**cfciCU6#lIfBv7syS-xg~=bjcOXhMi})q5SUre;Sfjg9 z{})|}$t>v3Hk65~L{jSPK}HKnjH?6M%jaB(@FcTgoE1qdT7gkg|AxN5#l`TG(fv1{ zm20!Te7!X>HI;bP8u96f^Ak!cx4bfQ(XRT+{XEZ8&p$q;g*3~M96qAYH4D-Pf>}r! zedFy&c9>!!uBXsRHGaYydG1nGtyx`j=0hE)V+kN9qZyUMSmhGbtH0=2<@+=Vpw5AC z)xYRMv8Ey{wTt#r?udEj>SMRB9nBV!>&Xc%4|7}s>9{Az253xYPNmjxPfSk~r zjU2g@3QP^FU-rrm#&o^;s2s=G#ADaKu?=T)4Vo>!g)k$;XR|1mlu6vLbFj_9i3Q?hYf0YOfU%YSU$#`t)I3~+u zRST19yv#4YV8o0NB6hZ+0Cz8;LI7&M3tdwIj`-N_Qf~}Q@LQ!sMdh7a-51{q=x6vErNd4Ejh z?pu9m(|zL}nJt@WwlSVf>-2ICFf1m?wfd&9RL#2T=#049HxdnY*UVD*Az5yG%&!I$ znb=>+y6*c{&vFIa_wf7`bk#hQpVS%A=qObz^s;pMV0hWgz%J-0%QJ})7DiWT_%NgF z7@u+1CiF7(k^7ldP?cZQ8dwyrW=ToUwuHkBlC*{^gD%gRN9blO)NRLB6N~-j^s8kn zu7j@GJzusqi)xgcbEl*AB9DQEY@P2pBZ+S)MV0HuhNC`L0ScZrtggg+wISU*Vch%4 zj1rc&JpQq=gS<7O@D~@?mUIgw{9v;cgjF*t*9RR#fCqPj&MucV3gB(r$eZhZoC)tX z*=MYDb0a$}5UW6(N+YbZtfA_9hm4f%&a|rJv3x?46s|ojCFzqsc6hi(x%9v0)BThYLK*^y5`M`JtJaCq@_L9w=a$4G>@VwHbBCG}zL zmoH=PbJf+Z74p}=+h6&rcO|lHOhWJQquEU&s-=DjqoFxx7Vh?x822(45^hS9J^{Bg zZy4dxf1i`V@53?KCam{XdSg7c{mm%@;QQ8G#Anxe@S7oEep(y&^jH_^3JDTdDb2jh zj&rI3DAfuhq8X2_|J0HwV?Q0x8HVN7__-fvfM%pH2cM^nLYXQVp%Ip>7(tP?G{bUO zk+Ui&(qMUX*6QJNw{>ddH>as{mzWl6u3wG;$}}L2`uFNn8w~{Dc168PL}R2`X~VX1 zME6$`Pe}1$2qS_ZUf;ieU}Nh=9RIx~N=SpK`hd@w z5gADpPRnS{Afqbxvz~3c=4J&?hxT;7ui<+(ry?lc?lW-w7X*ZS7B2v|BPakgR%u4K zmwx>Zb%@V%!YCHMsHBwn;0x1A_xZrM=Q8ULg4?k|>G=3)Xr*>SF0QZ0IRz|MSrY<_ zsqp?)YwyR=n5fw3V_oFRS8AXFjU!(M_MWn?}JA#J(XTXre%l9|FGR=UVD+p^hVR9DoAGz1ymq*Dviz_NI_bywAe^NRYs{}9q4Bb(}XwX zT5s|8dWWetUF;&TLiR3kkuE14D#KZgj=rjE60x|uwKWun zVh`8EqkYld)cocrXU^&r(K;+8z^uc;l5l;a#K!<^h6PSOG#ZjvOwO|s(1bZEhsd_Q ziQd+GnJK5$Hn^k3wGH@tJ_K&n3;2rSqpy0i%#wq8E%rP$7v8A7(B<1hU9GAQ-^TN- zPe?Wn4zM+ibE-e%^OQGl;S+krw##X)VpO}@k5D2ZY3!_V!EzL@qa-qtq9fl2xlbE9 zG~BEV`t)7VKk!svvzbk&T0N83)<8RJNHY(hD%?5S*(P~hpo@x7*TUCcCP}vM49!fn ztC#2f=xYD+#NHQrh3>_plR!0DF&lUoBu&OraDF~l!e?-;X`DlKG)`z$ih96VDK)1c zF>9f6lK!FCoMU(}2P^gQZ7>r84bwnc1pS~>XDs!D)GR>G=7eZPF~H&Mbr&x~oT}Ly=PX8bBlklMV7aGc zI1_QL2dh@j2j8jp(Inpj?1$|xlAOR738epG1#jB%-cbL0;QKn)J+r*w?!Vt!QP6p+ zBXQ|aYz@e=hL#f$D|rl)=abTPp)iI0ai zYqo4Z=0k9_fcluP#gq@M9nH4Hcnp*ajw3r(Q%WzzV~*t7#!X+B=#z{ek{oim%k90t zvJdI9wad|Dnw3# zCpm}gdVZ=mWtxJw-KMF^ORZEJe1_WG(z;DHMU0>WINRA`@Kn{^%#aG*6#g)3jqCZn zOUIycJr8euo?F;fdcT-s}cw*Sl zeB8!tUdpEuZl&pMo|Y%i&s?KB+1fenZMIaFOB~RF{@lbDc66lPuceeE0&O*SZtE2e z{eVpmB#fy%MKbZcFVrOqoy^EsDz+m`Dp_!}Ezzp#b0_Bt zzuv=6ary7pZ_?Gx-Umu@Ob%KO5NN@*XYn;Nz~c$p6?ok7!l*LGQ8zcLPZ_-8LFJe{ z-)yFRYvkHHtI{u8jO^gnPiOk)YzIhIEvlOxrzlDulH;MYYcAEn+_a(5M*7byo>=o$ z+b34xBLH{P*Y9MK@ZCYKz_OG(3@rhXrjTu zzAP>RxyJ_BRlgp+N@X1l&NrrA-1#><_4oDH1A;#dp(l2a_;&0)aqIwD)0nUqK6#TA z7Xd>#YiqrKRA>Bd-g-SjQ1_`vQuu?@-HY{U;+h=2VE$kL3C!Hg1{+$< zWkxnP%X}E~V^n1FYGieP<-JUPaID>V8F`$_mQ6Jr0bf`?keh&`?mejSdu}@=YIfq& zgH1QlEerAsrMJ_amYY}d)G9hF0Ywm2Ja;OAG01kju3MI3UC+los15^?%nOY{SUL}X~z7cggBl?Ex#(Yw{)8Gy3g$*z-#buRXQW(E5><6zNHV6_D z)Es#0TIqk{H7K>AN98#Sp`pQ%%Uy@481<1-*d`?MsPpjK|zVs$+1iYfpYtL@26?H>#v$0%F0#*Z^@gT zm_O%VMl%Wz7DzHB78GFJ+#AK2zBp~|Zvj-;yz}sChZ=Gd&eo}81a{Lz(V65g*tT3jK ziF%e~x|)j0?Wd{VV;vcrwH-795)It%--u@;DXl>PyRMllz;)5jk(V zQAOOu0L-Dn@J6doz(qNc7$Vp=G^Dos6N39|u0$#SEaBRdZ~{N_h?7aO5#&5MAW4Zh zyiS#+6cM_p1_XD;d~$6yVQ>TASX-rZE;NC2l_|19>`?X;?_9p~B=tBY(0x_|)}oKM z;vp7P_=B?p^S<-z3i;=6+l-oLWHn;0+&yu@mGPy`5$DN?B?v3f0n0jukQvV#mlqV8 zJ9IAX`#9ob@L=;nZS?V*GCG%fzsft^2{Ad%Kp*`hW)>A9LcCl$TSHtg9S^I5rKdMc z%MzQ~jP=#m)k_c^cD*ImkMM?ybM%5$z~L8htPn>?1e(!jSz&XR8ePp$vp8K#X%V9 z$1cTrY-@>6bZi$mc0b^)>4)D81ElzfongSMzShJtxR}TYRn@DOuTUQn+>(>&O;q!Q z_n)FW9?uMnr+_v#@M_z*>kiYpiztaJYJH|iRh`4z-Ry0ztKNNn%%y$qScgX@EH~%n z`K4HZBN9kBktvh}9j49DK%NoZTPMr3@@47cku~WJ@5A<2AKo<{I*YvN&=Y&?S|>_qIvdU4>&Y(-7@PnnzcX`c zn5s}{MbUP88sd847C&F&-Tll*Dl=B16^eRdqT?rXXzhT`92Me`XKg`&DDi6@0TQ|f zUY~8J^c>|`gxEOr9KKdvlQqqoxI^{ zH!8n+m;v7G7ER(Xa20hMoKGr`vNbwCG7hC{7Mej^1q4uBXk6t+C!Rgzzb%}jg^8jM ze{dd5Lc?7r>-vEgH~RQmHvWiZ0K?Ccp6k3U>dPNg>6@}FKOk4)@#!|RNQ$2_y+m92 zik|xTrCIb#@Ck~4E(LYxciT35cxRJyX37QdG0u=E_K{z$e&AP*&4>SdcfX&myKF0( zZu>}V7WASL6#5k*K1>CtG#^hKg7FNA)TJ{s_4ZwR@3;T{$(p|VbkbM>C5Z@Wf%3=; z%IT0yV|^Gkq)al~vl}wU0v%1T{3_Yr35G*AF`ld{Wh=lbFIF0b(*%i|(Na{c!k_Z0 zqhlGcd?W91_AXoORM+EMe39SZKiw~{-I)Jy)-M#$e+tA?Gli3G4AZ{RE{r;i?TRev ziDmg;%juTx?{qVj7ce_3^NmkoaPUT)2e3l?X(C#46@^Rf9?J$fm$R4j+SBh=$lKbk zf9Q&ytlpU8>*Q*O50gzORwVkifbfP=IJ9@f=aR{Y<-ooAikSOn>5i{lvDe+WP6ui- zor~U$z9KlWL{pv#d0|NJ)B!=a3;yz}QI-2U_%D}#Uao=9N8R~cWb)|r zz0qSlL$i zH>&VRx%(r@X7^zlw^sTkZ(pHf{FI$6Qu~cAc-JaUeC*0cseX(9PIx|eh{X{Ypr1#Vc29DBjw(m=!k{y<>%pHNGR)g9UZ&-PKl76BIJ zR_2zERZi>F5qm$_58oz(TCQBG$M>(bx1y+L>w%bZkWOzE7IPO)H*ZfEuzgJP6 zt{Zpdr@@JYsh+c6{4EyP#nm3S&Yt9&KrkMqkKypFQB+|P&&|qIjOADjIJXcECXlL5 zSL(|x?3?zeEP2+;X9>5;;f?qpT7!2mi-AIcMBMMN!QoPqh6>-Y5FXu_wX&$vxJ6sK z*Wkpt@OXRUY2r=Z3xY30=2?_W89|}V=Z2g4R*>}wJPlhoIqn2D15oPfkmayBcp50DJv!yh793rZ!7XS7^47rIDo z<-(_XO~6`o>-1ux^NFykoEHr$J*bFGP!s7W;&s9B9woE2)kYS3*#G8Pb@J%E!OQCN zU$1sQ&{h^nOT}cK{6Q5fhQ}BWl?3+$5g6M?6jTrspn;4hx|;ZRVMkNCwf7i$K0Hmb z8o4LZ)u?xP32~X^9*Np5HExyZ^ZGOuR7RT-Uw!usRj_%fJ?GAsyPXZXu||k-|Ibtn zBCVY-ZCnJtbz3IeyCX`VEC2gEuz%J%EVk4;Im% zD=(aD_jK0J0Ru6tFRgc2;&%+wT?1m+5)i|r!x|a0Dc6>uwSy(Wor+CGVAQ9YbkWS9 zVV&~^V!fSi4|qF9Td8%xHWv_m!XMqvIx&o2>TtEYJKAUkw1sL`>YuqXR~?nEx4&KG zIA5u)E2<6e?*7?^5{om?2kg(#tOzwch#L;oC-yaG0Dr-C`-jUxP|4R$PEyrZ?AK4M zhaVqnL8sT3vl{hKnmI40KV(HUa{{w$A^yQeNbxcfHY6XIuHPeoth|%?aIB=1-p!P0 z@=g(kokiu;$5Ai~1!4{$dJs432QhH8VO1cn&y|f!v6W0^1uAxCXJlWB%<7g_R%^2= zOaN&zk$eeYTY7NllhZ{Vc3CGu=Nt4G z!g^FfL7JNqpz(y(inY?$kEl&ks?ift^5WC6LDlniy?H!-{(AQSQj43hB8p-~G;2Yg zTyrl)2xfQY#q8f6Pi2;`;o&hnc6Mk$Ts`sJnT|!}?sm^KBoKFF^hqb1QLx!xPZDRg z4{tHDR!?Q93Vhh$xD=JNwt}x?5;-BHqp&6qlnjEO5Flb)5lKy-5&=$#=UuKYwRiul z=_P;rTT4saZJjgM;$^zn#a$&6LPM>CfQ$(^y7Bl%6)c|N)|QPe^R2~pcQ-S`+B)Wj z{=_a_$>}{$i(Ju610bHCocM-#NMW2cdM)ehNkQMD4K@t*q>Hb@>ek9i_g)A-fD4MA z{l-+bcHR?6C76RxP&j63MWri3EW|Z_u9+&q%7D-oi4v(3$8WM->%HMV(`E63X`%Z` zXWAwZ(#hA|)fux_gcOI>Z9ME2;SmNEwZrSu>Jtp9C;9~Lq|-lBOXKa=6?L7B{V?`} z#z%o+0)(1!m$B{>JL|q%+xJEuZ!frm18W>>gL>P_$iB)C=mx%@1d?+GHt80lQwBt| zA;tGV5g}NZpXXcejx9LCVtdY4&-LOK1yP)iDJqkpZ*=8>Y4`w`j*?A)LD}G;nu#{P z*vU2hPd`_9OgXyU8~hT)t$O%UB-^qi_oRV^8=wH(mhH|C=tNFG=w-7gF#-c(SswE} z=r4EvyfOoVg@-Vka>9IStMvq)SIgdXi_AYBb`89tZ}tHWB748Cwg>aDAaGYRn(OMI#=)(p#~gb|fmK%D5W>20cF>0Wu^Hehn= zMj&Ic)+2gRjGlZA zM>D;?#8)}>xEpj8fKywhcu`LD|6%XV!=e7${$Zt3NmO<*6-Cx0WSus92r>5gmaLO4 z*~d%~${IouQ-rLOJ?oGygsc-{EZGe+*1^p5ynXNc{yq0q-}|_}zvFn0=lEUEbN%5s zqM7%6KIeSi=lfh#=!6kSPsvRFksY=uB^ z>Iy`!nIaiMY^st7q|bnqYVjC`#tC5n370+qUbHxTG)v7;^L3@l+Sfz3#Z~tJjg!<} zoyKnP5SoFx*Ws*>(4?%QU2RxP@h-!(>hj;$0NSK^6k(vi3kK^@j0VxJ=XR3e3g*TLw`3^krRCtR4O9 z64Ad|ByHTzFP>ux_nQrY+Uu_tar-~js-;5|`IcH9O8C3l>~Z~Lj;g}F$Bh`R9q`>& zpNbd`T_YDW)LndSfBDYB6|>|L12yU2*pzN)2+}y)higo~5q;dDH{8lbgEX4)eo*go zfnchbnNVcdhtnV0jvhQUnY0Y->eRI*ONfAivMXd;P4;wtJC5`YX#^gCE}+J^#|Nk} z#@|MNiXrRMxS&AXaz7BaG=}AOTYm4V7;YD6iT(Be48Vh{(F9$yqyuS1E+DOFY+bH) zvg_l6mck#Hf4;N{=4q|MocXz$Y)2Dw$Wvt<)x`3hYgWfU>vOynt3nidwmbjG`4Np} z2TCx=ha`IF80M%?+7Q72e`!}6=DN)t&R2&^dUQ`c$T1#~{&N4Fy+U1qMnqag4XvDt zQ#m@7h)0%Xk)2mzaKC=|+qmDZR)cyT0v7M~*j0jjWWB2dX>3_cp`{m;+GH+NvOcXo zeReI4*&@=fQO)&IXxgD;BiA9<)co%C_+F?^HrvQ{et)rr@r^)fzVZ#RbK_#)n#(vd z4#aDk@Az)Zw1T~=Ny(yzV}OF^-dzPx(f43O3r=9RC3p2fnSnlN4pxKISp`b@azXal zpddk*gF6@!Z2HDlT+aN6P`o!rPOUkZuRS0$t*tft#a2I7@1l8nEA(%yYmtVLS<)Bx z7ts1!ckMTFQ6c^1$0#)qzWZ+P`12pR1spv{ot+M7i1$*)(AlHI(6bePikM0P5!0M# zilORx;LK)rh1?jbcgd+a>y;wl*7B+GYk4i8=_dQqUl4 z6>>;2xwsi`9v!;IOfw&19%kA9Ce~EqM?B}1q`QUJKJPi)=B9U08D9cTl1** zfD*op8PGW3$;IXWF?=B>wlr{eO!eMcaLNa|pj#8DK$-GmH2t+p?yTt6CjAfqgP*e? zEz>8U+wPSa(8t@@z8rL;b@ZC*OKc+QFj#dC)d$@a14HU6wYS4+ttjB1!WlcafD$_Z zU1!dTo)5t4_w&MbRB`F55kP|AI$PQ=gzoAltGV(r5 zZuQ=i`YE}Usu20|!&ek$l#RQ%P(1{eYUT;%{1ay6i&x(d5_j$n4_R8a*vG_dJUbH^ zE1*D|lsdWghE+H1qZFS{(TGKzyqCbmaP`6g*_VGjslO{_GhjTHpjHOsIdE&}JE&p=_atnOYE&tL^a-l7sch`t0v`=PA)O@o16g`}w2#S6B_ws^8Q5FwC$<_%{M6;hGDoN4&O2!d}kW z*SQuxM8yeuO-8sbG~M%=oD9Y_L`MTD2!Seq6)+)vsEO*UpVpF{tCo%yKf`m+h8ucj z=8@jzbB|Pt!hRrG$cRtSObYx>A3FwO}8VsqeTAOK1^|kIO62@y=bqFx()_EV~&|oMG9>U z60%J-7r+cBns$}skqIK3gPkk{k82J6Q=ojs~zHo%1=XNBe_2}y-l3D`Qe}zgU1zt zpj|!CTl5ax$_1o7aB9sEnzSQ_tOxW^?I|C`coNkGkAxL`^YPaVYQC^s-6w1s2mPj~ z$9^1Tr}E-n>Zby}SGOtpX|n|y&`4{*I8kjz$Kus#hlsTEKZ28O z80t8m6+S;IT~|<9zB>msb60}&fBjfn?3kci3#fO%H7Efe+jBm*FZb8LLHNVK3IM$cFuXla<5uJZP`NTMZ+1%2JmlFUlTi%f6Xs5e^?FcP4Jk%K=~re-7Kj)pE`gTiSq{`J<75hH~_i8mM`s~2hZ%N|756{zRy>@ z{2I?>Ph!$^wrerOdoCaIsnt1jW20<)eWiWh#iLPzH`K(z$Q3>8wMfONlH3mSIWgDL z5yGgiX;?{mXo$e;io9W$fswPen+R}{Ze?~^lR0xqOylAoe4-7C`qy=xItK!bIvvm1 zdcEY|iC$)6oVp+M_&MUb38}X54buK=^;!E#EIaz{!nS8q)3*xTYL-cG5bHU0o^Y=# zO44StV%I>NsN|6T66V5+IYOz)4EecFf_;J)P)Y+o9E87T^O_;A}-6}n0?p2Rhlk}K6tU|Rhzu?3`$KR z67x}RHj^~Z%TV%T_UC1&-HKaRM!&VlCsDP$& zV_E+)p<>Wux2cDU4#8sEp;MJu*Nly?3Cyu6PI(bz#Wrf=Js`Xv)2{$k6lT7<)b}c3NtgmvJ z$^0M#*4o{=+Y%mx;+&(i;-zHE`_^^QmX(j4;RXYeoMt9$!%sAi85?_hJgQD2ip^el zk)fU_UR5&!L``B=zRiEdTBLFg-cPF9DxKY6k#}H5NJV5nANrgyR5SMdp$WsNOfBa<7uPj|b z5-)6F=FbAbm-L;%=27W5OwGa+Zg7>ZAGc8q*nB!b7bezN^@)bgH|Y2`bTO260#3y` zp{a2iR3+98;Mdc@CXXlT|73_o%xjAjElN6itAcDh^bQ?B0oHWUD>WbsSkZ-$7NLU} z`ZFvYRtm;qWABhPnuhbaT3{&lZsh(6S6(MCbLWC=uWj+l`vw+G?h=i+Lgc-lbJ`)a zC|AB6zNoHd3}W#7NyzqTYgI6K0HBV+MjuAgIanacX- zGy+He^pgMQR&&_A)~lRO=MUYH0r?oPeA@8zwCv7 z+wGUF`tRrMUuK`)AnUgfz3WwZ`l)IKNKW!JCe4x4!-)$Y>HX$2P|Q0Lt7$B&Za0+E za`@eAw?h*(fk{*ThgS2uKt=R#91`aTy@Zpj{^pCpiE^@M30eLZj_w!d1vfNMOZ;Ox z(PIktxBTRGY?ycz)1@j5sQCWsl7IKK_`Vkgw%-&ozHhny4VPZ~E-hTLzC&`R9sQ5z z-tXsu?D|=DGq0n6@6^l|No)V$p;NI!`0exsKK|D;H$Y2`(<z^pTx7lW}CT74y5tQMBTt=*Fdi#HmDYl6?O z2*$wGyRSe_P^IP4<8`dZ= zdN07fezzHC$1e=%4lPEZ&4r%_JWfnoXq>Ih5V}z2yZ?+&Ur?`$C?JE*gir;^MlI!D zaJEv`C)VdG9dt*cDr`AM<0ejh5PkRcMtemod_+EWeC4B9D$aWM1}HiFMnD64kkDMY zsEGLl-1FRa0I~KxQr;n)7JP}+^5gT}1fC~uA0nSGSvCtXy0v08X&8LN&}MTqyH9At z=y@_MdNNwQythEvAiD;;@$kzzU(V4J`>U^+3GRR9X1eDbL3{gJ7z2qAovKRe44TkY zx4`6_wLHd#go+Uj7clW{EDvZ zsAix~8}c~KT0Ol5pGbBTyKnO`WRzcfz&oN*bwS9W%&6=;Y_#tdQ|s>7RauJ0BvW+l zL9H>Oy6%G>LK9ib-G6^c?mqDiZ>2N#whWqAY>M9>e!#?2c(_ZrMQTX@(}fYVEg0!j zsrzCSvicZa65JEPBJTY4=~SIExhcRvjR6%f*gEd)4W^Y*Spz1){U(kI@2EtjNHyrN z7Y443PRmDd7$X?SO(!JDGIDZnl%6Tmr0wnZ;mV>4ACS~H*(iIPY+gO*x-t|wJ;r+L z=mB^9-kPE?1~9#XL2O!|6ZL77jaJ}^4fh^O&Ci|{5bmWKag;`-BTgsm*I(2#S4}PY4~l8|FO!&k*2$_tiei@!oy-PX^mkI4Mr6nJ7A-6AzE<=ct8Z|al zm)_-dNHFT0F4Qs|0NS0t9&m)<#HB=P`K95F@ZUPbf0LgMpeqaQC2H(kT-D^>6gVGReiY;_jV`v zi9=MuyUB<^(9k)7v?HHO9H+Tj!=JLX=bGt@X}peusyOD zzhqy-lsK*vduQxd?tir9m^a=qh^@tR;9ZM7%gVJ-OnuIthxMLcdS86`tc^J11%6Hb zdGoFE7DbJx%4$2;ea_A3ldYP=wxgmw+#RO|Ejl(hjGc>K}XVmxHzd@v(Vt^Z7nEcf{zFzHY#%3k|2Q zWMd)qa85KP3AHR^KDYc}7@-0mqhe>4CrT>^jItnN$rSYp~cg%Y4@_4az^s~_fxVN=dWrx#4QZaBs>vL zLt36*4%;DNB3M&0=rX)v{Xe<%2RxX4*OsdOP#1kliYBK8H-xpLi)W}9VInBnJxHV{ z3*`hH8TbOSAdwuQ)o3PAn(9X0*zJ)tz_%?5@CEo7A1GZfd_tJ^Y?K$YO-8(Gyecv( zhFOCFX2Z}F5TF?T;m1S++S%Uq^6q3%+)T3xoNJ zGN?UN<9Q2M zGy={-Wg7E`b0UTWQe3m0phK10j(cL=rZ1Hl9m!X^b96FT?dS^w z0U^b6nT2w(rJhs%C&RIImXip-ggDO$p_1OU+yJ_XHR)5wX-CGKeNPfrzCC|qEQTH$ zm7;($B8Re(|F~TtkjHtM;cU~uWE5p=$2l<}$lvGG*4@sx7h8NPWd8#Z##wE#85X0B zJhFT+yuo}qOqG3f7JAfkctPi2_0@A&_QZtf2lc07CqAE&Zv4`AXqEZBdj+GdMzv3s zLEok5BlF%vw>jk%gKMhVkC>BYCYN{(ge8b`g&h~GxCIUOvlZ4R& zwE8?C6ta9m$G2d3UeKx{bxAgLJc-YmB> zC??0z8#}-@(6lR{e`FnMq3haDg2z0mi~hXBiiXh7?y>*`KaRrAzgpyf{SDS`%?b|0 zI3x#IHos&j?9WsNkKy#KqlWG|$A<e=P0aE7`fjNp z5ct14|J7x+!~p<6^fTNMW%$@ewc-Oxs77^1`6lRu7b0czfdy(b7XcJzMW&{@4fx!i9vOl0cnzxc~37Z)bE!kwWAP1Su`Ccw=FaK^SZzR5UfOYrH4TS z0S`R13iK0dAfzl5sE&1Okl=K-O*FL}>ywrnf9zk}(0}nv;cZEhFS( zhQLmrQu+k8(h(f{ERGaQv4{02FBhX7%sL!r?vG4uJ%xE)l!=((;b?Zk9T@Ld<);`m zq?;u48j;$-kk@>RPgRpi2R|KBE)(MTx!;8hjjEjI^fWme*edeeH;ntoBewEu?lIO=Y98&3*R(rSYplyl zmzKSYzRyS5JPWG6R~hhf(0rq)G`?VC$rl@;dYrJJm=#=_!rW+c1^8koZxgKAdi6ZSZ1Jfv3J68<6%$RfB}B!o;mSZ!6tO*s8pD(hnX9MmdL?((-D#eL8#fSCO(nval(hww%JdC8Os| z&G=^F@3KOOH$0#qc&4T-jA4s#i^7~k83tU?lE{!^M#owK%Hs@EAiMD{ws~>*zEB|S zAa~IV?~|8#T6j1nQM|MissUqQJ&9E6M{@0)qIV}ny#+Jqam+;7NCD#7GK-H(=YWDU zF}+lj<@><=aWk{JBL5hdG{2!Vk=XNLE=e&&Q@w4n4#xfJK~*Ct{Pxz8@;% zZAYJ6{2`EYb@L5tLbd03R|L8Q(m-HAUKpr_nEH4(^jgh%^KORQ42&oBO!o}=6dWG9 z0_|b^eD~%dj%)WKI|gb)C;<5A-wfH0^nF9I)=VRFX1^UBbR^0w8Yn-nJZC}ib*ZVu zBM=CV$UHex!RI?_kz7LKSL%3F`z08BVT)6ll;o=v#CQj@83{{0J>>N)?VF?5kaK^Z zJXh*{>HDun@<+y3I{OAg#8G$aFLKtH{58^+u?>fSY{_CGFdOF((CzE8-s^R!0q6<> z$^%`zIe=aZanjXqPE{`LD;O`Pcx)_aPCpD;*m-b}`s2v8+MWu_h?4^92rR3*ak2qo zx`#S+xlmS6S6GZ1hmN&>V_*$X8;1yu`rBs#0>RA!O}>Qwm@n~kx28au6YJp$H73TwDgm|^ct z!*8d99Ca|0dkIjvA1D~yiwypJ&+Q;|?&3NJt^mnFl7AX5yBu6rl3#f=L$_^xu}h9K)(r)8x4 zPlo;bC}IVA36-Hw3#!kf2rF|F5{d{DcrH;odS|{`G?(DGQTgHkbGw7fTNRts3-f43 zYhyh~y3}|FwsBL0Y<&jWl!?j2Ob7IiL!Z`ikbmrTW${VWB=0jFsk&JkHnwNEFiPw# zww(wYgFcpZd@|E8upuw%Ewt?+n=9xSE&jf1+`LjRhRjs&j~-{E)K3u4! zgg->xd8Crtmi>G}kaVf*K{WfgaE9hbIy7e6ObvPB9RP%v$UoW0AM%lJ@yhMb1WYa> zJ(x8CDoQMgrik3wcIhW$Ke4jUjwDT9kCiaz0MZiiB3Gy^zQz}+#!_L)Ds2j72XOVq zD2;FIu*aZ}v_yeS4BlX#Ot#`&O&>;lZU3ViqwZ*Iv|?l3|AvyTPBz92t?8P#n3n!z zNZsH%uZSjSYGPHIS|0ipzOW@#CEYeqJF^=?>Ut#9+m6WuL8a>!-ZDuL@eqD$p z_L*uAG_1HuN7wu5f(~}~cD_1pLY!Yfu03b^Bj9Ofod2O#iLN_owKw}g?vct}`8}0| z#1&BuMqSoFDBIiFjU(pe$NXeK!cOGU@fd!jLB&JpJ~03Lf;@RazGq(M{W@Ii?##Tj zRSD^G5@8?D^uQOA4|>8_2Yf%bJtO^Zl8DLHPL+|FuN-iG{;dPtE&VSa!&A0v3(0ZK z$+&SLN_*b$l@Vv8v@_>RHorq`u27?n2MZ*=PcT^Erdh~N<4~x7%>%kcHY?ua54!Pu z@8?1zKv-M&Oj>YPJ&YmsCbm<%N|1MU+>xxLdyyPnjVww=m7Sb~8?5hTFjxLCv7~8Y zH6xcH9O{1q>$1ujn98H#&3Z^<$bHv&%p^wZ72t?Z@W|%ov<1YlhMl z_dCLJ^+V^d6S}zTpYogHZ8LZs{pKB<2&G)a{S*1^S#z~4B#B%!d#X#i7v_2l&rgP0 z#Iyc<)HzD8H}x!r2WjjVxk+48*F6Uk;d^<4F;=o1^Sy0u59v)b{i9_8FHL|ePO#R% zIR0eVgJiLJfH!go&l#0&|2`jE(_68wdPi`rU|6pwuf#_=oLR|V1U-{dt;0*dVq7WK z8!Y;ee(;@7zgT~MzLxy_l7A@m1crVSKfX+Bgsx3`z_nlTI+R$d3<&OVMRDs28&UKT&XW4+z@tw*O>^gk>A31ug=Pqpd#R zIBHZg0&$Wia2)cc(|L>l$MM+mF2_+Ca2%U+``)M3djXE)uWK0mWH^!yIF3&@0mt#> zF2~V${ucJs_^Vlb+n$H{aDpM;K<`F{8Z`U@Rf^O zbrCivXEMN7ZbR~>?N<%}rDm>1C^f}QL2S{LyEUIjhtAXWiLl1I%l8Wf^&e`A22}xrG==c8Z?GVxz6p-M4lLa8F6O>TU z^m{)Y{l|~Q2Z_I3PBg_H%8dSs01VBOu77^E)$cb5?gA2on>`QExtDAIybOi(ZwhC> z16%xRQ*bZ8m;i%4r8`Poc+~|9rPGfno<`LVq=CAyC|ryC&#E|7Ox8m7sqc z@ZSdfa})mi3H;L${Y!4)FOfzYaFSj{rqx_!%5vnogyITUc|~zGlhSPxIX1wOA~wQd@ojB zWHT>6#8}Cc%{}noMD3&8aD5wvrWP*{L76@OpCYK%w^7qPr;}t8&a<&<6O%>Q9D@i}-9<0?_JSxlWb+$>0jz zHj?m*N0kTvWbn?K#R-ybdhhhZ$vUf`i32nE2PMl}{U!V|*5Pe^YYV#SlF5>9%rt(~ z;kn#NT=ls7T$NrJ)yMCpzE^b@OqIr{Ci5|_01cNypo^_~Icr7n{DhbR{p8S^Jv4m; zX+bg}P}Jx$l+ZT4gi)a|3A2Orr8)I6)z@LfhtWgrlQY2^1=w-)L7(XnKe-eS$>aw= z8J4+5xS?0gZo^&~iPZP)$gQd5DA4a{5DEQUC4^zZ1h$Fg!0>GntihGlK~7%j32cat zJv&i0AkwIDT)pAQJC8X_I(9NTiZ1hc040vTVogs7 zw97HH^9eQD6{HHi6NA?tL=J1sd)X8(qRUc5^jhUgtX|C|V)i|Rvsp@Xg_Y{F`3%hI zjmtmvbS%&daDNWfo-2XwrFb+bp^F|$Kz9`^5)w8(V<{hapeZih)*Z?!yzqU;w;=K` zuBTY4R{1IjW-E{0`Xzd0{0<#^4NhM>94mSWoN`%wN!!}Bpo|je0nV7DT{6hWP(bxY z%!yLH&?lg8&r_Mdg0?}>m48^M8Zwp@sO(IZ8N)xSzLSl-PcAJ;pJ3VBU{Vp-)^OES zFu~tv$}>Xs!7rhn85_?<-`^-!QqBdy!o6STMfQHSBLlnqYKs=SC+ORHY^jg9|T zbbf`e{^td*l4Ulbq+4BqOE^DQW`u!Uz=38+et?#>L^i6`!-=lgO||tU2ergek~PYc zdRlJDRUULWlPGXEnAf_uit)W{&#yOZICK5s;NP=^ehTydR}7p><*LP>Fr&Nz(5A*j zx)?}$JQ`!>2K=J3_vgVR80Qo{2VtCF8+aNM4ac4^aWh@+kQG`xcl*2X;9LoCc`yGb zm-l!7{=3v|!2Z9;9ByZkpvK$r`jqf)dT1}lFJtN82C(*DvIaG&(z)$)ZGfM_ui1CU zuMifuK=|0YV+L{&5PW8~ip^_pc{f9fpwTmwk;$&7se32{PtrQoW`nhS?kmMdN)+orNphv9mQhZ0~mM^O)QP z?U--)F9cEtp+t(t@AVcKR5j%%Lldlxz>m8N9kOV~lq`qJvItM8%sgEdzoZGB7`L=& zd7GK4+UMyE;v*z%Cc_*`Y?!mli`DF!cGRD2=ypli%7@C^b#3J*}_NKkel?pmT)#UUF?vH=k@WcYV5ZY zz)8gbJoGVY0D?5rpqtZ$kfrwMb7W*hmMOOVly zC^pw4qw^nhRWX+o!>pU+U+jtD&M6pR2>KfBM^x4V#LZ8~=2o0G1KX5#J}*j)#Jn>1 zl_}J^9K%1SFUgQj|DM2J!3w;7&^Do6 zCu>J8{v(B%2lqD6ddYfp?xs)pwa{I9k_Xf*C?BaA4 zyu*sC5EgR?fe|$yeADugK?-gX(jXyYAd}1^;pvqma?YW;|ImFene$AJmp|{b&$@}1 z9^Y{%mAAmTFJTT1)51PATnREN>V2LVDS6}K$@h2NKR%B6ioZ>c%`$Fk7}3Mm;#lPd zwp`qM+b2!JUfVBUUNs-Gz{L4eH&i3?!c%mN!y7kDF3gUiqQ#GEspf9HgP*yg{*9U@ zFE^(3{W3Q;Y27unmbV*i&*%Eo3+5plk*FJF1V1kEJ!C?sOtk5cL?Cl)^1+Q}HNQ)V z-dsN!1XKkqN1A?E>5hk2oQvlEHuXkadQmGcLC&z~#eITUH$h9ui(V-MOZ=qV^Q0nn z#yl6n5g;K|Mb;?lhVwfKa0&2{nr6ae`4yC}X7Nq>b2Ub@ItlM+bL?o0`n+o10^hadPth6#53_B4#lV+QY(a!mn)O|Y(aV|M_ zA+4tDC&L*xisA{+>{X9TeRqkOMWyt!+jC#HYs0oghwBuxLLRan^2uqII!JM{akszd z6R2``!yVA2 zoZ4en&xlm?gN~Dj^6$9)bhFE>Dxsg&pR5DwMGVAl1wNSem@yxsb#ow zpZ4Y?_@g!V9H*65j<=~wZr#n6%Q1oP>o(7nBPKD&95zs*v^XkXj#IZ}2HGYkSqYJy zA#?HCQ232oP`<~S-5ycb!qy|v4SxE8wHK*eK=eutDzyNoZ6YQo&ATo z(2TK%6!KFQMuvDL{TF&9Brf$=5h*VTrfsC#j&Q$dJG4*W2|4ULdFHV_nKioFHcv7m!$ zOJa1y^Mvbrhlm&k!?Smag_k4P8rM~U2XW9(pm$j*)+D(Av9j%l`Zb&K^tP-nS43vz ze_DP$mgtOK>)>dn^7ZF?b^&&NO;K%4U;e#f1O9<`3?JPp;G;Hy4cx-Yld`L+HSbI* z#r3t9$-&rQ3(F8KtZh(J(sYr8aFE^9fgxFwp_s~X_V|%UEDueZcSPM@+n{VwRpzu4 zKN+NCl35dXoQks)9;{T|FLxQS^jDEW3(oWVgReYfJ(5|cHsMCqK$gG1tZDlJPpih` zitY;*5%srY-I#=uw!B+XnY)#3^QcGqJZn@Xw0A7SE_?FK2$i;lUm25!jtY^#<8eMY z{K5_BqwBYUI$*&iUHO|aNzD_|Lg%>Cgl)v^#e!Qd&E9po9S;+FVS_->`qq2DyjDM& zBGP@!xeZpE@@_J-7Cz8QKTIBYHMyXxy`XhbB|nlnOWKkQbxO@`EAwn6}_kti5`m^iBa9#gG!(@ea+N z-VW2vy%`$E6dzi+>*ga%c7ylMT~(yN=*u>tdc8Tz(8aDz&zAF{CE{tMguK@qtYku9G`cL5AcoN8_!eWA*814S;3@6*@gB zSLoKd6cyAPV^z+Ct-4aw>B0vlo6QY)mNTNs839ZaAt*Te>^pR6)&+V=fH`D!1G*&- z{EAeb4n3fq^pioRM+7TRZ>GGeLlOXnNjIt8LvQS%%U|s~~N`uHg%@?PCetvza7bICx7|%udUC^peh! z%DJ#rICNr}y5plnr&}48ZZ^dWq3wK(YnmECQC)u?yEVBPIsjYwkC(apB%0tL*6eNMmdL&}!w$om~G@(D&){vGm=xwkF&R-Y!*<8mH#V6^2wJ%MluQtBWcA znx&7$ryYeuZ`lYrvBkEx+){vOH&K|tn3QL{6ITVWSpikoT6k06+>~)%Hwv_+*5+HF z?Yq9Ul`G$`#;>nj++I}4b9~SaKZjJ?kp>AID+y1l2C5J@OLt|ciXqhJU~8j?yXY_4 ztux^1AAK{yRsPGjfcfztUx=E@7 zNZD9vu>D0my=MvXdrJpGZ?HZ~whrA9r2u7cItz$D6Lm}rQe>rwn)&ioR$o@K62?9Z z3M%{3C6Yt;!K(fdF@Q1_=hry5451!{PNUY3+-5f{!I5~-F>|+JyYQy}4laQF3L!B1 zZ;%18h1)wipplihB@nOX`x?ildipva=novZ@RS*t^hqG$5p+y;z=yF3LwZet>f%xD z8&YM0&_QaKj2$<2w605EyEBtFL&T$PU|36yuDcxrPzhjx#|QumEKh&lV4Z6j`i9*Aev%qMF$y3vZv^+P zhr^Fj;FDgM42QCWaumZrX`HQhnkG>uqra%AL@Cpf+OywvgyH0#I;L-ZusWE)xgxTD z9p(}JNYMb**k&x!zvTf1(a2gbdcQB-)O;=^UZPPqHd0*Ypl3kGNhjt92tqX@2{X=B zhl?68mk(TW0psKNZLX!7kWepI_~n*H&&SEfwiG5P7`!}FyC%+`$P&e%!ZZeN(x+HK zS@M<5XoIN-g+uv$|pLEE)zwKXC=5} zK8DUtt2$)4T=zS7^5HGcPQ&&Nr(d+$m`5Cv7%>4 zR22y@H2H|Pd-e7rbAtC7x2ci93rd=XA4M6h6Pw-KQ$wmnx1PXB2E)?z!8`$YP7NxaU>&(m_*Q=Y^$Bt_lke2J|5)>e5*2FgFhkium*^du&^}I}n_~=_y z*Ko<-`mCJ%BhW(A=80|6IngV3+n;NGX4np&gf&L*g8dIt;gjgaWuhxZW36>v>cbXY zA2apVIn{1p__d9ao(juDRn7t6ZO@WAV{*5kcJ!~sAkpUw}U=8 zvtKYp`$Ar7M-rpTLB0_UWEOG7&_HExUw4l&kfi!to3ZM4xI^KIWcKE`dRCD=~`8HT-Db*^O3rVsy)E@Ki!XchJtXV3g}P|d!nKhew0q8J{CE$tGCmhj7-@aYa zTskhcu=f$$m$}gBd;SrQD{*m_s)?k~##9JJV@&^<0D+fgjE$4Ru zp{|KjKN+w_)2<`JBRjQlsE;8Jkj({>2;G>Hdr*m%#kUW!cs9zWUX1(tAr0y!T+27# zkFVb9@GqWbi=3W z(~h0_B*H{NV0x~THQOtOZKB-9m_I>0__2MhQ2_ur97mYrH&hgX7Tu|T zDAfKn*pFfNHCPOKEE%(6yF18uNpQCmoU?uScd4js{V`PTx+sRsl}=;L6q_Oh;#U0N zrL45JpA6eBv9)QxYkr2o5xwi`@$@e2Y*#uc9)E4zrI`FB=PTF=KPpV(YC#{T#I}qt z;U*;@W~9;RtS0xjR*r=)?Mo~Bh2rm7RDI-&uYMBv_VHE)cRrS4HF_Q(0(o{D&WuP< zvTiq@iS<^TJlj^P;u5w34yND!A3E;slSO}O51Py@e`+r|-QVHr5SYpDw$}OyrpN;Y z-s`GBf!7r%@FFy*lFRIXhHzxJ$Njig03Z$XIRk={RJt_CQX!YH+zxnNz0x~JGqUug z&sHzQU;Y8P%X&?Lql*F^BLr15<;Z2E&3L-jD(9=>rshP}eVg@qzTBU+b!ZWEp;0K1%f6sX6s@1OMDvx*F~JhAYKCd)_>F zz4@8K$3Dq>)s=%eR%tq7WixlfMXdGj+mw3Ic}N8@V+yE<2yshsiWJ&ZErR`*W)C~=j zWj%J)acY70iFCb($F#!gapgrmts&_+a1h`@Qt@dCNF=j$fet?(kVrP~{_RUJ_ZJzZ zR*vp?mSPv2RgTnD=eKud+BN54HMh|A;I z_2w6J3Yz1Zjo&XTMy~bOq|Oc{j=PiC?5F(z5}W?Ul_}2VHSVie)d(~zUdr=)k{e*=b>#Y1aAC?--wMvEo(PbF{#qjOk6A1S_kb{ zKs=S?xHq@(2pA7=p!;!)_A22o*8csb^Lwjw^P@Om@s~2vSLH9EAJO-H^+f2Zw_i2Q zDoG}Cc9(wXx#}vsIT`%AIdtUq-8=+lKayuc0ySxl>o&2vw%}0Vuw)mdl#6fE$G40&u33vwRH?xFqiO zDDXJme77X=5k*SiRz7LN&7pA%Ox+l<__XaEioFz4(X%#Zj!=tT^0*}V#WX${{i@Z5 zY8~{U1tg+>W5*jc7$!s|4Glje&i&ACHVLoH*{oeg(QExvLZ8q%eJtv>AJ=|ed%7q& z^v$fgT|B~CS`a|aZ7Ry8m7+{ZGLQB<`Jg`~=?7OskA`NN#)CXV(-#Hd$!P({)(NpH$k6I4aomJSLo5u z`#$#d2^~bbqEAjq^27N{qZ?kh>~A0z`oG9~@2IA_bzc|@A|OUVdI<^$N>jR00xBRP zA|ky+MWhKRHPk359U`D0AVfr@gY-_MiwH;y5^5;YA)$l-A@4WYd!KvGmi^uLJ?EY? z?l;EwM~04}Ypprwnrl9DKF_Z>zoh8nYvtAY)(fA0va5stoMdU|t}paBXTlm7z#g$Y zQ|Dk;1IuW`Po}mx3H2lnEb2ImN(Ti$YKYJhoJO==k_F)M-`C##FW~$Iol;)QBsZZ6 zVZjf-7Lzv@#`=~i0^_`lU&=m>sTVms6p40<^Ae72SG{s}i!~pPatTwkZ`CF8OGhSIEVJ#1NWj#mOh4ZU8zdC zpxQ})TC3#=n!G)U!Yrp zeB6J~R@*EE+b9n*y z;#Sih2DReWm%Ys2v)xz>Q2GRkk)H`^$hl*(^QCLS^)Md95FTBRK7fGflZ7UIk~JH* zX@g>2Ws|ATerSoYbvG?>I_emwJ``krKgoG0-DCeGEN!>>xx6=X7CNY>{X2mPSn9-4 z_az^7^p1xDZ2{?1eAdWZyzWuKkdJX#W8AgFF(=z-Vw)fRzBcU-+Un?g;JzNt&oB@P z74}xbFWsYasPVT0^l2p~p|@6d>O1jgSK-y|z!XM(BN#xW+D< zX!4cS?Fs7g^}cDVwPnE=)o1S(naY(WijcqW-)#3y`43&6T{SCvjVas&|0F`IKH1AT zZOb3hms(d|-Rs|bagCWc9W(3ol2Tur@p|flX!Z1$L#)0g1okhMmk281cwX1WZn&-Y z`G%^8kG$^qK+h>v;J9Bt6>~aeZ+M^ z&UK+ar~G`On##I+>36%7mRGf(UFZHe&JtBRBTcPGACxHR(A0_la#8CO{0P$n+?$ob zrPZO(GF0c#V=dvgS9qk4zX$|l{aa-Ce;a+PiSk8|a)d_q+OhLp{DCYfkcsp7TvuLG zQB+wzO7o;x?aX!7XPg#46*yVtp8>ZWM!dI!9x%U@t5UlB{X#wRu-MY6Ic6DK25DA0 zhq4J83FA1iTMl&ldi$@oDPlWxGCpySk&mE3T*yXIRB$2r4yP}-Y+DVNMgRA;TQBH@;$T4%vGL2pXBb+1Thz^%Aq&w326+(mGq8 z!tT*MXS{zR?^~?9ZGK)#8*ZxE4WiPG$>j?Zcp#41^mW}2B$u(>rneh3gNC)IRi~tV ziRVAJ)w3=oS|&JtnDg-Vd+o22{PCRgF`w|q*KTJ^X$jLD*Xr={{7dU_i3#br?dD|% zXrMpvGv@#{YvSuj$@=Zr^4LvpXWS!|2bM}j7LW^=xAG;c?}BJOeJf}ll!s9dks+;o zN4sl;^2q+8BM|lL`ZrNsahDcrGAUr@y#O`MNI<)mN6wI5CWpf~v~wK~Be9P@8%HBz^wkql7|Zy!A@nr{R?!TZH)mAI1fC}@V(w`)3^TB>3w<7ytal}iGky+svzwn zj+rQXNC^B?jUu5{+o9HJ>&}(*4@JQcS9y3v0w)jOEt+L@Hy?)Og3o%sLuOemL}P=i z3%9iNOILbYLEhpoz_z@df^!-tm{@fC#j)4Q>&l~k$-bt+TU@146||u%lx=nx)5h`D1dr7$|Obc6SiqHRl*RgR^lL)~e# z4o7w3s>o#IgTjgt)aS2SLjK$1e0F0vG}tE2s*-_L+shG9v^Y)FK80i-;r+^ zJ3$ehu+Vf`DV*INDINZ~F~}W@YN$@U^wvj#q=ekjpG58+g@S$%s_Q`JOcuJY(K$aw zNQm;ySa|$9siM=^?a(Aq;6Tw`h63E5GyMv)aufVyG`5 zpbSj57(=cmqLy?c!Csr+7q%A|cK)Dw-M@HB?|b85YvErVB=x`F8unLi&6=D`j@2Tp z{6vxC* z=ErkT>)8v&w78qIqHAn<{9&R0zGb#+B&@}rP`vRyE>zyjAr34qRh(GB^H=j z;?KV?WH=7otZcjg$EMpqX7acSjQoa(#Fy_?442ICGXd5;B}{TN2GvpREt@FCtA}+& zL;6cz-kH8V&UX8jl%^>uY#jE_B>uA*;}|{*dm~p9ipCDp$5_Qihuz@ADL2@J^q)R1 zJ90!}P`5b#!y$#H4LR*MajQK0)p0OOwzccoa3IoRedFYn&5R!No*+35n`(eRU zGtwBvz{Afno9z^KMF6Jc_KQat7HA zDs}DtLx0eOrOmHnX4d`Z01Ex|(mb*&3ps_M{9D=nKRyCV2-p*d`;ZD!;=7>8!>ZXo zhwPf&?FEPQt3!Q%(4g-C$OCdqpeM6H7NiDQfOH%ygi^kQAp?HpZ;i<50sp^yCj$`w zg3Gf2o+KTVnm`jY)BmPT@W1IP)Cl}nSlXuUA2iVrXML(4KnnE3JMzmCQ`%UEw@*I{LL!;2R_pMcl|3Ud6n(wE47^;*{ z@-jBwxRsat)2@PZ$Q?K*uOFTo5Qens9=G4rvBS6fS+%f#d*bK|OYd;oT0C;<&=!_e zv?ra|CYjK$&1i`sydV3Z%pCT$M@f{}*z)!=q<*JEVx-Cvk#;+*)dqVk;cu+=p97Qd zP`UDuI-p(Ka8h0z671Gm=j-kC?h~YSdf)`ebyoyU}9DM1BZ=VN|?mNSbY*z@C zjnIkD-ZPPPUpN(i?iX^5W_ee1R0c&B)_wj7N4-FVM<_YXtPG9W2aWb^Kj%4poo;u2 z15Hc)hB@jF^>R$5DE<)tg<3W@H_7|HupVd8wLS1gKg8+}8VUSn$`T(-EVdM02|YT8 z;S{47oDQ!aWXYKvkpTl zWGA*~C@jTz-aeFkuu&5qZKc!AI$rD}6xyOF;glZ@>w^9SbZbj@S&3m-7#j(%<&#x+ zufi-|BtowYQ<=#azKjz)aVjD`teBOo2}RWIn)$5)0-E%a;53OOY-$pH={NIzFXm+P zc!y*#EUg13d*|mj@*W#XmxnK*(h1Gl-G9&|hszip$_5^4Cmidf3Bln?%tk@+Cf(`A z{yArYjwvsgT_LC8q0CEM&LQhV3EUsywg$0O@&oSm0>hN^FW~`plrH9cMwYAO*SIZS z^;5}Sr%HSN7p#g8gz^0u#5L!Ay!Xjsa>6dX?zrW10Dk9UE!EvR0^n-{>>&K)iNGJ^kD3VNHayfAMn(kD?36-PFjQSN{m|0~Z$%w(49rvHeKgTqqi z#M7?14k)1;UShq;g=h*dvEZYxV6&4;U;F$cfaK{jAky|le zoEhg#=140XnsO?2rbb{Oi;zu_A$DVO>x7@AMv%CEgQ4ltbQxIg>8)yT2C;dKxrJsH z$UMgvtOg;UISyyCZfEI4$%J}*$d=G*h&X;rhiwfiA6@^KfL*P2ldvK*B{ol++USYi zW*)F025Npz!jFY-oD*{#(+gIs3sWo$y_p$AFO^DsbxFyl_MRQAcqAhjpTZ>EQYX=~ z^eJJPYZOa5rL*YL1QDi8R4-{aURdCRR1=m)`unh(hLHQRy_XY4Or^OmJ+|^X{BDwS z30Iq|<9(;a@6o&EcLl|!Q0ah%34wUO8>Na0Eo~z*BbY<43yJ2IbY!|u4n@xzT2;&6 zoP2(ItW7n%HHQ`0l^Hdy&})x(4I*z9RnzUt(wy9{vcPGFD)QcU4WSxV& zfpC=tq_JGzLN4KBoBaZZd&ie&OretP0Uw`Tr+FCFFW=-Gj6*h!9jgn@8n!SqEyn=ogT6JO>~&JnC%AC0=BY&g*)WM*-84B=8?F8TJSg4#;r zLJ9h%^ql7he2x3W0)Dy;8tduDqPdoKcJb)*Z_C4%#xs@HbD8K!u0@2}+&G+WWDPu{ zQ7}>_oOY41w#(3J%0)LTm(T?Xj!nw)$i+qtEbm@MYOO>?Nwe$Rx$DSjp*@O{mq?Tt z9+K}Cm$fUng3S~7OmN)IHCavl93wOA@3nx=z9TiMg2h z3(u>LCQONcPKmlQ?yvG@UHwx^*|xcyRGZMMCPTxj#fg;*i~;ax^vdL!vlXI}&o9$v z1fC_-#3$yBz9GG=EMaF?ZM!$GrGF?pph7UkwXdh)fkn?du%hdYqh- z5naIL2Ng(GI3h{2g2X%u{nx2sQZhb>X-_X3HMQIFD z6^D@-tLlwh_jwCto&k-^MK~?v9IcE9-M!b!4M&;l*vcJ6Ph8t^;hhlua$AFb;paL~ zs@?d6RXXQ;ywoQ*+(N^aW#`V>C{q8Cp3mk^oObL=ABG)VE_>56BAvEBgR$s7 ziDrOJrTe|0GRqUvgsz&5%P4(!!>jQR|IW2Nb7FM0cC_Fx7#b~)FrPwAO*04T32 z-&2r3+`23of+h(g9R8rucE@etRwe1c7WOygW|?xu`-JxeNwD!W%<4l>(EtslSffkYomFeHSmRbHEY>?{ey;M zxj;h9pT(Yfp3H}#^Oby$2-fbGq@Ll-m6$GylGn+kjV*~5?9}7ZI;u`5$P(cvIf;tk zg8`a}qdq>PAqmdZ=<|Ng1NW&spPVY3f@5*%6kqxfM5ke69XVY1gr*e~;fRdmnAR>7 z6k>!gh?oIoFYe zkPtM(Bf{dEgSSq8bS@d$)T`_4EIFvXn>ZmFVioA&F>yrhgI7W%RfHmPiK z$>W1ELn}OFzai?$ph~ThHuc4uh3zxa;tlA8o1w+W*r!g1!gZW~Qh~|~c*X_0I(Z^f zE>)hKtmK&(l%D=&!7f9~DLOCWi74;#D=)p|?*>We>gI>OSICk(S16BuPz2Hu7EUrz_g@tKLf`MO%E$URkL@Z7a|WGY*vfD%lm~detaw{{xe0BjKRZs^3k7= z4P0lixlT(~h?vQ1iEQo(o7GN;(Q}g?7CCRAA|1}u4yp=Kz8TPLIEeA#GCRt`cL&}( zYKU^5a=-2<=;3gCC^^*iKodxu|+jm>ei9c^BTHdD>fLUpcV=g&vbC_g)OV9lrj z1+oInN@gF)1+9E%dnw`{d}Row%eBs4G6y~ToUIB|+)S)2rq37{zm{8V{&|TZyj(>E zP0(vkmTcHIiYkS(QADc%f2cfh?aQ{sdRM_u+|Bt)7S5Zly3BPmAy)#YpKw3Z1!qZm ze_CxkCNSkZK{%YRhdKc7+ZfDeYtTH8jo;M&$|9D1b<33o!Ot@u-SLDEO1`?II*Wm< zLxc1X?EszqBBj6^G&sRa^$p<>un+- zi#?lAZ1TY}JJ67nLs-~ncV*smpYU@M*9ejcEKZRTF6x)7oX)E$T*A5bF2Lzk1juxg z!VyaDggWFgd$|;;u>R6WNHfc@X|8-6(d(S+;UAikOdP7jqGPYWK8-aWnORJl(8Aat zLsHn2;Jxm;Au5-#L741$7r{8J=o2Rp!i5fuJctruW#^C4xF!ANNK`5@c4D&;6NWyX z>+AWEEKfZN=bn$TC7RkgyKQb8)CXvNSUdYFWSM>QX0D+E;g*KauCS#R5rdZ~B#O2` z>D?`)ezh! z4lGL2^Hs%*QIj37izQzdx0jr{Il+0!>dQI?XN8xmBwBruW5jcJz-|(|f;W70d>S`# zpTHho-ShCf-0B0);uCgO_YQpZo)p@RvD6*3X#gaYdjoOp&54JxiPM;nnzYNwPUon7 zn_{_Y?lB(m{I5K|`h?LKQzdU$!-yePDF+m+2x(1wIVU*D-77c=WE|gyT3q zX$K=^AD*lVzVqVbw>4vb{@qL^PjJ9upGVV=HEvsXwQ%QIDo`%p!MXS3uX|}%^b7Ib zzxPV>RF>F-n+bYXUI}uHAz#2w`HO>W|y}SF`*J?roTPHZr_3I5?>zn?8J3nMO&L-+e-z*9AePZV& zl8;@zvKkr^!R9qciV0UeNqM;Y`6B_{qGN_1O`3!Sr_fapc3!rtiByz07`H zJa01b%)$3YUR+mEvNZZ_1xn<|9aZ%mb^ne!4s1y8t+X_XVRwo06$`ehVPjn-dBW#J z+?4~iAET}(yGY58Pelw=I>70vLI~Idshr4-?Ak7&8jaQRJM7pO^`GO^#(tItDdH?HY8=acXA@U%SP!oP0xPc}#1+_NwNEA=GeSWlgy>ZuEHk&BdYP zt$yF9xrhqHm}%lRR=r2oq0yyS`D6D%b2HW`g`IaA8PEnA85M5-UfgbLtMf66;sC{f zFw|<(#O4{xh@u%T>iF2@%%wd&+_cHUrgakiF!0e0p3JOupy$e^BBe7i;*58SAFqZV zo%PQ9g|dQ%zU+%noWkbb(Gox;3zSEu8>)*53Ui+JHpqjF2RAwDPr@6KJmerCnuydU z)R;zA&+>7uy%S0Kc`d5A=3>HeRc?ls%(TcU;+59%_!jhw_DSe*iUgLzLp?z}p5>E0 zI7|*)(i@wVvMBVrb1KwC;^f0eZpXT#-!YHa9?B!yph}@+W8i0>UpZcTw$Yd<)W15a zmi5uIJW;UiW0zuHUXx>?nXkXu4 zG$t}z+z^J?IKy7w3sP-vywTKNdFq_%$+zA<^WM|n zi{SHTEu^bM0n~29YUM2&Z~D>Z#>vpt7=DNWhTplP?nBX8kfEvK`tjYv0^OvmS9tQhdkk zPqxa;-%sWULOH+guDY{jc`R==4ef|s-zd(HRZhQbm4LMOOF=>6tjYZuc?w z%S|x3Gu2(7xKl6Vs02$jwY$91433?4*!nUP+Xu>0k_q7#U<;hj#eAY_bFfOnJ4b>>~MKDHOh=xiU zymfus#bF($N~nv(YhSbjwxpMpGv|(=AHNdbU46#To6nFyI*UAbeM5v$$ABhs$9IlJ z6XK$GgnfO9x3K*bT|DHFSvg^j)H82%j!N(=j9uSTrzPCltfFOfcUTh4;F=(j9X6PEm|%O%Pv5Ud`@V|Gxi1m{IKLHA2;jNRDr5}(k%<+TlC z-0mjQM>@WK+o;fP+woJh9d8~`i8d^Z5R@Z^bo+-Lp*8+cVdm`Gqo+3rh=|wU@9{o< z`yJrCnq_$4b;Jp8t~EH9|91dg@IUSBh{SIOuY8JKG0zLT3Q&<{g)foVT8S_ghx;-q zGv9EnAbW}Li9;S;7!@Z7Y+Nr%wwNmC`yNXt=;OrN1oG*HHZ79Jr)#8%u&cytS6`PA zs{!~L$al6F}KbvZ-Q2s&w9dGYv*(56r5uax=z8VGT$KH>5}gKNH-q}dqb ze6+dE+@S}$Jrz4$kvQStWy7K7%a1VQIc191z@g<)+2d^+J7}K&hdv{ZC-!VTW+Q66 z9W{Xt%~Et4d@uNzyi3-;-&GO4PFQ%-c_gDAu?<^;T`Ga`B0QUhkVWntu-lKQj6Sbz zD0g{K-^xYjp(EuvF36Rt4Ljm7DDm_rAFmTjA~w=&I}Mz;_sc4+IS;mu&yf!i92%(y zFPD>MNLrQ6K+c1LlF@b*Yn&P$dLq<1sw{083uhPhl0$XBPYk;W^0zVHa2b?9K-FIe z2bh7g6{u&h)$2;2HfjzI+}#Bhb)cxlKZMx^NwIDEe1UNYB?bbZqXV38L(Q28rg|bg^?7u$~6E=gIZz^**j~%(5GJD%4!M6#q8z!i_!wDsW6b;-_3clk}7vs9e%O zS(0{-brUoLlqCZJN9(t%YvaN${qzw@0rfs6PM~yokfI6>NUlI(QY~%Y<(BR0Jd4V& zb-DI886^FGzrMiwLzD;KH({UBY&C-Iwrzwd8 zzdbl$vN-JtHGY-ZcfG@)ZCY}05zvw+bf>TZ%}Pl?Ly}iH4T^|duzlCdE|%zZHV$4R z7VH}-kn#TgdXlNg%>Dll5A=ZEI4qP4+LWWLF?r30xSQljVr=)**RJZ#cbr*Gdn-`y z%Htwol_-2T|L7An+0p%wyv(-p2f{k)7JNzSrwl(GPzScCJTSYJAY?I;BuNp2GXo5W z;%@QGZs6|0`Gw9^UyStbHQjQ)$4DQ}k(MV+-wLN9+}j2ty_tGgMWGtoNtHVcNIgfg84 zCW(HwAy!$XaeRA z9a1=KZf}}-JtNBcLVq|@p+$AhBI92`V!g##(P!jHMM?{Q z2_w92Dl(O3LJxv0zgZjwDln_D_4TM(MUKcFOw z#H&q6GHYg<*x^2siiSg2w0iRxFO?s8?1kpz7c{Bw)+UMK@>@&v zEG2;;!$0d)@7J2<>j*BjK-qq%f&Uc^Yvm+}eKlXK4FDE3Y$L-9YK$E4wP%K!*=Gwa z7M}dP7GHieJ3B`Z?^(f+O(Qri!kgp&pSkg0?V*nxU%!W90XO9|LAa?cZ<-h!01obz zjsuS+^X+b1%3O_oDV285#oK#8{o^c&>qwGwhMkAuwiDW^`gCbY=z}n!^}Dxe_`VoL zgE;t``1XF~Y|Y~POGZ{WkpfUu{!>YE)T74#*}?x$jgj()Fd}l_jM^`Go=JMFoW1Cy zzE>UGryRatOkHpBr;owUjqpAAuVs4Hyqu|H@~70SDs80a;iEAlRs4}$Mu#K}Snsk4 zGrT`eYik5{U;p>V*?=e38f%qr=Nh4Hm1QQ>#B8vpQVGEgDk1|)%2|l;+g$m<@`D>9 zOG=7aBSIxfSMR5X4aTO@DHdM$aJCxC%!lK7@n>Rl1@*oZ^X zswOGH>R#ocKs*aWLzZu}a25lMkW0ZsH>!V*9_M!sN==kkr&l2Q*HfAb;X!hFWG+x>Ua!6;I9ATJBy;uF3hxxoNkM9G5 zXCv|?LSK!@SI(D|i^yJED#~{h*TO%DUzZ#;Zj5JkgSvwI66T-n%uDLSP z;80n};)3?jj-M30Qi9N!t5Sf1{?S~GU;GaVimyb|_JK?o{^|5m(?0GT?xqaG>jvj7 zmnN&=CKTQ&>04+-VQO5z3ZJPp=zgf7ao|nRQRQYAI3d%Q6+oQJ-hk^?CV>Bzs_{0D+g5S!`@Hebe4W0_MstM`qw*Ve&g*(E?wp|s9dTbC!720++a|ypw7f8oJ$~$Bl8?HB&NwA=b}D`x8+Q=} zZn!Y3&wbY26g9_GOOAf`%G@i1slGIvCld`RpKhIMEAoT;kVV%f@1UXZ)WVLdJ9yf5l+4Jaw@_mPEL-J>1GZN+6YDq?I&7r ze$8FN)q@Zo_c=>=4*6_vBbvF!%zZ+V36W&cTWS|WYS>Zr`EcrWWAhocGvfg?w86I~ zJ|GXC>qt*}dP7T?kWLtdv-}#db8~Z{=u{9TTadxqkL9XXf}e-$ z`HV>FM+|MXPi2J;8iXkvD+r7YfZUj`QJ&O+T>VjFmuo)noo2WFsL(CmWxfY|lI-^` zeqmP#7d&iPDU7W&anI9kg>(5H$X3HEyWZ@PA-kW7*|}3>du#Khs-b130*82Ha|qN8 z`zUXm$+Zc7%wK4(3@d+fo}Evm0#j0+d{f(#tDO;58~L^VIOK}zOqffW#*KHv%c2_F zd6JjlaNX+6tcj-=+_QUjCu@e?>`Cd2CSG+zwdIO=?2{e|Sqqmflf60 zJuJNXWXO}&@HkNNJ4pKg!96LL1*FVV1b*cE%I00QI$YL0=E-mBq8%gc?>2ET$ZN|4j0Gp&Y*MN)wHSCS72P^t!9hdtV3Q zcX?Bd&yOiuBxvD$oMA4BxaBcw7XSJwCsVI%K-!Iqndv&`Tt*}ufdA|+Zd>B}eB9ZZ zX^q>t!+z;WQ$=?5)hY{#-?mLk$~9iLwl1-8I7v=+m+mAV9MwNnwa%uNJK*4G)44l+ zfKKdGl&DSf7@JH1!#tNqGHVJD!@fghai@(7r%Abb6abhr#Ijj_%%t>>SHYeyQRK%% zDVMT+VML9>iKM4OxsHym-ZDcS-|Km*ZU|h%X^cM|!&dL)C~MP=i~OK`LXgr zsom4z>Y*!KEw=A27?{UhjBe2qz9cxHev^$^x>OMphD+~oFw5DMEb5y|P58x}(P5JA zZ&vbgSYl-P-JM>?k#bLsf(973&Lk{!?BqM`%+~u(RrZY;^W4#S(Dv6i>P!@_n0U3C zAL?T5C(ld-C5-dxYxb@@-rydpHP#^+m+U;1R#Yz*E$5%w3gqAzk5Og)<}2u^uTKW4 zPjk!372ngq5WuKV}N%k3(e3CzhmEXC~uZa?6) zRxMYbSqbz%Z|8rZ0xNFXb^C{N$r~+6v5m|)dV$E-MQx22hMswK@h+fIq>vw6qvBU| zpiOL4zBn~5M1TWRnXq_?+|p1$>~Nm71wvkbK;^uzZitz#okI@UpPSoZ>w)#lkG2WN zp&UY5Gn{8Cm$VHA+|dC9U#0Lb+f#gJ95~!E>>hvn*`jaHS=YTOhxa_@8cW6!8t$hH zS-WjXu9&@%^`Po5&$}3Wzt}h4>+zHn=bN`y@vYm?%)(>r)AtRpQpR&C;wFQPOU7{I zgEqIa!ta>Dt`iBjtA+I?jPI=vB{n$Y7_XRl^udZ`d-44hI~R#G#qdI#@2l-I{<}#P zBgLE(@z)6>q*!A3HLGK5KL^Qbxu$@o<9sfi{RAbw%IUPWW9JhlFNyTjc@GbEGyz4n zYEI!IMhtkxY8Stp`H>OZv9k!jcuKSrQNI)>a{tLjV#LV0g=4iR#$e%5yg~LcOGWnJ zJ8xv2y-iKZ7SkS9sZ?3Wch7ct5Otdwp-c;fvt`?sITK9g^`tDZbp)GHkl47=%LD@# z*XQrvxy_lT93y^0Pv)J~UZAZAZcAQr5_djxQP)`tns|x5_!#QD5sExiANq-6+*dkw z0)aFeC~>rG8M)(pyv@xLJ8`-+I)TW!H3DX1rC%=Co82&$F zHS})>`9Jf?`XBjsqwmlnI3*5LL!GIu9LV)((YfudPh%u2ax5^vL!Y2%jU&UikwHsJ z)>)CaCR$b+7=200XNbmyDJ4L(RQsvbko1LqtZQ`O*X-D$F);{=l> z_tTK1J34Ahldm(!6|Yt+4;>zepgidJbteXsbjk7)?SIhlj+nqL5Opo1bNs!Jzja;e zkGO5l)4W=8`T#?PA@xH42BseV%mYpDT%%TC&qYm})a53JT^}6PFsJrXJ3$%{b#^PCQXtx~C0+e!sg za>LNt!h97H2|eb|4~|$y{#_&}p<=^%NiCwdc)l zDKcajfFU7U&_Sax>cEaL<@9I!&p>P4F9|uMGIZ+V%=b~H+7v_o1*Z{;!b}tqwm3Ht zLX||swptz_$4fpRYBnSlI$)V+KhKLk6{_*StN*2QVd|?u!)ks8%^QVNi=v;R7}dxf zL@UePw*V^;+t^(mrsSMXF4pfA69{X#Boy|I_v*LZk*dQiT0Gp}vM#x#38Cn&lHAD6 z*$B);>@1i7top50x+L>hdoIn;^@rU`S3g;Ze!^Yp4dLALy5o<2|06!WnX@L z!-FG3mZoEfu_pA%6{VADV{p@q=tmj)6+xE-qm-XF84~1n97s7@ zLuBn4g|L{D3vLeNietGWHl= z{pmpxE2wlf*KI3LM$xRjzN!Id#VIY(B_b0sc)qjw>+o|Lm6tn$lsniuY5y+&Q$**G zW(;Y10Jl`Y*Dt3j7`N$a#(5AF8drop6rGyg;K%nYxf%{kbcJ}2k}WCZ)lbLJ0v?N!qb&+;IXg-eC`fiP5>T21O%jH*Lh{`#_N!hTvsqK=_LS{Z zs$Ge!^BAjfEmyKRFt2Uj+L~ApTS+%RmtVZo&%HMWF( z`*j$9SBK9vIOLvnc6sFKN^ye} zn|sI?SEQc8ANk6X)xbe)vlV~=DLT7(yPyV=QEpcEFKx$eI1 z2vm_L$W`arn@$6bWQj3-EJf?=*Zk=^u}Gfo?8w59M3I`3Lkt4EUV?oRD309^HLOI% z21$Yz#e~QQ<(nITvD7G%-ez>a{~UuwmIQnnd4K4lAHh(y<6AQ2glCnwM$%B!AkDlE zt(fB!S7<;f>V%oukV9NKPRicHvxW9yfM3evTQS*kBlX{2Wi!0f%e3HQBmyP9aFsLu z->LL9#C#Mo-2n?@af`h4t4}7Oq^$2j_#HK-iJ_Z^T7)9fSe3b-@Lo`vDmXX*v!=z? z9YG(`nshWpokDJ-v~mhW6P|9*-t`oZw1@uPOyVUHP3g$^0*@+-UlL6hP4c>Awpfe9!E%7Fpj zlXyR9Vemp!e)D)o5>pEwhWS<>TC^!k6bwK5pA*}_rZs7~q z+Rym-0PEVobyd0mca3y8E#K}0Iq+JaOm$GS_)6YXS@Z{f1*1D31JpR{mzlhbDCbce zpnmKQeEDb)K7Y`>0X)x4B5Ic%4n>aB0X0Me1CDRvvXxS=h1oeDcNqZ1Ghf&TyKRf= z>m-sKaY&=*m3g+vZ7rH(;~obKjG}(OT66ycZb-#CzOX*3uAwOKbGCp7tQ>li&~1r2 z6@bWt0QJFa6xo39A|QS)053k8J+;R3**dud7f(wvrVfoDKl5yaPZ? z&_M5N<6hTATVeRSh*oNN0k8->1cVN=)2OVP5y;sp^3ESLG)l@~&zyaU!{>TRt2$z4 ze>?YeMF3*Q@>g4&Idy;Oj1Mo^VeV_)zvu!_gz`sSs}D7<`y05g57EITp^I*RX{22) zkfN2N!iZh_1~ha3s7Gn=fg#>sfBn4yd0j!EC^>+~k%LTpujU}q^an~$XT#^czm}SO zd~G^}%Vcysd@(yD?*Rw>y8tc0{uXg5VOgPv9x{6B_O5%xf`6+&{+Isr1@G?gFsMz5 zlRbeT7j#!)#OQ~?+6slmm3p%rKepS1T%UBmfjqPU&${~uO-Li_@s+W+bir=bi$h~pbExI?6#-47VNGGUXnNz$bV zpg}rTr=o1NW<+rlJ^xT=a5Z+th8xkMxD4B{goBPMxr@J7^H=*WB>+t9p$y%jtNIcHu(`5L`diW)tPG(*E;RI~NY?n=ZvKo@ff# zU1CiWj;fJ`yclYhLImH?J(jECflVZxekedk`+ca)v5T|woR2-`JC#7XO*Po$JNXx$ zJ!C~xtjA@9J1L3UOmrMyX(H`J;KaCIOZu=~UVb4*jM_SdW})2o1Upqc5WUvC|9+lN z1k?L?Dg}fPyqA-+^`WUyR)eHpj}NLUa4IOI4Hpi}lt{)IXMyPArFedJFOi#XGkk;+$8{gxP)|nVt z86bd{GLG>e9yOk?GFwedE5=H;hkfxXC$B*y34ogNxwg2Qn?aX?I`C|*#U^U7S zus43K!N|r+qz!Ug7_(*Chuk`Y+Sf88-5ni7@dTz3bA%k&qH?&JU_YC}_alG9YzTJJ zZordWy5?ltIpb08kbbqhG){Ey-c4lsf&&>5927j?Q`E>=`D8k6!l&9aV|%vn^dWQO zHb>c?A3mS|M*c<-Y9Jg~0qaM|P+{GrfTs}C9 zGiW!l=L+rT-%)-yf57%rtpCs9BX?NmrZuOSf!3mfZUdA>2}(R-Hd`Y;Q447*GRDMvB&wt_4VF74Jv>>6 zLn!gM{nIS@Ypqy^;UJSPl#_&%WLKNC_6hhYhpN|?B?40$`6COQtvmZ|__v;B8rryA zNcC&>Di5y{@b5;_`^pPP`0C)h4w5md#f)+psbt-y?s*SF%pn=(8zvH*2L$uKwlO@# zqDylqT`LeP$n>?be_m$NsJ!hvieRg?x9z~D0TLbSvUf%kmil;ndcU0YGyUt^Lw=jqYq%tP9&in!3m(rIj#BYNTd zLlOLeogp*M`b;G)qVtzgrU7vo=M!K^aBPL1sN%WpxE$Krra=X60ncN@+YFYtzkRE( z_rBGLKmS%;iJKFnd4#eB{6lZ$+Q=S6PD_~zPmM#9=b)pK{P8Zf0rTs}L<2mJv8<+C z`~_pdJXY4lODqG5X>0xxq;Y)0b-H(Ou;W|H-tHS@+ zWPm@-`STZJ0qS(&rkFsLOB61G^_$YyRq%*qcUyP*{FC2_jAm>VsJ7D$SrYH%rXNSW zRT{9E=nA)_Be130WgwDT{6SPp^^kV9%dvlD+J2!^DpVj~nE2(NJ`m(z`b?KqWc~a2 z4w5062#xlps*wnG6#+o`h?2vUhvvPHeh71XeSKcN=q;%vke^L&wDthS6<4O`1geD?I(+vuxljwF}N2F z!dTXfd%YdrQRkLVYc^Z*vYuzy{^hj_$LNVmioIgj-%e~I(M$&AL==C#SBVn`nIffC z7x#)lUvsCtJfNKQ+E*EWEeYx*;Y;z{ ze316%lkad0mu+tQ9a)Y0T~nl{l8CzV7NlW)im-WD64{c?)d0eewJ%8EFGm+)N;|u1 z3SeLMWuHHFN|L>H0l*~sH(s4z=#!6oF_ZHry!bC4>-UfLt9GoaBn;GIp8Dw>&Yg>h zXs-*O9&G$nTBHt@Xur`XoIvN*eY*M}k;U1Xf8^W5fz>_K-O&#QG8UWtVKGz&yVuKU zscU5$t03PHumDj6fVHy!Vx9g2)|%c&#${1VHC!)%CF&DU5eFb9=XQm@IugFjYV6Y%@7$!%8Yhv{NyNe5h4(Wzp#;f$~v+u9nJ45^LcSa5%iqpWfD0ye>9zh|F1jf$a+HzPJGX`k#(w@AvXVpGayA*7`AV6@4 z#7Z(+Ia*G7pYUi$YxEYla2mF{#XM&ddig#`_D_6AIR0}EPW^6~|B8{H@Gd%meL5k8svQMJF+3Y#o`wrhKky}cm8&MJ;; z$-yw8Drd1dhM$4>vPKds#j$2BYGFgQ{c+>|!nx=*o_>c};>dQV@AHf#{xYw#sY))jZk;{`l z@#fvSqk`(fb>Rnn8t&X#aih|-)I!Mt|Mquq51@WY@uj@LdQI#{eO;c6E0 z^ZeS6a;pxW)RQHA4m`JkZ9z^su2?8{l?UTX@raRnS^|*p~HZXlU%X#%aR>T z*LRkKGyZkh&;R|fJK(cBcTdB9JgOUmUta>$rxFdTs8Ei%8=tN8&rwr9*IF)P;*G&J zagZl5460Y*Y6Paxeb9NQAKYbce5(nKeIi ztCL{IfTRGbJsoTP6+ZQ7-&DF*#rfH|lU#s=ZaRpQq>G2NZ&P<>of-BSv@iQKAqT!- zP3fAWPOp2>e?*vY&A*{VXl{cCYWwfDU+M2Re7zB1OHSw>q!^6|bLviqHRf^B zcPfUiQ)!w7GYK5W#??F>=NBf{EV`fFOv_M+Qg*~{)0x(H&+MP*8HFt1p1x&Bs*i9B zJoB$!uUHeaS$7Ij60`XY-dx*T7 z#ZHI_T`J|51RUQ2;P}KI7%Ody?I^wGO*qFYD5e95i3}2;CW(h-l|&oKlIG72_(u!E z(D=kdWM$sIF_zMOc4h13Th}4ApXHt4|Aqj+4o%pw^P{~HY%>}nsh5Tl5$tnLe^AUz@-Q=% z7fBHUT9SzEWEKSgFPU^Ig&>|(7gIFNJ!cXMBE3zJ{8aA}T7 z$nJ3(Kn?SzFv<504O!`Vds3b3qZ0 zX+M!VHt6IR7$qru+Mzidv_4ca_2UnP$DIwV_}6U*_?EwY_AWq6=McRl$-!r<1^@^z zo!4ya|JCcA`FpR2z@uU+A>2-Q z7c&2(gxtIs{HF=pzRyzq^384UO9qS32miHwrR)s=5CNb0dlv@L9nr2X{1pxO-&_87 z#Qt}|{1&hM3;Ouq{qldTxv0QFrWFn$c!x-z5@KkZ?@f+Gm-BNj-04xfrQ6suAt8Gz zJx2Z4C`AfSe`O**0Dp#@P?IPDqYW7DpYlzqCpm~^^%2CD7e1SudowVA&zU<_xJl$k zI=3ul2I7GS18nuvQshuFaFT5Y>MKZ@+P__azzvNCAO6HFm`tqyndJ!vG7&oDR>VFC z&qU_s1QIG}@Lw++<6;zx5K71G8~Z@&C$PH(aGkk`$IVAfL~6hT0MXVxS3vIIFINHy zn!FR>cPH3`=LjI!w21ialM;Ws(nS22;F3K=#ve6;0ciYBKS^88U=;xiwEl%?i6B6^ zVGH;Wbs)P9d|WGnte^AyC1jImPDcLs<_1;dpRNq(^P|;11U}_GP&|OZFE#_M+#~plDB8YSY=b}p?y4P{w)Rgc(G?RtIW9#ANVS5U#h86@b=cN6ZDe!H$e& z`I|@1H5`6PJsW>exR?7~OTLpPP_V#L259kgWQjKs^N*FteOb!HL}%N}eY}c+dm-SZ z_AOy=$?nM-k|tJ#t@DT%E#C;V5-cQMqq~}2fX?fz4<43vqkXB;Y1LOtKy@&v?i!v{ zN(z({Ou_(N3BAQ)5A2nyZ5kh?E5nw$hn1D5H>h-L!>Z>8Mz+>+vcQm0AOLl!cvAR& z6#m2>w+X7AD&lv%0htXo)7^<-XyAh8!U7Le9g_TS~e>^%7{c8CWXt@f>#40b)5!IUM^6;dlYbl*avRT}&=RY(&6M6V- z^wr63`tlq2{0Ry*7-&I&;;bTDF_Gq&xC4FggYo@A{!3r$YDS-!xlvh6SgV!v(S9uV zT~#uSqFUtIZw@Cj43iA~aIOk%H#xF#N)0DDrd4B4V)mXMU}Oq>QMF}nM(m7@@AtKz zF|}h^zk^e~L>%$J|Ii>-0i?JFZ5UQ_Xf|_$9xhV}^clU;j81al_Cawb@?PDMOtrcH z&Ggn8w1T5gpkD4KglUynQMO*^oxw$nX9$xjl3>x21ATG)VGRv#crS5{re*G?olznk zAg%)nK8Ss1(Vj(v-;F%5D2d*!Y`je8ywcmduoj$|{5%6)gD;D9#)&ruFUDFQt=aZ#j0~}qTufABHzURXHw%~ZiJj1}V`m?Y!OSf}(3dI;{C=Nejy;c#k;6Z#)72XdQ z*jYIh#Dh18CY^Zo#`U;L>grJ0!-qPaW$22;M- zMp^QVCX(|-<afA4ITwK_zUY@v>f=|)=lT+6ceC0=%L>h<<4dZ11r zwDQu3ECZeX@V-4g=e@C~OEon-O@~5ioLMBpXSIMq!R|n{Rfv zKesMEDXHFYBab`OsQSIt!neOr#HtyFQ_w#QH5{7nS*Pl@iff$yP(l0${O zdVC!{lD^{CL0fX=sjQ-e4U~oTr7>8bZ18<> zc-Pr83SHTZq$>m=gP3lhF?57vXiJ~<)E7MwW|EY%#1qm}a;n?o-cl`Al@ZSwscdxY z3V%G|^NzLKxOvnY%n(b1d}0hC;TL>V~XqK*~PSGIpXyN5nzoN7O^N+?GZTR2yz9hDvd{2crD z#ZaC}ZhL9klwM&A$Lb<(hg~vIx{hobIKz^p=a?CRI#gMuY?I^lvFe9H=lhnH8lg#F zv~TsJKIC~=5)t|h69BGLI80P#pm=uJyN;{#HV&i zobO4GPRfJCV2;*RiI0ZmkP$~#RUL{w$qMy#?c$7K&-&V5stF}Q?h;el>O|ta&GrKk z+MlOnqgz)EqbWcM!t$6SoA;R6TU^?3O_oo~Z8zpROIOFG@Lp<} zUnvw08#;UM3@2SSCDEiKO9YSE4#!h3?#817w*6WaGO%MlGpB@1M^;81I8rJQDEqyI zE7U)7P>PqUl!ZRHWOR2Lpt<=1x>Zl%yFMxFJ)1gIy^jClqQJWL?ZV*OEGR~U9KXDH zX68kkMs`Ni(zr6{{l>z@(W>KlwtEdz-bokNkMcTm-F+N{t-hSI7xI2G6@k{$KKliz z_{j!PDYLt)l=8zx3==VJXN;_}BNup;u266lETj{LRDJa`(hJ96+fL>yKgk5!ZT80- zDnI;)hO3Re=f5~vne-T3iv&TVBOb-we;0vcg>Rd58;3mk>*Js1r1DM1x}PMi#Hm zQnYMr|AkMLkJL_!hQE4GOVQGO;Xfcp?3oZlNb(3 z>oHO{ef8qD?aGA<=gY*isE@_g)3QLs!DAigoAxC)gVL#EwlBvFO-l1hH)MD-2#GPC za#&s}EXrnAmdbGf)tIz#)Qw8GnXk5IY^A&@O&TbP7#5P(9(eSOW*yy>Fb231r@DxI zME<(5u>v}`Smk7}s!m&SF6IOKiakaBnN#XdUsCXi*5VhE{L5ybe%wZ{jt1i%s6z{S z-Qqo{JZ%SbmW4cvT`gazDQP_ij3~Z2%v{c+i60m|kY-)zFJNAdbID_thVpWL@E z`R|v4ScxL-O|ruY(KrU`yHnn_H8*|p9QitHXMZ4kH%t=dJPcm@sp=CIxB&n=6;~ps zuie@t_#Mu~YeFCz+)YOJe1WX6Vy&H%%_i&=AFI>$o#QomO%&H30Ww4(=ES3R{Te{H z1}j=UcCa#DTAC~!!1}4!yX9`q>Z$vuDZMF7FhL+<0M?ym&{aMXTME)jLyv7S>*h~u zQfj(3Ba-vZyCxzAKYL_(WH=<@0e)oAl$XkqgM6WJj9VAE{%(M2J){c@uAFEOzw=P@w24v!gie~N;?^D_ea8SJHR_*>sVj=oq zzs|^JQxG9~TJ^E_#qDz}ZZQnp<-dY20K?)a#Sb3HoGZ%!xHWu_<|ivSGMzb)KJ>m* z+#zZqI41xs#V=EOiKTmdv)!!vdA=S0x1a8V+BWV;2a#@||&Y2=Qg7 zv}P&0GoHt2xIw{rNwn0vzSP1DZ?ufegA_~jGj93;tzrS;w6nnu5_v&3=ebwFPKhx$ z6I+@*chhz@-e%TgQW+5^dRYHJcm1$Wf{ikvzmCJf!O(+_9Ok8tMH91vPPW_Wr1A zW!Ff9tMG9AjdtM#>q)zaXT?&l74p+$Y#-Lgoj!_u_zkG{BYOVOIEjOUFEt^tU*T5@ zNjiz9lV{(ELAm7Jw4C?KEmT!#^0Yw1<^)prP$uuC#v@vRHJ2(=H7{0a1ImCLSORfG8j)s4#6e z$&ROycW(_#zLCw8Gi7Drby{?Hq&3&2Ejx6tleUgsM1?oe5uZ)DV!`LuR5_s)b&G3t zcJdK6H#zqv{DNdHuL70au~?JPDr7jKQmecW=BPJbWdSVvCazs6*EHw5&OXmrC~sVP zWcRW-I8CJRas$dN(BwW*{Fp;Gpv>7^DuR20b=Qb+HTxtdUjuRSHpk#Q?)q&e-3b~* z>LD+$$r?jqxVP2SE^;4A=W}Jn_~ndmdkb`SIy?5S(=v3=(<> z+xU7ILeHH+`}FMPxTiVC+-+y6Z20O=zqny(6B~}oAX5>2B{>f(2#=5iY7K&Xy+XJk zd1_2q1dG_3We%Dk$Eysq?bs^_!dK3z+44BvFw7RmGsZ81!-442U2!Q^YnVL7(A7($ zx_TmGVR6O)?&fA2d2(_8^!ho8%!FqJnQASko0jqJ?SNt$Nofpn51${gpGfb#L$)SJ)3ES`!H*;xa|+_N*XQIbM>no$RFx6ZaSWT#*FmfE)3 zY2H&i3U+3iL3g?>bq25DI@{mE96A zr2(Aqoxo^~dtU;AD}3qCR_V2#>08$VZ2fwe3+AxurS_Z3WU-34 zPS-NsCu|V4aI-tPD}DuHTVJVjSms6mfL@OUGoUxJ%WtQ^5^?z<4Jx*@I(35$O>_$U z7r#t&bPo(Pqv?bee$Ewd(cc?A&nUiT<^M}jkf9W7MEoEZKlqa9-HcD@$B8D3`!yXK z9tTjpOtnY>CF@YVeCsQ>A3xOwI&Dp?RW|ZiL^7gqVS?!v>34#Wj}pS}K7@5omJ$20 zyWv4X&V*@LMf*)~jD!tBM#;*dP5f)p9~2E7nva7C^v?;u)0WWp{!3R$t2$O) zz*f%YN4vRJUa50k=R&HzPw{%J*z^XQMb4Z#jT&oIu8=1+?T6~5ucAX9caCCKQ4mJ> zDQq)K&@_hUVBX?(`S^jWwCsf~H>9AM`M65bRh#*jad)noJ-au2J=jvDMF|l;3bRiA8agd! z<)nJ=0{i9Ky{V@`w!!J>+~BWoDT7U|g^7BL?p)Bc3{;dd7k1mvw6c5VfI;t02oA@~ z8=riW!#a=w0O@Q%ZJHOc|M1~S(!M*d>0p6{HSF6tH0oimbBXF;QKsfA$8Trn)Kit2 zbp%2*pF?7rSYe`Mj4E|3nZ-)LLVq#yxKPq~kcvrG!BFBss1Y z5(Dl`X5S0czFW~8cyq;FB4cA-SiYj@i$u8_wQacH{r=F1Ct3RP5QcR&VhF}6<<6HB z%bJ=19iPiJ8eY6tTeB{u^MtS#-3bj*Qz`hm12fo~eZNjVPh3CNTIW`Id($ly6jj;( zdaLxlHm$CF3RJkH;KrGE(KmP}_@MWzM!}08vp@lxQ6;0t*wl%4IhHrnm4ROC>g^Rl zu>WD)HZQIzvK&uN&)YCh`{4KqZ4TLROU78KpU$muixSdGZRtM(8M>RExE4>ec!a6l0~rxp%C0~&D>d% z5W?fIoVMtQ>On_9(|yr1BbaXkQW4~6j!glHJKO#>;OY|lvLF^oC#a&(@$LBY1H4( z^z!oHDKyc&G*dhA>ZQ6c^E2qJ<6r0FDkWF>h41a=n)6MWl<4VBm9TqhpX<&o+z7V3 zC_|BFLQz1Or}|{>XKefG?Uchg0v%lBrr{%^_a+iy_nO=$MV`zzVD(3cWY-KG&;3M% z!wLU)4aRV+sF+vZ=WeY@VWBKI!*33L+&&I~A z_L{$XE`eZfwij)>=#p=Rw;`JEZrx0eE>-PkpDdBwZisgKko4w(L*#&HS!Zb4s*gMA zk}41pkBow$$jp~V(I_(eLGxkMNHtI;4EP6mygWR!p;_D)gA)PEZyX^zgBXUD{d1a9 za8^wE1ddl*d?h|mot^g%d^EbJ9%vc36azCKsmg|bx2*6S6u$4uy+bGXsZ%vOzmqBn z9SIQD+ugsdK9l+Qr>Ab0zIejwE4U|+m`FIuY~E5RMXaao)FMK%=>c?zNdQ$^=a<2} z^e2(o9mCGwth8p_q}@-_9lGQg&)wJKU1XTHTy^RmHCX_S9_Z`ok4wqU>eZh}FIeyt zE}Eo`%+tG#D01EE8OSZrg-Hd;c&bzmh33H~gmR<<5gDJvu>46^lExDB4Syf{ig1D=>%zE9^p0tFZRYb= znnU-tOvZrzZ6=bGGxkb@ICTWjvneg3$D1MHD=nE)QZgu3Dd@|*wKZ^Vxm-k1jwv?i z0#T`Zvf8&v1fLa&Q-TWBP4ZdjN309n=rXi>aDLG@-(!xL(jvpB0q~8cyMv;1~!8}UgT!pZ-LCJ3u z-kX~7b6wHAHddK;qlDsZ1hNkhqAi{Nj8(@9L=5h36UUgc1~xH5m1b1d%DUeu@L}6U z|0){QL9w3l*~pqO3-{T|>Yvw2v{m%J@=Gp4nC3UlnYv{OjvY*qWWI*Pj*NO(jscL) zNF)DzA|oB5aUF8oob6O%Z`&CPirOv%=OP&N&ZIKav9u;rM_3iWZSu(g9DV@ecA}r; z9!EGtn_9N)k$2ohyL#Z5YxXg#k`QxZCKeP7cP=&GuOQ3b`k);n-jm9Ot`yJD6 za6o(0gq`_9KA!G4Jm|1wwe6MIQ9HL ze>T{1Y9B?vp5k(|E5|u^9Us^47kIOsmC4vwfT?3biWaun9;956w%e{MING2IWqX<& zc==E~S8E@r8D!usP&9=8lOd}tO6{rwqU~wV?S347b}=awPOJ93TP(ZDkC9HI=|8iO z8O7_ywIRBq-WS!3^@Ljh4U!&<&7`YWfuy~m_HUyoekVrpzGsfYKYMmWHgY|q>hOc3 zZ2{Wa+IdexA?@&caqrWu52<$dJ;MsA&bGFmO?bpx;P~5v{?l|h8=X5ik#Ti#i6YK4kxOCFcD0!t8mPzeX_=n7b4l=7cQ~ zNP=TMfHQs{(31JrfiQn$*cY10)%@3=1$d2+oeU;8dt{8wlq?UxKfS;|c{atvJ%7Uh zK@~QGo-c_OZ>0;It$IL9&#s(gxvhCD2hfWnmcSlCt$1Fz}fpEV=RElvcvwMI1n7$oZf^zh4KM3m8EpFcn}Yu(!`jUON{M! zo*h>SG+WCR8(dDu_DYtzsZP)%Hu`%^YyEFNIoJB6+vUf`PgFPJyef#;-j2>oOxTwj zRfgKsa7?0~R`2P}WmPArPvnA zs^a3#z6oT%g$}{Y(4fnSU0Ey&(iSeds_)oK2Qze9sd`49n4T42Zf{areztI90!b48 z7=${COt@-3bU5^VU|ghbXpO-AT_mv8BkL#X`C5=9Tzynk6`+q~2gn|C(;;6c%3M3Q zNz6KuC6Y_2!v6fX-a9T+oO-)DMe$PxiGW`9ExKxLKr8;<1 zzdy^eXmZuLDZt9i6=}Pmtp3bZmhX;-|H)h1>Ll{aihq+9bM|$hOJ%dBN!l_szhj=V zjm&~C1y6I;znnlksjfUn2))p3+72O9?VE>KLYUx)XnpZ$nrR)AFkT-+>&g zE8dlTG-zO5SyzURQo!u|HTpW^6}Ny0iqPp7(CLoJcQ>Oo{9Jb@bzKqHTUxQ}lezPH zGYVD6+)I*M7z2%_(8DNVfCtp)CM;UL+9yHJukzhhg=-S>ZTEF%Xzv;6;OY#RNSb&| zjIxwM)c(9VO{FPS1hi(JIr4FTB;%=D)WX-;TiYE^lPA+3MA?3~Xie&YIL+5@m(`-m zhN*{51SOa2!0l2AUT7!$<^!pitBKQ>qjTfiwV{n~IMZn96e-KbEHM^Hy1F!HFG;pz zu^;m%nCPhkUX^>Mqu=!RRLl`5bGJ2=eZ#}ePH=-;ueZK$}dcwedm_7zoji8%?E9lQr0}% zh)WE|p}N^%eG3>!wXuckWge*bttaU&k&hTl)C9Cl@wuO=XiLjrWFX8cLoCG3>b;X6 zLYa(%1~VU3G-NoL>v@~_$Peqpy-&BuDSTGX0+0?3N`?liN+r$mX1yx9 z#4eJz9T+6;6A}P*gS^@GAlZyOC5hM~H%I1(o(#7h^1UlX`%hg@DCp%?>UXo0=e%*Y zngm+frtY4cy1r%}vOs61T)5p2E^iQ@Vp&bF&S46c z-R9Q6qO6@$l;SQB&texi(!;DHBns;hQ@>a*6(YTMl->b3zsY^M6iLjQLtCH+ksA-% zT+1w3TQAbk2lSoxoDp#A_OO!msIoVg&4W5`oXpOfZ6n0}-@cA(3VXJPM}THlgmC4%y^+f z8zWk#@m72l`A^DhHuCX$6G6G%#MpmYVFQ^?&)O%g5G*>uOj|)0 z;P(ha6BW|AV5~mk6YO!?b~#96)6_M{W2gl+1%7m(DJ& zl373}O^HNz@KKF>Vm9$M;_&DwEk>4s36KQ%ANVPu?k1UXqSqpXg;?=OHy20%$*(@D z)3av~*QC-H52Jcv;lg`bTcy(jgRlNYh;myTAVdKHgec}?=39|_$~pgk-tz!KwD^;A zg&p8rVfw|nBKfPBs~jL^5wunXh*^Mo0mfmH3Lrymt_XQtleF^hFR_nk z_y2=pq8<(h;GobS6g5-Dz<>TSCch7xXH%=YP~TtP;y)}zlm9SRiNDY2N?^!>_N%|X z3NRS|KAqOc|1dnD-^Q@-_)J5h=x@8?-Rn{p}t@B%y#0QO1gha@?x*-;CtQoLZ>IsUGQ%=!iM=3 zC&~XIyY@e5BL9#7DW0}w<_5{7Vg$H7NO%;k&Un39uPJ63u@nalimy@kM*YvYn*Za+ z@qa(?{~ZXES9MaaI^jbP!lNMkg5UWb4y3nQ7inoLW zC(0f>3ipEbqFzD_14!;7qOHRcdQ_ugiO}P|gm_m-%C$w26b5 ziY>h}*{!BmE5{I^^=2MEi>l~7ZQ1gHcGvzTb+6?X!*{l>?{HaSIDWN@EHx3tH;66O z&>Tsr-5Hkg%6RV!C|#oVyO3MAb8Dw1v7GST0uZe{*TCdhL^NooJ*YRi^tRga36nX5=rI4#fT-w-jSaM!dO%=D#gsNYEOAM{Gpz09 zaY)=LqZNw~-}_+1-!5#Iv95ZNDAj$G9wSGypNmJvDT^1(YO)dCJF&Q!p+^hcrnw1R zev$O`NmE7mcH#Tfa!W|Gwh1u+pV*es9pzmr)NXy1aQ3}yW$dG7&`jEZau>AbjlYxf z*Y<)7w%;z|J#T~00uvg#1d7pH4AkhbW+He`J@?KC&njJLw7M&{#YX(gSE%mF=-sZdM5WuN-@PH${?l^=6`exO03{%uucWN_~>C z&va%D!sf%o`>Aos>Y>({Y%!FhDzv?cHwzl&@aB!~*9>RdxMT{k0yaty;S7pzzl6xh zf`stZ7Y*tkVZQy{9AwV%PmPWEv=7f-b{7=4jTB#bd0`+)dhP?UcXRrCGaYcAUa}?< z?0o>}{HYcw{x%zPth#ym!{an*m&qmQ^|NsocHoWgLwKH15W<&6gQNhKc)uvQM0#Ro z%YtB}F5zsbaQm^+Bbn+qg&JaKoOW1c&e^xvJ)?P;#RyyVHUNqQn3Zh_wrx#-w1*$I z{~YNGALh_F1)HLb(=D^vvrBgVcKZg++l%j)ms8^IpOuOP0v}euA-5jIL!{Qi=Qf?^ z_~5ihBR=9nikTR9!A$=8+k zm_CiY6znCgT^}^0H9oWy$4v9&Y$_gt!?a`0Zh)EKEJ&iuSnKzGZ`l(U{M@I)Ty+m2 zMV%$J2PSbn$Gmm6D@$=8jXL7PLaC0#@T2jY>9gLuH0#_6nLhG4O1%Nz-}8#P1#K7) z!H%9=1B|Bpp3hDV!9YunQTy)@xO>5bi;@?thzk0l=7D)#({dc?v6%x)8xq|U||kR>_fhE3I*nzt0t(o?-fOXh^UoLDcL*t?5iGM+;vfLKqLo1+o&E^iK*%5dTl6=EQvM z`h@KgQdicAN~SpO$iyIiOIVOsne*gPP~6fdW8A^^&7CD2oxOWyfz_F>P<5Jt zyEo3Y(<(^Y-#1;(px96qg-H9i~g0}OP9DUElE;h=h*}kAgmnq5(ZeY%;$`F@Y7tZ5XqvZ_Fn$Hlu9*pjc z>{ob?y3j$D?JC2^SU{4dTlZ%?n{O5Con)%o%j+}zYO7!As9EK*hkx;8)LG(v z!E~$QSgqoPRi&%X3i9o62y>IF&HH4vA&-=YfG{unUQLI=Z1U`(=D4cFtoGudMaT25 zeB9UE$~vb3ZcE2wraM+&EFXVUukm}8qogBTrj9IviZ4PUy^$OSgl^r*{aJm8UL65$ zh_VknJxE`C5}y04rzJ%7<3|gZyVc4M4-r7AVhV^Ierd!9a)G2mSU}>G((9m^%``-F zv;D5q-we6i$WP`tuEluj&Xp$XKL?SNdD_AIcbb5hKb8+jW^h8(NJ>O6EGle)`3t@# zY`CB-SLOSbv8f1V;17r21zfe`_Sp`Tkv z5bF$xu(JuIc06QBvm-D!wl(3=$@NQt`kLHlBm2R4OdaHeg(*Ips96IPt^w!GA9}#u zf`kg-Zh%B`sp>%Pyz{iIsu^%LJpocrYf;bBzc_?*run;ILx9#w^H@aXP9U*HS#*`uW-&5H%RhtkI;|@BU-^92-%6v zP3cOmXm2|**x zdjf0Chl)glKPVpXsWv85M|34k0~)Yj+G50Gh|L5s6xJkIPQ}yTRe#Pt^sn2~AP)KD_{VUj#>^*7)O{)Wh|; zeQvRL303(UDOTWCJL=&7l(}wvh(V{EKncKG%aWzk} z^xMlC_w%DK?XHNte#_QFUmCLw#@@iM9_n`iAsU*q=_ha81tXxC>h9%kfiA;r-{E$l z5U)Y=VUl((F>7Fc?IoZB zWpP$GigEOLSGxb;uJn{>i2>-&LaB3r+!=0wj77S1+um@f>StEnPk>a{{PmgNg}aeP zzeQ-rPpttW-=4f!hzg=r0leHF#P>6e4-YKw4@Jh#h@e7bABxf*%=KEo~lMU)%Tz zJp_V)DJv(X;GG8mORHV#fh4@@7zmd2?vW?6e_TPVf4Q@4r4^{BdHzupu+Axd{o6zR zd()Zqp!M}*V?=Km=?S2VrC9uDfUZ*HdZx*@~~+r=VgPeF0W4KxP2xwq~}?YVN5{MEK2}T7O(3 z(WGwK__28#%V3pwHt=il-9 z^!`CnbucmNIys*QOSQ2*kCe@Pxh&>-U(1v&hT`x@yxoThos79n6mnjAXVHqDOewS4 z(0kvC6-Qkaul13C+wxMPnku29@&I7O*?F zPk!`4yitZXq?%5v(N+FNs50%!YteUh_7qsJZZ8}n(OMes-r>rIjZY6@N+j8X!*eF{ z$Dg`$-YdT@(RBBdx&Y$h#lAZ)M^&v%pzt9Im7SQMk!`>SM|ObtkkR{zOmODNv}oBo-&G?SmUI;1zTcVM({T7^%a+O0H+fyJ3V?IR z&3kpidvf%kW1;V8SfS593Tc5I>6-p$pK@p4SzbmX;Vm6usvRLWYV!kk4!9j*J~5`UnHFY?_1Yv0 zrsyOZkMBrTAwE0HpNWQ4l&&TTW?1!M#JYbx>vobowN+`-jp1!}ap!RXeE}yb+b(9) z>tc7=iN1$FG$%)Q4dYZJJ)X|mWw6?K*K=K|e4-)orl(81XsW?Au*j*I%AG7iQmm>W z0J-h>(x;U@rOLOzm%b`};&l;{u9L&cDwIS|UmpbDxpDr~ej__p^~%XdWqp5wc!xDD zASk>^W}Drmm;5^Aj+FblU#w^5kT{UApy0GD)Bia_K-6gN)MHr5iN#^Z7~q*5RUC1t z`QcSp%Jx-k_~?xAjU6FMvwC9)hPT74u>y`7*Pfl{TXI_RRihprHwkvmu1t>s;9U%v z31FoobD|cvuXo8rAjjMJa0_sC1c**_?R zezY_n^X`BT_$Q&0#l1oDKvMk#FGcQFN<7i7%6Kpnz4ct>yrCta+4y&2KkR&s;|fVM zk5ezlB~x~_MyR(g2MklMK(=O&!9>n(%xSoI$p%`AbT51R_J(mf>ntj@?#-heEiOSd z-qm31#(a_-0TcQR&PgclGQWwOtD3x|Z;b}4l@HwDpD8hzUM1_dw}B3S{y~wKGF55P zVS8P|ir(I-E$7Rt%IBpRZEv_x>4UcziHz|TU_9b5mFc1Qs{l1MvnyvI?Aw?-i15G$ zlr;OD0V zrXkORsg%ubR2%TktWDLT57GM%*0;I#C1r|&?n2SBQmoGWq7|GO^2PSWX#R8#1sY{X zGF5KOPYp|;r5G0le`l*xKJl?j9%3Qf4$?Z?uyK57MfAV4J6ky@w$yI^^0+30VSmz7 zWLVr4l=|HJ`!Hsij+Wfz@XUnxnqb@26$vA^2`#DR!dO5xRV6^dEXRnur(F$_ePpe9iB%@t1VzebkXZ2uGsaX8AyJRkx zZs{^({Jv1Av6wzkQSqA&=IOt=#`Fr|u=`U15#D?#wvIT`Wq&RQ1r*irhSL>r5S~q1 zQJ84c4|rR_2Z;77BsKu|ssKC+mnE8s6Rz6~{&7X(CS)#y#q1N7T?zn`OWXfJ(NzH8 z;D^6l^z8tEso|~}f>5OL-izyzF_I*c-3mSuS~vy3W1D_prvs@y`h1|LKI#WR1@Rh4 znl3#GQ2QoRPzoX1gF_Je+6|O|f$RjJGB+T-``70j1`pU9nm6P75J1i6S|W@Z08p_~ zWH~@~_pi^{rhMJ^u?m5GMF!x+2tPomdp6)@>JQExO(>eeC&fno(R)8SR{#Ihz!?S&BKAN;6aYV7 zfsbrP#i75=T5%QL@daEOzj`O-EDC72(;8YJ_u0=%R*mZk zdoq5tf2&^BjBEQp7s-UKY^WF}@n*EDFPa0cdghr{?dB|ymC z>}}xtGW@mcA&f92d|XyV!DQ^-yTE+(!us+V`E+K9B<7n8VYZa(hE748BMsLb4l`6! z{xA04JRZuw?H`_26+)t{QxRpYY+0w0Eg@uymU%%e_jfN*=#(s*D&IJLV$Hn-MYe18NGDH@w3KJr$P-j z+AV8s^6gaT<9)GhVd2k@f9MktBgrM+6-1655K2AW=b0dhFKgxCuw?f3C%BXo9BOMj z!bBYc8e&bX(dD^ED%i!oZINm(j1nsz-szt?Pjutux_|ZCp0yc^b_rr{-JvO*#Ji3Q zFTV2xT>nY)yy6?kc7h@nzVyI#Zck(O4xOO82Drt9zQ4bRW0zTnw_AXyrbf#v?Jtov>fkVuzzE z@5`+tzJ??BrFSxq=nn6{n&f7FPUqHHnAN7I3E3zDh@}dFT;N>ya@pCt$7(A};`9=Q z6e~@%)nh?2zb7Y>k;0qKh|dMlftD;<2t8=pqNkLq^ss3M;&@5S#B`zc$EyiNuYZ73!58nIz z=sCUuKh*NDma9@wRX}-JqW-4S7vF63wz)JY3t6|S(wP=u&^Y2AAv4!k&d&K zB`;G8)0~ykOah}V!_pr*bL~9<;aENtqPtOvDU?L_FrmKIx_ySZH;IM9ttO(0eu$d~ zJ?+1%)=#4g@AGtp*0-{yAp(L}tMSW2m2J=LL}H#^H|^fqs9q@DDh&Fhb&LAXt*DcTkgd_Ag@d|xY`K|gFQ%7yAe$ow~()B6r1O9QN4 zRv1869y;34T*|9HbuSBjkKZ@k%JZ(@%~tHnlW!_YNmA}+_$2dBZ1)=d!wzm+MFi8K z6b_Q(qR1<{2_+)bj)wSYU8=g?-p?yj4Vd5Ba2Jgo(X;wi>U>}i(`=;e!*mR(W{3*- zzhTQ;>U3!IN8FguRLPt3Z$`g0zG)#9%8Ya+2c#aqFpxR{Vd1OiUWB`KofDScl0)+0 z>-c7wQj|lnh__|XM;Z!!hQ)<-X|JrTIim#?-|=osJDYuFhmvS=kv=ts=2<`+YMN14 z4&~Qrg6$C4$$PAu)Zby%joDH0>|(`%2DMzcRH$IomoFZV+ZQ4Vln-)ic$ zt{P?1o`o_j_l&TN@X0Q^63Kcs&pI$pHBzlmVs1(cbv7{Vnuv@hyCWe%ap@iZuTW?HzPH5uMLKtRrQ7#1YXmE1l^z= zs;^UqhbGc0jLAWTBy32upC4&?!CIEzYQP!q9qo0Ta49Wd(60sh<$bhcb(79lxg^Z{ z8iAZq+B`M9lPTq#d?tRUD|G*j5CYZvVq$ypqPvM=wD)(!$zXAT^W(~-w|ZNANZ}_F znnXPdg9P)`HQn#g6W!NRqZRM$Bn>&=G8~l^?pr*C6DmE8V{<)d{N`dTWMBSn2;@HG z3QH87o#sh`ve0kQW{X@lQG@c>1M~epRacq!5!7cUO@}ke3wST9oWecuZ9m=S`x?KH zO)7*_Z_@O0;4}-Jex%KqjYpsrYOimIuGstD#fi)b{X5H%$Jn|q7sY7qa^Pz=rZ$3G zNEWcD!YcaVhq5Tgb3bDCAbH8dnMtC%ju!VoEo-Zvq%5^MRNe}{qNvSizchL7UwEp*xrk*_1dNLr$i(?zxoTgu< zIgW!I)A<8MJW7M-8}bU-J@>`6q1h3EM0k1l(79?HBTvR{GU?XyN-xIRtqVJS_i0Pc z=bzXy4&BRa4LTi9)uauR`wdvN$weh318cL&uF^$C2NnNDiG~}NpOtl6E`;7SKir}< zYtvpyTz&*USjrKilPbQFb)dDVI``U*rn$PcvF5KiGNLVwXLb`b6m|fD!D+FyBJ&cOm1>mn-fez ziE|xt%6jmJIig?*$3~{MtY|t8xx=X2E_hq%B&jkZDShh6-O>4~u&1L^>DhWBxVvh@ z!&fcdTO`l{)cj;?498R? zvojcOn}G2K0+hNkBh3EjFtfAnaDsJqKn34Q+h+~S6hXVzq0H|y8tk;#Twe?kNPBOE zx$Ul>8d~HQdvkGjZ-`m{g@GNe+t2u(?eHJZ;5% z8+{)ndxiJB-4GA5c5!T#BoH#xd8X0ab*jGlMD^(iH?KC6?vvjOW(tk@mkxjBI_pWz z+wrbJ!gSZ>_Ky~tFkpAA1{?j9BG;VUj+Zby<46Vj(gNNf~;=m2<^V;6qK8U>b_yG~D6SqOC(lp%7==zE}Kb-y~dn%vk_Jr*#ftYpjI zH(iF@46b|{w7o)p3m#|7+6<Xv$%PzH7viIpRw>qJ$6h z(2a0LGBHCJscBO7O2#zgAS*YLzCLK2R9J_KG#erHn`U8?HkhW=9*Zs=x zHmM3~vDZ^e?>jKXB~pKzmi~7hZ#YsDKv?;i@f0<`-hkS4zqFZFca8Ci2_F)m8r#(~ zsv9)b-``*Rh{?;({EV2)Y7{cX!tF5gW zz13TY;a5r$d6MWq^gevgfYg3=6A-Lo#~m5m*q^YDAE>8%aal`~c$zlwS}Yzwn7=&< zBg)bS0pzm`ARk*>&;yrND;8Dct#RA0f({&t0Bk1Xr-mtO>nPI{pgv%bbjPN6&`%&} zW&H=_Y2U28aXT(G6F8_}pXx=`!zm@q$WZ_h)zkv&w1U6ELC<$nj_JQW0VSu<0l=H( z128ICS=5GsZK9Uof^zEA=&w)c{B}|nuex+p_y3EY?sZI~D=ZdtKJn6;KgEurw>i6# zr1@8Mm2gcJB?73@a}>W!IQ7Ev5LVSk=3Y!(+0y-=%Lxi~*B zly-a$Yhp*&n)w0IoQ>KD7(>%DQx-LUKs>O_^jDZSuD_;Nm<9cw?^(~p%+W+T$%OS-mfKA|oPW^xkq`yVY9_(A_8;6xS z($kikpj+Ht@r+|>wQ1$K#c#dA9xMB8k5OPeL49e5B7%pSwblKg$m1~8j^;SrKc@$@vKzgczve|M8TjF*3aGqPO&-A(Zb>Dt@ z{pA6N2^q5(M#LO|`x+D>PmXhdA#;s*E-RpYRn?wx&_C$4Xuj>P3yEq^HuBG8-f^xL z;dzonw|h_Xp(Iix`jF0~Yj0~rhy!aUkH)PKM+D$!+FGT!?1>YBSP8aju1^Xi+uh`) z5+ZPFNR=|08PO<8g9Xdq_mTn)Me4YE!oo_o42CdlayGjoFU5|STD2s2(`zF%DXLUI zN<=)&i6gR3p;KDa0FFyb4lSJF<)7eSL}T>WA4ZDG>=|f(MHIXK?9~y<^9LLlBs(cm z&6TJwcRfY352=Xn+8dr)*evMtIBzTKYV5GtqfbNCB4>{X2G?H4fMiFgq5-{QHuFfz z`F^$2o?g&R?I)OPdahosCy3(?UIGu4aYbg~dqnx?-uzJ#%8EMipFjT}m4|$NLdR*5 zls)tKek><|R0<2d0tyZnqKc)b>$OK^zuxudR9>66VWESfc_eDh9JcZeP*q3;9pN%& zB5nK5fK3?z!uPY;kDlV?om((lKHxWX?I<8~Yyp@|08W34nyduYxDgoZec(tqgD0L| z0XQxB*tb`IK+tD?YGSnTECR$#<25)ISbUu~UA8vAz8Xe^(*eSk1(1^D$%-Ek9-V#( zR7Ywc82~S#Lq^O%@FegL6Y<7bHPwHgYk<+vSXv6`JhTJtL9Ga;MEu`;GD|s{zL~-V zN4E%PYx(*7@OTW>CvKaH0`lZQ8KU=hR1)5vTRO*MO;rYA!Ci6$qYgD||9z2% zLL(h>6d~wa9pBp z0AC~HcGuO2$iD0Qu9=kxEa1D-WJ`CJ$C zic_b*GTS`q62;^yV6jocsZS_fLnG`X2=5{UQF-$y*7i8>6UtUHNw* zM|aQEwM;E+_-v$8(CNZ`Ddgpk!UBD2J1B`JwVewH#8QnG>Fi`>Rj`9c^u1GNyMhUz z_Hx5Mu{Y6ni-_JmBg|TwfX5Mp(_dPB9vTrk)2({{_IHT~+g;j;`EDSC|AbF5 zzgy~hyy{&|nVA>7>aO5;$hJ>{4_xs}Y6358eZACR8WA~dFJ8GGnDwyI?$3{2zj{%- zljUa2zMyoX4Yy8OyEQx1s5rSDzo6LR=JKSJcf)+;$^*sbB-7G+NrLyxq^$f4a+iaS z>vLunbWqe(af)?L4#^XhG#g4?C5)ib&GsVbC;dEEQlO!Oc=f85n z=VDdHQ=emUb1!?x$py<nUwaK2` z>LEP6S@2S{;b!Yqgc}Hi8%5t@dB|c;*ChCshNSe?z_(WLxHf%?<;yr zx4Z_~3t#CUG?vW*V%ya1h4^QSf%{n|K%9F-5&L@t4x+pe53u)BfA+wAh+sZ)}oW;mmQ_s|B!m@I=wHK>b zV`9enc-lbWgN^_$A{h7tP>a!DW?t_vPOwhXdgagHnSc($1b{nw@lz_T{=n7`NQyk_ zx7YWr9;zSul~e}n&%Kk>3K zMua284$-(kVpDDS?iWk3W3}K{w@(5w-rq(K7$;0yiR?N7FeXx_Iy&-^9zSu2nsK?$ z8oQWXRgO)C`VCfkJ@%btg~C%>TY@EiSyO}dPGJphWggXCPao{>hP1SK*&d)h0TF{y zgORYW^_J=amK@#caEL%>fS#8vtqNt*=>H3EC|1Fqg#(qvrSWV ziGKzYVQ&=D_54fL_Q7%SS>WmvD}ew04Y~hs{?xEy6ksRDe?T0tTYSJ|uEo{82AQ|Z z^2XU8kU<2q$@&L`A%e+s$^3ltZ@I01>(c%ikN*#eEBsQ6{o}j-y=p@An~prEtf(iu z4jP~PXa5tn*#a)-Ge;wgdIkxymL{uf5tZyzB8u$8MzBWn~e{G*oJUQMXlq8cR+Ax5=x6W~yStWfsd**fRy~ z_l&QU4V70h-@u4DOny{n3rJB8!RYSsG-qr$dhHBqA`F-p0gQ20ZX&Q~FcC092g|&i zd0l6<8R*=i=ubcq2JjeRGoZ}g!ir`p>CojzaYmbbu$2qbfNzC=)1@OO%M8-_rzPHcc-K3XxLRi{NFtpLSV#)J{0@yZ1j7nh zR`-vs2O}6MJK|}aP*dC7Lgy><1a)Srho$w%`QtU=a^Zxzt7DsE{T87?P#Wr^BFv$) z^z)soF>3ir`r=#dL3XsX=E3p6Qiny6R{St(3M@#X+(cu*)eXF?4QveCv8=($h(k?$ zQv<*)0=;=nJwVGByWj~T6(W&i3rHh$x6BhHNuK{{M`QCStv_SN)tImFJv1d9vRK^x z+Wj31iei36NQ}Y+W1`6Z$@_`SYmrTN<(hUGlXG!UHecvf%I@r!&RouTE5^sF-ZUkT zk}pcgp}i87-v$Z0^+fT;TJX}$s%0!X@A?YfW?gMmpOFzHG_%=Qlf6(xVqRU~T&P(5ddzC%yq7{v)Jxogoj}pr zw`~K1aF=;kB`bT0CvDe@uUJ>Ns>`PU9Ha;(@gFi=M^Ty-Pj# zV+O^EK%oiTQ}5LH%c0Fs9=aPTnW<{6`m{$HI)Cs6x}~5u_pv#uF%2GonxtiPwz{@@ z*|9d*s**<^UMa7@%vPP-akjQP?pRRpBXM#OK#N^uciN69q(m4NkE@^e-4c(G&s%zq zwkthJXAe{!8yzSZiw_>Oe@F9qWz)X@@@AFdi%D#4YU>!|v~6?FthbePnw2xvo2r<% zexy9oPHR)*!7l%+D{4}($dmd@@ac_=&My{2Pl)JusE6M%JD6WIc6A{&U)#)7)xN8h z8yLONWx%^NA7S$uCnE91Z$T?y@t{T5m+RdgOMyG>T6u&4+vV!P|A>nJ;ZKVYx(H3@ z72S}$R6&Ar&Ic)=O;Z|eMaslFn)&;lp5&=33qY_rvmJ8gm(V)svi;3gU8MqW`M5K_ zs?3l&4e%(I1T0FSA0IL|tIHPjc(S#OEpP6lfiQW$(j(M{N<8hh5euplaI2v;-ztJQk^$y74zEyp?+Q7*9!w4nbBH)_}|tC zMod)`0t3+CP!zF=0#XBUjMc_Gb1$e? zidmDFnIzcey{e*o)XrLqxyz_Wwa}Ts)4!*y-`SD1fHG{PPks3VQVAT8LAMzr(m&`; zeswc`&9SR*1;4NkE=B8>73v#+CUaUO=d6D33Hn?W1KR6O5pb(b#nUcoD5AP90@g9750+k;`?>i`hhA3A z5c6kS;hM494scToE&qVX021Is&=bfwX!Nt}#u5Nn7-`CfJxaQrFh7fKzdi-~?W5wf z1^?nnWIu_JR2Pd{Mf`Gd*}tFE#LfRX-9Ikm|E#Wz)4$&gN~FT+CQR5UEB)p;7zX~J z>?(OF)~_I)hita6d&2tT%Em3DMeo7t+W>#%Yv;JME|J1pal8No>3@J^J6f@hK(})n zEtlB@Fnq>d*Iw5@`BU@`<}!z7A}f7Qn4fn%*wg|f=oVxMMB_a_qw#Fu(3)rw%#UDm zY|3{<((+KzYd7KkC=!EjPTp9{I|HOa|5tK8ZSh<1*HpI~5I#%M;Ti%nBL^!w)kKK8 zcOF*O;=k{%OnsQBmTI{eeL^JYRCC2yjAaj?d}4%squIxe;`pl?M*TbL{qI|OcQ`y- zvimLxmyT?4y>;5mrLs}0rL1rb@nstLEk}Hw*y1e%O_S1YHv(yMPT=gJw-pC=8q*S^ zRx<*q&uEUsjhaZcYmR{)%`WF(hu8QZmf}yG>KL~j2FT+lSPNwz%IDC4uXi}3_ct1!5P@C&`HOF*o_+HMAaO=k(9I$Ga*S}i2nuU2o}Um7-%#IaH91aP#I{^>ZYfOB9We)05B!{Ln#*5+mtPCNyX9c^@D~Mpi%T z@KmHOx5XR!uCB7^X3afHNu&Qewc3h^TM;^sen8%PM~DVp!~;#|fgx%T85#x)BYy-YC;V z<}KLbXzv`z3l>%C|2Lf=hYBMVHjy?5+N*s5RBR%oU3;qrO$F&$f@?mgdry)&7`k5k z-=pTN1qwqd(fsr)V33Ao;cJRd^nKXAW{;s8fvzUuUoYX_n2N}ZuN9@$>C)PW175O( zEH~?V;WOmA49c;N`o}}xd=bqA)9cRbX#v+SwMf@|>KebX)km)#Us|$FwbRIpuAvVM_XnlQf6LK^^^t@9V|=GKaK!$7)WY zbyfz;nF{2fio%(+iUn7qd<3r37f$R$H=ejtV|UXo2w!E~(_@mjXmVasBK&w7qL)O)nDaMp@UVriw>-Jpw7?}Y62Hpm ztSN)QX+~RDocQq%p;u_ZB%sQzZ<^jB=M{(C z$JpWN`mcOVwLbJK)S9>Ibi3h^_|35T^E>r>q(vUT@K6@3lcc$lvp-d#7ZNEQ*0toO zl)$%eIIW=r<>OLfh%Bh?;yUO0J|}7;aU^3JC;{%r_M{sj*ox{#_{tOuIg%J}yxgLxu|u$csZ=$J_#MCWy33s` zGnpyl(lT@7x;-*p1{zS#Io=&t;`O&KNH;uzyqwUD95KGu%lirPc<)pmExmcu8~xh1 zUR~0(2T^mLIIN73Dee1k?Rc3qcMA)ezkI@Cputff$iBo&Ig*)Sh+Tc5 zeq9E~uECo~iq|7>1>&vfRNXmt2ndHhL8A@E(Sy^Uuv|zBjr1&yyq80BRS&-aLt9cI zd)V>F=jmJ9QIYZeRhV5!U2;l8!AT5I-_JPIsQCDup1Zuw1%c09ad-K$Z$rrbn-(AW zM+z8aTfEFK+C%WJ1-gi9-mGHXkzNG><2SQZQ|aut+o@bqHySE-tAAN~Y1w3WcN-NN z&J+lQQa`-^0E)MABq(nyQ593>Y|901nZVNiaLbxh#BJ-r2a;lHHAN($k7vamsCSSe zn&Vl0#kDAIN%2uN%Cd3tvHZ&>p*TE$jP`8W31O{acR}5&NqkLbmn0p>;$ty&$B~D~ z9MNTG3&(oP0G(xav#{4Zs&${fdZ|a%i`%yj%01t%*L23nie^phpr0J2`Jf}=P0}x9 zS zjLx{u&vqXcfiKGy?_;S{pa9v4Fk$vyP|!ig`42CL%U3Vn+5{beVgXy#W@(<2HY1Nh zs<&SeJF|Uk_wk6Ydh%uZ(dzmiN8j}_Rg?LcLLIr*dT;geIr|a$LOlDQyR*`B&-e}b zp!neakS)_sSTIbCB5>JFryhvWsTt)KJR>Y?*F`NI!R4pKK64yW2qv#LTX54A5L~q8 z24SuzH$PM#;;)Fn=yfNYSQ3r(lq$r%xboh9Ot(nYB5dX^X#J$pdO&9dFIo^r4!7Yr zTole}1yqXbK27go8o@NYztOkk=n7Mtx1)&|uAq7*XHSg*2}RTFMnFe1RLwI#+g{TN zsyl)W@T!S?7wi7@!_A|66DpN~MJOJ_K4Nmy673Y0A0myjXgTeN29GyIztQyLS6aXJ z^Y-&qFqDlE-hpnCC7z(W5eL=GV~k6W_Z7C0Z}uKmTJ9jirF~gV@TFjuSEfRjj67=3 zF!IU=8cK~Se1a_0@NV#%j^L*oaVu@ndvaHWF6r954}Nguo-Owa+U;gj#-laK3?ouU z$QrxGVYirmYS3;aVbS9Li+MGYu@^6`XN?KgHQt^qe{dH9GjVz&g&Zxm-uP;ZkoGA# zVq(2R^nvemnbEmjzU%dn{|Cg~?Ayakcv@?<`&zqPEKXhP zluDZ9S0W>~ZvS;LTAdV)->h9pJW}d2<9Golvukv3k>y+1K{Y^Ofhi&!yH;U?xsDMbN6<44 zH2^_mqyp&Vu3Ge;yx(l+_0Cv}NAuSL#94F{j1MVEb~SomOEK$X6xL4U95x7l;`iqC zjWZtQUAkXR4?o~(Ldr6V>$vH+@oNl$PXuaq!3eyxzG>eT%Hm#Xm5HzRi#C$2s631QG5;>aMjdC(`|C2`<1QTqn?0mmGN49bH20ikOV1mPSwt0O}YOjW#=o3YaLmnZRrvfAlrHngtOJJh7XLPl=u!?!ca770Ik#8du}K z-a4{mcHQ%lw1x)VpairztGf6{%_molhTh5jKwgh7>)6+lt(SMv4cYsca9#Ug%faBT zcj*Fqykl0jQ5ASrU=2X3iEd9jI+npg>Mx^gZ@u2dyLCApPJZYw#dQtm>ZNv9)%x2u zYN!O?#X)68GoeJrr9PJy=9ERenE9UtBP>1=oYPNzQ zG&2nx$Hf_uX&ubRZN@Hj+bC^Qd4BhtDKf6Cdbjhq_#S(08MZN^Se@5;)4-CA<1p4vWw0UY4crhfhJxbBBLG z#!SE-d`z<&I~gQAvu@nx*0}%4BITzhAYSgF0vz6zUjld5;Ej&@w^Y-tS5%UG} z08l#Xlx*qBU1bc(ao)FLA69(^=gYh1?kTopKt^S3v^no9W1PETu9$e?Lxd`PC|zVc zz>6s@wqm15Y~HeUpXlhkRXc0{tdHkx)9n1qRZ7RZ{qE26JxUjrCjtj;Z74%b=V?Y9 zzYnt)!~e%9GoiA2Uoa^zrbZNa1&?I?KqoA2hR+cv^m$wpu zhCH=ayUq#`Eqnw{&P(n zfM74_qrcPyfL5Q77^K{{E}H2jibcF=JI|VduvUb|vQkR$a;SBEm{nY7^lb%6(^nW} z(Lf_Dq-Ftg2!2RSs}dNpt4J_qkHT{DT=04uwfqF6jGI|%!@*uOcjg_Rq1;Vxahq{XO@ZEr{ngRo^nc9aEfvlY#56&G%<<@40i{{YlQ3wv#y zLA5CKfl!Fz$b6*|>)+^mDCGWfG?s0RVXf6oKR%Y>V~}XuuVC#u-*L$H+GUFEoP|;p zX5XyGLx)4#_KV)QG_3}@Mris>00yf9F!)dkmY!1-XYY33f6`M}kp>^@hR_tn*=`fWZvr0RgvyRr0Q$S z8Rx7a=Vb3q6|D&wG*6ZEhxo$-B0Jo_c{fRld_u7z`M^kV6!{TN=RCQBFcjE9l}Tks zKgU&XxKzjYf586Z&w4X;{%icjQgTq}&8!ip{wlF_w^z2Mi(a`nXa1q~9y$5O@g1&w zti^ZYpMkMo@`E)z#Ci0-W@2Qi&>1+pj(f6mw)*qiZ8x;`J=t?;17san;EFl)$KvI$A}R4KMQA`kcv{VeWQpcqum)@0$I^)Kl`L#QC#F#_$HOW))TT zCpqRgC!242r#tMkwi+Z{EY{qq`_e>S3nG#KV0-WbaCxi$`sMAC22<$=oGQ{3iXwA{ zoDUVM+BlO5+U2XIU)xUmek3$qSg;nElG1B8P(z=}6QvSudecFK(qv(%y0--oLUk@0EvoX!z?qrId^@*_Tjks>TD(UL|>$h)zo_vOkYUgO7ft+mx!6)591Xa1V&|_{#AzI)iIaQ=u*B13?_g5LyX7re>Qu_E z<-zsE(aDW`pj~-+oR1*aX<@2$N-Y^N2ptQ=Eg>EX?`Ao?p{DK>y22HOQCjdM%Mi~Y z9@?@&JXf5OXX#HZijB1fmO8l7sUoz!cZbr6vSH_&Gz`=^gEVPMne%zj@@y7Y-ua6LeFNO_w)LVr4LVf*JceTAq8wZ=Y${eisxRf1A3JOFRBkwyfD`OK=fP>=N!O=r8c%u`}bbYsUUM zO5^uw8{chYul0Oy^+p@imG>U8a3WY=OE%lDk|&}2c}c>0e%|d&yK8mpqeAJMCM!m& z4=)<`mIy;OyGy0p0(UtP7#!fUe>|Ell$Z(qNxhpt3f#5Q2|iB%$U;m*+6lr-dK(Cf*-tPxWG zob|PF&w~lt_oNO$j0ZTkv7{{(G{YQaH#og8TY~g!KOlMgUmBlyReH==Px$4Zh_$T! zncAm$DRZwsruWPLF}2TC#2!>j7xE&Y(<$}h-OhEd&fwq2>nKWYI*d>ga})n!jZftyt;9<+fKJzH=u zS2vGZ!O&cxA<(sJ8c={i7=w(j8EmoeVjAGnXeAPVK$twdFuL~}$NVl^GcX9pj;zUy z7RLc@x`Y(!r(SSBY$t5y{U2oatmYq(xK5V;k6(#T4r+t#z!Ec<2MILUEieI?;}~^i zb#c^wOL19)I5kufFz(K%9B!8j?oR(}-$?EyvlTWly07lAuN?CLN|nipK7b=ZD^QM$ zTaGDeD;uh6J(JTNO^a>oo^f|o0n9E09B9NiHl$7z<;p-axoTyv^q@*K+Nu|!E~he> z?vxTlQjc@{Qnq0CpR9KL@7H9zwNbDNGV?U8IDc;4wO|&;Stu=qkWZ-?DAr^z82TKx zE=1Duq)Wy<|Bg`s@97}Rha+}y&_qmW&2L;g-7iF1@;~V3< z<=M;+dhgg(sW9{VmF)U`aUjMN)TdFw7Rrum6j*SbxKencb;zDFcBpkXtX!%u>q4~j zdp^>s)26Fz?~5TxZ5}D;;@a1kkE1(%xp5;>mE8*QEb}d-K&6I5yr;i$EOJS$sJ7hU z<7{f0x;Yv0^88_CzM^R+fAd%bgV!kH2ZVMNWMvcr?6=$Z1sfF1+71%yqZ<$rLNh?q z->$RngtI@4Dx6)!&MyA0Z}D=7d7P`<35V@hY!ol=TfUjxb{8(F!953M$J(O^EXH`c zb-=h0H!VV|wzgM6@{v~Q-tyjNK}G7^*Q1eK+PkMj6Z8+b_JdvV>)y%!{R|;S(3Mv@)GlFE}_BhAU$x%qHkBlEJ-;u4C`^OA3usUG<{{8C}5gO9eI6HS1; zsj(o-QN#P_#&6+H*wSU{?pQBBd8(BW{RqET$G{6mX+dGvx>?1OthJSHm1=2U0@)@4 zMYNEhK*qMdbs2D~38*Qh9vBUbLj$ai4u()J(=JdBm{D`@)gyrJwE^@9m#L{gAlaa- z@)ePTqJU{&TrG5B2p zc=PK!7TI=;EjOR6J2uBJja}rRB6!c=6l&}Ms6BCSF{*S(?xr;J3;dQFBK<3i_{$yQ(=r;qzFaUjKW8006A2R zf<~@9-gxw5iDK~I@ydxr+#@KzTt7={gSHR(;txAu#KZke!#duGfpPgUaF~} z0_T|^M8s_Q0it%QYi|LSF1u9=)7pS`ST3*UV@WAf^~DMuJ3*KEAhW+qo}m>_`DQIr zG1g#jKFrf~B2QAmGvUlrA<{yJS{V)cVnzuJo(R;mA=``#HI3s4rTuMac#Y1iO?W)u z^VG2t|T+Lg%TO0Z9Ev&I4&$o$BxEe;S=1+c585L645H1uP6^fuqU^B6)U9-^g zlqNX)BK!bdu2ao|pS&*2-)GfU3`9xpHpag@R$7W4vLjv$(lFxn#nv-r$TIKCQMUqO zO?92!&b9kQJgl^fPw^gkZo#AF>l=6YlX+QDAtuns1AXvTQj*h2>xn?OF)SgJDBB<_ zeINQL2wIb7RPAkHMK`yRXHl#Sot=W{klat+B%`X^w%O*>D>|hkfDk#@f=i(Vp&CBaM3nPqD6g4sea6Z^_T7 z)FqPpzsz65R#&BMnCrU+q^9;024s$1G`af%)7E?|y9^oonr_T^Q6mCPv7>P7Q3Br_ z<_(hQ>fNr+Z+U8whIh*f`UXFMR1&;P%N}Et=eix<=T4U_kU6-&6(O+%Tkk{Gy7M;~ zHwHR4)=E3%e^*yg&asAbEsmSF_m}jnY7nyO;GF=-&H~j!QC*~NrU+RGCsYT2RO)Q( zY z(bR8UmckOWU}F6Cp$(=eML6+=X3R#~>jm?%_=0yBZi*89AnO=9V#?K8mb-%#P3i8z z^>f>l1PY#r`2iUlijvWwKsrxQe2%VC4k& zJUn95$#q>Dvvn@0%U1T=?n)b(E`@Q9DGs46Kj=^ zS7`a5-RH^Xj_W=#%rm~+L;{vB$~@CqddN7DhxziL3G_x-1^2?PAmkjAouU(a2I)Z2 zX|fg7-l9goN!JHOw(`?A_C;vYj)ST6oc)=H-#N1!(|^P4-1z=AEY5W>8+SI+)x=qt z-Ce34cj<};o#V~-W!j1OPg{FmRdl+SbqLzL{8T|6c_QDe5&UXTE?Mk@4V=0}3odd{ z6Cryo+(J|bl48w{Hs{=k;RU9rASz@;(lYskoo==*kI0o5fsi~&LN?CQ33b2_9z51h z`g#=v(7T|`^haLtG4N8Im*3qbWJCCd94rF6&iPZ0vMo>?9%jxIt-Z!Lq))rIcK#=J@$zq|hTHyG zkKxj&Pe#~@rn=*(Z@^X?djB!Y;0I)v2B^01(~^Eb?shPg31&L?6)G1GVM|%+8A?nI zrl_wro>}c$o=2Z33RG{pXZ3%LMGvqY+h1n^g_;-SPbaS*QiY>n&qY?CzUp zK9tlMZ+@_VG#IP(xr(jAKDj?EHs^GIt@5PHZOe=Vi}UNLMwg%8>n>K?H5MO<-haQK zR?g69?=Yb?K!MQW<>djLzB3jUEfnUj6D4n>*)YoC6?n9*)T-#^>o%9hYc*kMx|f~{ zx(^)w-gJ*pbLk)>hjs4zk)`ZDFSV*G)Ad_?jl(jpW%s!BZ*tTdaGO-SC71h)9y(BQ zw_tlQW0x-?_I`RaVz9}^@rpqe=8>1LSHSZauBk8kWIK--s(RX1lZ+y&iYmz!7s(HO z6g$K8Rc#Dy7Q-uIWYdmq_}wNz+RuuMUd8fjKu3X!80(C`+#umw_#K6#5r;kmH%ITbYZFiYGWR`~I{qri}6&{%f`pf307*pAPKKE_U zZfS9H5;^_%*UnM?7klp=*W}i2i=wEgfY?B(QL0i!njjEe3W$hEml_qR5u(xp1R@~4 zgMfm7Qk52w-V!=0O==|6gx(XR1PEDovhLpJe4l>zo^|fNXaDZGzx@YNOx`l{&N-jw z8RHql@ezOe$66$(vtYosL-`LHQDk*`nJzoUh;O%2Z`gIu7eW?RXVPN{T zHf>veOt-`ZuD4x%Bgm1|@*&o{s#9L@!@ZAwUsDf1T)t{7p;Pc&^)B3E2;1h9XGWOY zT2q|&wu)c1#P?#`)@w0n8Do-2vxQcpCI9!5*O1%fHPeU;^RgV0P;?z8TG_Yg6%#|O z)}^z%o?}5i=lW|j-+g_SPcR)CQF9h#mJ7#Pml7^=+XPRhM5Ml)@xSkLj*JN9kJGZ? zU(gyT`60VjRM}H=E&qIcU%2@pL+6^v!{@>cF)w`>s;KDn`Fj?%>Dp+t(B_Y|sfTcf zvlmV@on?$lRAI7=$7?M>Oi=hK8b{d5mdU2l#T^7<#d3^1h92*nuW{aZ4rV@+>0SqR zbJ}+FJo}>SL|SnoEm#S^8|fnscu_(3Ft@#Udw#4R{jHq^^B(@26M5lZA`P!K{-2tT z|NXvL9z=wyEJ~H?_@Oa}THWfyNiL;zdr(XlOq*-pepc7KH~(2Fw$g}`=P~5=433zm zdIaQ94*@iJdILstD*)K=-7pg1IAB3w-ZSD>`@HmCRb_g}1eDyL1`6i0pY}A}j(SPa zuWHcbY=O4FxavLto20gZlnY<1t1}q&ijqck=mJvy_fd>suU7%P46xARDh8GT91sSO z0qIzSKmk7ZA!9J;;@>^4M)5KI7WA)|8}-|XFgEE?Has~XE^9PGodN`kM$oNuaq%x> zu3vA!vO|rpT~UVkrhr++uXnsx75wEq8x9vX9)3f1{Wg*y!~Eq$rHg&Ki;e#^w*M=~ zwgPmUM)`eKN=L!Sr>c1l!cLi-)OokN(71oXbk z_>acRS74i-K4GiSH0iMwLv*uL2MA;>G>+@4oo4vQuR#ILjK?wT-GN6xeSro%Bh1ue z4j|*6VaY9TJI6e4JzC}2HC4%3FYF8r`9sDiwlY)nZgPzv{{1+t7}Jn;cmc-ZqV2Bt z-BFI$Xtv!tm2#)PtczdjThoVz4?M^EmlL=gvlAKoz723+(YUoVaTpiPK0Q_pp+$qo z7d_NPf7NE0WnOVbUG^NQAe|Pu*pN9BlA9&m6GsDG6@|SWzmgO8ephLu9|4Pruq}C$ zPJ(Pds|0N!(_O!*FEr$#Pg>^F!?I{swt)tZ*r|Kz|6OT~v0Z34qlTwEabRO(RI*>S z+5Oo=xt6%`2#4*VEJ%8f^KHrEIjF0C7x%jUy1#6o^18(gg)fY_g8PHbiZ`rEG6RfB^kw)&u zvIF1{*lW@hQ~@3^33y!@dtE2jV8IZo(_RVbi`=4%?ic_6I`aQ6qcM*UdmwV{-A!FPYUl>e2au9hk!tPEH^<}aj~y1S&ZVrp1RbGzSf#z*ACO&r z*oe@-kXa~lI%cjAr|APMJ)MKam_g~7>TC8gcw&!2Hg?{re z;`^=`Ws_cG`==%A-i)p|48d-pgLP?}krEZjVqf|2Q+bOAhc!?cmdnwreg_0+ppGzfY z$F!A>miUul1r*OBJTxMJt7*U(tB?2(lrqIQe%;p3HgaL(E!9`{viPU3<2aToP3}WR`d)?6 z*LknAFS7r{EdnjD@trfN1exFDk_ zch7+6{T@|=2iN2kb@~P|JF0#odKm70s(HWm^KSyB5wDq2;)6%opSwkBwlZaT-~&)5 zBnt>>I))fhWA8Yzt3>Z4d9{Xio_*YFs{BO!;RHO^;LyxD{5zT^1=I;##JnwBydo^W zI*~$m7Ml`F%bx^Q9A*!DIUy=tFQRN5X-6mza0LU3JylILh|a%hp%6UXvy{p#|DtvE z$cuZ~f6y?TU8WRi*VFLE1fTVZEJDvNM_1?O=S6RhLvg8ms6MJ3)1a2;?kQ>UA&WUtu?6CoH9_&_L8oGmy+s4$pNfOS9Nn+xp$W{y^??GulrtgcWi%BAd#nzm4^DQKkMP)RgJL5s!lDFk{?p$#otH4@Qx%xr*YXK27Am6Jw!PNVp_e>+& z`k5;`;gyk%-j#jz-ZTFAkC>aW1JkV&t&MH)ugVJ*C~0?|9^0#LW%osTcfwOsFT0-9 z&LKM#7P#knF5`&Z{rXZlYGrYe@v`wLTUM?@*8IBW7^ z`je%>jfhku=ho4}fy?#EV*VzJ9zQ-1CRJ`co615Ne-(d+zh9c!@S{%aIh?m>I&hwnz5kD?R53QDeRM zEyRe)Li+4zM;@WaGqwLcb>SvOGRPy}J~4YmyCl{wtK$A+Mwgo*@_z3s4)7Tqf7Sy3 z>T{fC1M=Gt3S;w0b7Z<*N!!(rq@wP~R}cLci*g|)U!PI}jju+1Y0tD;#($*AlTk1> z8q<5KC!bHfL(!K;>LgS)f?8=s#fQ%(@nw;80%3e5wdcw=;_)G__lbsAE}vpiyuREB z!o~fC;WsJFKfbQm5RCh?ybxQ%(+#~1Z9~nDF0WEb?iyjoRKz`ff+$^ebpHMV*&)`a zn$F1St46T{$oCZwQw8F`3#1;CFY0nAC1Jw(PLgf%U263WANLuZmmv@cIxdtJPnqDD zGB&qn)KAu#K$H#kBhXDfkU^ za}}f>XUZwkA%AeQtqq9aBR<09Cla|s zTKQ_+CDJu9nfBF;=gvg5JfjH*Tjh=s?rMr~IgM1NWUhE85_38X+U$k~^~0X)<#CU> zpB)t)7};I1oF8pQkamR^nvE^FLs#5N;ZG9dOt+lmn_3;p79AA@-YIV6Ls(G`WLUUV zM`}!=(BuN+uCXp?+Nr|m2DiI3LY9_#Gg zJl3X>Ym0d7u7T-o!a8JBCz7~nRLTFXVg`J|=k=^~*;VCi({RFWE0~1LrzaFos;yf0AG4*hP z1I|BBw95TlQ%L2cK!a#P8TF)t=)_|he2`N`Ml(U0MCripaUAwc;fgAWt%UVY-Z-Jb74nk=_?b0jCh{>?}=SJl_oPz3{ zEQjY0%QY>)_>@_RyLuZ9o906*uTl+ro9&gq58*Z+#~)8WF)YJk80A;o~&uU!QM%>WpU#+so12@+ zRSm!M8!!=e5k31zNN+M)qpe)Ef1K}9-#PM15U5uN={h^6&!t1kEZK$5jXmC(-EiOs7Hf z^kaDg=m9IvL$#_k85@ZHk~FFzovYjBjp^=_k_zjpngHkh=9-DIyt|VuW@6!y3J;>i z`WJEzUhHB!a@W|A$K>UC9ri2wQKC9G0ZLQ{f~aNXagf|I{^}if53Ih1KqhlrlnY1S z*JunRqyEWT&I9Z^WNpYG;mH%S+T__k8B%@t`#RBA{moy8UMg_%nj0+Ll65x9I+`$z z8^up{?X`;i!|^m|qL?$FIC>9kndD67oaO;y)0%-Cn#LFy zMK!dP0wv1#c>vAkk(-JDl;kn0Y7d8rviaLdQEK`o<6L4+7x?QU^`Z1Z*EHT6k=@k7 z#hIU08hszY3bEJAb!odm3JTz;H}ep3ZVe1nF>io@sJjBHjsJKvmXiV!{+vAksTy|(=#r=iiX>dMx1jWupO&GM}HE$T*{JqpjlwPGUO z-Y5z~xG&M4ow$kq;!1IA>PH& zY~GF)L4y;S*_MPTzE+G3ngvZu)U*pW5my6)`d%5gp~=m3$pD=}zz0HversB_?K}U< zhchbl`VJ3rN%iq=HddI4C!*KvQ&+`c2bVkJyy-XcSePt~)4m0Wfc58>CJV*9hlgX?TPOIPs|teDd_AB%^L_dZs2IzsE!>_0IFB@Q3RSBYBLdXQNj4J zW8&F0vG$I#gL>1O20E9bZNrz#@6|1DHN8Bwxi7t~VzK)5ujMpLvmI%mfpd(E-vu!2 z+?~>S!yTV9yQ698w5XYU_nm$iMe8Tfz%kZs`sd2Rzv(vrUQ0}%|59&!PDGE)ZA*Pw zYDL!?{>g9}Cl8L~$^#(p|0b;S?`Ss@sDaljuXkM4-i`8s42{AF1oI0xtJ6P4FR`MZ ze|vh@U)a#K+yOs8KrpgIqzQ?*eh-()bMCF=M*M8oNU9q>+}>3N128 z=d7LdxN3Qh$PWHl)Pgo(kK!B0*7$m%%h!8sAw-*f!H-0-GC;dw>f2_Xve~U zoO-@1Rb*WDfyT?Vnw`4CyAC>3ebs}1GQ8Ki1?C=DwvI}LEkVGnjE0>cK)nFBUY`!Y z2%&CEg5b0*+`6pqjt}aMVMWK*np>I`=0-NtC~&vcC8Gk+H0o(b`it zJeTu_r8JxjuKD1S7_YHsA(sRi& zb6;H4x19WyljtwixoCVoZhK#rh+WymtHNE%K^r08v%3pQ-=`yA+UbeDk%N8mM<8PN zjn@CHzan&l;kisR0$EKOh#%jDY4-|#?khFxxII!RA<#2^>WyEtJf>aod~xdQ1F>RX zl%b>o9fRGpCp01J@g3*KyI{iQ~wT5Vy>%#S^ubhj92SN0o53D^me?K>#`jYU*p*r_&& zCi!P~94!ZK9j-EaF@*Amz+_Z#JQGPjSI#2US*1v2wfXR*L$w7!J+!uX$lUIv1xI;; z;3`4!B$M8ChD(CSWtJV@rn2@Csjl#kK$N7iceJtj8GJ6iwyD@+I$O+WFD!i%yN~L$a7*CV! z8T;ZSUV5PeM|dRt_9S~~m7mh7dVxrI8F?jS&5QL$tGly9Qcg^^rOkS&=8fx)!Tu+l zm_%QzuqhUbG?^}EO{@fuu{q_}h+qcot6XWH%`F||r4BW8$AERR#(TmymZ5_y=!I+4 zDLdTRwF*a@(li6CagoROmPGDH;Su@BbMZr|Z~OizFjVa1qZblP=`&liHysT0US+Jn zv(tvbL@wx|E6jNYEGM$J1YzdnJu|-|tUo$%@L#nbX|dYp&%wQ8t?qoEz}BK-tV^xT zqdrF*n{U((cyO!8!FSw7P_`#cCOGPz`rWIB5eg)Y;B_@JJth;Wu%~+S!>TX4zhpDC zbATOED_!w`hw_r)r*4x}jTB|m7+cZ3ezoAL`R9`I^4ia(_U}Xb;t_H|+nOKn zbK7EaBZt4OBn;)`jBO<;#U%>p_snmbW$bD@-k7jSw3W}?AV>m{*QzH4tfP53gZT;F zm)S<2o68)W5i;-%?V(6DYjZb#nx)?{PMRA1l zfpnT*(JA{rrdi#)_SoyII8V&E3rEBk-exbRGW8S(N}VPRFP34WMh3dl=cacSZ$4vR z9InA&lNNeuJB#*6rd9=2aq1YRsUX+nM`K3A-M9~j!iPGhABIE;-8kW2VS6qTUQQ`* z@|LM{H&w&FKe)K+5Ua^vHCR7gb{(o6&7;hBX~9&j=9N&69}6Q14ch-o~K0nGcvqjW(c?mco|<% z&OD(kbySt?7jeVj@)%*{E1dOh+XiR1-sO(V>sCQ}FTDo9_R;Fi)ikeP7*{ZAx9!+y zINmb($7RED4NnLer`WDuHzAZp)Gqu4f>8Tr9Klz8*6cFr$f8O+c)ZuV~YN8PY) zQEG+U%&1@wLSmA_LO=uoF^I7IWXJu@Z9IFd~Yac6;<*-PSvWgp_*j)A=AO);@8MaGUt z?YVhz;c*=nWJIL6GQ!?fBGXvjUEZ17E024&CNIo`f$3PK%Jl=sZ>iS8ztZf-7lM^N zGN}GdN_?0Cdpwt*(HTwRmhDNC5dkMb!DIQ4t|t3Nv&jvhIJ>QilAAmO$^)HfidJ6F zP7Z-cZdgZXs;A|pM{Is!v!dXI@d=E>Y*sKvTthHFel%%xpnGY1%L#tB)ZS>y%WYtz zY{v%|GFPIj=EaoFJ~4I#z;szCSAcqa0|(nfJpyaIh)~)qOeBzWGegg-WEki?vM$;~ zZ=7ETc-XeGy}*&HaYbkniMmq<9ZW^jg12Wj5CmG6)GqqV(1r(E>J9>Jx~_E57hpJ- zYcI<;t_WQi{x-U0&-vJhux}PxrbPr_!iy=(?*>TZgIc z_XkvbMo3pTx(eOeK~#0SuNOW37F`?}`|SZ0_F(E+RLPh{S1-Nl&LAf9L;9j**ST&q z2zY$y5YZ>}lhi9pyZAJ9KR3SdFZs?ZmT%rsVd~_9oIw|D;Z}{(g~=agf?&K*@Aj9> zW&#xU;y;1U{|H3?`tyI1-#0*+&+UQA%0ms1RTtO^=%O|mPfu7VU5K9;<)?Z~*kMKH zQ>DqaVY003rP_Rrn$^X4)K%3?XtzjBf}#KtQ?N0!)Oq+kmK3cayRw)T0kXGye|35t z0g-)M(&%MPu8(4W-!EhI7X|?&s9$`c(pNx z(dp{7a=0II&&gmUS=(`}9AWtJZ@Ryj{uJQ*FZ-8&Gt&A0h+ho{?w?CFp6tk+CbmHI zSydqQ;Eu_`G?>4ZR!4%Gqe3e&OC+tg{A_l__wT=j`|;^z3f^B10}mBAy;*ArJus+ zo)&kCFw;$XjuQ8Kp1WzJ{6^nlTErF1^`IbRdTQbZ27QKbAR+Ja8-0b7pT5lqobzAf zul1??6rrqg)eROi6s7%Werzjk`mdDx{J%=ME7A3e=*f0E-=7S_HvmCg0f-^(aiI=F<*cgL0y;UG!oA`>$2otz zc=mRm5snC2B)uwag3H*u+@#ZtP=PRTdK9AxRfa_kR%@wy3P>Jm9#`?Z zv%n8K47k5v4-)nJnPz~_%ER;;uix(o`s+=nfRXy&?Q(yOr~cJ5;6#(BKEWNsH(hQc z@tQSrrPhAjvnbXdP|NKQOdlBSzh-84KEgl^e!T9^8d^3iRGv{jt2rJs%p#m!cs0B5aEYVT=GCbDfQNd>2E2vb2+uH{Nt z|27+HB(HgY6-L!l6$KCT90e4V7;65S8&KjcY92idEW5P*e?8wo^C6gur#Nd+*~rx} z$~4Z=+aqJPNDzN~t|(*pa!J_PABD4-Q>{ikHo0w|79*X1yMU;Fahj>Mu_39OQKsbe zs8B2fxkFsf#<{ZwrsN|NZwEZFu6#whkaS<3v_vA~=T`fXVn@geD>C#e;yNzX?mDs{bLU)+C>OEy4z)sf%wAmuO$@n+vOWB82nER z?fC%;{F&H-J3t*Hrqu&6*G6sX-~_xt zqjCEny}vIZ+hOOlrSHFg%n)FGMdSbVhm($ z79oSl^bic)xD-m)%%Jo7@5rFL<-mr$kprL&SvxL>UN>liYy5<^L2L`-wk-RAae#0X z)s1bj1IXCn0at*K211wacXog8)at(P|IhyCUoRh^WI+$;?PF`&=+)YffTyX=b?AS^ zMOtc;p&K8l);l){m1I~*0Y>iZ^&R!7=cuq*>G~1o=hx_~%P>#iLHz42;$Qo;mO>@3%}oh&t6%M{?8c{VS@WCRnus;vo3;Dy$*1^#5$dk|H$pb7 z{NE#8&wq$?wPSAv%6o9?o_<`+vyY0`ZBh!Ikn0LhJxm@YqDkJ0ygqAB+l8O48w{gw z+r;e2SEP6o7&2vcX1Wn127D)(| zjq*(^F%Fl1O}hvhZR^HbG4t8W_pt9BeK|Lv5pD#HMK;nd9FA<8kk2eCA1wwl_w12f z_e37G`((je3ev_uE=eM8jAS$_23S0P@Xatk9VfZkfg$E zgaM}KC_x${h-CA}T&Im<%hS_Sg-b<{+L$-4m-^sh27YJ(q6BP1gSt-i?#MuvH>h4I z2mSyOLcLBr=+6SfFLv*Wh4in141&%)0@Y(k$mkKyVJ3Xiu0VQi zsN#lF!Yb|~aez2nRZgoe&kmZZf%4Vx@~-@F)f!4;b6{LCg&RF5e8E|?`EKEDGpRwh z@uZ)!9k;u5qc}(Gm2N}jgmKs9##rb0E)>gqb|zZq5%|hB2j*mLp^KmRYs{L*K4I$q zWH@`f_v`po3H}vDvxuXbvKu$%IXDbxefUz1P!McL+3&Xco4vHhjrYU*VYH}}3z(bz z0Cr1_$ZqY0di(8t!}ZW zUZ^Yx+@Vs&EpCqw$>WZtx#Yly=5K8j27TXp!9tf?>}h?Nhd!2$mZ>jDm;i8JQzjmW zPQ;9L8|J%;Y3uD%iSJX0PK~{O&J`LgXH#AN<7X0F9prfX|KDh)0Q#T*-NODq^XLCG z2V@{*e(kS}xcnt_au&;v{*g??>`LI_c(E_E^T2~fjdF_KG{%aabE>wiw2=NW@&p)W z(N4S5@1&8^c9CHEXWaw&b`uIt)ySYBsTLS28>|vr+`axGmOG?m@Ps70ky`W7`ePD4MN@2rpAmz4~)(cmR4ShyFeFBB;_@^n&`+Rgj~Aq zF#*TmW)GEvo^GEBxTO<2caY`vGRVdz>FDK(n>|lfu#hsb*H?s`cptm)_C&xI6D3C)rnAC7(Iy7C?~q4IQ7^ z4OEuSGo|{BSyEP-*iO|GN7TFYy3+^bUQcPADR*^I(>;9b@V$UcHsPLn3wP;rXE$+Y zm(d`=34?Hpqpb2q=lOWzZmO!1XEp+QJBm&yCl8&~iYpOsV;0nUaj=!aINzt;hnJ!e zh&@QX4dHgc6S~5d3(zI5W)Xi7r(+}~&$g5Onuz>Am9uo~PJ|z&zzEJn%5ElBl_pI# z_c=~EVCw#IJ!;9qtND!l$?Mr#kIqLTJ~hj*CaSvR?XN0yJ8O1@A6xy7Eu7BWL9$RF z1~s9>lg1*IjY&ZKV~ko8SN$V6%NDO)?UCX`BI@{wa2FjfcYZ&tDQBjPN#o(|PKY2*9SqxS8jbJ9*6vAiLL zn}`lT(4aXg9rQG>4`cKClIePXbh$qhT+R8Gd(5s8t_U6KdmyC2lb@v^9mSCrOZ6wHcZ ztl1;wvMZ0j(T8P6+r3_SAF6OUtovb=j-qg^8tl?RV3!f=Jd9$aYLgHUD#xIYfF(6c zTo%H^cATn)x-P46f9Ld3)y~9@(vY^2R`?_Btjl#|hKq%X1wP%l65D35Q(@&PIswX3 zvh(#*ombz#Gma z#Qd1&iV8sRznnj=QQc-Tk#y0@M!TKk+WS79L(LkXMWuNBHTLj4nz;a4l3${tH?dTX z|LQtDf5u(dB~>fLCLV^0oK=uvY-K!qmSHuD4{6&3+wUWT;^-Hr3sT#fo>TLh5bqbt zOs8{Z?ptUGW%eK<`!0lz#FO-}dWB3({MhnB5z?6k(6L}tIi6yyUy1dbXfUl9$T#Zl z*3*1>S*`5em!|B&`^4LCeY+f{P)uYDtQ$l`m=IMcu5X3ETdrD*nVFYM`+DK|k^K*s z^^_03xO5?1x+1}ZC+#i6z5t3<6IWLwgkVNQ1jCGQsz)h$+T*(#+Fn8OZksD_79}~v zmrQtL?=!zRa_mK%Zxq`AT^c!Y58yh8V6gNc@*T1KsB676PNG@j>n{6@Is2#Lru4I~ zUw2QrR(k)Kak)9ez*gWRi#pfsql8lKQ-%}{!+s2oXtGeo>}3v1NeG7;CR}_Uo%h7` z@c|yb^)%NVRiwy0>UnE&>Wa2k8k5P~#y09wNtTV;3#s@EF;8#ie!oy4BBv`d9jf|N5hp_^XDJkHPB0UF0w|XSHQTC zGtEh7miD>sTqRJK=_*W3g#NN@thl4Pd!LJ$eUDd&i(19k$Eo^GcUa6XH;P%s-`*Ag z+fVVaC?c>4w&xSW*O-yUf=ye3V+X?1)<$AATpt=cX>Q)STdEnrSIV2ro zW!~B35LI()gW~E-kJJ?{m?xM*#Bnx|5Kqf$3g3>_08L^d8FS!1wb^9?{b8&n@m?^{ z>Vb`pDBXTAe0s^%MvsKpJI!&?=?ON@(PT? z*CtcnJR)s|k6qiR!M-rcyPN3ZC_s`5M7e*Y7B{DgQ!HO3>Y6!lO)XwVgd%IoA~GUR zM;NetTJ1k#l_=#Kj$x;_s&Z0}m6Eh-Wty?FWVaNOSBPZk5?iC^=b;}C&bO*1b6fmW@%}9;-h`nnLnSt@~BntXE?(?N`F)< z^bE|WaqMu7CJB1hfy)}3Me6?!8q)IAPgrVXYoOiX(;-aK)g|&@dPLd}L^h_v3wM*& zAV;Xl8>tkX;2ME^2j~eJB$Mj-jaQ~QX^OU8^aKf>FA)dUtpHG8n2HK>?b=U8H|slW7tnprqfZQq2QIrN_0^rINS$mM)q8W`?zIC` zxifX%3e7-cidXil@_8?c9wIO-%L0ukZx*iR*ZSPO8*L&nd*JNGdvyZPwJ0iVn0A8+yjiI$~*)-e8Vpfg|!6v_Onp7ekJ8x(@Gh4EOnlFM&& z%jz6L<~1k1V5$}qyv@Q>W^gNzJpw@tV0ZK+tbyD90hMXPN=mn1WD`|fI&Z7!p=51J!Hh&A$Qv876Tc9F%a{le--r>X!t1t=MTojol=hNLaGdhX z(2&CUP%+=Kbz4u(Ct&c-|GUog!GF`6{-^H`ka=Lw381+mCXsQCo;tzjTgIdeib5x? zX6NBPq3-Yp!&_ZVz=359lwFowt%+}dw#|qnP342OUEA8V7J#my@cZ)Ub*lW`n^1pM znT4nPt7TY`qA)$yS(XedsXj)Sb+^Tq!Ee zy%><5%Rx2%4?3vEBS1ACZW*O!mpu^hQ>GJ2bOU%)Fbv>Ptb2Hr1gOmh3;_)2JQ`p? z3IGEt#b~5Qy79FCjG|g#_u#BEdvF#b3cy)yroX)%-aVU<5+tKMK|g~4I9b4+wnN<* zz{v*w)!WxRFU&k_@4E8aIWvBHdo5q6;t%Klx?qX_8sUF`qW)@s=xL8&!3-i2b)o5 z^#5%g4hw*Czi@jFV3!9YFKG2S_WabrQiI+PnGb!r{JCjWsS5Jw2qeOa{(Y<%@=|pl zO^m|U^e2P=Mg)1HMfFsfQjMOy!YPRq+Ed!9nS~i*bxPw}V@&k3&V7gSuRb{VfN4F@s^_d;p`z2pxAywKGWkwhI0C~LmT^UA3Mvz40(6j zxDNL@tqI=s0?@?{B~uOo{iu%U8a&3OB-uF1{PGCZPpcXyRJd&NUV{65^7$hu{})Es z5^6lfpUw_yK7py6^sn^qU?b?#ujs}0Z=^{r`WTkPtqgtIdb7GQAr?L(lEuNy7ompw zS?JTp$6rkui$X!i(Lv9sWl+Y?t$E22K3ACs%^Tt#_4G9@T_B7fatkr>J*s2cL(z#; zRv<+|e7ZaXW?rs{;3%veOD%EFKIllKw16VTpz~e2W5wB+Z(=uf>2r_NZ)H6wT7G5R zwV76u6Nb_xAvjD>vQ!HZQ1L$ss&`!3dCv+Pv$qn9F4(^-jmU5o)2O&8Ze=mN|Eaci zp%0!*7Rrd?rCh!CT@G^G9?L%>Uf%5oc!0PHG=%Z23GQ$^m=I&7V^G38JS9<0jC1u#)A}8w!nz z!hjWN=jCmPI{u-F_h*T9#=xzaO^EfO*;wgH6VbHAunAV?jmNuDv}gJ7MB~INsh2d} z0yEC1f>qTQy(VwId&=@zQH}S|%;JS9W{86>3~Ea6NtGmb1=A$p6e;3k?4fRDoeyf6 zbA(cC^|8$JHLp&wC_Kxzdnl>?{o7-U9|ywyjce3)Zcr0;Na$c?PVxe=JykKTasNJy zgMw1K>0^%7cE6kDFRP#_QVBOstcp(VuS+sw=9^JN!GPPRT@Rg$#!AIc>{{LJJO6^F zjTa2lu$^+f?a{iUgOn}qmWZI3ESEm z@uVxIb`jsyt#_1(kHVW4gC~#k9x-?NdU}yVWjb`Lbpzm=(TPh;Kv>?`2uQ2JMZZ&; z8>u@Rbz~|3W%gusa-=Ubd?wt_Dq+JUBL0ojpxT*86|xuO?Ru-R510-|0XkFwdns z;L6KC(`4_MGiNkvA1s@g&2gFI={Mi=f{RbaS^7+2ORnidpoq5_Ow-LUx8v&aEwm~g zaN+j)fHCj5+~TNK^EDve_GgBH2%`PgxkvG@?{9t+tCNKgY%Pi?Il#l4kIL89kl=H% zN}kK+eKs|g*Fb16Vlr}cisM52-39I!_r4^*;I8a&9^=3{u!W8tMX6=lKS!PEakZCQ zGM`gAEk7y;f#VbfwI;&^rgB>pD#eYY`>U)61EM}$z#OBLFR|wh(liHZ*vHX_2a$Pt zWZH4<{NZ^5%~P$kxIOUJyNGbTlFzRQla7Y_@RBm2BvlF2&2s9@j?e;7!(U@IanWI? z9-E#pi5wvIXqI&7-;PNl?f-JS;<2Wc&|N;oS~By&sN4(rm?PBCv1U3)cj>~VHv3$Y z8{UVV-V0t^NJCvJFpc@AkojMQ(f?V0L#+dfAZVD0lc2f-iXde7%82pz(w}(HyGp-P zP8{29La$A@S{VFg&+!_#^m#(RZ$H9X?kVGmWbNDoY(vbS4B2yH^IThA<7u?i#3hh| zJjcc+~z{EnW z91Zt_#mXGZ%T$k)Q$YH0y#sZxoI13lg|u8CE~XvRCu#(&=bOQk=tYI5FpJ&bnCWKt z!Qxw3t3qR!aE1ev?@_rQax0dRt-;tc4)*}KBb_^&^nmX)xiF+)u3YAbm9FEAP^g)3 zLwz#;kwXT@<$s2UPty)ktw^wgKWNTTCcQo_bRh~hFW>$~R|vh=UaquCP2^jvji~}R zj79njC;N)5UWNGl*jw4?R-C_A z7kz~DN{9V|l1}s3o_irHHQ9t}3-OcHIl5qcMqyx6X{umpRH!IbMrt|PHla}WnqFti zo%m~+X}NYePb*j1T2@}F@>6ocYV?Jx$xt226fIVojEhiK$TPD)vvK_OD-GU0jI48D zr1qq;TTY)Kb`Bg10A{swoF$N-idO+b)TsJ%nRaD&iz>v{l&}4(}7W z8q9cQTQXGgjOqijPXI+{!>wN#osL8h3AF3qn?y4sLI9bShJx(Fmd`z)6SKwYrq#cCMNa4P`P~U9uhS zuGqTXqS&H#b)%5#PGSqOWFlkJh_%oi_2sr^g8WBiAD3rO%KVlD->fdaTe^3bN#v7o zZFZIHW4_h257Ny~MbeK8_IsxMQS4SU|jp1yDmpV1KimG{Rd5k+v5sS^BA^ilX z`<|#__2S{dGbS3{=x$vYZf2#aAW){UGSl7Mz}yi zaV6G3Rsgss1ci}*Uh7_8knl3=D=bYm8!MH|*a|_|M9WCCo%`eQEA>D8SL2Kvm9J71 z#?dX9U_xR5ZGYNvWs!-nA#clz1?yp6oGxw6zUSVEoQk%TU+r#GKO3pu3%jFxcO0dg zN&iMyrQ|H)DH;uQ(OwsCnHEo6)Jly-&QOP_g?*y-Mn$2(u|pG+T1CB2{GS@NNg@l= zh?@-->I)=?;;~5#+z|AXcBI)Y=us}91x$?Do8;=pIL)P~*=ib2D9=waFlv@ut z?R>E7&~7S?8H`9E^pvX9TvFK!?ndzJ$#sRUjh$%pVAeQa5jiU3@ZIUBVedDqiozZx z9*W`mSsj1i9_=WiTcH-&+LC%C?sj=HlZ4kp7yLn)`S0e{=pn`x*tG$pN}`28Q}LYs zyxZ|n?W~c4-SAkpq)}HRU4FiIOKz~IO?<3kz7x1&TqE=(^$aPe)xyfW*QYTDnb=e` z&vj=MrRSZ!&nXz@Abs)Y{!8N!hhb{oxP^058W;8moZuQokWD-0kd)H-@!50>&Nk9Q zl5}_oe#y2ymY;ZCK=BF)2C6PpKqOHp>OR$vlof4iGNh50ZQ@Xj>PuN(okq`G6Yem# zNVf#;YJE55^k;$}<}EJE$Gqj5S;1MSIFmH$V4sd=*6)QvB#N>f4Jy_MFLU}J?wgQ1ws{_Zrp&AAq~j2yl$`jg(dsU z5v_A-jz#vgEr_4H{ZyH>z6C6MUPExzFY0eZxlg=sH(E(+hTp4UU5%-xIbt@9D z)e~PFoS+pw>e8<8W%BG=LT{1IOU+{L(+3FIjQ#{e!y3kF%XW_LzmM zUHg%_c16ba4F{i`=*l-+eOSR`_S4B)2RAGc46{jHoE^_mpSZtc+pPI*f2<$+3;bx} zer?UqS7CO|xQkM;_I
or4;%cE z0S@B$KFI&0_6B%U(N(!~^Jf%Y`D6?%{i)-~72it!9Gp%vPHopn8l@10DkkT0ZtyG@k{y zS05Ng$F-f&Lv01HBuxBa30%WC`@sDlzQB6!<0{*^_6AQ^J^^aLaFYCo%ku2NGnZFa zCeElPxn7#nN=wXJkUgOgx$#p{3Dia>P-DkCu+8#r0_EI9gdF zH*rQQYj|62w6aEO%#OAlN8660U769+akO+C?ODUR`lD^f(YE7g+YvbSG&;}&92_0( zS&#OtM~A^-b>pBL2G<78cQobIpPcr``ai>t{(p1wR$oRRVctET7j)EJUU%-|AN8Ia xFKp?~z2`Q6*=^N5f_X`Ax0x`8>zp~xJjwHY$j)T3#a9YjrtDn$H>dvpO#snn=_mjI From 9415053712bf395ef1705b42a857a2dda28cf4e5 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Fri, 14 Mar 2025 17:22:52 +0000 Subject: [PATCH 41/59] update fig Signed-off-by: Can-Zhao --- generation/maisi/figures/maisi_infer.png | Bin 0 -> 130987 bytes generation/maisi/figures/maisi_train.png | Bin 0 -> 169215 bytes 2 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 generation/maisi/figures/maisi_infer.png create mode 100644 generation/maisi/figures/maisi_train.png diff --git a/generation/maisi/figures/maisi_infer.png b/generation/maisi/figures/maisi_infer.png new file mode 100644 index 0000000000000000000000000000000000000000..4bd18ea1883abf1a1d26ddec5e0f73f3f537bea8 GIT binary patch literal 130987 zcmeFZgTzq}8}_d=xWOYFkW>z8_F!?qX{ zRe!myjuU{CDO~F^0kmFqZ8ON_|~pXgyn?9 zcK7wEHJs^;G)pW9jKtyM=B8~^h5v$=%=&%`-SzrwlgYvf&O#a4#M7%)$!GY5GGk%; zpjkX9&s9y-CBDeWz|cdFU&FizHivgwP!Z1}-`X20BA9SkPTG1NL97FJEQA z{p%QZ;Ex+WD2Yf&K>sQk0ZmM79L#JTkFwxLprmHZmDL^9Wu$qHY^|9LjBO1~m|U&x z{^$b3@5&23v^H@xAa}L4vT@*b6`=UX9lX%vKQ1#*Z*kxy{n3Y2~fn=8ak$<;J>HlpPm1+@t+;}ng4k8|6s+ReE!E( zD5C|D_?iFjqzNMNjGmmrzzD-gh<;FZeQ}uf(o@M4uLoT4I}F#wrS1hBr>7NM zP=u{fpWQ+b`1D05r*WrK!+lt1SaWPvZcTTX*9p&BPzt+w3@2UO&JuIu_5Sm6z*YYb zS6h|0Z!ln9{Ey9-Tb$fGRf`1|1(qD<^75->>;M~7ua)dSP|364TNp}guzEzV=8YGPOr{=c%f6xp5ssjFi>iY~4 zm}jc{pYH_&%ko{=VuT*o6czj-T-^=}UKr-@H$$NpA^w%mq(*`NlbHSwn6Cey$X_Ms z|ARd74N$1<@7c?~g)=2G)PjDLO1`|Le!1OZPFA&0qiX}c9> zT7Fm~Pm-|qqfHf_WpAC%?ii&{GPe`OlRym~>Hc4I?rQ}6daFveqE3RgE^z+&fmABt zh?mQL@eLvGE{*%+U2RP`9y6(V=lK)5K-1W&0UVuyA3X5!Y(v=nfjdMoS5ksbDX+p3 zV5)N6x+N|{=qYO|3(8b9ks2Fr)uI%b+(y+Z?A|Heohnwty_bxo#f-og_{RHOp51*= zx%Ey6nag%Le#;mf-)#Us_qh!L8V@s$%y)EO>`hRk5>LB09*2k>Qb5AT!*`fNg!D*B zllBHp$ns_8G;T)Bv7f$@+#K3QNakJZ)Z$xefBybOHbrbpQWe8s zl)L})G4ZwFC%sx5h7SFsCJyrf+nBzcb)U`qRRrtVbM&baoiyDxPl;BK>s%@y#;8rR zv1Uj(-dkWhB;7KrlJZL`Zv@{^Dw}`M6KlZ!Wbsi5Q`)?^-6?kP<@3n=6$J_|Z6XLQ z?bNyGeIK^-^rOS;Z)VdHJI_ve%U&L1x1mO9oYiXk;)!@~-^v~cQIPQM&qemlVw!Ih z`1qGxVNbNBP;_8e|7HUX(O%>?jfa;;o%Vq7`)4s1Ig~jGHFHMIz2CYB|rDV z&ewQjPeBS)YbfyW@kQOL-3S>;t9SY&XUdnpT@ue#6>DsKx!ml>F4D}VXVj~XUGMcN zpHMAR{V|}%B!0Bum=On78;cE9DsU?vp%k#uKFrv{8_IgDN_cAZ`}DvXU8d9eDJp## z&$7Kw`ffR`dAvn(Y9O{WUDJj1%50CN#M7lWO9Y1AzZvq#x2ul!-%D2-D#ER*jQd4~n!qk;g^o#z zvDDUt>)hrd2q>AzJIoTTRN)C@l}5eBKEn@lo7?e<*X7!6pPozRggtH!^Bhc><&XJ! z3W0I-mpV~pn^`?$lELMCQYBe?)%g?Ks43mNeIg^HS)f{K_n+<725p z-dvanwY-OhOOL(BHD9JQ&mkx5OATvM?g?621QiG8B}vr<&S~??S{ftvi@k97Hxt$? ze98s#ANB<@Txef}mflR4>dJYzoMa0$-zfS8AQsrIeH>_QWQ(KKP)^A*wAj6mxdX|c zDN>out$@v*+a7B?k60yBIBTnF_~qF0hBF*?h6`Q8xagdc*_F#YHTKVS@ET1Q$3}hI zNBgm9zIk~+nX@(0@UO_~@b$TA%~%|Y)@KXA#pKDb_7sl~4<+C5S*N}s*HqY4>{}&-B8B4QN^j9=t0%}@ zlL;To$UBMXaldY#zM7-GnfTg3muFQ*Es=0srmQ>E{op(4!`Y^A@02^DIC`CVTI0YQ z>A5?rbHh1J>*-6L8n=rt=PEV_5;4?qrfnh#wgsNuRUMtY-g@^D?sXFgIU;MjNnzA;}yjLhqj#cQTD zyn^i+MdO}}w&jFV>uzj2=hG)|?hmBLxwAB`FCabJ~o6PM^V;*uyJzH%kDZi~O$624TTmd%~E))b>v2oOMd zN)!OK-PaM}D6TJ)2no60j_Pgacl$U>ZG95lk&}$Zu`Z+ZCKRI#j*1t-eedt*zYD3} ziIrcf-mCV`#w|@5;3z#|-2#tlP#q$?eA!PntLDi}hZejS9jnitIB+%qzc1$ z+tPh5?+ROlM7f~A9;o)2w(_Kf-{=8w9)56mwJamnf+RsCcw?W2&?Bh+-R=4zsjGJ# z=@H>F^GW1RQpelq=90|g@<-b_-f0fsKnL@x!dLATA2Vh*90tHWrcQP3h#V45{l)fD zv2$gOll9t?(2>U7Q}Bt|Z6e6TYknaM5?*-0rL#NHgKEAOEwtsRKJo^6Skp(Fr^Ga- zoa-(p^fmRvA}+1+SDU34v8V^KegT~(Aj{Na1E(6YN)3xu8k>v$f%^PiGQ*iG^yygZ zoQJ1JXthYW%x`%hsppxS{V__zi#&VnG0zfuxv?DAvN%=0H0N54&^c5ql_26NHT$oU zzDZ&Tm3n)w-VcF-IfzsVeDo`KcxtZZrl&RKAK%;GUt7C9U4dFt8-a@9@k3^lAuK&3 zsn?BG^M?+}&35tj=GTLA_k-2B*ES{^{bE8k&mSHORx*~2kJ!4dYELd@4!DBvmz;Zc zq#K^&%^k*lxX!rQ@}8JhuvpltO`+5b9<{-w~R1K{4Q0-BEkq z1pOy68qL{C7d{-uZ84fiP>#e#en8Wn6$z)s;p-;H3s{|2uh1uLZn=Q6G0NWYCxKTY zQ!}8^fDey6miOJ^xG_?5%G6(OCJRec*fd4l>3;zzs3=fm?^E+y(%sC5?o=FielVYr zX#&vr(OgRm37I%#{hxns2n|B!&!?pJIwGnaHSF&-LYn}GS4UH7kC_%2H8r1t(MYpA zi%v|6aVH?frrj2HYY*+B_*T;`bUMvmVxbnzyU8mihHrla>eS({S2@j4Cq+#sIJaxy zZ({wZweSk87jQg%A3@3$J*>kc!WHY_)ALV`G zZKM~H_FOtW1dKQV1^T->ap3wVDAx63{2 z)J+SD-1+!hL2RmX$p}UdtrxaQCiB&VFyZkSHONE4ZB4ruJco`S!K4EtKo3_!u+}YnUiKwu%Tm$VP=Y~U3@dD?^ipD;;~&)uFhT+ z)ox1UkqOA?GARKRgqmBn)D&XYJu}Nu{44)TtYEJG=8>#(`{+>M$!VV(Lisov>i@}7 zM9S+nm6tK!VF&T(j!T!yXm6=iuVCh`mMB+7l%Rs)%7O}IxP=B%qEZqnbkwLits!~5!nES7`Q;=C{nq|xE}bg><=g+8;os&BX!0?zb752sZr z`>0Wru{hcIPSp>RVBP-R%UNoBy@X?7p1v{T>Ilk-Ops#{LFQN3+}5_lDPSCmI(}&-AIOo>XksZBAxnp<+YK%%{}a{ z=y=A#1?OAIk_flsnKJEa5AQpBA2kr5Fx0 z!~xg9jp&XopF(HP)z*E2k7t2qHWDf!>*wTBomNiei@q|3svCbAV{|+%kjO=#EIyw` z*GBCAN_54 zpS%dx9oQO6@Fry89xR)Ej6g=_nV^y5DlBf)(P{J4=wfu1nHmtk5*l}oSlH(%!t8Z# zwmbGaOQm$ajk-LQoVL>FHat0A^2jV=f_Q|6*}E`JLUQ_}$7xW7u<(T2%$WT>A?E~v z@TC{hNv!=$&EA&XJ3?f0txgBH_R=(75{Z3cXyvpAq+WFD8NvnX`gr98T|#x21}ta4 z;C#;2n1+@I2EHRVlLxC&xVLv6s5R?;)_~g&4-Wod^#vJ#i+)MvfZJKy8wD4U&qGbo zo-X8*shu5FxXF9dx3U5v6@1?S1MJSo&@Tk_QT?#u;4uGc9t(h$Q1*QF z`EHt2QyqS!NFP<`-HRU0H8QdIht}AqR4!1R)m+i~(NuQS)Xhc|oIAl%Xk)3sNO4PbeK%< z%xcrYJc9P2H)`&<@mFWIS63CkhvGuMwAim|3(v@mWVG0S;61b4Kh_I<9*;;GD79Q2 zd1UONn4H&ba@BC28!#Fyu%9=NIYOqIw4?BQLl>Qr$|bZ4Ppi$OZCAo=CK#E!fkhP- zuC?kG_p_trA0nqM zFmc(gSuu~O0wN+J{@AyAk*)D9U?#ns4_I3FEkQWcZyKqWhK*S+oj-f5vuAX0k8N*n z!n6l$MFM6ayvl`Ar+@niE7YiurMpdDLJk_x%3{ZP5GPnnp{nZrYib{P(;970svErz zcnN1s-!tkpeMFpW-t1mS(n1?f6<9KJI}G9Bvjj+i#t1nq3yjg0*{#pn2lPf`pi2J% zj8~qKEgl(aX`f4vQ_2)tIu>8{kP`IB{*elnSIoh5Bu0%MC*0|4Sn~|Tha~r(6Fnu- z+Ke&1!)yRMF_I^6fqb$8TgOzJ&%NF}H1A5>90#5D7yT_2fi!qeH&Gk17OL~xS2ac+ zGr6@Bb=QmNHb(!@Z)>I&6NB$kBysA+R*>~FgER%&x^*N2* z`<8jK8?xB>GT-6fK-re5M1!JgdB=O_HRK-7_`+7G#rz6qnaqFDR6pYv>>y=vuy+XU z9CMzwXXD|5$Ja|1JcxGTcz5Bjcx$egNv^{+EuWfpQ!nN++Lum#@114* zi-gPITSKhS>o%zyvU>?)gp>dnR%ZXO_+r?-AfO67Mt(YHy&w%@V&4;AJLi6-ya7wwOd9E^LB zTv(fsgf1U+Bh^t#s%BmA`k9 zKWH;Sz!=Dr={w!irhJ#u6y81Q5HZi#QAU!hCEO;{PJ+7cn_17keRhGn$4mEBU?IUA z8QWWXspjjNP=CPDBcUa*BfK5= zNhS&wwu^Dvb!`bvOWi&ifhkoYQR)F6Vd%U+CLdVRYER6{Wa{Y|OJs=^)8I|aTqHN* zZOFrB(j$tvTULv#+)p(KuI%jHRQ~1na*&zlG117P5{HC_9Dn|KM_xm_77?IZBfS{K zLuvl&BV@}_l)%g@sGd=^1Yd{SM`5uPtOl@DOoLlHQF`&HacXoUZE14Wk&Bh`T`XAW znf2Yy=zdVk`!4p0NH-$pCl5Tk)~WpJnIe#g=ykqqGX2mtW1`5^a<|6@_5**ryGcd- z_4-8GV5h16bW?|;A|JDh9!BL#pqRx}USrqDCVsLtUk%QOqw#JjEOb_v^whhz z#w*J_;$KSS{TJ*E`@Vv5aGPajY}G-e#V1_Dj@oU8jq=B*cgM39^$Ya^n-F3a=R~Xu z2gyO1$mY2=8F9@fV}hgEI{)PQyT!7d)4CIqlyvJyZHi{wlMAa8*XbwJs+;B^O`+*+ zC2fs3kXk0wZEzL+6hn$KpWEOnaIlh&2iM4v{@M;2D@~UF@DWYo^WpTGYAUVrBw<I%KFU%f>yZ^vbfzZRP! zn~kJDP%Y2jkFy+;^A8G~Gr=tXI1o1YJ;LR@i>^!Jb4?e{;&-Fj$5MFCbgqQ-RuBA{ zHwp;jKTvIxrPfD;P_vs|mU~4vc~FmqMDY)dTs8K`w{$#Pd+CD`+74Gdz9U?+!9xQb zQv-lblOu5dv|apLLTan(hA`%$MC>g2?%oClF#1(p?3Ssr-u3Y^O<8X)I8L=J<7GjJ z8fHFB&6$ui|$V~S!caKb^H$Gky|Cmtkn{YaLv_)Kjlrl zYH5Jukv?R)FCGl>0c7}ky&4O5u{m0F8PAdER!q9VPHZeX=V7`Y=3zA=_*8S^b&H}+$4L){Q|p}C{@Tbvp<7)A~w9I>mNG4VEpql=`34& z2#U$k3b{x7LdlhpwX6x4$oo5ifW^14&I8kuo_II7$?Cz}kKIT;7h@*9q)gZR!P^J- zH4<>i>`s2E$5W5uns+xy7xdf+|DBpiAybCtVg#X?ZAQI1zb2m?G>OFw;St?BB0!@wEgKs*B_?pF_&qwEeynBthB*k1igzGU56dpuKCl%rEhh4L)N;|HEQm-c!`}#LWh~i*tbUR#X1fhuY}t9*u4kK*&Rpa&Ighh^3~;@Tn5BV)V=7e+n?9? z_NVHbE6JWZ=_ydYkpxtCCViOW{azX}fXggLyz&hj&1$7h#878zAWcfNw4!M((S~1+ ztTwWVLl8H-JSJZ@`o>ozRsf$I(u7eQt=|Un{sxA3jyBH)-YI)e+eB0htpdf}Zj3v-&ANIh<@Ps@ zh5N4*2vE36DIoxChaEQK>mNY^Y$zu+4U)TBB7}rQZ%L z0Vs+smF2LU$#C6oXH2OdwWN}*x>AWu&D>}@OZMmdX@lrpKk1CJW8AMUJOp0q!QefHAz5-QVd)GYEDU!|Ozzi64gu+mCa5*?m{ zO4Ft7zoaRK)7m0@!eZsja@}g;;ZdvVvw3J#Kf#8}$vow=>a?;D?IqD-ui*l^+qf=M zonUbM^rgx@XIH-~LLnI*ug`=S>p->2d!*l-`7T6m9b)6+EVuA43<>q;?6${b>t#h6 zD)tx;$+g83Bb`3f<%WR~#jB5nYO0q1X|2bQ(qYjG9&Qe!CZB(txf? zDy?Jw#=>@_f{}jNLGb#P&sam{hWY(;8U;#@T9!`C2+_oCztK?fyrb1nZm~gj&}KF( zeS*?)g8`!dJh@0PY8Ek1)kLa>16A?9O5Dra{pN6}a4Rl2TkrWSHJeREXnEkj`)u|( z$AFHzwf5_UlBsMSN)qenFiEp$g^}gE;oZ4{{(N?c?hxsCK@nF7o#l?ye6qtr|uv4>Q5+e11~m*DOCb$*9kSIG#sTXVf)Wi5VFyO^yoX($w zbxbyThE20~+8!LbfxZm}MbRtM=Q|lKKFyBk2R|a2#C3atF zf$%)ik1D1IG&)LgjGA3QB4wrr4-I9tBm8=7a?Tw)C67eLCoUVjvUvxg!!5%R z2eZ%#^wzd)7@VR2AMN|Bt%Mq<8Qi$EMGR-r6rq3O16UG-krFC4XnGgp47UF$4y_Wy z#uY1Pp)O2|6pY|7bj6f$Rm3|J^PemfmaxKnV zO|R|aqrY#0HRRzD(OO2%YW@8D`d5U$R++l!Vqp!R7loQ$10t84Z5hr2Y{lmt&ASLY z;;+|ODsrPL_7~fq1$AzwezOmG$&A=qXeqy2xv*4dWREf$G`2hocv3*EC?<&(;RUg%_4=)7JPnMV3 zZ9`UEH-gLoA4yGnr*&9UgT>9p<~1zqYW1|HsblKbA{Y;eHLSci=chgCXX#o;B7yDY`y0MScoI7VSifGNZTRRO zF-ZpYuKChw5$DUUq>L6(DNO#xcZjHN{I;@vyfV9&Zc{Aeo%Pynf(bZ2lj-@uish4b zfvg?HFt)zhD(KbYN2s03N#XM9CP|EGGagCOi3Zf3OvWn~4R$MNoLY%fS$})^&q8}; z3d~}d(amG`K6Ju~<$hw?bXRH;JMsfBU&BVeJ*9XjTJmy^1nVU{h2&SGBl~L;*@rhe zNa;trK$`dH|MXcylAy?>p27Pd4Jd%YlN&Y?@lKX|GK>WM|+n7tQ|vSHWw-Q zH`nS9#FX#u=btk(3D+kpa(K+TIj}F0XCj{-F63e|v8bg#d);5>f_$EmxFN6!!RTb;Wjf8qE8`2_2-rmVA9g@K z1qy@VNdC0zqq$ zW8!}lQ|T450wkIX`1K89b~M_(9}9?ajeNfj{fR@d{x=TA9_?@NS;Ug$f#G}_5bUYU zjg0DD@vkQyK_~>fPmlLqsy=DFPOP%$VCMFmm&myCrJA*72h*iM?}a*RpR>p3CCJb{W`v-F4u2Mr9TL-kuqGam+#M##&AhgdDV5 zbyloGWkgt5SVc^-U%$4(uc}%xvrNAFYZ&k+y3daG1#Yi8OttdU)4eUvV?PZ@y~*l| z7&!pYV88e)f{1H;wi0UTTJ1N28knkYxosA?-A`9v*r#@?mFxDW)@;(6kLR$|k#ShO zxjLAUi-d;MblwlQhD&|8k*NTa^{&vKCe3Gnw?*JiX9sz>2lSCCkMDQKvK=@3Fz2gG zknORsoy)g7jx@-j2xIP4yFi^n2$VPUyq>r3sU)IB4+ym|DpFtouZ}(iA^Zm6`15r5 zU4`SG;{I|g-_}q{LY?i3EL5nu>^bV3tQe;=7*&IJMnHJQElvm13i|pP>L|cekw_D6 z(_!L`@0QagnJ?fFLgeeAFbMm=F9%(yM4a+aG>92AmSeY`XE*Ya;;S|t!8Q2KM(?ly zJ;I6_p&;v(G4_>bfZqDXpaNmJQqC*0792~X>&2Q|)!ruJgOkKK#veTfBS-YX)9qU@xTflf_ zI9s@jHj{3PnHAV!ZE(PPp)Pm2R7=SZ9#NEbbCbsF?(&B_IDwgKy1u6KsIek2YnyQJ zqgeuzPJwnK@bUrT&^Wx2&hHkDMI#H~2X6Mg-Qh8if~uwosPvVBFgYt!Oo$2G&5-|! zi~19DxoQX7>T!eXqA{#I$nqS9$IN8YA`*mBX$g>(?w~Rmh^1H$oZ@ylUNnT{mv5}C zy$!zO{tB43FH|c}x4N>m+Ul)*IhVy;GG1e@WQg2FB^4inLrx=SzSzJLLCiD0D?50^ zbHR$2E~Gbshs)nsLG_l#ajokOEjb1zEXFI!kEp+cl>WplrGEMv6KxhMF{_hM3JF3d zlhyOO!ZDwAs6!b(dv4g>csV~VTihA%wH?St?ob)~n1~%?w^jlM%$u)grB1d&-UpW@M0KVqEQ1qQll< zebuTg&5=j{8`~uOlMa5mT#p&oMXTNhb?O;PPCB(>a|@hcD1%?*7l(cLX%JDcA(mNh zS(5Qy&H)yaiI`!uES6Q`qTDVY8Kon7!_!})x4cy;q_UjkUpe=6y7#caWDKcam&5xK`j4&Gb#%B>R zo_0YiP{SOs&y__}!yS}-T5z%hHGsfAL}e^0$((lM#T{4wRP7wGaNIW-807t`cQ+@@ zp`Qua&B(8~Q&{kDQ5x+IKj0E;z)$J|;(?1Ix+%Dw^7I?1@Q3eU>_=tw3()>niqv6K z1N@PjF-DR(z=_}eW1PN`3TQ?rSlmKu5BKB@i1W`{O;PM}~hC#K&jNi7c(=fNbzP^hnmDj1L-vk$?(JmUBDfJx{ zRGl0zwq=Y~fFD}zwPYhZ&QKfW-e82#`i>4?U>+eJ{f2w{=P5ZXyv0lzW0J30iTYa& zSDpiAWTVt?n`7DH<3=-OIwBeyFA)$F|M1M*?d&QlnLw-XB!E?WKJj?y%Klk(1G*4n zX<^3Gt4Mb7*S*_pta{J=Wiau5u+?%457at`^+b{|AWCqrA%%NFL7(x?R#$z2V(m~f zqOjED1XT$88WS)sK@1~Bt!voPd~HmVE1Yi#)>m$P1)$lhzvb$CSgSK=uztBWv3I=G zOp#n^S(cwqQ%OEkX(Ya(o(yI(-7_iXEp2FSR+_IdFN9XQE{C{Q^8NAATx zoGQlSu$&&8ZTlFAWQfWGHJ728eRI)nXgt8p3}3icSY00|=?_J}3-e#WwAW5fLnEow zYoYeuag)@)lh|Oa&ui217b1yg!xqB)K&Rr&WTql9?&Oig{nx*H}cV5KIB&uXG?DWbqR&a$Ffw` zLL~c9_*ZEEJ?DaNpw1Op7=I762p&=hPy7~*!q7yz^*)j7@7DT%c!8qP!m(Y%lY_XF zJZkL!y{v}7`_?n3k7iqnQ;xwU|LIC~al%#*f3tRZbMWGKWkrV9U26VNZljkez;~n3 z5T^9!vSuGA9IK9IVK)RvYO(V>yb8K_+cEjx_ABwYz+C>RYZ#P>KY{N*Epti}{%)2I zDZl?@j9|xSv-swPszCIQW^z?*s7w{xGUNWOQ2oQ~PebiX%`k%6?@S|~#t2E^PAW0E z8Ey^X!?!%9$A$evGJnv(*uOtV8H4+sb6u}t&!QWDP^B0|4^m8yuBFv|K(}1zq!_659in}=`U2-jC?)#K6?;-O8@eunU=|ozTz<4$ z=?&H!;YLlzeCb+EZimm8B>srRdn%xZ5#oCDmlAAW-j2w2_U+6Z$F?cs&@Z~w$s8GK zQ9-{bIEpkB`1jlXxwz^Feib~ zRV(>z347flLO_|Aq-f)QlL=ywwvd#ZXF08J3vFZGMMYr*8-1(SV=^ogD6VWGu0%A+ zf<5~y+p?q4BZU(aDGu2sAGA5Fj44Gjia9;cSOcgyuNX9veKK4ddsK}LErYXdeiVWVQeNLw0!g;vH8<^Pl7$|^GD4@4pY0JG2b^+TpCOhY87BeZ7G2ihz-6r@x zIqvpSpSxeN_Z1q6^$wRaJ*iB4TX(-n$=_kiBqDh(WPGV}R@n&U3W5%Arl8(PKcgNd zy`U9@4|&3sRr$Pxht?}sj!tB1q;DgJc}u(Kfr_q8%5Xq)L_gGhFUnvzQMKj-dtJY# zT`ZCOcV=)$3I~Sa=dwm3X{_~sOQ3JaIu3MTi`OJQg<4Y zpdYo)YLSdkr+4DUo%he?`*u}Jg2!dzm00~9D8y9MRlmJC=k_ixts)8NJ^i7GpWi7& zHo~k}#btoA<fo{UU^h_(09dM zRKCmpOv5#-^m8u8!u*U@W$ipt(=g!9&G{q<>t37ACm$41b{iFEDU_g`j!>C)gH7eK zgPIFaR;`;ObW()%`Pk%R991RV%CxgSuGP^{qh5mZyUkkX^_7f6UDwy%!I-;RS*TZS z(w`BVL7DruRC${KaNr!!-e9x$onm@-)hCV^L!GZC=O6cS4erRXVZoCmSSlrj=sBGtdEL{^XqYRJ~LynXZmmWI4}x-twZ`X}nc>V;Ew#O? zYSYbRUesJV-B6$p&sVG>l(}GcZ)sb-)+`DkS;~6Y+=wGrn%x09UY9~TyfQZd# z)`+vaLd>JxvzNGOC|k3usuVq6il1>zEXP%3t1RnRu5&ki_Q?g#nk6LIj1@1KWgxt0 zO7=t(mNF(DCqLbrDlK?luC#n)CKbpC=vR_2;t!-%1E+85mK060kWJj*YqEZ6V&xF4 zXl?vLt)s^l%&J@O%kfzmLf+kvri-NqC9pUzm0mgQ&W9z%wtdtAUH~NO>zPzp z=%W-H5f9=lodw+=ALy3UABiBKS(-`)iN^OdBgrX1_B~O8Vh)ZyA;7Q%>ldyv}jU-`MYE- zV-_ebk>4IJH{wZCd{`~J!8K2-QT=SOMQ>pmn z#966jff=dJ-_&YJ5=UCAuHn~;7gPImWi!@B^h^#4Z8f3AU5Ng2o{=w^bFtKx{P4^t zy5+M#g{%FmZDw~mNa$mzKOkN}VP+OpnHdEb?3>`JtXH37>%HMtnY$g{i(t=j`k=r& z-ku9w^M31umHSyt?&NmhARQ=H#%;t!B^#qqwxoVVl$OS#e9dEHz#2ttp^YPc*44i0 zbBAn_+o;-|%wXd*7>j=Lc|qTVW#GcZV6de{Q8m(&u`=ZseMEUX%KOyjO7FL=x`I+A zl5VJ)51&DmX}iO1L*pubIsLIxC1EbEx`=rL90I%h1Op4sA==e}Y~?G81~1O_dj~EL zeGO2{3Ma-asip;+U9`HFQzS+n%9O@FTL3VFvX&l`tWOJ7%V6xH8`i~_Q?Ky`0-1-hwFS*ZRks3EveORBl@0IslsE` z(^eJA3%O{PU@L&I3L- z#}SHLVu(Vs5sBL&p}?!J(VWSw^X5|@SmvzlI2{DH~<=4ODy^!jGL;gg0POg^8rq6!*kE z*d`GAh>?Vlv%YbpfDbNj%g$Sj?{heCZoU1w1tITx(>G_*IlDJ?cPtmQy3eZt+9@vQ zlHE(jzvh>tKT|zdiW6@dztdKCl}XRmPwb=u+w}b`)gTPg;EC4>*wP0yXR4$9zKRBj zf89W<;_kD5^%gBioZ<2+A?k!^W0h(smeMiM#T1q~J;Rd0l&CzPN!jn*nbd~A&Plu^ zb=>65i|s9=XS)=rr(n8%`&F}lDX%<4fVlYtBCzFN+?BUVWV#3OZ>HdNr*qL8;z(X% zKz`;F6r^36K*8RFE?8nR{(E<4Rt>M^x7Ioku{|GVte{hpzRX$+Bb=TGsD3F z<#^1+r94NvHqN7h{t#<$+6tihrG^~M;-bC=wdsLO;HJHtsXN<~B#1yv^CnBBFI!it z>Afw=5?_l{^7*1)xn8V2kp1~Xz=yV-s(P zpmBer`@J=v;J83N7D=6ghN`c{B0MQBG@T7Wx@>Asi8j_+5^Vz!>e#OyodGf7d*JB| zQF_iy0jgbSPH1Xnyt`2-iQVh$r=1TsAA56oRh)il?SPIdPH1I=L0Q9n5G~j0*J=c` za^)Z`)A*buhXAY;zj6Y@waSozO0=u3sfm2x?c_XhrHXzTM-jjH1jmuf51G6|g`r zvCSVQ4vYCD5*uifb-3m;-EqVoqzsMnL~P#VA^x5^h@{BSK&Cj>?+OWOk)rMJ`wTmi zqsk|d2l#OtC*n2;G=x7P+m~P6s&?FqT2nS`IAV%}8sh?2-=GHbGILf9l9Bm9*Rzf4 z!o+ce6;rHjlxl75i7z#?0EwnSV0txcuu7HZB?p!K*#Z+IVL4W^Hsx^9V7Ws(dVa}O zGDH338J0(6>7+Wbr|Y;@2_ANSKftDE(IwtpFMuPU+fvSkK$NX*w|-$Y*{X0IOoL#5 z8VMw7Yw|3Tlr>PV&n)}!zIJkWZs*o*6n;g23pmUywWVU0ky;m5ohvxobTXJ@<2d4x z-@oOGh)n=uh(UHH=I6DxTX4DoSco&AS>6VvFSS{z5tXINYY->*=G9Dxs&ll3S0@%_ zyk)4YZ33nX(L5;P)}{WWV!{dWx8JidD*at#Pa|R0(LOyW*hw5SMXUq_BszAm95M|( zY7D#4-xzp(S-oWnrBj-c6C*apQ}Y<`cj=dFkV#&jIyASaEwgkN3EIM7uo++$E!@BT z*Z%^5CH=hcu1R`unis4t(p{v)ENcg}eHN0u)XJZBi?p%Pl$IDb%}g!54~bI`BX*

k-4g+*}Rlq8g1JyVkl4^1}3q<2tEG-fg3;HVxyM?^7jvr3ZrLl;M^eG(0}9 zGgSDU&H6;XULH4;#~IcI-)h-iG$LiLAC6ItTG|366!DGjbIZrSk0*!fzA#0XA`4a2 z9HzG<7Ovl@w`S2v6w)1T*kX|G?~o|Uuq50rOEHybd=fQ*n>KuDfyzx}KN`mdESQ=o z+p9mU^6<2i;!!EsQQlJ3{0clTo{DnJae=GEx)iF_3-u3DC8cNSpWE|Sq2AbfbLgQW z`=w}1y%9)n$;cPJ%}e459z_rTs_C}xk@Q4#!$>7>e=Ye?rEDURPSZh715d|eQL8Fb zSMCQV9SI@V^}D%nyL$IC5k9co{!ox9*_Hm)$#VMr3fp02TpnsSV?w^M6k3yG*tpu= zWKMVYoPQiq6PHQhR4l8C?H4>#FLA5PKHL;-Bp)tKtPryqp7662mn6?xhZd6=O(td# zew8}dH;zkYS2n4yYSU0hk)b%tRZ>`S&fniQl`YKQ3N@bJgEVe&tz`gX5p?e2Rto4qfem>M z)p0x-B_-gIVL1pTnq4~I8@j+Eii)9IoIS1o76Xm$=mi}W-kCnNxrHt2M*oVd-c3(B z6>vj+PzqokHuOBM7aZ8S^2Kt+QY`A2W-L?xj;tL2)#`<-j>+=Siqqo#i)D@JN~dI1 z)+uwbot?;xd>7BH?j3>s+A*euTA3fs1zmc^^;%@G7js{E){kv$0MUPk2fvw9u}jHO4$00%J7!*k!+iAEURwHf4Ge6rb|CR_Iql zlKQkoWNF$aCuo$dVqj4&Nr9%TAs91Wn8QeSL1-uj98WA}*y+<2*sqID4869S-X73$ zCW$;*|5y%c&BA-~?vVn#CL@~E?l$%`#eAEutBKfi zg4W9~`OHk7QsasLn9e)QudZZ|#}eA0A5u)rALR2A@l*gh(H6=8DW_uPkOTD!??0+l zA=vO3F7Matlb%7tjB+pBJe~=(H{Rss_cG92+ZNY4(I89HS~jL`O&2r$UG#~ zUuB%15A`~yCiFQ1iO5${T8Ya0g!eAFBR3 zs;aO1--iWJQaY9HZjkQo?(XhxB&55$ySott4h_=Xp>%gV8-3rO-}4;<&fpK&oW1s1 zGhXwW^L%STb6mYjJou7cgP$O7ym!6YB{AOW?hzyrY*2wc4iEC6W|bTB<&$iyH+|2| zRkg)wGE}sMwT7-ApKhwG9^P6&Q3lrc=|R z#+w+&%riiH5y7JJmder=QT%AU>FP61PJ-Q)^FC9VFcGzs@F*!g-4@-oVhV=S^*7>vJL$}%sl-#?2a&emY2ih^r%6n@L{Q>&pS{gV`dS3Nv`K{AYAs+nf~!*CehvY})^ckWMN7flW|DLJc^3aWg(3NlPj#A=RH zps)|Egk7wQ4C)CKk5_DqODtJQsEjKRvdmZ48mg@mQaZ?Kkj$Wr9IiPiti!o2tc!0> zF?yL@O0!)QhzMWPkS&kjSYO!&Lg{XPy5D=B*MK`ql5 zlFyzlpT*hJT@(n-V8qKdn=LD^eqK(zO8X8g%qu~SDcoj8*B-D*W}-iw(42r|`_RpY zIb~`ivdLq`GnxC#6@K|llUtu(P;QPm!mznY1oAgwQZG2S^l|Kbq_{*UtIq0H=rtNj zDXuOz5giIgjF^39_c^VHvbs~91{k#8%qejNBLzZ}$%F-6Dqb{+8QIS0 zRs58>5|g`338y1jN*mwBMW|#Xtuwr%g7{BQK1Kdq;jT%Pfqxu!v$Ebl>DJs)c(}k* zFGseV04d@@O=lCY(J0pwpYe8+O@+$>1l0pntAQ8+&L8++{3#H3s_<0JETb+hPyprT z1%BtnkfKNJ5hFF7$KPrD8v#|8yKI^>LH75cTy`Anw-F(HS7bTf`-iUs z<+{zeE$Fvj7Lp$jPA+>eVtf0!2cBqt+8+bi4qfeR87*5`#e;z9^RAmM-N_^6{CJ?q z{5Aso;xGiDhyWs7KZksHxoKfp|MU_3^OirIJvJx0fVL8=!Hci%Wv@*!wky4lfofUp zf~c0s*rCu)%Kq!<>1PrLS=rFIR`ug8K#?9wI837rpXF%M+34CLb7-W<%B1mjaM5lO zGXk4J<~;eSGM+TBjTuYe|B}GwOiF_PWyOzXw*EF_fTvVc$1`0d{Q)qllRd8#uzDl{ zs=8ibS_*~Htd6j@gijR^L0mA^X3m~Ni_++B_iFF7Iq%iR z&A$<7VqAp7YGOI~>ycxgeSG^xhNQot;2gPe6HhA5*h@w|sy@`ee7+Pu!BUU|Vw#I1MaXg|`lhT{gpMm@*G5faRY`bFf zR-!lkmNmrmm+s!s(e+(b_dK8wsXU!1+NL_!tCpyv)48_ADeZybYl8&(-{c<=A#sF} zpZ^=k#79jS(=J50mE0vHM322%-QERz^l#{k5ED31v}yekw_pAuf&CO>W1~h)TY*&D z621awAaa;2i#r`fdRjS*+(MeGWzEx3Xfc}eU7aKSou<;-TBl6K$4j5Z^~ImUBQFH- zA|uP+#Q)>V_^yZ$v-DbgcWF|c+|Yv&;quoz`y+tb(H1LM6laU)zD_7-NobILddP9P z>>u?wE(?}iQB$S8rIF%68efX#FQD8sXr+}dpkP3L*aeX=N<_A+0v=(>At)Bl76>L8 zSp)$NtkV3OE;A0veHqxT9LM`S*Z&ZUkXeYUZ@GJE70vW;lJrMwxs^5i zPrYi-A$s<2aV6p*G;lISDs&BvFb1I&D95-_7i}hSAu)v{u}osLCYvIngAP54iyml~i?nEqJ?3IcIZ)CkgV1+^cW5HT@*n_|E8 zl@QOauM3}nVIBS97^Qq*i9DTBSQlsXEEK#xPi)t~;GP+$m6a88#Ud%CRtMvUqxuPQ zdjpWM8Vn1j3|j_Yu}o&j>1zhNBRPO%z@Osb03}BuUGOyJTvowO=Uh_E;Qc5#Siwl# zzu+rCxgKdm{H@pFBi=#*HAD2Z%ik8MS0aT?w)0gu&-eS;q%$c0A4Im8Qsw<0Jr(b< z2T4u)d^y3ZREE0cky0Nzc%CmBO^%Q6w*r7a%9PW@5I0vK^+A9)Bb5f)>&&e@USGVC zAnqT&eSifzAiC&v!X-I>U0-4)pMcF-NbuDY+Bfay{3%I_riKnqvN)iDL+YMHCiqt% z9)V~$^>Q5HuhQrZ{My7PCl6655JSQ#GML8xh=41lSS(YR#qBxOV!v%=VOTnK zxj#XnTBYR>`a%CCZ!3;gnPF@+1Hi{F%Y( z|6xCW@PLbn8Q{^)MB#%qp=);gbT$Bp4r8rnsY=(=g=8(UAr+saJ-z?1iE@LPd?nDe zpdx)!{r{nBVSH|98~pkB#uZQgE(^vkcW0Y~-+}@IG5c!~m@~~-tlrSOSuc95H!e|@ zj}FtKYN!b&Sj-Li`IOEC@CJz9P**3*n$(b>h|ib&G?4iSNTU#Miuo2iP!_k}*YTZ9 zTx)Zd)KZ=aB^I#SGy)p{?$Phv1oY@SbU5rv81N@lVt-+^5(6F+$_VJk4?aqz62wS6 z-Ynq1i!3+W7Nb$Ah;a?voo_1!!JrvQ1LIm|5yYFuWqED32O|GO^Sf zuDf4fJpahT&wOz-9O(hBnBsAJBF%-+^v-zRVXur1Aoqw&Kmwes zUVSw?IOm+o+zSANd=-Rx;`9v(XGXmh?sYcSBan!2hO_y8{cpy@AMhg_V3chTK%V7q zCi9K~GXMTl*x;3chE%;qqeUqFT(K{ zF;gN>WI)B^Vl5p{8W|F3H8waTr$#?5)cxhFO~}UJ76zJRXmfcpP!; zHbA#10u*M*<52onI&QDKn5XCG-Oug3^>zb6!s6)bYB8Q?8~!t!Y~j^9opFcMao#WY z`&Wn6-4ePFmH>=X4A6Dr+&=ZDK1(fGSy_xqlOWC@@KjjxCh4JSfcMC01VZYaQOQ2SRLXGpkW2Tv&WhrfV=PicDB;$NCAL6 zTkzwzVeS?|r=0aWy_Z|8qw%ns@fR8m28V5TJfa}vX`bv~u>^ZNYTz1ArN0p!Cu^yQ z>WDGZqH|qEMS4*jIo_I}Lag8Hi6jH0d*v^J6MO^sfu>^kHy*=|73}2;(;IyLvjE^l zuK)5Pc{h#4R4(x7@76Bo|#jq)j)AObx zB!K#&Hl!JVced@U7D3*PR*P}8x?NIM=Yw17KojI2fX}&_mgN=KMqhfvsSI#oQ?15a zfk7rni!I=q@6#}C~S45H*y=*~QDH|B{!V`{4L8!bq9HA}GE|TlMS%9gv@Hwgya#12ixQ$l3Z4zLN zLV$g&Pt^vP^)NlqocR&>SWtYiT}20;&z=9mSW@K*TPV!!$QE&XmxFi)8-w@badY2E zEj33PMiy1OfMwEmD~2KOdbZ)`r|Ws$0a7b-0M^1pT2oWn86CYDomhxn(x2Ct+2|Gc zg3+{KnCrARnmSdS0ku|UJFrLClUv;SKk!8$qXnhZ3zF#v}bOQ4?rod&QARz!$6TkuO3 z&&bOQhTOk^fUCiw=Mt;)6vYI?{tbg1ho>Rp_uHPR4W|vavI-hNH#<_{Fd9T`_b@K! zqy=X1HITzyu(+feA2$3)STb%dxMDlXI+E>qV1JE)z3rdB5_YsVmK1R5bar-59Lb1P z(_P4m33t)=kGs3M z4W4X+q8kSuk$e)?f-uWA-N$XAFxOG{R}6~L&epswiY}xHk0FJcjV5GtY|W% z0wmSWcL57XLWmC=Z91Nj3$Tep;V2l^+5EWXga3k*-bRy*5~(HH z1FR!NuIRYL@+?0GG=1&YyDjQL+il-9*bApLRdC7@_=rK$5&2Umv-5nAm>a55&gd~|tRVLUN!(iNNgtnhL$NpOk;BQCf#>Qy0T(xAzdxm(>snzh+gV~$b0PwOD z*yZTS{Q_v3)OClHWHp#d36BNrC6U;Lvr=)9r+TA0)!*6o;#^jpD*W^g_KT|D_xM)1%~pEv}~| zH-KqCGl=Ez#@3>{WdtSl)5?hCk*sXAc&8y25*WHpTP@ZN5ro@oX!G>+;=@YpsarKR zEhRA>IB5LJ9Aj|mlLesL^u2Cy5fXBjy`(&7>#0S)9(!=^uKh-+tp)c2Hx4S4L z&-XgBEi-gq1;uA5DH`ko*->v~Y_ToIS{E?#qMA-}zFeh}@=0CFc;2RI&X7vnh2&(h zzPQDx{#x%>o^WVAMmC$hfRMbTx4BBNmGaOdfbKF5xG-0*=ZhjBApsyTRfl9EC5cM9 zmH6OodJuo7oaGuW=x>gh2^~m=d7|qsS%jAqM6eQ@e~B}&!iRr@Gzr3q@Gk!u_Lly= zgP=z8V>1hd8D|gr0btL97tnY>dHw}x2@rqUddwn|#kH8s0h3N;3|jasPjyozX^ElTq`92! zoaSuqq)xPl)CuLJrkq{H+h&lV)V71wXCNmjcX4xUw#{-R^a-p}ucVb!GfcMxIyjA! zaRMG%Hg6ACAE3DpAsSiYdCvmq+*0DZd(F{Jnf%Gc7ombSHHrvVrUokY9rOxtLR zR*L7)kLU=h8Yr>)3Wly(v7*xc=%Dg`oX7L_M0*%;ZheQ2b8HVj>@PXh)pYldkKxNy z?eCbVEQ7W`*L`tJqE;8^A1ha{FADJkLRhjU!WoRHYPC8eafvi$id4|)^nCb{kJ@Fp z)%x60z1+wsGlr4nHKAix_9q{oMHY6}q?EJ!h{%=gi!^5YNiEcw6tmYg1RV^|mD|R~ z{;Nuoo=GohAX&5%Wi1Xc1mp}y@FiCtH{b2ZJ9p+tY}PO};x`@P(#8%Bh{O0`FkICg zbT##x2-O7R9Wpy93{`0upNOcMIQPK#yt;XE?*?jRdxqxppv4w1;E7bMu7AiqY zf6lfr1h8ks^nvCOM0M~)%5BK}K}I3Zzm)>TuRYM)^7ptId<9d3A2@jopJk7I2l_m-^{LD4T%h7ZW8fDV zY;^6vYG(uC+1~6Ij?R0^=jKgD?k*gu32J74v3dD;_PQ(T z%r&PVnAtJm!Zb~I{>_rwNrX2^!V3!gZ5rq*=j~LAyrEb<@e|_ZkCZpV@D6+&0Hi1H zBNzm*@pC4MzCb30lf2>Keu!?@TP$OzHL+E=?ZyH-zg9s`&okB>9T&{^Rvl82-<5NT zOSPmA*e*8~w=u4`et#elPfgL99l_e&oCP`knUo%mp^Ic{6U~16H)lP65$f&_DZr%x zB?t@fL-N6&-~UghJ0kd@-miMK1|ULpO5%g%Iy%FeBviZ9>7n4KJj!!{s%G=2qJNI6 zs8ClnDV(+D$cNFpEteQL&gR9-o}BH=rpv7oBE$XPRu!KWX%cAK^g#T#{P->?e4{-J z-oCXQZ%^~>fx`G(cjJ;~A14=_1`C!=g5Fbwu%*!33rJ+rkSL$XMRaAU>sGCEd49Kf zTyY)dnxuGp%YJ^BQ1~Y~mkW8Oe|MM+gdRI)=E=vsf2tKAw+8-Lw4OIA+D${t{nqHH zk>Natj74r*OYL+WNQa~Clf{T3V_E?^hnB7A6q}D;$%RyXPcD6vkWeWka#5E3eQL*! zX>L1{WVPv+BicyKpK6vry(j5TyE$~LJePi2iY+1d>V=97K%W!tD*K}!YxH@MXczy~ z%h8g*VMjq@e=ol7<|ddC!% z<>08&(R7Ccj_qIHfB}89+>U1txxEz$N_$HM;NGHyJr%>;e>((!0}~v3`t(KG_fN&0 z>3#1}!>q0IZ+AN=5(V@i4QQXUgcqyso(0hZDI4VOXu~@kP4}#&@y9>~Q(rp7wTDyO z4=b<*{p;3fxv`I-06r4pioHy~GbD+03!bpycK@4(eZm>{jwkQC4_Jp}wQg5ZWi1^E zn-05!;&|a;j8fC%UPi$f3jDB-LNhU{Zlt}{vAR331ThEKG-UJtBrW?OzSFKw5{e=J z=f~~gxLO%RdStVu68219*I#ahe*vTm0Qpi5sKek&F!tK#!CZDAy9qR1o63ponIp|-@3k0HeGB)fvoB_<$1obn`Dcc0jS zXBr|-C5|^5cJ$$Fz0LAGc!i^NcMC7qeQ(fX_fzw+u>9h7!luaFH;T+fevbNAarJb} zew!l5QzNdt8rJ$5KLh^BD=wpu+6sdHpS#bsSC^8SX6v`0AiX~l+(ix3TF58B6X*L;0i4eq zbv!y&RcL=`l+)tkY&Vhe*;y*p-OG&DYV+A`FbJv4cLwlj56}kB_;S$x=i8v^9R2`u z4vx0CB)3m*mgE7c#hyT%_3FwQ1w1R#CsRVhcx5y%MR9&;Z82(~b6OWB;Hk{NnJLn{ zoVWF>Lt?M~iaAjI+S|At!xMU+eM-3+b&PFoes*PZ;q2jnzFV+o!CHCcbM)oC+1+T? z&ib0@$>vHEIWoIS$@1L|s)sCWT~i~eE{Oz4vBzUkjd$)U}91f)m-w1 z6Zn^**j-wZa~c;JL-R|MTPTy)k|~FZcLZ<^t?IHwO|AZ#&3amL9}z9gyojoP!BIL* z;IKI*rbcPqDcGl)_0WDVc-qIi1OJt-WTim!2AX=B*GR6WLb9d6La_)8$SQ#)2Zyp- z;YUTRAqH<^WxX0MuTEHGoImG8MWiPK66>))%(WQ*S*()x;QX1&y&72|*U8wKI-3#L z+4K~Yk#%tnM(ydbl>TFtv6SPwf5uNpdJTJpp99&xyno)+3eEkER6?V6@sM!$-6}W5 zhp7B2?)>iuY?w#`=%EAu3V%J1iB%Ogcwr@^K8qSWxl%e8aPmog0~h{UX~62u(11nr zsXAOb#Kt>Fb%hNFH{VW+=KIFkziZ7*51|_*5jDb_Q?64~BFszAw(!!nw#Xl!l_Y^N zAxUw0PxWh3}()e}#+4;>b$Ps|OMn&#ET2u8F8s}6Es1LED zq9Kv#IwOYiwm`TjspF99O6K==2&fl7>0#^!4x-*;$_*c@n&(d>q7>Ru49?+p=XFJD zmLiw;Wm9}PJktoJa@yWoGcqiGD34DJ5gjDqEzZf)KmXfNKKcz1TGRR&3{Yl?bJP%e zEo#!<{Td^NGAjmS4ka;+#nHkepth zZ>PWG`H*irP@Jkk>~#4)UnhGo0qx{PAY4zWuwbAPF#~kFwz-`XW^#Kb0U3)FHfIf> zNY_0Bif93SN_Ms^pxEy0?h1=WVdwt*jxe1>qiHsg#cgD3TaDX(^$9i+NNXurY*tcu zUhcN1s{d`ML}bkN8axv2I1%Vy;#Y^^%VvY!?edp8F9&cxn`w(Dvu%%Z8~UUiE^YWK zAxpMy+?(ylzT&A$v?t1FCW}lCvb&bHD}+WLBrCUUN)2eS*-Oax zSU2XV{zB~0_A)No@qXpx&o$JDOYa`;Wl&XPv(dyQX#ePHQ-HfQjy)_y|Ax_MH!)(!cd=QcRv#==$6jqJ9KLUY1E{ z``v`3DE3Sv}kq*Jk z*jPB82UB2OT2uDXmy197re9%uF3agq0%o-(Lu{^>@$^MlaB%TwZSOsm$6`Txb;?9o zo{U!UE|CwxX^{=H{!k8#_BT)NrK~&z&~z$J@}KV8XMrA^r@`TYG=qsg#io$-g$hah zPu`{9J(f0V1{^BqWkAz)o86O>zOKdNhMJ}8+aKS;=h4u2Ae@y(Lj)T2jzH9}+e=Hj zf}YV!pFeMZTo@o^~Di%R>H9@h+P5Cas}qE0KwsMB*y7- zgil7MOkI{M_+4SW%R6*6wB6~C0Po9Pej8-_WnJPX(Dh$njIViW&+dLvfC{1O`|Rw# z%YlG^kjCX+hYBg1$u>GdxD~g(;kKG@CbD ztzyT1zEYEHo)oh8ZGcBT$=OhD^Wabyn6$+!okOiF%Wod;0PR1&=tQTo=wzoBGm|Ok zm~>$r1YU_w`D8C_Po;@;+sh-!F;*HGq+j$faqjj|`!l=6p*7GbYvA1eSYauPB5(Whs`8UwG-{5+xqn;fbOHln3u#E*2Op|=>cQO75aG6cwx1Dx9 z{$6!mrf=ht>VG8lmmuM$6QmHn@=Ia zW})wJbmYTum8itm42(wVPU0~M}Eofs273f&acU4 zRl?ofy>4k~DGim1zYs?k&+Ckz0=$j{A4L4L`5kWuG(8<}GM*t0hz)aAw5PzJ@Ts?Y z7rmZe2k31JjS$3W#|ZP{Jl4$#3A|v81oMnvD58~7L)CrAt3`837x{m?YbEjJx;*ru z+d;VeKg6Kyg7syz;!;w?@_`pA)859TAd~UA zA2-g2Jf35w1!^zWPX(ofTfJX$XnK87z z89^yD_7WfjBS`@nyNe(15#%f^C>zbEgMo5`U`9w_e5&21)j%O^coY|cCUe*An&%ZD zXC~Wx`Z^#V!T8HT36PWI1wo3rJ^IS8**K3~M? z?o3~!)uC|IWK;kbgiWoDPNPAr9g2g4V-8exr=;DUAFg)}44ZAYNp+Rrki?UBCl4$vt>}7+(0ys>jQP0g4mPSn@rd|BbJiB?YqW#SKwPBb z`-4^~w2!4S82&?2-+pYWiYQ(z8hTXDE`IfU@GjTY?EF|EkiY!l9`$+Iq3!9?cc@;_ z^`P$eE#(#KS`i{-EaEzOZFWz*gxRUtyhg{ zMtn?&b0i9`GssT>anApi+rM})7y$vEk#-saRX-I7lXa-Rz2cBX_k z(c<9;q4Sik)kfBbKXkF$+^tNF2aV*eF0YeNj{yA1=X@L{&J)I6lK%MoWM1|U%=4C+ z>c95MsZL;8n+}AccoIFU{;-&_e6H=+JJ@xsE0&$_+WG2+3?M8*3}1W@omEg-Z(;S7 z)x=bNA5*3XCt@V|>MceUE@?x*H&5N8Ig%fGG+>qZt2)F5>Eg5nZ#6O}7Cv@ATz*$b zfbZHTu;BDg)fwUJwE9?>#VMnW?xR`_H2)NCJ8{w_uV}ay)HJI~hA+?5Q|31F%PCsS zxK^yl^1E;pdS6ktHvcJ`e}S6~d40-wHse_3s*!%V?O7Vvc_I3iSa~mIu~&~Gn8`8l ze@>@_3H<9g$<*F0!q9zi=Ttn9wjO3Y4*Rn$UGCann4j?Hul=s9|3vxb65hN*8w4`E zRYEVmCN%urW*|qFdQDBm(&Om6w#;nFP|+sE{Iy#%Y5mhZ;ZNy}$H+;CwFKi~VNB{(f;l z8x`;UDm=5i(4NcCN_#kDtE;7Z#HhRqv|6O)747DmZ6h5KAIV>d;Bs(q#uq*3|F?i8 z9ANz#lPg>B7j&<*FFiZFZr?O8jX1{rD~DA2hN(u!3%*?mb^15@?BOi#FBN46HvV!M z?nKk9GFCYXPDH(a+~1k@jzPo|9l`XbFeN0XBMH?4PnRzz+lz6%-PCZz2p59-Ri3Ok zRlAztAQM8qwU}>hdhvT9Jx#_#9*V~G#rNcg<8ui)T{K7MIT{PKIM;AMzwLci-h5@j z=t8+tLcCy9md*~^T(Sn&1IJ=pi~QY4eo0GB49B<@zm0Xf{>YdnR<6eCv5$OiX@whPyKv;u(cM?z2Q`* zGTU9V&)TX0H{2GcZi@V3#9bgG6!9d3p|_=8t!4Zn=G2d2<|3ATD(!M7k1S}G6jr;; z!R$efA)Dp?3R8e!qDyjq&PV2#rK-aA8d1rtB5b0`#V`X>^~;5>gDs8(j)^bHVFHu+ zj3--bkW=BIrf*R~y=cjN-KSobb{4c<6UEW&Y`Kf`&b$}$SUY_i#F=ktQP~n*@5wA8 zCro|smt6x}I?^5bQJYR zQ7ZKpuh}+#d<@gM3@R~|UldX6ACqf_&U`MM%Lcm+*XS@`Ipt~7^h$8xt@Li zegB|aKhu8`89h;c=BNDK>ew&8^tugwaO;Lyn@IR?TrJ-WJoNTn)vUsf zAy~gCl&dnui>oXtI;6T^tMhMLn9nqQ%s7SkEaH9M;dv_s*%S}euR{wK67^0<-;W1Z zKqc$L-hH&a66|Ds8J6k3vC*vXy~gUqJmOqFaz0Z;vU0`uyd^X>jE4DzyW7Vd*oB<^ z)(^olzRZ$5%dBQ%<39cgG(v4H*1ifCrFLzH@tJkLMQW-?1Gd8eN0fA)w)ja&5pxI3 zksa4iE25!qXTNt8j&gK(OH+M{4iF0*i=diEx$Z|#x);B0?T`@|U|io@B@ezkq*||q za@|PvNr-n7tSukz}O-aU0Je+hA zuJw~du#kx%)sh(o%B6WT$*3&1F&B)Mz+SBNjMXdX)B7AEBzOss@K{7?E_$~o^Qice zQCX(rx+^`glgEs$EUngB7`@1JNmCt0-Ol2_v#H9Jn!~s+!o1* z2!w^L{*=}fe|sn24ipjRXmun0Z*Eu)Bv`hdti!FXSS)0|`EkeU)zQKE&SZ{5@~4*% zn==DcPS<+a+MHSVaAXyO<*A(4?j1xQTvS>~hFXFp;pHW?g0+TPE9FsrMRpIE|K#WH z?6`ogC87)SpxbJex73vxUS5Uo6l0RK z%za%fPiVEAjsqp>Fy>YCca}|C8ukof*$atT+~-h9=wj4Y?f!u`x zy1tXVG7@@Jzq6GvNT)7Gj1-f9O*KT)kUuP5S84C?a5|9nCn}okuTGW|=#z2oPMC(= z&3^2io|4l&~-Yg|1i}SAuR1mure!RZ*wHe{ZgNJl%YNQb3N=%bnWp5NBjtW zq#s$4x$}aVRh-f_6*i@)6Q@fT-2S9 zURvRVEw%C<>dzO{)t*k)oImPOCD$|RKbb7#)LdK&^-liuMX@dOBE#dPro-8GHg~v| zUUA~6SPDtDwv0rtZ(BL5yvz~P(K94qz>7oe%YU(>8lu0w?QQV{S!HhD0_xJ$iO=WT zaX$&yZfA40<9iXbnx(OkAcwk3ikG@U^5#qANN>Y;&aK^_3Uww5MnnyaBM5Qn8EAvz zwiI_4gg?wi>#P0Qn*1AAjVAb+tnfRo5W-3m$aYCk6eA2p{2Vfkf}tv>Kj?C6L_NA% zWzq~O4WuVr{^;6Qc=5o=0n@(+iYc~RHH)o0rNo`~O6 zjOmoHZf#+*Wp(YhuzBc0}>@GmPtq_wXAl!>QAp+%alr@r_aaZnS z_*ktlI4UVgjILSP#|woX{`$rAc6o_%{6)GkkUxuzNdgTvT39K{xkMeNUfFUes++J} zd)W!)9Q8Q}_S9K_mOU}U3q8g)`DMoK2irldlVXrfQ)BDx%=F{V!=2}!se;HobjnWC z=Cvi4V`+@H?s0)OrM9f8PgS-r@@jK08f|S;rcF1*1S=XA{%mHYWYOOLfqH(iNOsrG zx%y0}d9&5ef}(R-@hAP(!OS$SiKfk3iM=`H#jufC%h3WKKPdXRfZ;r?n=jp%Ls)C$ z%B*Db!ph1NN{7Sa@9DKNpM$>zz-1zQ0M+ja(oyK^EsNCasMP)@7{V(Z1fnJrVY@oj z@2h>K^}d96b{F+^9Ud*IC(Qj$X_J<(dfjWRwrut~Jboxo-Nhy@@Mmj5EYa&*&``yK zm^10_)G@dSj|?lJ&vGGNQkaW8V>zR#3eRglCn~c6V`FBQl158y5N>#YI2pXWO)m=) z?4)iR)}q-J6K$epT@?Y05H$}vdzY`UpmNL2jVc}PmZ1HDON=(FoD-OQ%YAyxzBaHocD9#V^{9tHN*xM_&TADUq!YGpW~m)G*-GMc%KsbDEQbQV zhw4hNtCpv&(I~eCg>>?hRmN3|oBgnQ-LF;wdo&`d0=ceFP;=(EQwWY<5LE|(YYsY(ib_ey1WVb-mYtbt4GPBOD^rlM^gt2RK77> z4`ot9^MwX9V+Wu%`KHwpd?IBd)*B?7F2Tlu>HVHkp-Csp*{n7?bxj)#^pCAt zFE`c{a1KYUNeEAP?k-pYp8^Zh8#{@tySp2;BwGW1dl2--&J@u5a;hSjuVS*^N?sf& z%z4Cvn(I0ZYlmn-xz)ZjG}tIatcqJbW2oJx`C@$Wd`Q*OMFicV2dG7GU9ORl#aWz$ z*=2^QAYT>W^{bU(#<;fywd7h+7I#UNgRO7!gU=m*;e&uSu*p!`>;!B~U zpR);AuF5J8pSr3AlGVoUnNPJfU6Z0yYs>6Wkz%Oow`r$K5sIKEWIPWplI&p77QGiO zVkN(0ti_8n?*!cSRP{9nITx7J*43);qMH;VoARrKZRuntmxZxc%v`d3Z51+_y>vBe z-Xmb7avCU%pD8(28_OlXD0fewuDAdXmPrujVRIMneTgKj(xtOFzth6XkJ(237_lV# zT?cu{#G#Jj_(`9V=38x$f%46G_(lP<7EGNIL(;AwJ1=VPa-(xf+Xu~b2d=(h2|@ed zNTxU{%cASGC6zwcra4(337T39ryJNffr>dkw-Od`K)$Jzj`p_*sk2I_j}1!Mxv_9V&)%cGZW*tCDCCC z%K99o^t!tExx#-CIm7-MJX4q9`x2KbF?F<(iv1ACRYBm+rd6zZe3#6IyvsYiu39_i zDqJ))o}$m{DU?l;feZ^;^DKb>>z`j;#Ss{om-DZIY-~E25k!*2`XNv2-yLXWe-(A~ ze$#5JfiQyoVMd_S=>8JGDJ|)fY&61Ec=yROhPFaO=q*q9e)wLLE3cz!s!=~&hVg~4 zeKkvmHB(^Ut5OCW>H5%tzzr8gCH$ptNGqkZv9l9#5{80~U56WiOpcwywJt=`jSQZs zyTS1?c1Y3&V`sYZeY%kVVqv^tKA(HK*Fy-~{&eqZTY%GYr26fB<_?p`3+^%2l4@9h zs*0}+521OuB~{|akj=d6d_{t|0{HjxxaPA0?0JU!6?>S^UzLe{>3pHjmuN8lS-gd;(_Eh$=IbyOGzmR#yMpMZQJiqG)#|EYJ+&<(iwjI2?% zvPhQWlY0mV3a6riBTg=t7GlkH@qdOAZ{vJ9PZ>{Jdk1u0N8LCe`AGhnUA|U`QHm42 zc8nysJF0C8D%!B9j&m__@K^&RP3h~A?tt&jA+@(=Sm>%gD0#yNbb*M2A3raNd>-%e zpLu?6B$Uy;-ujf3T2YX{oj}YN*!Sg{MvWLs)#EfY642-R%b#OeP>|Mw*Uk|jA^6e@c zq9nt)@*O2^vM$XqNNMmUyjioWs$K%U9;5PN_Sf9AouvIkVZFo2R$i}%x}baf-bzT( z(P1*h@hHCpkEa%^B!_!So{u`^wg|^PD-!j{02qxemtlFgp{u18)lZLq)jZ&pI_*cu zyhp5T4ViI~(R9-8lFd)Jf#SmNJMnfDKpkz#(p#pYTgWm<^5Wk5mql>KY>Xs-ll`-? zVkh5QP0r6R6nUZDG&{8FX}oT+vK|C!8Hko;SC1<2MYE}v-t2J2&#UdGQ0H}(GCqNv z8#SEjo|+n>ke>X3y~Q&Wt~{4;`P}N@KAT1()i<%LuzD_vl8U>MLGrUY=SuB{;zRAB z>Hht?;^f(o!n@%%T%r#IO)apPj8SdW8q%y$v=WZy{sRbUbAAMh>xCvL#V|+>xl(0F z-Hw^4AKjm=v(f~?el`Y8`fZZ%3>aki=_8bYs(ud?^*zK?zGDejU3hnJwFGkk-!4h) zHB>lrNmfWdcxZe3#K}09JJt0(Dmzpj4>dy6H{UQ=f3dxkSIJfWk$)`F}< z$!fl$uLY-b)?mmHnb0Koa73$=@VDx!DXZRkqr)jbMj?U3h!NA$>wG%JWnVf~P5Xlb z#bsw|xVnYPg!ISesnBLTMcl(@kIl>Z-)EKxayz$pp{GQzsTdfWVcz<)o~S&I9f%tH zGmoDw>)ugqek*&P_(GDu_*J!Zeh9;5-pFflo~fX_V^3GUFu&^D5A&ax`@;&VqsE6& zTZ~OmQlzqAyvsFptV_z6X!vz~;xYWSvGrsm@xA@k^|Y@zlHJ3AxG_4S_s+-4hk1=Q z?UIK>2|H0=j|Z|@U2Jx$^WfW?xOCU|X%8dbGc~mb7`fk81Rl&5WD~M7gyYxoL~umP zeB2ja$0(MrdU!0BhO%*AO&UVX3m)mVjnVaY&|pC&J`|w!`iQ5T8ZJb7aPVQ@%TMQ= zxVGP!(AJr};A%f?K8zj0)%^^sN8jQcnJyzsQ$YsMRw4awoDKd;$QAoj zv#BxFmS;8jnb5-quRgk+)tN|AYAhjIoh9^7#-sut{$AZr<}DhQa}1#7VgFF_qAy;* zrgmCR1VibmU)~s|MW9|H&E1+z*sV#}4grU4Nv|IRX+$OF-A54>|C^X6m^|3!(&g-8 z)>6F#a^K>pC&qJLtEB6Q<#A)p3pxeFo0@)luV?z7dZ{-DQDLp@i&i@~NU!GcQ_#eT z>~${ExKUah-Jo#n|Hsx>KvmUkZ41&M(jg%!(xG&Bcc*lBcXx*f(%s$N-O`QHUDEmQ zqrTqv-tQk{k2B8Mu-9HS=bCG-`8?B~^krdx58`x3_t&wb7Cykrm-F-ltKC})PeZ{7 zyc%<=I{p*yKz2g#T1-+Tdw`!_AsQM}FMzZR^adF`;B;yrpcF=SB#ehw>5T>Ux5O?Z z$&9WN37x%dfzh`vGc8OZD}VwM0S=6NFu{JEPJj+5R$)FK9Sd8fde(K_E<9d0GG6bQ z>z(Ke1BPw<2?J_%A&>q?``6|wW$M!PgBBs&?|(NakQ6x4Ll@`kjtu{{ALbnqytIQd zp`dcT5G}6#!Srkw*B0%#0`$Mz01tG=Z7R9rKXePR9oJ-BnHS3>Nho(zE-Z?ih%tpa z=HHLZO~w0Me??Dj!j2?yhB+S1^WR^8^^`-2<3@OC<&Bznp^0w!U6vle;Cs`9`1$G6 z;Aa0R6^bBQIZ@Ip?KTAe)&a}Ic9&XgP!^JkxnlyWO9V!PE6J3EvXcR%|Kq&@d zZ(p&u35xethDjQZ(yy3HpuVoC1GGbNL_hj|ni(r3pJC{U7BCZ_niRnJxOq0bQ^vKU zUlCA>GytOS64?RLgMoE|emsR9KD)IKSHEbsw*`RqvZlVf`W?wEL*t{&%q`WwPPN1+ zlpvx)L)TKE41Nda!^svjNDc=Kl|&A@==K`Rk#h)2*wzt$Fn*(B6KSAkV&0g#}; zSt+g&k_~jkzDNZYs1*e$KqRDMS3uwI$wQ#c27zmyYNByZF2W-xEUbi17@d&Q0}H%b z7~0*Z-Mfi$ueCR{VC&iu$ht57bPSyo_-iM=*w95bY#5(f zL5vO1piqGE_u`EVNYg7qsQlu0Pox4xVY=C?{N)wr(L|E*=kY)d>>#S3k%F`Xqymc) zfF*pKjq7XzwJyUQ7=mFVKjuHxeF2Uk;@!&wwDE{oCm7Nbw!#3Y6a^~Jawi2bKQt=g z#KbCp8Jbe*J6mlP!&;!DN-&u0{}O4@L4$`XeouQ_Hh8p43}MUP1N$*(svrMPKmJ-< z5`mxz_t$^#Q2FvG`tv}6Uu%cmsAVydY0xjwjdu`e#XKYk^l>kH znSknyq=AApACdL^fWI1w03w8qPf_=eaOa6+Q1B6)ygu&Ca~@Lp@Xyi!vc~>~BmJ(V zw>Kn`HSp zaLp;!uu4#Yu9*gfZx^N$%mSE4Z4;pCYKtLyzk+^1s&*Jc1z}nlMxZNNjSB$*_!pql zcru*QxfsX?V|&G;D;w}{^oEj9Z}xwdlhZOQn`NL6bH|S(Mn+ytPm|TG{`eLA-2+G| zFGxy%4aa_rHz0n)!HtP!c8q{(K=J2EHJ14m<6|hCVb#uMSWjj?Pz9sG;T6NBdG>WS z&uBb_I6d9%Nww6K0|>wdxhW9ygE{|n0}mdO%(CC*AnwR8`8X%majS#1O5!h%9PlQ? zb9=Sg*iz`9HGS6B_5_|m;NaUrKrc0>zQ20;=d>4!K>I!6#0&9G zB+$?S-6-v!{QXw-F7_q(HM9Uz&i~f zHmr}Av1RDSOafiP}3pZ2!f@WAU6O-emIncv!I_#F@g^~K|9yTYy4 zHTv3D#c$g>Yo6Z)qGZ_3$Mvr&|4H^jXX0TTi~ScW_>Kq^7jw8$UEcP^+l4oy4`N;y zNy(3~FBmL51;~gL@!$xH@d|;2#=(e`> zj($&4&^(pksl?FtLHd1q*uesn>ZiCWd|*K z)Sz|yprvBb7DghSD-}rMmFsyABnO&EwrUgg@$sqgd~|ha1(vmzbWS%WM&k)@p(y-P zPoqQT#>U2JfMTF=1VCZNRVCbMTmvi;l0djQCeYk4fJ)+1x_IaE-*)}`%ReWiy*Drw zg4#d;|1>^Nqz7RS3@3n> z5Wi{@D`XW_Q%|eDB)>g8-W069Yse>a^i1H5)%?+?FFV}m+YJfalZvZMvNN8vfUx_gQ1^k?z zUT)dVjE#*A0tN1W%MLQfL_y&L5YU-G61w80N`uV`o$l%J7RM(l)hYqae#?IDhtrpw zXFaGrdZ$f5N**-ix7y~H<&%0^HIWW|hZFHQBJ^Pg+0qk#%zdytt9FW*e z=nla2=EBrlt#Ppd0pAQQ=HnX_mVFPpK=5e|kXdaZ4~UKY93M|QH)=Tp0PG1I!M3vw z2%7*6M?qu0&dmy_|0pL7miB?-4$ma`$EO=j8Op&xm3_oCtUqg<{Y& zvxHEAS#CQ)ItpVUs_*lfmEq}#g#n@k0lMfM)P60{Jd5zIEu_Xefb>~>*MZ>$sW*JwJYK}UvL1b zA77%@>RPJ-ALicv{KT%=?x{I8i2b#31;ociPcOSiQ_g`I_9bN?08$i#VZk-#`s};k zYtz~p_wOtb%gUmmU9({iBk91i>eb{Re3(QqkM{Ff&)e&a*?q-onSr-;#*;NPC6NJ{ z*{P@zCpU?%=3CoOQcMXn+i!-&X8->3XenVnwLv_A=3*m?yC~sm zJ&ab2k{@GMMZp|M(k3V*GzrLOPR7kQ;kU@@XEs0R(_FT(@2=aQCCxbpy57QjYaAXO zk<-#juP)8cPnear3IPmH1k%#ds>%yd0G2~g?L(CTaWjgYPVoScm~mn#o|2q~MhY%5 zip#a1jSK(?Fg}o!-jUg#YAs&2Fu(`|KvxG{ zrl-%dYtqUn#I;g-)67L!wWF=--}n(78bq4zT%1Q<<*d0PDgOVnhx1V{LbbYlPp6=6wp9_MuP^U((U(kM4Mi%Xmm$ z3}|^dIsFxYdkeYAg4;Zh3=se!arJ zgs0R;jMF+?faZ{4#5dParxIW{NJt6@kP^gF*pFvheH}Ge3FCn7XlFIYgY@PhBRwSGYcEdO`aeBysratuPms$JXD@11ITrUnLzW_o(s8w4F^ki(RrH+e0} z{%nx|?~q>;Tr-KC?rRLBh0?^hqzbchAob=}5b9@ZhR*Xo$m)@zc zvaG+T=E=u{dEW{Kn`q6PM*D+V$!bc_WFJZ4G4RznJZ6mb&O6XIf$v^Y5Gm`ltin@A znRVP-(}!TZRN<8+PpHi^-T&r$#~k3~gMr3jjI2#B6N0MpRZ!b+`)IIM&={Ln(~JkO zbOQjxgctr9T>BoF_d=2x?!Daw2@$|A{D`X#6HPgyi5#AvdDkIeUifqlF70>np1 z{$57asV z+N=w#>r;q{icgJU4PBc145$XXc%-r?ynQWN8L3~a@e3sXUXllBLh~N`Lih%z7Pk%W zDlL1_P`JioQ}m}da&%O`tn83AT`z@RP8DQuY$X$Bw=l@y2tS@zfJA7&`krZ4iaann zn*Ho%$kyB2|IO`$HV??6zNukTd3LdWkx+Q!i0pB3CX9x(OLicQ&n;PP)GFi|@o3ze z@JuQ!{1{GWYVi^a3j%=$*j9iM=)xdPkN}7T=yj|BfMX*rM35!9Tn>Q0>8Vv4MudMb zwy=;q)OrAj$A-c?Q!?kjeUoLfn8@%!d&#O35{u?f{XEYX{Q(|W@sfdLG;%EDFzUE8 zYBeHo;b353=jddJsukKkBMKZQlO(x~Ew*3Quh+cN0l*1)x=R^=7YkrPwFTgc!#$b` zY?zps;`Mr-N+oaS%0EfNW72#+kh{vL>Hn3fDA8M2v0!4NhUTL}7pj4|rn2Ng=VINP zhvOUDA)0|%wLT-{BCMJ*$FU3J9c;CeQyDjiA|i-$ifFo=t1)n5P&|cqCCdEWHTT8v zc>B_<90Ld+;yDudLV}YccUDb zMj*5&IouOqHp}JcWmW$X)+Qp!}rxU<(xAh3H8yW+|W(=^fMkOzQl-5UPhEqQ&#)DDwrd#>+W7vw%1zS*ok# z<^{igK(AILgbxPzw9XJ*QUHLLR1*uO#qCfRQF+iDVC8y=DX#-y7n7rbgYp8<7JD-)k$E~~AOK(oSuZbtM34iyz59U;u~aKE_W$NnNl zKXN#*{*|lL*xA}n&PbFu0Vy!$l$0<420Dq**rZezKZdYiz~IgYiqdRbSMh6M8dE5- z`r#ocC}^U}w7-OTv>*5ae)7g@3U9XD(laHpH(=@^bGT6Ybj3+axE?V4J#YdJN#X?x+aFoeO-O9EQ7N~jP52&KDt!IwxCSRg(Cdf|u& zklPgo*s#B|l~H*IgB9A*{hC|i0Tl9IfeYT8JsHgXnB1hABT|r3lgC~qmrssGaR$E- zp=e{Es1eIZjW8_koE2WE0a6upAB%YfVHDOOC^*oWh5$=fV6}@#hR~N1Mg_`e)#Gj; zheRBMmbXuLn8o%G<=2)BXwv!1uJhl1G>{MwpnM@M1m5u_@D48Uu7|b;EsPiA8WHvOL=m2m<9S78U145;a%l@!yrY; zrWu!IGo2Izs|nk8e2wIMpGw}278t<0?{qQ`Kd1YXP3;g0XgUOFG&}jbV}I3`0wOm3 zL?eFNaOrAh^wn-6UG)7KU@Mc&+a_A`#l{rVnx>kyzbm^Cdj08ZMvo_32~_QOg&&zU z+68-&8kn%sMHLFINqjKV@ThNq*^dy!Xa% z2aL6hrcK0@X62FE1y;Z+8M&{4PAId+;!^=rf@p97JX-)NM@azs1T;JumOzb5)K?RK z`JR9~`zbF3I@yHF(kd7_WzrD_1FeM(>193dp9E*yyKXv6ch$gBlKa}FFEH_{n{3!m0hJZe&4=B>^sC1Gi zs{3;@#>z=p>1o=JmLCIn%C_n4(XTU*e31&m5cWZp2?eS;i*7RV66E7V2Xd#X(7$b6 zCCX^lN}PcC}j&2%OzTgIp-z zGxdgn8Vd=&SO|S(jjN=j1ce3350v8Vtpj_pKNE(C%c4EcOaJCSZ<$86#sR%+P zz*S73{crt#!4r6Pu_xOdq52C5Wca~vEN1~s>Kuj7CVaEmM=o8&S(vwMe`0NawH#D! zz@zN{q8Y_%{sD7=@jxX`X9DwA$r(uiE0T(&_3keM`rpI$=M(UMCsjiIEKm73k^wMd zA@K0P&iI|=f@$l*_@P!<;hRR&1032&;aIXmz`s-@*D zwDgg3QYZNrVf_O`vuSVYc6riLp`u%xqg;=3^qO~<`#;Q#4@H#P@!MXQ3mhLc#jHg` z4|KBgxF}l9t!II=-Sv?SNlWHP*|ibvWJI&6i}Sj|X+C)(8wjN3JcKKlK6Qj`GGF+x zCBH@W=^zVQ1@}h*uQd6l4F-~l|GH#Cfr%~0L-lyJUADHjdH-S8k+%&44v0O4OKtUS zX-15{6kNdmjE<8#Bjh52%FO)QGCORICU>mdODN)Qxwxe$!E7u7ON^sQdMwpmS2{J+ zFQ3~v5(UvB!A^Z)UUn>^K+PZ;&*|%V?E2t0ATk_0X>MPmkBP<0XbQlA8Z_3ohn1L{ zU#RISU9b*?HuII0vc+UeXJ~my6n;xL@?^-}(k=b38vkm5g9LC)hxd|sdr$5ktfqT1 zF2^sd=B6!bSG_C{V#QYXUGz=W9k?nxi-|?{b^dd-jBWuPb<8ADlx$N)tT;zg0-S&D z@|jawc10u8rrjK;n#cZm=f9zveq=KX)4jdD_LRB_Ddo34Muok^M-hfS`kv2O<8j>sM>-Y&56^G3$=kWd5s0WMB~R!$qy*2pLPajh~(M*%|js zsV^9fXJblAO7zvE#$IkRvNOdJ8Aj$VZwjzoU45AT+3m22Fpl{R!%5y!3}s<{DNdXR6%PL%4KmzWLX9i1og*|99=>p6 z1buV1u6Z^QTUiPSrb^e<3~{Ae61XbU+P?+>siG#Bs`hg6*+22oj!MX#FZ9qjo?Iw~ zM)MN6xidF6+{m{MWr==(FA)?#GU9Q^yF*JUG!>S^-ZRULyiJ$euBH5(5Gzvm>P@WM z@YS^Y&~T40mAXgTlXakbe`?u@ny_fUV%fAN=A#f|TEyN&dr+8*-DV?;^KD`OcCsHW zLS|P9La9ferHZI*yBAH*_aEnnWmb=>C6_RlYIVE~hV^-$uDYo_jk9|)ll z^kj{a8yAw9y~HAHz<{qed3%mW7pTU?ARJnj>+&{UxsPbN) za;q6*YNAjjum{kb0D=JyOlnK3xEOEF0L~_=caXn492HzYv=GrZ$!>5Pp3W$Sj%kpq9c3R(Mkv?c<8zA)8sX=q4bRE~V59+uLzJ<0k4KC&AMiOQ3*Yo|=8H;XMwKP)*T7VJq2S8a~1vg#|G-I|PM z$fg-^d!kX)4=AEXu;lF^f$1^Al(CgUuD!PX7;>o_^FNBqK^)M?E9XpK7Z_;LGY*~~ z_(X*r<%BLs8;?7hPiTpK73&D+zToAuBqqmF*%vR#nHLvG^RgLTc%(fD=IQkd`Ph5* zQepF-+?BnWKHhS#s;kal&mtm}kcAhO0A20FS%C;p9c(Jot<-b0oi9OR__H> z`l;Wo5ytI}0JMsIxSKaoGt?l&Vo~MSipgXUVi7h^%ig#Th8*GU3J*X`yUV}h7G$Dv41}FMBK)CYsS=C`-$H9Dfx<^l82G)uz)mKU+eAt!b1R z)?wW)+4(-3Koeax>HY$Gxp*X=Ekgm(Y7^AmWR)<^K z)+D^~MN-(uqFm_FgFW%mWn2kQX@JEok=Vz_3T?7N@kqkdERKznSK1-fM9!>F_pj|c zgi--xa|*aOp`yr?F>+mIa)j~o3mBcKE;*K*GL^>L(^TC_xiWG)+G&)-$NG#yd8^pEbRBHi%mr$ZOlmxL#iaP3Sp-- zeY{vQt=Eo{dL*~*L2J6XL=g*~ACxqZt%9mHcuy8k>2#eLIJzhwNB>%WF;`yAw|G*x zF)#RoejmOCySPsD0V|XK_gybhGcgQ%RbzWhAf*_5SUuq31`vvCMUKoNSI5JzsMuJM?kgzgYH9VpKJGt>r<_{~ep>gcS`}&xb>+gD zakH5zNcd^Wu@v07c|&^e{mg({w!B1`GZJg=%g)Xd>7%pf=(TNGtW%Z6U{YfQbv=4W z@r>Q)k=~<^_k-8Eh^n-As$rgWLw19U7J{vrdU3P8n1v!e3h2+-uVTnAu5;1eV6Kan zJ6-4Im0dZ|b8i1Ng@1pcvjqpvVBXwfV{M(Qn8Ui9D~hpti1b{rQ!Oh;fe?&8%9@ij z)C2Zfx0jQRAVP-fjcstOA)ju>v6|k^yPS5@n+ESIfKH)%r zuGOcq+#uF;za6GeR=A^p3l}hKZGmC*oM);Ny))R5)gwohH=t-`Dw#^=uSj$FMvJ6` z@M}5%t0Z!}nf&?wg5R=rn_zi%Wz z>Q9`dXmh2|QPjw_rs(09?2H!_C2u*e6{u10R~0N!yjwx04*0IdkcP(9`+C$yUo)g) zkchneN2H1xQCa9Ug^eOV2fA6<@Ozn!jqkV8-YeQ_jBZhBPt$ADbjh`);871*`gR2Yq?*#o(IIIR`RL&())73$;@z9X$m z%m1KvW9OD8sm6MzH>-KKjkkY3wm;NVyq5ooi}Zx`ouG9nVRIu9qDvb#AJ=#k{q-FcnDMDMO=&avLf$ z&+S{+o($oibp6&j3_?E+dn3O-c+{F&gi4fYJ+~cICYSiNtgMZ2Ik3F!Nu`fp zLHP?zo>SVvF%6{>sm6KuS+hdfOp_rkk`0b3E&|pq%R(?qK!T?V1xmkw?Bt;JE$e?U zmo>{LQ0mvInp}P;h({AiT|=`{J((@U&c?0NKMA)+2p1-abK=rCC5o}V`sKBD`CI}7 z<#4Ic0D4SciO_(q*o5+chY|4NCM^#56`9$eZZQvn57L)fv&DBS+gERn^u5m9jc~t7 z{ZvKBN~+&fGu0UNB)aqNW651icoQ`DZm!D=4fVZU@K%W1jGBwNfy6?aK0@xA#aRh! zq}(SeAcxI{O;3Zy=c}UpSTPiM=FBdm`>8#WyGIR%g}OtrfCPo-d2K%R4!OU&{P%tp zL7;;O;Gw8wIM87UT*+00-@@njzppSf)HZX2KF6U`3iUf&FR~&>`l4sDqo`zc@y*I{VSV9$6Nn*0@>7 zQV4tTd@BoI%J*8IuvSLH$_%b-U6tAy4!JBmnxa=+mezlPPZC*vnzGAWR+L-tR6$c5 zLi#K+K-@vS5-K+p?PFs(Lr{ZI?#{Q_VS|lGN8>|@YX7b8H>5~V#g_&=WdG%JphE>* z5u=*~W=*fu^8@$iJtOYc#8-0#$`z1}BEwRPo3-RC>3{^JHbm^@Vjb{UPr+J`7bd_y zP?2dEWx+ch%TbJbOen=>tHfPlk^JOic?ued&a)7*Ss7-nvwKrGL~PaOC3#YYvp!=dAqq@V$bZtMgpx$HK* z+9Y95M(GCYw^`nqs0O~_^AVqbl@8U{Uh^SeX{l?Zs{PPT$b<$`l>*Z|_htW)yqM?E zD~A-GGCj<#CCC8M@_=$(QbC)?v2aD32{f=jsLYs%#PVVF_MzIh$;^V0^G{n*0r!^b zrhVzqMw35O^>P>u&j&sDaH-hI)m5u{-z6Z@wveF)zb|D8JF_56G{vAqBv6)vtCkxc z^)9KA^|zdx)e(!=uyUZ%n0fiV?+~^^Ffa8+Pq%-zS?x{nte>pS4f{JyaoofG6C%OR zX1_OYX4oo!g?RsGk@LADwBD)TyZidK`VA~tow3R*7&oZaeaj`8NW29JfCk=FHENwxS&1r4qo+ zkAj?@@@>x})txRM-ny&aD2LUOnk;3}RMB8KFeV%@Mb|m%5&ZD7X4}mXudYrxB*|s) zY+soQMtB65TKA1%;lWK{DLFdw#jKB{+KCuRu zHc!Hv8nba3eN)V=zdozH zE4Q>=(pY^MGXdl^y4&!f*0BZVXcd12I1rd|ApitsEC#@g!zBE&i%O0bY+LAJT(#*a zQAhbNX~Q$1I!6c9V05sRY2o|@H+T0WxtvyrgUJHEKBcL1%jfpH%_5HcU0%)*2L+7}_qkd{v9qZdHBgst%*CSo;|` z690+g`+Ok3+jkuC*|!736i8MDNl2 zgGIqTr4fram(;Y`HHsiiY4T_33&JK5U1>7%2!gYt**kiGuFnhbS#VF9a}cQW;Y#lsB<&1$eGOee zAf*ua5**oxX)XLg z#NG1c+um-|Y-`n5CQKAHyA%;Zk+L%h#isa3C{yFPyHl6PZi*E32YGcK5LpBW4~wmr ztq^SF$Llyq8B18>Y^`>C!ZO8{lk2@v)9&o1<=qXM>NRv&Dve1WJvSYiaeZG2w~^Oc z;S~04Gq{$Xw)8xn)4nB9CNH0y;Fq|n|Az};6&up%gHoJqdI77W%=nxjDctcyd6fgs z-cF7EC0pLt%hrXT>3dh&=_qy@u~Bd9((INCJP!Pl%(5up;u@@iJ~kX+n_O(7DL%hD zm@w^cYmf$q{Mw<24*nhiV-u&RG3Etq^sftz1ssT+wPDQ%lI=8c^rMo4exY%0r&hH0_lZLUy;dj}Y+3I93~~-)p{r^0k9iykxDuRHIeEX%OB)(d})|YU)qu^0pW^ za#|W`)mM2ubd=2~f4P=*RbR;Sk~Syq!*lz|>9%x$J!Hz&J|?s&OiNI}wJO~;UHNlq zil*)drWVR-X2}Fz>cC+S3n7Dg9Ib@c5F=~uR_=Yn5qmpxJAKP4@b^>?5Z?)RcR5AM zJG;C-g)PEyJT@QipHOv&hAqbZ$i3w8hD8|N{koNV+`9OxOZNwCq9#9YL@ZOh1Kzz) z14_Q_PeFG^*>t!gt`4&<0ycke1D5iUw#2Y!J&lJa_7 z>j#7kLGEyPtuhNFO*({#_kUew9N>L!NR<6d5fGsiV4;43mt(e?|8D2s@2o^TQJuIy zHhKg6U-WkJ*=Dmyq7Z=n_vJGT1i!e`oLGl}UK0Ak2N92Lph&U!+?^B+`X??cG|B%w z0ETi3za*o#S3Z?q%V)B$6lu|3FP{f9m?OA|L>RRYl!!V+njchBLAlw{uw4n zU!a=cHaIM>|6Rv_)nLF4e1vtEbo&+^^Eh{}8V7~Ihe+hbpt;Haz78le8XM@HuitEb z;>LY%f(;%0pML!N6Ale55FN8%sdMnR*#Cb%c~+4#SQ4E|SW*6{2H=03<9Yac-kEvT zEzSBZ|5vyAP{GiZu)y#4fE8&#r1IIv4-O!*@K0sbAm7av2`YNgfzH(>R*pj?GZSGhVJGxD|hwihdf z5ySdBLD>u?MJj^~C^D5*rfW(?S1p~VG;Uv<&WcsIW}ZnW>D-bokhn~6tz3>pL;Ub1LMCSInf zR(>f9)nuI_#pbp5l4-7=9>B%+?DmXMNy*Q(107kA%C-8HX=6A)wNX-kN}`BoU)!wF z)WYLvTUQokb5)6aVq})??dv?(l1(L0|^2U{I~ z-=(d98atHssq@1xR+F;s4-Zu-hrRHao16Oq`;Ca7GiEuf%*UZv$#`t&b`XGM2>tM_ z74V?RdP?(;;Iu4}yAgw-4A6tA6C$M=yo2f@ru#rRv7k@z=FJc5t}yAncS3X@R|CG- z(7Iv0>7*-JqHf~sj!BK#q6ZZ;@D^PC$*Eot>*6dv!_Xp^Z=;yh@){~+p++t1`BqRY zVES`YcfWq3M!xhDN^Z}cBZEeQGW7l{ zH?0RsrI-vyPJqgTb1VeCefPRUjBs+lwmes21*4Jc$!8&sDHvxId~<)EaHF2qlyD2T(o${XNbcvnt?m%un*g89x~53yMDzwb zNTxRNd@ep$+nQGQ9_$z03yY7czpY1XyY=ecoDc1_C3>LEU)SDS94(2YLRWK~noErc zk3N-sNkYmb#1!epvnZ`bu=S>{@@jAN)g!j5ht$=XgBEmc&QQ$KX(#gsJQNS-`?Z?F zeCd7qVMfzrx+<8!Mo+rxYeu>kqN{l&oTqIpa+V63_2-c<_+-VE3iEJZ zc;qfRbRR;|%w^@m!l+{LAfg@$(X*r6I z2PCynoTP1}52Z(v&*;fo)Dh`V6RsRv`Zo4VyO+V;eR3=7H z>h}g0$2;YUByBhJbaI{E@H0lKTOpp2ZCO`W$j#wMF2Ula$VoOfK zykr!c>6N%=<;TcK#j0iBiZ%)zT{J4b)+F95ESy}A|7vUhO*!PKhome21$Ei8H|rIfzXG*3~vO~!pXLuIwoZ#PscvBxI= zR30*oeGX&}bREVaCh;6MG<$7^f0FBbFLpY^nF`lUfAI7Ka6mjGDOhJhSTRpUn8taw21ok4tvBjDAE#V~D!ccax`{Apclme5 zNs69gM@$~uj*eo~ta}k{k+nn=Z_gdAVn(gFh(e$p-}Bm+SVAM)%)Fcvu^pjreJew+ zGC7`}u!$x#o9Bj`zVV~=E3`%-tycGET^tD9Fzj(Se7%7VXy|R+E{$T9 z>TmM8>5nqlnYM9V>o0FW2{SXPYZ$Lw3MvAph0ig6RH8^fXL6t=E}i?4_M`0!r@;?H zP8*Yr4yU*ojhWVVeQn$A@0P8~x#C`Si6@Ob-s{>lJO&AWR%IC(7#^&Yy0e^-#W%!G zOr<2_wRs}Dd#&_*8O@OV=S##BQR5@BU)rVyJ!TxeXAjALHy_$|(d<(;y1o831@ zv5Y=DIX?-jVfhy%q}94ODT!#y0D>}^@Gjn?&RBx8p_&z66${UT>t4RTdA!&1h2gG za}tgW4$Stsb?pRfGeyELdgtSKqYn2awup6Z*G?g77r7X|gbj{qb!^Sf&Z$9wvFQpE zJ}Ua#cFv$f5n=U`&9h=7Uo+1ZNt}7~!!q{gskwCQj4tV;-QBT(j{h%h%TUq=iXG%)I9>wVB?)Sy}xnDH7lzcd-$Wm(1`hNeY}+5@P;rxWfk*%U4FO1KL0;*(oHt14Y8%@@n-_I6>*_83h+hNg zVE8S2DxR)Qqu5}QLZIu!Z~O2r8kcXr8E;}SVv2{NT)b`npaGjVkiSd(>i8|KPm#>4 z627o9lv$(T!>_EeTlo*dZ|#DrEq6DeTQJ6PLnjW(8E-_X5wfsV0yOO64j&yu&#$VT z`PmvdhWTxls0mjGU`2=7!GR4#B%?49V7~iskjb27OS*8qgQl8?z!l^>qa}}Zt<#of z!!$B#0gnRgV*Fk_LZnweDW$b(vIb=5#vu5e;L@~Z-~w6s#>0SNETaRz4C16bb7MYe zaWFzWioc|N9Y2xwGM;(f2tP)I)sxk}+Sw#*cc;j&gF;71XQJk;Tpt!vsvg!!a^4CV zucdkl0HznDXORHogJQKSD8GBz#`z2#j82RQ>;J@&79EG zzS&^I%MOy`O$;w zaC`ii(8*j7vR1gV?FATXoqc3V7<(a~yB-NOm(u7r;Q;Nh^OBJjuTSyKLv7I#>z;8- zCs_g`&NGg8u|@uhDr#LOqROk$$F_9`o0E}NAusl7c>>z#)s|`E4`fv#gDOrBulf&`0SkPBQ_5WMpPh%y0IV|+gl?fAIR^{3W$GEl zN4+Ii%p_kjmVUUwK%_O0fDo90D=Ml`YE^6u?+oY7y`9E)XM_s>T!pWICpBNMc(DW+Wqk`7h;n~pp8?7XKNq33BJJmOxI6XGqxyEBhN7o!NU5*s?!nojku!+i&zjL zS$gpDg4{(E|yr?`TEPi8i!^W1j!T%ESBlHn-`-E^OD=%w3+B!boe)zIZ3lqOWzXi)$xoy>K$kiLlK8j~N_ddZfmWaBGv?)sKs(4s- zQen041v=CrqLFecuDnh+^tMNV9j}&UHbwJ5?{M?NyqJ8~*4OSY4xg=NNpUz&V-Ha( zo+HLAXN(IxNxDFMawvii6vxYNU7)aKAqmC9i2{dqW=*_ zYIvy|@^tN_h_&VK-_D}q`h=7TIa+~p3{2dM(16L^O8t~VWo*Q zhf=Lu4PP~*3r~R^u3jqqsQWhD@&H~m>O!acY`IDYX>!e<6p9h7JYSwZy+kiMa-r!A z!?b%>0;z>!Fg#;Lbo5S5W!{!?Y2Y=CT|#x^SPR)(b`tef4>cFzJXl*qrFI*C6eD(# zGuuvEhycI|eXaI!uJI>+IfWR%w!e|dg6jkB2I2>++Dw*R>e$17_J446V8XH$FQjKS z3b6?%0Jb8kk65Ww#1ncwjgGuZzB-1t@WYh{-?H1_QukIdYU++?>{h8P$=uXU>6r$==( zUOP}pw?O{kKET9xL4N!YFkhrHE|4GcjXF)eWu1>k$~mSx#TNGgHI`akokQsIj{4I= zsro0``)6M;9KcTWnz`u(dC65Sp=$dtn?7nkW4HO7*HSAb<~~O|RzOS}<^9%?n9I}J z4|YoU&L&i88BO+;(<@w4-)tJ1>68+TLLnCbvaLu#1bK7e$N0jG9lG}3_C2vyTNy_b z@&|D$c;gN0QK6~p{?_GOiOTVZB&w*!KKrE7mG4oF1EVd5TifN?eLBKZDMH?~ne^K$ z8&)<>LJSIv`P<#kSY6BFG!X4QZeR4Wgjc^{1K%(x=Fy%eQn$(#e03B zn5WcXJ6EyfnW-*0-@sCMaw-u(yH`%Puw>AnvAoDUZcHK*tQ5(C}O6K~=`qn#9F0s1aU{qG5n`fxpeIR=rb+k5if5?gbX2@9(@52^Df)_?xX@tWps z#Mdw%S@knhu{Qjaw87wNFF2Z5Ee!=%qK=*@#E~rPr=fIa$;aPjgsx=td?$yc$Mpl= zZ&ySbXZN6rrB%v%=_W>`{%9?8xNGKBjWMrPhG){mG4MtccH!0OXuK`e5@D#1CL+)M znoxAw6~OWSz=d4j7HWY;762Ay%}R-m{)xZs$MT#=k1sN>rOQa_m0)h2QmZsZWS*W~ z3}361fn|H;ECrC%;QsEGJ^)Nz?*Wz!rIC@5M)Sew57pxV;fJc-yA+rCs0n>{d3}!~ z$#@$_$K^?ObwAxOZ-6_I{2|(I!s~2I)9qeMT0htykorYlUP4`gQJQ71gWcc8U<|xS zjQ~bS;a0z2KPy#Tec~v3CA%2=QT^EYHW|~l4*o40buKm9#ToG2#}{Cjy@)BLgL`Hl zA+pxbEn`1Z;OIE#I3?_o=jNG*keWAs>$RD^F7Lv7)^U?rC>i-oJ!*1@C>*M9Id`75 zvP7~sL3aE(S!3S0>uJ^ck0b>3aCK))nGnA3HOAN4(3Snkw2)R~+>O%#i&rh0dD2?5p- z$Ep~A#)=VT(&HVkN=z*KW02@ehNEasTgT@3nEIG;LT>VzWV{zb`3}wT+6*6p`&0^Z z*5~76ekW)ejag>RLO4=T4r-Xr?;m|uYBU?gjG9ZGd6cIZ!-wf8;qvMdA|{?Myeb({ zPp<9H1Oj$Q!kbgB-<$V5ou>gjiCTgB@foJVx(vxG03Xwtcb?P_P!X7SCUow3aI@Dq%H3%lkHQso(}@2Evvk=XU56tk`_*RijY?oe%&(FM{|cF>p3aMG4aBla~Z|% zuT(c@?+r@x&i^ymPkXmnK-Pi=#$swxdNwFLRYB*47mgO&XNcBj*QZ;@*E@whQB=}pgCRFD6G(Q7Fpo4~MsGNoVBUeA{s5$~!+jel z;{wSFMS=8%6U*5aAUa&!shR1V&+^AYy9M!ZXX(2lZ4l{ zBp49Z8TOezvd!4Pn?d~@E^@5HrhB(6BA8uwUs*zh>~djd4b?QYOJwoaMvuRl{_J@1 zvVsW!Km1WKTZ3L#AIaWUVz#^(pi9N~#?!WDDqHKg>#|4#l?HqTYb)N!XC;M`J(XhS6#xj#hc@$8lhVos7%D*+d8ssDp4`#Z@`^i|$k{N6{ zU47yO;e6ZQ-Wlwz6x^xr-+wLL9J#vbuTm%Wo)gfj?PKFWHSzdoC4?aa3?r zJ=&U7k@q=`7mb`5-+{HWE{Ns$^dK?UdqVFnC40jV&V$G6_DFh96sGIkMYkEg1m~9k z@^?wj>`UR-e4KFmQzQQwlpeBL4z&0=Ud*}hbB&pJ1LG9=7M1|Yl5;~Vv0dGeEz6TxK~YORJb_y?3%ZReJ3GaN+IevTC{u(3ylP0YcYTLH7#G=kv8Lg`>aT zssf{-3+f?q-|g}RIH8U$BX}3cka>WK<(3L2=u6lfDQdXC-dT&&u^l?mFE$RqUuGAo zd#O`l{nhAFK8fr1j9nu$$Vut}JQ78RGw%%zw8lV(P>SzL-2fo3og(wq@n5=Ej$Sw% z1T$uxBu*D^o2uv(Q)32%Zr+UldYiccym8#^u3Sm*R8&h>IIVJ_;3C3Hfj)36QV7C_ zw&<}ES`}ePb8$G*5|)USq_?^N{<@#S6W+_@wI!zDll}sF^Km~vA{vh0EwxlBXPBO*AHEQiJGBnzBP$D>_SSF=%O8x6NE#HIg}YnuIr1 zHr-6aN*wxUj7_+5?A!Ik9EzUIAE$X&#E=GUE{2P~z5$Q|Wrs>7?_{3y3?A>5R-flr z&X=r1ekrB<$xbaY0b_d0z0$i-9G9nvBnXm&L}#z1*P{IGC%!9FS#iLGXzA#zim{E6 zJBm1UBj9Pl4#_C~=4igue0!Heq&=*~8)Bc3ZwCES$ zy7Rr6+yh9eGiV6PtW_iu+9&1l>SFGEioJ8z6Q3fX^@PCU35Ax%PYroA{{%D%hg+86 z*WbQ_eF!%Tz+HY2$+ZH4ROfdzge8kIOV#no3@DLJv2unRH;O$=3#M=3MmM?Jezqi* zo7hG+;fXa}puROr*UtG(r&VdImwOV(;|PH0wkB*U=V;T@(~klE25Mc^B5A;_TL;Cf zhSOm=ZE!NDRUE$d;u;bJY56zcij+;7PK$5)7a<>r2eX13DJ(ye32KKr<&e&Yi3Hi3 zbQW3aqB*U9{jNB9>+?F8v3F-Yf5geRi36acFe5b2U@lc9Jgu9GjK*+kt}}#o3Ux?N za>6LekO>m73D3f#$y1Nn^_qInGjQ9asWe2Z{OG6!hOGNmyiY^|yXy4P^~ zY^{XzDPW|fxsRKJ5awN9#5UvLBG2nnY$%l&iTg?z_i1^A(DirxVT$8N?vr*|%Uuu{ zQXOLd$J3V zpP`ysj2F@#pG)z|eyNYKsFtt*WJq{C;Hb$M2p=66o8Snj4%tv}t>MA&R0Li$wL)GB zjbO5BlMbFwxy+dRPtK;K39M5`^U%Be^k+F~Sgzqo1UT3WP$J zuG-KBnDn6^!z5XfSfNYLJ4?egS2GGvd!Sv36&`@^|5ytZg!hGvA-2mf=7|xU1qo$` z=x8x=Z_EQ(NrVtCI5$lEqMK0Jro$LNh-M0hKX{#9B%M@3ESRDqa8Np!Ie<6-Dok>JXz&h&TN6c`$fYQh z)e%ry9U{OcEqa5)2+5bS*r3+UGZ;I8Meq(M1-1Lb^LAPWz!Gszcs1RkdIUh{D%|3h z5|~b6BDRpzTS0RHO{o4)g6kZ>lZ(9ORR`3 z;vN_D(6A2yo?JptZCn!i0^OCqQ3>+<@ET?x%mw0*vgHRAg4LF?QJOJZm_l+B1~q;? zUGbr-lao)d0cOUp`XZA>K1TTNSbR9Ar;7?+i~7wJ@2C`M(IG2ZRH?)Q9N~fG6bR3) ze4B6ZV(VtgEHV+__s+mZ?WRg37jQt!>+|0P375V~BnY7ULC%XS031s{(aVTI{aXbQ z^Z?Ba{`LJ*!{ZOjQ?Hy_p`fFn#hQ1&pVclrb?GoAe%56LFLi65>&Uj=akyC5AWkxn zgrIfUmh@_h;L$yY#*5^ODx&DW0`BJl_#4kAX}JClVyZqkl0uu%x3ky@#6){aXaj+Y zd>7-P7RaIaE_&%JI_ukP)jPC^hXg?&GwfLGLlEEmkqPPimd_K++aiu7hS8!C#shLd z4XmzGbsb|5qJXgxws1uS^^a3fH!ng!9CuU^Df!O_#GK~OllLleJ$lOzw_m|W@S2Kc zC>yqnFP{nhV2wNVI^}!BtSR-GJ`lJY7ZS=7pww?GD=@JT18-X7vC|XCr886xt6I>N~fkBo>7wFa0OGaA- z)i*B^%!XifN%wsx%NEdzYM_THQU`$^VIBYNxlRHK4{G4lZ-Xw>-vL)Bi?e^$Hq4)W zH2R))~k(?-qF+v1(p|YJdVmYOsHm>1tP$HpwlJS^7Z86TU>&if#h!ZC(`I z?)_|NKAn!S&y6}ySloU3zJQyOITfq3E+z-V@kZ3X)zf8yVsuGpX+Dv}`%tT`6b-v9 zPJ9VZ^G?j(85cP!6I@NP^xIMZ2ba=of@K1C3X{s}@e-V2EA@^ z)LwD%%Dm0luFv-_(kB;z0;wj6E|SbQb@=^FhAhR;+khM~M-4>95JvKXe|0-Ob^v{| z2ixbH;)Y)lRKFEF^nuh(s){e61&e=g3ehzMBSY>f=*R@Vlk`E~Hb2Ew_cSt(SSELH za0u^9*>6Qd&%hKIjvN8cfUqQ&&|qn%ofMPbz{o8x0B+V!85)$iMac&dGN34M1jCe< zgyymTA{SFOU1vz^y>@iQ)He@oT-wdcFI_p@e5x3n3VIs2M3#AMo2w8fpqE1*gndnN zQ9B{rhS zA-d5Em~gELRLam){;)}$p07@7$p)nuXMn;{w51d;eXygKbG;#9rTPl9;d=%;``~$X zM=5#~c!2U>2HNraRO0W_4nIhRT9rKfTbwRgwhtPI)I0i9et)+0_#ik&0KHnXCQ13J zVdij4m}GA&+9gZ2!TM^4Phg^N{2m!b1bRN;cRM!0PPApuD5HN*A=%M_q(G{YMrb-5 z^KKQLMPV(};Ezgz1T7T3#Y!-t&2=nLI5;>`70Wm(A0oUooGs_f!*TrFxS)#_*%vi^ z@Gwt;ObGX@Pi`E_TG?Lf#~D?jiiw(9i~|YSyx0eSrC3t&hS$MzpeZQ>wi%Ky;P*z0 zNbJKO=V(t!Hn6+V{4YNzhaDF9`-I3g99D>viQ;DaidqMX#=v~6#cCYT2jJVdpk2)~ zSo5yyb=Z4@{X34_JQBwbIRzQ)NtOcgJ^ENawRJNt-WoGT7EJ zjKC*>o90&GMlG~4q;*YA!F^Ro19}hlcjeNI74`2U1kV%pQ3trMr4xA9G4TPw*y%VM zkR>mXe_~7#&}6-`5;UEF+v;R;!Gk<)uY?j7!S$E&~jg3`9oQhhsmFvSwtqib!pM}KJ8aVLw@ z(@XG}Awj?y1TBe6aEQ$v>=I*B&IRERHNwD<8hRn)7reZ0I~aV?A?38IL`%KkOeI`+ zF>=Qp2sdRL4OPb1os@d_?#{R5Z%M@^brUkpCV^0K$J;@{Q5k$Re`g@8ffFbnT@wqTkxvdtls2;6u|mIvllrf3_deUtF5UApt;{R}Pt47mKL*4J1V>V=F! zlvVE@g)^LD#>sDTO!Uk?$j9c)Fc=@qZVu#|s}-L#T3x1yui2_XcrT>aK)~<*ctu75 zi~aMP1}f!EIRnLE3*8v5@$@>DM2 zsEuRq0RWJ0`LP3!?}Bqmj?R>NDqWHchPute?fLpzy?wD@d4S_Q*1`tkhx+(op!z&I zz`|IEe6ZlqylV5Ob|Sbm&E$DikK5(YMf zOrCF#$!QWuW6BDQsOG#Ww&}Nd*2Mvj`}72z6Zx^K{MXADUIysXfU9E$ix^r>6qF~W z9DVi!1r^EGDGd=UC4+v$kjd+^D6$Cs51%SB2eX0S-{0TZkqU(j>cC<%=wUXmY3eYS zz9Rc#7)5~KBoW^S!*WoIw`R^s;UkJ46Q9xhk5?55!ulo83{No!q`L@ zEIBG{ElTW)gkl_!x+WZS^d)7affm43-#iIS+k>VaJB$bI>)sXZO^~$K9<@3BdV(>8 z9~T#wBS}>s{ToY1u^Gr4n3;caN3sP>Ba}H|Xplo`zE zR@Xp`TYf<%76VXgzt8xiI8sap+VIyN^oPH71HjP2H2EJzX{T#yFSJ11O^Qvc35Meh(29LL**8E#by*ewcWc!2}?09*jKZV z1ku-_Eqyf;6C$lC1(vp7E>W8RbbAUld|Ya3)(069lGWq{{$+!a8VQyKu4k1_hCHUMw+2t5RN0CgeXmE z7<3MTcaI*ppdI3~E^$340QGOo0s}m5cax1@(0Q<)o931GK(=DXdEW@%8qaXJE6D;> zV&EYQM?GY7gL1+zlQQE2?SB%t5^m8fTBLkklIv0wx{}~-j%V4wC8jF9PfqdA<-0fd z|=-L2SXPnnd*nw}XvC?GLboE}N#Z zdHIk;2j4<%75F66DpCV^x6mg1f^zRf>zq2@WWRTr&cJ9%FFi3DnY)Lex7s&G@sFs% zI@qBSl!{cnBd$B4m;ML^BJevCg%b?VbeTylh!?-!04cu7Pe+Ft_tIPzab*n2R&IegmumXk$P&pYDqXEEvPjB4D;}+2*gUb zd=gOS0mihDOnL${1bJU7p^4%ejX_z!&#+h_AiOTBtv!Jt$s|TiY50th*A$3|0!dN1 z1b+C#s1%j0-6QV2>&4oI79_nW%Zjk?-Z0?!&fhXUxwzD zq`GOjdQ0`Y!!8K&m{y*S0aX7Vh+-SeP8*vX0|Hbanf6I~S-)hPLwoVpIH-k9nb_%_ zS4M}9KA_7ks)$W7f(j>*ered|ur7|zB>I80i@3kuMa+YlQW-L3#cw9i5aGNGC`yV< zDwz1Njvaz_C2Z2~gMf+*2Mr`vWWyaI;`{JD^%AI7BN&v3Qb-K3tcnLL0kgt?N1s*f zVXcw|Kq7wRvjs;QXC?VGJF!#-h<;wN4CjeA2xNsjdeFhOFX6nsp>AyODNRAH{3FWx zS09<96!4hozg*iM>xxhv#2|CKvgzV9KgP%~N2x=sk2PbZvl6FNM)jGZ3&y%y_xCz? zXW-GKRLnRs^oz$~B-jWTRI&%z(^5yeAVk*UEERO=g)Gzpg$%vimp+;lFfr&s2F5YE zSOWJC2A~L^Qx83A$80!lag8O)ubc4>%gBG3gtX}rI-x|0mF`C^^yPaq&x2&BEmXO{A3QhT2h zG@syHpzDvl?tyFmI7&akyLb5T7V1U$4hm3W`TU-wPHK^>NDcmH5BMOI&;pfZD=BN` zV33kEg9P^uYAR#fjvs}gL35>#Tq8Va)@eXcn?yoiQB5qU3OOC%S)qD0E3JYM{V66} zm3T?7ke9|n^L8pE6~TF*mvtxfTvG7Sfie%4CkD(*TKwo)7IxEQhi%mo66yZu7KXF! zv5`CRvesFp!B&soYdq3Uf^`=K_2=4p0w@_|b$P}3!Ny&RpKU;i9c;*ep$K9hwr)CH zd%lh~aQ=wA2YMdlo#GFI-}z94kknua-xlE$vJ0)>0@VUJ=C%5WL1VM!Z@ErLcF;TV zQ3SeKWVO_wZ$Nl5)RYI{{rNbb?qdiPJER?6A9a$`2nYyhhP>hJ_rLj%JE=5@a*2PA zXn+5oUoF1=_O{eiTernw<@x5M>0*;_4H3J*q9G_nlAb+858|DkUxloUv7zs_Mbl|p zGP_W26|YVRj?P{5LPDfI++Gm6kt*~BRis-?9*oiIHY>tyUuyEz))Bac@)>tvE+Xt zwonZFQX~_z2B&r5e3BgsM_R(+)d@m>P6<%8M_O0}Fd^m{&`%}}2AwEo z?PQj60*0p*^NOlu;}8#K`=yY=$CsOGqw^~tD8pP20@W59#jQO61wFk_IGt3q{aB-P z`nuQDgWW5}RC zwE;|N>nJsS-f8panLeKKqY%n`z~B9^H6-ct#PR;Q1KX=tuNE|60#J~FRCseNljimW zz_KpiSaezNG&}Qf;!=*j864xY9RKx$m18gY%*g#kEw5sfOOamfN4^L0x)X}&>X#e@ zE*qb_JBt<<4K?+qNxAp1-OvX_%{~HrYrO;{cpy*kpAFu`2|NJ`rVi2ny4et#A|OL3 z=mg6FJb0!FjimxJqPjCMOQ?tEA?__uDS6lIhX`*cti~jzRmAjDIWtfQkC=F;3qWcK}sL2(GzbXERd-&a%;o@7R*5boe7kMyh&7s08 zqnb_fP74UHv1(qj=iy{FNdsvd=K?`o=IZUdpUDbCw=Ci!uqr|@ z*w6;q!4b&*x{*VAs%>26U2}EBp?n)af6JzqG7n;afl~e?VV(>X_00HUGe~^D8K|&K zsryK?NJ6EiluARTKQpjTIeH>@%d3lyNW7-c&UhVJZKyx})xI z!-SgI1cEUoPsI&%Q}9P+lq+C( zO=JKX!FC4SV_`?E@M!dviTLT$==pm09{z(mD(~B)AXE4Oap=1i0AFsD*uy!Yo5`Cl zylM{n9zx0VcRK-x-7_&p{V(m~zrT6_%m)QqQivZK4v@LGij<)zkg|_}8Le3iGI2m# zNeQx7)#12VpG)?C5)i-QkW4P@Rd=q`rVE}9VQlE~MqcV?|6FZ3mUj%G{I|hoWs>C! zKU8xq?W%-4uQw@aOy6iaNn|fhm749av*8P@Ja#JFz}OzE;Ylo~wMMJf`|HVbh?x92 zVWm5yiI(TI^&vLyiW|2;BZL*JNU%#!9CI`i=!Ts>%`)-g}s*^DofdfsvUuL1&IX~Cw<7q>$rT(UHu5Kxj z|HglO!EfK68EQEw*Ovl7=)WEp|Ks4DDBvyP`#=-_W-R~ZM*d|x{~z?bk5@es5wNar zpDbJa-S+?Em_85CI^d7-_c;G0T>l>@5-EZYe8dzkIQKta;GZV~lx81`2xEF+xuIfev^Pmk@}zO_t%B|_knMQEbxv0Kffq*NQZ2HvH<>HUZ4=Xu>gnvE|R8R zWbg$FAi4@^LN+c_z;vuruK-lM)w_#@O8!pLXY{*|SNEL1>*_xm+&@Kuz-5Mpi5MDw zdz>}#@eZ&*atFc}vrZp7tHt3ZeV+gV_ZGcXqQ7qU-w*1<76Fq4hJyHr!k0(@DK`;t zpC$Lg9R>f|YkxnOqJ4sW2<*%Od%Hm@V*|JIBzV!#uy>Yqw+@A^M( zFH{8I+v{O}USzbky$$NWU+8}vQye_Hc<$>YQUBLHiHZQ+2*hPB`~M%L?0`Tfl~?+I zEA0Qc?!NGk7R0v-BN^fUYGhUgfzx#bCS3m4H4rJ1P7N;ISA2V-W(UYd-}w2-RjFt^iA)wOp=+&XIua>nav0L(_~(D}xj`70V2QR^vfDvZpzGYekgWBN z;S6FXN0%=1l;zVn*zyXpmVuY_5wUy|lRRIc^6cCiH_ezP>&;w_O0gt2gR;VoZVBy27G6!B8%MXcW*BdKh2cC?Hp7-60Fml<@}=epBFKf1xN3szgx5Uv-E%tW1;zJ7guwCNW5uZ28&J~~mx8eo#)YiRXKlB$ ziOW-6AQW;zt$w@K*vuj%%beg8VQW^d3`d(aqO6gX!+|i40ZIB=lMS_2Ziq~ z?c#=xZeUr}!qzJ!?ncFsn>c&7aAcWp^v{B48f3ZZuY&QB?wHiR5OL3@8^Fcpfdm%0 z!igaSD$yJ->PP6jWpaOb_l)7J%eLM~$&K0GWkbInKT27x-R>Z~)!UWm6RT^!U(%hk zk6It1-CP7%Ac9D1Q-i=3kBMHwXI521LY^QfGag zO@DhzjQmPq*LD1U%*k(=Wu4jM2ii2_{=4PHH8jy8K#BRRephpfV-K%z;78Gi*A7KR zvkd*w|1ccKQ6dZ1l`?BDAs;z>CKfR;juj+|h?DuLBNY)8eqHOWE+hKJ8&=wJ$z{^b zj}%te^$u~=*t19)kL%PeTIXujeB@wIt^K5CJ|+Lyx{go7S9g(w%36?x(DG`E=N|5p zrcqM01tA^BPa(ss(fS*pO|#_Jh$;BE&c*^I7q|bzoQ?TG`tlC>W-P?fq<}XifzIhT z%SK@%cI-?!v>~o^NIJ_SGfu^QGgKjoj&z>cfuH-QlaNY-g>j%==L8J^i6-K za4Y?3?l!GW(mHxMyGJ%# zW1>qv;tja5>E*`>y4@*?s76mn@r=e#@P@eM{#dqH^v~?+Gndx^>w3G>iT65n0`lFX z76k*@$)9694H^m%2G+hfUdLR<(nGSvtYpLBE@aHpYS)^$acC@m3otBvtA6Eal&I%D zImN!E%z3W8#^4%_Pi8}G?P>yRJMjJXt{B5)e~?l>?A}E#OouG$kymh;8$t7N1eLem z#6K9-mszjAV6^6a)}sVT*VI$E_?Vmbc7@Jyy~|5Rkd&%jHa_b3Eb(;;WWZb*Uq5Yz zRxo{FDI>E7zw3v5oYFRzT`%54i2A#}==!m$-`5S7$m-?iVPRttXKF^}pN6eRrBw=) z`91r)cmFMd&wJhZUsUi6zWMKB^FqA&Ee@NYv1uhGdJjY1y7PFt3rZO121klUG6+9^KZF6y~7LLfDtkcza2>w5NcecEw^mFQD*t(c9(nK3gfNb zC*x+#iL$c$k(b zl)>Zm~3wx{IJ^(OPNUKh3vtvv-1KOeA%R)ogZjG zvNB_o;wZ8$xzt?OF}J+{>3p_hpB_sAd^4lt?+8nM3zdRm$v35#Ib6@qZB;#`wXux! z3F*$hT#Q##N_Q9Ig`T-Ke#us=s_2fdbPu)GFq#9D^RMrgO!6nl?vG5Sc%75yrRQ4! z0K{mW8d|x zSGx$cJ7V+`o9?7FO7`89HDu}f<6n~^3XF%5Wrz)`o(Z*d4UmNn7veC+)SQgIUnYNd z)D^CM8DQ-`Kq)Ia`r?OHs}8H>Z^dV|&iNm1FUd&*3-}!`;jo?mko0@felsr0N)u-N zO&8wnTb(BBcnYyN>egg&jTd}h-Jh;g@2)&0xA3_ zzVP(=$_J+Bon-6vVajftl7V1kB$kcyHJafX5m&seLH>xo`ds4_eX~uiWwYg7g3;GN zW!l}hX4pvT2c$W-w0fom(W$}`ug41RPN6{ry)gh@#o7E7LS<%XV?`1dYNZIF#O4R3 zq@CSweA{~GKjr&pdF1WE5!!k0_UA|zani^Psrkxq@!3r=ZcJ46>iSG7CZwPVbO?Zn( zVbC9d9%JjWnbP9)(p|f4j%S}m@Z;*RJbaUk zJbIU`!pVZ7Dr+bw_c`|28smF6*OedxoK@sk8ZMJ)&#pRkvj?)}x>4|I@rIn~HZWv@oG)K1v9PV?w3XNv+1OZ!hyn@W;7lR`cCTzELxt_J`OaA@O$uy`LN@Z^^CPRXGW*M2#?T zdql#!6S*;C0d;wMrG!s;{z`|1GnTSPre8M-;b=~-P?&6s(cQt=d`uVTSEylajGYGq za+(S9*WKLY;9;{nCapGMGkMk64TuD%RzQwkGUspm7q&ODXP>nN&^eNOS4GUz++V`D z%!X(8RPST+v}bZi`03kO1IBc)@Q*Nnc=Q&@$@M?R*ghNFgbdN12OlyoQAH1U#&i2S z;BF13`~T7|51~e-fD2~EkQQLgt;|<;JsbaVIEGQ;p~Zw(z^@3ft=7&ztA>*oS1{}I zWxB+jt!9UnU}#U1`Yj@3qw+4iLDYtowM4EIygLhYo4NHlR4dR|CG93@G^>|$DB4xG z89N(O>)KSFdgYacfD{oen_nv%Yx{I<+l4?Iv9X9au;-pv)!Cd~A`KAfqab8Hiu56^ z*YocKKi=H+HVf7;!`N55OUqBp2Dax)hi=aJVWs+LYE?xq0rmgI^Dp==vi(1d=^Xf( zN&0_AF3pxXYEEs0yqUEVpk$S8`>eSasG#1$h(4A-5*ts{)*D&y6u>QchrF%2b&{6* zF_{&M;8LY;iRC5x;$orzt(Fv~xe`m_DH6_{lG!c^GtF)9%yVDOM4b=rUodxxO5*=b z_rho){0iHKHDcyGZJs&D(I76*HFl37eny(Wn@Jj9C*-oqdUaJ|^GsZQ|94MDc{J@5 zAFPGi%a_J^e_+)_`WG*E>DXIHUGTCYq|PGw<=^5qA18RkA2pdL&yVR*pb9=Ml^eas z+aW5PM5y}ZNTJ92a_a3b3MP1W&b^Lm%Dg;NXGUHFGXF|Rg__+}^AUa5OTV_9M7F%n z7I?3Uq!d)!C6Tp< z3P=1XT6%gSGKeCgzHh7{;gMgnf$K&_@>Ulul&3r8P6EUFpRSAi*X{RZ}BztnMp_J9D)hNXKnl23u3gU|OC5g=tn- zo2TcTHSz(0%(MITvKKm|2MV5*GnwgjM^EC>1-82+P15A71PHuaBttXYC(q`XYpmA0 z|A({t_+yg^{6-A-`26$2tsSDgL!+7QCIG$!XpZ54B4+T<+Q3`WquLkxrg-Y@YIbi$ z-pdgl@vaH3_o}QIpDs1YEp_xyFA|D5MMYyuvKqG#{G4?OrMX^M00=)Q>bviX`b9ya z9J_(F79PAK!QIm+H2T7bL#pAsej;swotFNis0-lt7Kf zKA}+@Z|gQ$RX=PmN%N@#$v>maf906ydl5nqeb~SXnK86we`-{Cr1osqXL;cA~wg#DWW`V0KAZn_4Q-LMjuy*-!J!y zwB4I!?_~ldm5qC=mzH^Do=z3EkKiqoq`1MQKlJ_|wAPys=wxomV(AL28e#9mNhCJb zwzb64aV~6!Qk}s=y;cvE2VFJsw$Yj1{lh20x1KrpNXi~rfFAz`+g6fVe?vDP_uLi7 z^8j|Wm_R`AzwL7J)b-|b3cqKs^|lL)-rzOA_ZFeRGY)Q?Yfsu^A6xVF@QcwAy0csE z@LE-MLxu75_QlTxV*YYt*3{((gxQR^tUx@rcXn2+^CG=AJhP?ePEh4J*a!c+d9E&StHWQh7nM8z=Y&Q|;0wRzcjmbkiMokG3=CEYm7 z#D<@C4NLbE)&f=HxbEhIh1kQ~8wPBfq=uYF&3$-vgZ@*^99t)voY-NbY#_T(0E=*{ zvFmkyM3ofH5#nXMebddom#~%4;vr3ap&bBf@?(WIQDWnhz%duH)`C4*Oi`7?f zCb+0_^a&Qs{IP`rVQ~5V3tsYw7YR8BOwg4160vciAsqst;t!5P)A2vUUOMZ591lfB z=5j7)+$hPCIV1-jtWU9rs`qg-VgA^B3yTn%uIZ_4B?nDJ88F|jP79@FPKT5ZYd`{l zP+sFRvglu(MR0bqRDP+t<%9dlr72I=ia~O`&a+b%dQ;qYDnin?UQL(}uhio(EOMNL z`0D8+Fe!7XUflhv2klxzRiTI*Z;r^!_CmJV0rcd5&)q{)L{>U!yQj_UqO!jGm)x%z zX+Ieb5b4)K06_-=&VOt9tVA@#=cVTfc!URL(3ab}48=RCwXorB~5(uf5N* z)y(C2e+r-sqzPtTpGU0^D8%{5Efw}7p#hXR%7(95jyE*jjjjCODW7)}E31=+&w2fr zlzE*X0(*r&ZUN70B^o?*f*`E5r2#R@@cUm|Z(g{fsh68d{~W9<l zr`%76(hsp4kqY(B>7V})zn^6j#9Xdfu)GF^F&k*y8W2%-#yzm)a^>B|q;slqZe?mG zNOnHT-c82b&|}1fy&*yP9@(hvztmzwh~T=_%ha& z>|NSrXJ|7awBs!Ge6Z#JRjIPmKYSkMIv&!#iM%(&{Q`*a0QI_po!R-*^v;^$65}nM zTSq+YVEsOT%!N=kldU$!rWZqPw4iXLY!j(j)EXrBK}g~>tb4F8?JIS=Y;4?UmDVG; z(tUf3#6CiM!~J<+;b6wkS46y0>IMaQt|G5r@t$qZUF50RPRI|S0LbX{`n~yUefShy z7l(H_tW~E}e`wV$8%WD1jAjd%wF%LG>BO8LwIDbS%_x6 zike*5T5zLES=7~jTWGY@-Q!9zJon6NPltST7f1alq0N&&t66X0btYZzW|3_!!Rm7J zTv`MDj((jUrRk3wudNFVNPn%_o5=D^+o-v;@Q-}gM|h3fD@L9rtmgcCRgom8%H9;o z*R65iO_U!@Tv!K=R2aE-^KN)5z2(AhjTB$4W8LRXGw|MnWZAjd!4^M8hyp2 zp3H+;?k#k>G+MU4_z1$;3C_&lel`;pKiGA>t{&3Sb$sc*>MkevoCbg-;aw_8fT$Vh zB0>O?qLtPUIx9s$G_Go*ZvNvLFjlWs4O2{xzR(7sObmtKwHC&$CYY$04T`Hg=ntUz zPM(|9hK1C1p#()L&bn*H0ZMTBBR>QLofad#@}6xTN4Sh9VSlQ}kskgMk9R+-*)2;+ zbO)*cEKi;`S4$|BznQR_LEazYRo9#R7ie0{;}JA{a~P3k&MFQHCl%7lgK9Z)O`n^M zYrgC)MQy9_T%Gv1+|XBYytGr!Gq;x{uQ9|cyuPxvz^V99Ph z&YT(M&b{5$Rn;Y3Po<5Zd%Zx%Nf>1;AfiFkEju~rFHR2^>XkTy;TH*tV^%`&!6^W0 zkPV$a=83Md~H)3*EFy1nodVGe6i9$fo5@s{ZC9-&id!_Z+>Kqx_?)YCjl*P0)t2rB6 zJ}oas+Fz6^>`sX9fkwxxO3Tt&Rc4NGw#EN1760D)GrF_o>7woNLo=F9U!*zrBY#5U zNjEI$WZT>Jy@#i_Va3{r-kbET$05(5bFR(K4g`dPMx9^uBCUx~kOKMQqKZjSb#VJA z?qZlVaXW)Yv&s-euhaN@=bPD-F~#y|8@O?4DS-xTox0VSUh#%j@cy0;Do^;MvQids zu?Z}Tq=VXVM~*j+S3*=X%JhY`I?IGZe?YbV4&WTX3U(64L>YzlpJsl@Bb*QnP5vH< z6cO_}|gxw;gwlJ%#1U zzf#zYgu!dXMm|!k$)VcdXfed0hXPKqA~u?;KqN*OvgZ;cPd{Jr?v$ol*~D@#=tdKs zJF@Af{GIgE03Vl(g{W{v`w#%t*4~b=R>?{ZX09FwplyrE`xP4RWyj$d(0p_FR671< zg4%R{*nCT5(J1c3CUzF6%5kjb7m*3!)ZkpQ$#R|&N?pF3-7Vu9`K*UqRO9qB2j-r- zy4+Z#;nNVUq4>qySsS;Co4Xs)OZcjdDKaPV>$%6G$GDJa|K~`atEUh16LH(LNwHnT z)CSfS_k$)4P8+xqT&eF$F5;*hK7J}s@#$f5>i#tQx=PFufB7ReO}2OlfNw8wX|@@< zg_6g1)3@KB63InQeT!hTs^5Z)MtG@x|5kJJmt)L>SIPj_uEtllrdyI(H~S*IfJMsm z&3y5Oy=f5@)$9DsgsR}fTW>0!S{Y{HlIVaajx2lLHzt}z8JuHJhHz9`ef2`-2j6GO zT8`qW=R=EPKVa5tD2B{dika0lKT(f020k7)?Jh@565en156`zm^xO#SX(dO)XMN37 ztU|U0P>$YbO2j#EN~@^Qu0j9hKx60txP=HOgR2w&WSO%tUJbEAqL$^|iX4N{BSKQ@ z$0R5WgdY$CBREfRo=uZm#L<5!27Tp^0PEX?WOGZzfRBTsdPuEF>GcAHO z)Oj7$7Zvv%j-d4pyYlA?%QvHUZ`_f2ran{0PRDR&b#gi{-QK_I0Xl^n_K#lLN@;Ez z_iXyFQ-~2&R*6z-M|L)Nj``DFe(g==aFZ=&beRN>FOEYCOyy~KH7aT^%)*tAp^|2LfdT?93-_e~qJ2-X{=NC(IK&CK?+zjL5p-fl6 z)Y-iYy}bEV3k%X6g=Mol3g%YgPW!2YgBQ^oQ^dQmLmY=ubmF4WB1mWN_K_Y zS)ZO9p2^kp-i4FfJv;U*P$Tlgx;5V?mai0F)*q`UqD0w~O1S8K7sF|6+H7JtoYFS% zPPo5Bg4yeTo~=>&p0s`8c;{#T=CS)x4d(&q%mGD{zZbL)6Y<&609&y0l~boXi~M-` zLf^!gnEF7UV}-<^n8oLQ$93ztM&BU5czHv3 z)C`|2HC#70xEYSXL-IJww#IOuVQD~s5yZy)Q~Fp_;AP^H0D-2(&hr=^x1iB6nFOI^s@_1~S~|L(Kp{qfyM8?}tuvx(z$Zv*-4 zL=CFVlPv}3<~xV8qb!;$2U99R3gJtpR1HbE=xO5b+lIkBgKfC)n(=euE<`|xlH9am zk{XHMRh&XL>&`N=)H!Uc%tWRc*P{#3!qP>t4OLsn1giE*6VF)mYfLwz9d!q0O0x5} zX6K)W^dudvCbSQgGz(>DaA>$@62`=P#X%NcByH; znCs_?i+X?ZK~oGdyZLDj?-M18IeU_nAbuo0Dftt7z})wn(545N(T>pZ%#qv)rf+_> zD52S;GLyJVagNN!_&}S#V)UKK{i}09#d4s9b9Ch>_CJ`1w}^N}XZ?N0ul+WBR(^53 ziDaFW0L8eZ=noYna%}-TcXCQv-v*Qf`HfIHfDTpn22LtcRFTmvZx|gzL2*f0<@I(J zD_To5;*DV}(!@^~nECsHnRy)|POc~pDO=zDefn$}-)#ndaV)c2(&(D&i}7H;^$Uo= zA=b=bYhIuB#~Lr4(%<#dvXb?lS?G*&pqheX_?Y~n`%)0{_ZsH10vwNN6oB=ohtQwb zJtFTyjY(*nKuR-F*h1=}@0w8{yAY61tQ>Z#hfBd!jRIGK>r4R@^=GOH^}1k*qbQcG z6-l|`Wts23h6_JyX5z}!)#m~vIg&n*uxPrCT~sU2#>3xNnm^LNlCMzI%Ze4qU}zv% zFn@pQ&%(Nz{PP)MSxPyQW>g!S1-5@w=u_S}qfn84JpNBeSTCLN(!WI?mEWh-ttHCNSm&s0f)*{+1@C*(Q z8*%5%auDPkrtcf#hdOjqFpz3=^oz%C_n|Zo4awI{d<2o=tJfCH)rHG&7O(Wr6Z~vw z+2`d+e$rU7?8O4rL|UeH1utsX^4qZ|T0bb6)u6zsZemkd)%6GNVY{jM<`!2)Mp*JUp-hi zeHLcuuYQ89NpK4u@O0a-FEcfR0e$8{Am|0>5DSh{Pz zE~L~p7e4+gS97o4O>bZZQ#Y#Ua0W6A9ihO}zc43N+MTq7@?7}*n?>(XUy3Ex*7(?? z-Go_(ow|p!x4qrgpwOJFBXyZNmMKYONsqJ7{*Zf)Vu^?l>-+NR%IMp!(h*Zw?7#YN z0<>LGGVcoVNe38e0ud4@_)G%`Jq|QH%C(rUwg+%gcJNt)zM`U{{)qg!d?|V?$sP|B zxkNv{CnX&P&PbNXo6hwEl^YcB-~9c}9PAJ0nC4nMc^-kI^cO=M$1?uN_?3M~O{}h0 zRGKXw$>kM_F|6N~lt*0#BByD1cv==DPwQ=Qfy&PhPPnzGP`s^;aOMrQDUN=YWZK(g zdQJ<@Wdb${3x`f$o`E-ivZK1krObZ{eb7wk#Sdza6!3Lc;zc@K%yn8P zE~j4VbQZgQb8#49Sb4d|&i=+uCoisyK3^D?p|e; zcV9%WzgYg7p4C2T9oRl#pPnknm}y+cakn{nspk3x^U{pLH)WSi*t}2oW(IVvh^Y!{;q>)(5EYuMfKk~Lj6%e1JN(2U1bZR@O~@05+%)@?&}K`74jG%OpyK^oqTK{o>6j2SQN=>rqo*nr?RFdhs|zl1gIR_ zDQS87S!cH`$IJVo%o$u@A7jsP1{SZhF0`Gs`KTi^jua~DJ^9+#=d3AOUh0%C4y}+? zwOvsasJLp@_$)nddoI49}QygD_4B$)D|oi>FUj1I&5ZxUitbGTUQ_Kg*%vYyw=alB2)bh@Wfi4t&qL3>iyjDpmhd%8 zuT3VW`#oPYO;|OXmlP8A#eLfTxuBmxuSHrCB(0rbw~e+7sUhemH`0$C<9q1N4)lv$ zhczX0Ka}3*PV91U3C1hL-2hDYMHUcWstvSLY^5r2FRgC6)Dmn>iV{|DP6q zFcO$js5%9$x98j9Eb8)z%{J>@V=luO7O|6CqZz`6(Yo#5MuA$2iAmv5JW@yprYz}Aj9VVgf+|lKD+lp897lfn!0ZBCtDvGH0^nEW94$7C2cVKv0x?%t zmLk=PZ|qJ-5LH*Z8)EEkAaSXNV}8dxL~K7Gf-!<)Fg@zvCseHEvF zSXPx98;k95I2WBQl`J+jRYNUOX^DpY>eVX-@fxn7i}UjgaCK$<)>0!#eoEfM8j)|S zt$3P~8w(jg%o7F5qu(=`&&cFubDvpSC$d;&3-}|CZuZA!WxX5T1giJQ#l^)PxVY)R z&^#RLFV;KQx)*EI@l#bIA!N@E zBvgLjiy$%JC}$=ZL=Il~HC#bpV?H64{lUROrgeweNRBk2=~$*{yw44Aj6CZ_2WE0| za+V)t!@-$9^{EzU6Vm~gs?x@s!|_lNt@~b|MIWn0#eAK42sq=zgv(zNn_2n27C+v3g-|lEvy^vX zQQ6JWdd6~opFwhl?%%ffA1hA?GJs7b?6pm+J}uR~jPngsyj&A>e*8<^}3wDT_24lr*)M z;nX+F=vV;+uemaN)e4O`_zJwM+f=!m&oDz4IyHWMDaZM0bzGD+@gKsk3J{q*UF zQn5y!0<~ijgCTsC$@sS!hVbsr;!gAU8=x#*B&b!7B_KFf%S9C42cU1H9Z-XY`*<-f zJyGvqrbi!4P3172B_5lpNUVNKp9({$P?tuJJ!mkX;qmM4JbwmHq&TV#ylPPd~NKt?j?m zE!ReSdRvqR5u*O#(MUo%wAqF)b|2O6(+q;v@t{meA*xHqBmzZpO+<55L(lujTfZ)- zTQ!jaqUjRW$t-!Ty+{DyQfTY3s5y!PWk$92wN}f`7eHLo3YTf?fI4lHjxT5AA&P%aq2;@Gxt2%jb( zAh6{&*X;QU-_URC`fz(SZ$$w>batsL6vBb9b+GDdd9VsJH8l-60I-Y5p9??*IzC`-NC;9)D#DwDNvV^!rAs@iIqhWZr>9&6fUt))4A5x>R+8>ABxq4)#eAD;r; z;@5jlB26m5hbsJpuv~;lMj7s;1p>>XyrN?No}O<~rIv|i z+@s|Ie3wAXb9XA_7{gg+gPzxV5fPopj;LEV-cAcPFlD*fwdi*1RBJZ2_V$?o(2GaBEj8$1{ITPp6{=ilQQC?DZ&eX|W)lYG3oY%Dmvzp2G^99vdX4 z@iaak5#=AUeE@G2B=wOr$k-uD66gyFeZznOWG^fXz59GzlBy$tAMcZ6a9=Ge);sl( zck!Vwx=T0WV(!oyBF*Z9DW9=sI+LSpi$EojIe6lf1%>Q)T|X;}@Wcx;RI?|PL%)S; z*EHJ};ay(pip*~|X=?|(7<{ruhWyL)1T6csRNf*i7|^wZqq_j4cRjzCrZ|8_TF9h{ zi;MdfsFADO0jgBrxIdhBt8LcOAwgils6OcRb1gN25z8j#QLSLxM9Zqj3p=JflOm&{ z#1|prG<^Z22k8TXftay_`hLPtuAXI>YTga|%RNQV#??jLxvj3HW%0s@AY*oVqv&uL zfN_uN39ftIWf*|`)V;pmJo`=>l^ZjNk4q9KDGQdd52Pv1u z>n}e}xi%u42@|?!&L`igrP4XCw!Jd3=y|vC^%X*(s&tw7C##cRHi&FP#N<|1R-g$M z7F8K4P)PC4&Q;{&|!m#*`P`s|Kiv0xwRE))KxiZZ0k^POA1f z@!Bja7uS8YEp}Ddi&4s{EDu~N2^OwE$&z$86iYdhJ@?3xFcH>{mlI}-QS$HqQT^kt zzPtQrx@h?McXNPWmi^y8WaZ=*Xr+6>eDN;P&C@w z+D?4#3YB#yk;?aN1r&$m!)(1n0md0-MEJAC$u%0>U$**RK?8V*10@Y*rMKsW=uj%W zdI+?kcHJ}`B9qcyew8U)u6vl!Ap6EQusNxoPw{JOYa49OItVoY0)vLuKKj~^Kq|2^ zKca5jjYW5+D6|?W)Z!bCUs#h1VPn;Dsh#3WH@@q*A1|XBxXb_x8#SSu-eRiGv6PIrn%k>!0R8_Xtj z*&(ir=mscZ3{=OC)rU<-P{YldhWPz@!}!H`N$wzt<^3v>kczc_SrE$$-3V#aedlu& zDXP0N2cnu9ATg>p-3QcI_h|a7Dr{xeiI)zH+?-CxOQWh|mgDrUU!9*v(ezU>movR1 z$6AF(#>;IE+8<70i+Tc}BG{y>R}hepq9IAuY05wAgCfi9K!9S|lp1LCIUxz--}o{TE!hPG*lbx3I_}>WRq!_R`eg#47;p zcWfkGVA9>`U`7_+&3q7tkdRjMqw0bFsTt6SLGD6cd5TRph!=|(WMmk6*ohl@;;d@0 zj_=5}M0#EHrlM3hjG)qbm7l`4d<59WxG8GO0-Ato{WTRoF*#=Z(WFGV>;d(tGmnnjy4K}xccwZq^c8}OuCd(SY~X<_`|Yx)Y12Il_mlM2|y={ z%=Z9vKE1xeR<6~P7|(Baf!6{3W_2RxlSBLBMLWe~nY|=^bCYt-sQ$kHC`&hX;!jZJ z-yPWe52eJo&?v1(uvN59I>;7-0_l5*ZD zco-R%^}(GypO-dgcQ#+NYRoy>a3NW7#b_N>v{oiBn!2y1#2ODrx8$Wa;XblI@x&); z#|dK>r^aj_a@L0%aviGFDKeCPJjg6>o{zp|NukINYr&P|!?aNH^*W;pS*FHJ4{((f zZJl{8iM@DMt;I~VjUHhJdyzPzX@#2K#mz$fXhPj{I$9`;XkyC%V{KtXwe^!whXF!Q z_B-Tsib!C^RDuB~iQXB>hd**vzx^p1AbRZC!>gRK^@;qsCEg-vN+M25h>M0`X#EsaiF<(KL%Z6RM>*N{(< zEd~Pqq6xDja#~Kq@YG-z)8A2FUQ$#b4lGJEium_`e!Go7{989WDk^W!O6-}BSU=|o zg@@Vcn;uX49htJlu)8=+o1X3R2+1D};w3n3Gt4=HsFMTPBp@ukFYaC(I=k_tJS91P zv-~-gf^V$!99B8E64ZHbG+tMW{)tY9l?&_Q-R6eT_&0=QdFumRc0$jDJyjJs{poCb z&j#V$&@b0m4cgk@=+9T~fr#B`Dz;~~-6=W6N9wPfc#R)gt1u;Z@6ed+kQAKCHpfaT zj}DwBmKqS6#+*>g5>Gexw=}YUhX(gK-8-`M`_`na95q~6c)FW>r!Cv zENaq<(E>l~*XE?#1hC}zS&!O)T=%;8XHyLP+~{B!&r1v6Jd zwu5J~hWX-;ce{}dj0TLDu&5#Hmo|6S&t{dh<~f`-73Db^qup7)c9kDinTYc>f97dw zMHrGk(m4{W{v1@*LyiZ%;h!QYuW?NC4Q9P%qktN&f%X(zbW^nK$YblZrO`s>F25h? zK){G4$RdD0*DGkx#GUrbRh6Dvv;|4h+gQ8q2&qo>RR94XCa!a~*#_5C7dkOSY6oa3 z-n0a`6%N%w1g|4WsTU2HiFowvf3etKjJ5^^507Acbw!>Zi*k%x%ZRhx~$iGvozWw^NbgOmz>m<%tY`AMKkh^uJ2 z;8XRWz8WY28?VM%CdhRs%&rBN-Wnmqq5$o#cIc>duzYU?>sl z5(m-BAjHK0%}R~L#x|c%Wh-V{(|CLRN-7gS_BnGUF5^cyCN&j$lZodI^6bJ17Rrwr z)deGdNLR;#LSy3LxX7cokhH`!-R^cRs+$daJ(+Ph zP-tOzW?`&|^a48?Fjy>Rl8VW+(_8qbWYb)Y%HYHkg3D$mm7gCZ=#eW;a3CaO_1cek zssC&&ePl0c*80c-ul~}EzuADd^#!E&5x!y3%mt3vpe&qspGDm(HjEfIQLXALg-Q9o zU^ZkpoX=A+%zCj)W|g~R(r3o<%io}x8l)E`fczGSAkr;mhFbaj{5*cxyGA*n*M5E45v(kQ)oGb#3hrgJioFnOPt&ho-}Zz8+k;X`L1Q!KGJuf=z ztC2^ZD>7{(&p^qr$c96;fQS}$YhuvSTxV?~`f7;imBy{Q`~6AGoXs_vqg%pI2Ywd% zsQ+%A2YC0K6WEwBVqT^>NKsKy;gpS-l%16;)dZ4QWkL485~cE&;htwj>?I zqaqPYYjT{h^s$uG<_#L`gb-vYUSUWQAS@wW?$EK<*=6AnecEyTz12!j z1WBLrYvI>)2#|o(j3sfz%H#+w<_5s;ij9%#^2JI2PEJ@`S460-8}LO$Npj&9J&e(O zb#sY|&G=}Uo|D1y3Uob{KUCai2jec*t{z58NX}D7A! zVOFZPZB%OGQe$l5wk}(l`d{89_zOLfSzcg4PrQJ7=+wt{KhSG0@iI{1OQCehwB{_* zCiZ>BLkXKs&>zO|^1FGNC$ij8_)?)vGLrUiQrTQVtGyMX+(S#HRQ5WZ;kV_-dq~nc zMUW(Rb18pM7W@WCGAg`*czPC>eDXx-o@q$Il(xJ|8M7_vYu&Z~@gte{mgT&s|5q%4 z^|-GR#lfo>PQ6K4zJgAKssv#74Kvs zxncVGD%8jjq5rANi_unaT$e&UCO;xFxMJ-(<87is-+Jh8|C$PKmXiD^iDTe>W51w# z_jF^#iT8HmA>}PKVxQ=jo|bJzDu(T3gA#f6mvhC!g5bkh1?(_BXYGoL#^%#DDMQ<<$!;(2RlcC>{7K z1$f}0(O*$Rz{C0X-wv?qdhQD0Ml?`Q3$=dGmxTOQ!2;_>!lf!I#o-;5Y z0gj5tWs0!H?BuGL;*Th$IPJa_bVWYOkEH*J{yLWml62P&P~cQ;#xVa7_&-(%Z_o>k zz$)vNHbl5!Jg!J8YU;dWFK#)U4dS!@gEExhi0)B+;R4~so@wQ9q77pr3^m1nA- z9?ZJ2fTJl>ebe=**hDff;egl?G|k&@fVco__>Vs(?-z~~#c&0_^n36=@TmId(f8I+ z-U&hg^GT}H2V?(tPNpvaW}5c;-1ZtgHs063@|XPo%g>vI4)A6c$!DM6EQ1CxljK*- zkiRm$|KkAOMKVC<>zkh9xBz}01e=&!g_uGBSnEGV2)t+d3QXFpgF!bO`~?vp?j6_= z{~Z}`nj9RU6y$7v(t`z(jsoC0vd*Bc|I#q+2rM^?>UIM-0H17u{b;*sWRk!0_5Ty* z3NU9@sG}jmf}bQt3Jgwx^8?gBJT9=>0v_}Y7+grYUp;tiGLL{#RC<1^1NEON5XqQ< zRn`<0DH))DWQ4&vRuY5|lmC*$D-W2&%!Kc#ha#GFr6cJR5nvO7V<*)mbXze0C5)mV zU`Z-Myy-%m0wZ`za<^7`L*Ia2}+{^!t zN#Uh;Q^R$hYciKr%pD#e;PP-9&Cc=7D$V?6HzmVK)FnAQ_mxWAG`wlmfJmvRE1~^~ zoH3G}95o!vDJjKF!2%8F96%OUR?^^>3m@BG{rwQ$L88**mPgw(s{z?Zt&%qxT_5<; z3?OM6&p6%hk0&&|!v z80a_BDb(@XApC2s{%@~0$ARc{k;y|U;PZnDt%?Q{yS_Z`_qGZ=U2~sL1u0<`QkV6w zAL_gZR2429;-nb{@VnPaK1`;wbThVKA(67K1&-qY3aphCvVO{^^zElmLQ_CCe)ByW zpdZTEilDpn;2{Gqi3*`5`LHb#a3Rb^^eidAp8gg!YO>ZTA3$ZtFbWp_QfnNEd;9yh zyhY?XA7k}|&8F3ckv;R=8{XdE2Vp>{bXnpyva%5%Nvw?D33Em=9bH}7)lojYLOT9W0JXpbLxfO4d@F$9 z@E6OrK7hB}hbbr$u!MOBg82y5r4}2?l*qf!xj^P+P6#o0ih$2mpiIl|d_rUlo(ix# zdwJz)1X9wyU-+YVWgvicp?rZ-^OaYU^o4RrLcJ{x3!q*;!>zm${S3F}0Tx>#hAeUV zn?EB~-~#8UE(bFr*P&<7g zQ>EO&Oc2TKrNkuCT+t?!SnR#o?FK#9UrIYy3i5WB!XC7#!OmvI?||IC(#+WQHE41$ z6e;JW^mWEgOEY;j8t)%%pPLM}h#9AuE*JRNFPAESSgMT!`LioQB%!(!?b>#D)On7a zuBBsH@9iQ}=w0EAq&xSsKl|VvArt;NYh5K${WGN5t#z?%h2{ zDhzr4!bcX5dM1+6wH%-0u@!!hTsDU%}8?&w3s*T=?%@< z0VBWJ*Qp`PjhVva0XuV`oq^l>=Iv(5Qs&8}w!GX4D!kSwKlb70&->hiL0uWyCRz!b zx6z8nGBbB2na(cT$LR)!+Y0G6TiF`1*Bmu)XvOo%A17k!mNgU<1hKHN43Wo2Tnw3g z;mH6~Xe85llogO@Rj4zo%<CUGQ717!5lGaQ7^- zCosdKy)`*9VPPKRIZB$9TfTq1Gw%d(@FwJRI8#5|9Kpj>f9_F5*Hck;ZbqtA$}uvC z8{hVoZgd5%+Ql@zaeJ;>`@_=;ONNMJht%0;)mMW6#epFuVoL0 zn10~7g_rA9}>(hkSXlQZj3K0vPwlsRqZ3z6sLt z%E6MUw2lGV)iL&r)DdI+QgSXLr2%H;VP}kTyAy4@7@wviIbF6UkvSi1nKt{BjN!)**n@Nwr;aJVHAVx`WUax5}RY%h#Ic!Q=ZXh zYh5r&ez@7zV9X||SCuf^&{=L<*?V+y*2l-w5LViut#7ij1=KZne06KswhD+78XhlW zA(uVlPBJC=lgZBgp2$ok%u$wP{R+(yOguW`buu2$GM)>Fni z&U!X7jcl3w{?9+VuDR2%iwAOHP2zKRtCLa_1XV|sDacQ;17|LL=uD|(mJ?l8G4MeS zpXwYA_@={-<+sm8+g2BUL|xEbYCV+<7(pDb$UjnW&`W8}Ghd{AO=O|qApX`i?IAi_ zhB{$#gi;>tne=?aR)2N<7PQz}fw~wF6#4UwQKhHJ_ZZz@vtik?3*3`r@9-E)=~%+X zXkLw*tfnNRdZegHfyApbZb|(0nS_M%wX>H7241(R$9%zLsMHkLR#Hp-d|SsKH;Br= z^NQ`;&%{oF7K6|JLG)k729xI36{Zeu3d?>_cF0Q&6)9ka@5Y9Xm(8O4MH?0m)Y#gi z;$>`-ukH}+HRy%CCThyU+!~F~@C7>HQXI_P-@tj(MU1u6h)PE#s_sr~JiRGLdY~hO zyc{t4fCHR^=G7!=ZMlh*Dp<;_@~~xgxHqoAfNW@PiDj%Gy*g-4Zm%9Eo<^gLQM^ss@qbuy2bS zos0+^lK;FM%@x~cTJqDbXy-BETnGD(lE=&8i$J=>uX4Buos?uI{!S^s5WMS(33vXZ64vI_s+ zELx*+@0`1lT@;12;N=mRp1IybN79!=NooNzK5L!aIZ6AY&Sf3-UqMI>%`uMi=4r7sG&*iF7K1SH$R*K+Oc;nk9ut!&x zWewRiV_edV;@sWPLMUoNh;1 zB;C+eWYDtt7?GGFBq_ut)bxBE>wNqC9RnmiAA4tWA=GM8n(DAeNUSHEUl?4iyjWb)jQG$ks0 z-|l%A{?nqemnyfuAphs97QJ(-!RTK&*kOV74Daz~_#;|8`QoSBGF?U7VT5f> zMnfUVn)#_j_Rppq)>o<~V%MJMMe5c<*jmb_7T(C@B?3TMVw~m4Ygd6cyzfX;*(7qX zxygo`ToxrCUaKhP4F>g3Xd3#a%BRLUj5&Acx9vHhe?C+Zrp_6n-5Qghdi?MwDndsK zwmC~Kb9;=(1f)hMkv?9_Smx+9&`g|}M8|!RXlN9-d5L0Wk`1>Jp{WDc(MZoq7hk0bF$iim|Z0WpihsPM*uTCuu zy#>!W@m7ZiG^q_FHBTR?4{uZlJqPSBxxOXgj2F|}F=ig0O_(rRq{OW=?x~N{;AJIk z?61vzRpYATeOheuD^7+EUq1P651RD`N?c&91mkqu^z?;|yk_j+aoDi?k2}wbROeu^`jcLSwW4*av&dyKc=V6EPXja~ zyTmzp;{XiQdKtxG1#D*Kj>Yomth82CEEV%C9J(uWP1sVMYyhut?)CO_pno#&uIaB+ zcy&$!O{!wCiJ|51i;zk4j>-wq-D3;zwO`XMUue{nYrJ9TOw@G5)kRfdS(H$&`*G5q z`sE|QT{6nSZ4aFJ0h9t8$Ep4PVg%ScJu~-A(U$KThkCckWQVg7b3ZK|bjivE&OIbk zo$Wb=Eqz>Vp~5VF)AnjzX{~Cg(M41XbjdihDQuFFA4Y?JCx)<;v>`~0R*Z?V6sBvFqGswQcEFl zkuYn-=MQa1C9+ud?BkZ{s@;Cm_&>(6v2=GmXKUsmIk+hL68K?bzNRWcf%WWY`qjIX z#;AZdz0gD~Yx+AAUrqQ$QlyVGp~I(JM<)b2@}@$xizabs@e%V=~$NChk(T*G)}Ji zs~no3seU<+43N&9&g~Y{=?GRC`T|r{DH_QgO58<#ee@02eimI%@wlaC)o1zY59`Bq zq{CQ2&)wd+Op7nnB;6C=-ayOr?KZtT>K2zvGhp$$DsiW{A9FBVsVO^9>yL@sj%}f! zW}io32^qUnK&ek652O?zScdHlw-8HJ0%QLMfV6-C{qw~@Ff8fQjyPV}Y@C64L)XAyoQ*=JrP722-;dN& zF|pfsDQQKe(1*7z`1Yq2@qjyZuwQm|{DHZ=)ZsjH$gTwyH|>Hd4{k#l-8=I6QgPgC zgc`7a7D->1ovKqrQb5gdUN^gW$rdAV&E40?DN=vL!2R(ay#eyakD09W`!S2Q#zj@7 z)tc-jcYjecZe)YP%*+gqO^V~RLKC>n*=sG+t5$i8>MaB@3fP({LN~#08e`H-`pXX6m|mUnn#R2{TKVxpQzu5Z5@d{i@s&v`bq6iuw4u-GFAv6zml+r3g8bNH-p%u-v`n`GDTIPuud zBeo~BKU=q+Sdb|!w>H{BaXPY$alfoomL~y;@2=k%%-2J}F0usXus(sBY&z^&yMyHyP(X2a5Rze<;w~UX%MT`r56_6 z)&0oVBM{aZD_o*0hc^%aJ8r8ebEFu$^it?Me)P5cSbWCCu|V?Yr2og>TYts1G~vPt zArL%4gC_(D9^5@Za0_n1-QC^Y-Q8UWcXzkJeUQQZo1FK&a?W?pA8^;YYw^Qo?cTfh zRCiZbJ@s@|!Y8fQ4p>aMFQ|)+4-~Tlr|IO`tr$J!y+wP-kzxSuE+!Vq6$kOTbHFky zi-gX~E7p?`Hb~fVD_G`(B8+<`UNyO_wPd@icXqwI*fCK$*-EAyEndPMi{L8|I5*W% z9^hI*lzw1BcC`tY1S;NQFR!54vvM7<$rzz1YV3K`zu_@rx0;w0xK6PdRxjw`%luc6 ztfTaI#G^E!V5D;vOZ(T%b)B{FukqJkLV7tQF|4*(mD!qN7vp6=+xJ)4-UX0=r<&wl zsx6ij$cwALsBQ{^q@A{FGtRxvTAZX;SpxdW(FwjJlY=(t$Pum6Z#-RmL#hMbi{cOD zNuU^i)fQ1*S}H2xfQ25a&FI0(NOI`gwZ^ZXa(*HB)p=k zdzl|V`LqaEoJd^Xu$FAZY;aVx~icHsXl3n;@l73K=T04IDp_+QD#RMunoWyZ8_!D2ZG~NpQM<`FGYlfWNw>mJ zafm)ASwz@9p4uzgI+pJlUr8-5S0?2bNQ=ciITxZ$UHC|J*F|p3>70BP4~}(8<~dX@ zIW*ecXmN%!e0i(Zs>62*fq?Boc`t!H(o1(bGnG0(Z9t_n(nqm)!geYkm7Z@Jof4j| z6^X>LbzhMd>@00Zu=WltprI7(lT#mw#8@GVa`DJsX zq`v0YKbt9i9AB0Epr6C=dC+EA2RG8|_6tg%MJMd|KuX`4x&nWG9=j%@G(c(Y!;N#j zvu`TU201@;1`=wRt+4Lb+l!*1m{9S9Q#1-NivwgpIK36OpGybd(4x~8_NDTFsELV* z8}nzFWhJ)#gOTy+&rr^Ycu7Orc~)Qnmx4K}gX`F~DsCWpkTe+uB?GRa0~lXph0;%l zJ)jJ2jRVXk`Jf!W|Af9#BmW5<)W2-9pL2jVt!4a+fm+%i8A*gzQFv#J}RL=VqvCj~| zp0CR^$bnWZ>r@zs{{1A%qXK$_h95%!`rBh{cen%SscR+ip*W$cL7Hw3Dh-sWIa2 zrUAlR+4~ahVOH#7I<-w(c#Czok8lHu~n4)l^IyWY(0+`Ii+Y#T20%oI{=3rTseEl z-fHvK&gE25StHU-;N1WPmRVBP`4{6O3UWlSfcE?iOSAzn3T#H!j@_E1yaHdI+hoF6 z#9enKtDI;pRIVbefHjO3jxrbo1XI#cguwUMyD(9mn|r0=I2Nj1nk<~rK`PY#j!XXz zRn~2pbd;xXVOo4jsONi}Al)LY`KAOM^Wtp2&e3>z5T@*7X^)jB)A~F>OMSP*+`pC~I z5EAy?*D(pxTEjgxxgpBN&JZutjzyIVb9Z;B1%`loMtc(x+h?NWR2_Vm%#wS*X63ezT{wDsnM5Igwm{Khg>8r583<#aPas3`HwjM7m#-AJl;%8@gF3XZ+ z_c0kG*;}jBoLQ|#j%GqSWj~s(F9t4g1EkHiJ2a$MF(1W|dL2%7!^A~dn}z!VwolVs z653|WCfU#9ywUS4nG(K0@=WNFd_#U;=mVXq8q)BCC=XtYCwyblaYjz}XoGC@q1*jY zI$rr57SKNpcEHAs2ZH62b8SQG`KIP3Lh4>DL(e0)YS#zuIW)AX4#n*)l^4`*hdkOa zIRWVuIDZ8Ne2A*;R!V|xTB$yVBilBU;D4=V({qD0yoxA-xsJMaZh>2+j(mt)xa3z4g z)h^N>A!!h*luE`tWRO7|j7RzeNZz9ijECClvb2@L<$x4l1gqviq%^8cwue9ir8v(8 zhn>%5&7XnP=<|Xz5C>8P7r{EOXNzP1_Bg;-P|Mf#1C6>>?}hzx0(YF5Wn)^`quQXD zRZiSnl1D=>9k_F0&CNYWfh8t=b5WjWoeYRqLuyWG*r{R!-GMt(xfcv}DMs_p8%{pI0HYbLH{x;s`N1Db4p>a=)Qwfw>cs z8|C^xjG%R;6myJ9HbX|KG)V2!peLC7II zKai&_#vDP8z8m&biU0Q?`=iSOrD0O#Sjp^;1(ZTpo?1g^fc~GkCq_BkX3E>Uiij;bPX3l`* z*ntGuv5$0*zbbQ@te2R{8<^tmDr9JOuzcOxaq29yB*Ru5!^Cy>wiMaz882#{b5E_H zMMT_FWW=5-|7^oxi^s|0RyB>(s@Xt*5SmcXUlJ299eUvlPTjdPb5ntncm*eoh z@B*5B^eJ71^RlI~&>Ed8*c{FWli$^Z-z>jfz)K2h~6GT&nT+G}vn?d$FvZqpq>{fttLVb_`@j8PPjJGa!Dzr8Lo4rYD1RF&OME&wcfWG zB+uniDu|o%W%YbE53Ciktfv*J6UvCs5Vyo#<5?4&7p;aJYSmj9Na-Ut{ORy^sQ)K$ zjjty`JpOf2h_9Vaj7&sOldG(0OJ0)|dI=T%-)?pn z)Q1Q#?Ya6z0fAUUa3fXPwwwIj7mi(ceCKmWO74B^jXC!RE%v(=IQF}f7~8WuKrimr znYMr^dy^PUn?9vT2H#e2+?PKN(D8OW{`Pzx=ihx{O!Wk+7bmVhM!UbDaUI;^>g@NV z!Wy1#j}Z|7EvmDSD_pbJes!P4%b-CUqr65q?-x05m+X1`&4E<@g9Ew9jQFz&de;ww zMYYUw>-VWf2yMv}S+k4BC1;6FP|rTAB`Hewi};w60GydhI_xlcsa{b z&?0w1Ci~c#CJQ}>elw~Uy8HOTYoq0B1#x?3G@$&S!}biz?%kgHQs<4XTa@MDeIJjr+~d35csf;UT5?}4!d`Es1tTC=Q^xaJ|h3-f}o;MVt+ zLtD^&RH#HUd{nFWZlxh?r%Z}>XUvf7OwAR+ZDF7s&g{%7Sk=xry}5BZK7NHZ5q#z%>R4ohdLj}ds^wLy(i23v`4=T#XJlfhx+SzUPkp}6|k0IaLX$qqqnR-kAnUG*PB9zLXj@Hp)pKy)^LSZH3xq#ae1lqNYzhsp5$!pBXf z!gh8M?!SpXub?TSAmAS52h!v}7*IIhU(vyuOlWHr{u76$_75WAt^@>yajHNO`v1`@ zNy#7-(3memu#gwbe$OoYltEvfO1k#`y$ED~#yHyZ{?lswm+kn+?`HTN`>o33X0w3G zOXcBPT~ig1q~;jzQ;Pfna15OqBh1IGiqZu(zGZuB|2d()wU%9sy(xgkm06B5g7$Ek zE!s>#N~Rt64l$jV`J}D)@TZ;7;~Elrwebh1D_ohKEE({$-2PBR$k#8#iD2IWv~L?E zU~KxV;t!eaINvkBv80T-UNVy4tx5Pfu~~0lyGs;34G$0Gsasr$hqp0?(wFopZ!`0rn*s&OPbxK@gKhQF61vn1CH)4esDU<^>r$1V+iYXY} zf%-$iLAi?Gc;0%G&JX#uC!B>#%*w*c5cJdh@VCFAGnR2l|FSgo}%> zAn!xKFj2B!o?emw6$KR}W?mn3YWK$a$l4(3NW9HiV#_5bwuzhhVvtR5$zMrNk!Npi(O1PP+OoIUP_TPNRe>2kL z1$d|P96K`I<)@9P4@oW`{H&SV9JmTGWxRoIKS>Z(X1+8auMX6ip@_EHdZS9=M{oMJ zI-nVI>uJ!zyS2SfaaC5D8m;{>}0%Q3!iKvLeMIV4yY)mhwu~x znUPut${yB>4`r|AAT*o#h?A(j*IViNyD`8_2vtj*-TzFJC%+J6GWaKNffO{nf-^29 zmH&mIe@JV!>4Y8v3B|JYY{vi&wwL7wAmCWdd)Td)<*$Bxsw`56XZNDyeEcIhu6sR^fnMdgCiN;z9>zW#}Sp1J(fp0cLmonr|=Akb&F6>6Ulx;XLG%fI<+e^Oar`$6;z z25KK6+O=6Ui<2!#xL)HKoT@GZYQcf?DkEURVOK7Z*;7c%V@=G6Dv#4#@=Mbc`+xvK zVLw}urWk1Z9f+oq$Kzye<-yqAm7P!p$~~?D$?q=RMUmLV6p$si31%`>cphFWGh5Ca z(m#RWAoyY0dv+AK;o^7I7Bh)z%U78~4qV^M@hwzigv{>z%gbM)A>2MbGtFo5RiUw~ zugyeMU(q5?^HO7pt842?U4W$9Jcn>#{O7NJFPEPMPt}BJ%GhOHw0iSW#ylQV_a6e^ zn+yF8N*iLp(k6==4_O@2O=GXk?LYm1} zw%bp7y&7mhLc-2=jyh-K*UA7#<2pjXlQ4uVgQO?q7ji04A1HPpWX|_gc=yFZ_KjAQ ziVB6viE9PTY4Fs?s++kgd*3un+xT zo6vvtC@&#oA3Ms?cGP&>099AJG%TCf+qaDt0gH2bamYu8^Ago@b=wS1vwDZbhJSfQ z`qkx52l&1!u&$ska?gK%<9OlMT@g;Y)*~Q7r7!qY6CP~7TO>$MGaTIbNxL#ECnX8H zx;V^609#SI4-@@-vAO+)vZ+?FH+w`%BD-|IWTvAgYKqFrmA&4y<@0yy`pnv!b2KO| zqPzh0>>4ox_$;cl;}qzjhsIIN2;rEre$c2qxMu3B@!qMn+THew0!e-mE*9BAmVw~1 z%psMz&%+=_`F!e_JEH`AxvRO6fs|hpk;YoyFZQRkn%IA_g+fAfgCC!4nk%*2I1P?| ztfdlBvdcERkY%KN=SncoPHp^=L?(`*$F*zCKq;1vwX+Am!4xo6p(d>!LU)v3x^F4N z6Jx$xc(P%wClNl5L)llRf(BVn+hqq`VOI;sLEbv$E&5n%xK@+km``d&59JF2%;sy= zRL;u|w%0N9+iW4p_TEf3kob61^f+GDTE>fNHbUiR8wX?TpF|b~xvYy7ClP0w@xfKW zb?tGE_zV>q(TW;RC-T)-1PI?v$3imN%Nt5-#c$FDAgqE!)RIcxiqed*2hb@6@K?bH z9(o=&ja7fQ>3Urw9m;3?T!eFbtR@GB6JTL;7LGHNRi!@-p8vm$p?~is5ZeKR1aiy; zjHVlrdKqa`?KMSJPc-KtTC^82b*1BySC^F5kYUN^-nAe8pD^$Q7rY<0Ld6U-c-l!A zXiyZjaIi2p9YI6mA<;JxvRLD{)D|s#Ra#o~L+J;8%2OsfOxhccUU^;pXbdhZE_f^&+3OuIz{JOT^k zU=#kujWK+pt?K(=V2uX6kJ`yxUE`UO^SH+2$vPf8<=kX3p<6cgZ*Jf8$a_8FWt|na}!EHM>_6 zO!FYDJo0IFxTCNtmP4JCZtni2{0F&1V0pJaA_nt+9~zwN*0olz-3dAN_k-gIq`KM}J# zr5|n{f6Cq(nad0B&eVENs|TgLy4GZTMwD8%QUT+_b&^Bjd7;rz-a^lo9l5>Laa$_% zMl%I;&By>kY|Piv0$&mh;SHFQMxo`#2ib=t%?S@V7g5T_pBv`Ki;C?SiGo~|c7$wj zQL=ctB`x_VZM=*8xmDE;u{6nH{@g#b+TA4Sx>}Xw@InD3j(3PSa{22;C;M}Qb{_yy zF%SmLtoDbH#N%YVrYm7KdwZuVLI!nstE3N~Wzo&pp9LWpYpWU%@bUxx$tkJQQBE1! zx{X{m#pt3(DP4vo&3>csy?8~434-y}HdLKzrAfi-Z5CG|b&ad6K&iWm=C*2+Z>s*g zc^X(W{R9wpGCot21!|sOdY1EC{b6%5nHn(GMvAK-xaweq^heDy{did#j%AH*FTTQQgyJ@nD}O98ACDow_@`c%M2aJ35yC+Ts7& zu)lgi_G__MI=}44u9oLS>enR1T1s}d75yx2RQk$>L*M0X*^ow^u1|L19G9S;PAW#C zc`Vya4|OVQVw7=`eE>=`RIT?D#hx{w&)HF6Y158!lxt>y?1A)&` zd-)3HR&_&ranI>&jXN2Q*Xc4=0EnL|ymE^Aqs@#v$04Cs^m=_iTV+Wu2T!%7t0+t_ z5rgnS^QRN{?=>zyV-PJAf2H(sOUdcQ;FP-~rFe0yVr4SXo4sU*Zr5&dS%}SKqo?ATAIC3ho0_&( zY}{{1jn>jP630?&-?~c@tm!D2tRF8GO_3-^V?#JvRTJh@?Bs$g3Mt*#2lT0FMUMD; z%9dz1N;>4}{bN-XY-#Llbyq3&Nb@HROG8zr0qOKESF+zN)JUkwQ!;g*@~2e5&Oci( z*cQqwg|7`5;&CfrnyWfENs(Gn4m~ytZ4$d4um3s{u_6wFcqfWO`!ftJ+h{ImZ+|kGI zdIbmi{0wOItA6<@y6sYQ@>TzCGB^D`f!aCc*V72&Hs%N4eW@*r3^-dXlb6v71{%X~ z@JcPaeduX4_>S+5V#V=luqJU8k~7RSBY2e>IAj`LzBb$l+0SwbLM8OyEFa2bYjHH# zS=R9Z`pNK@K*nUgVESN>tLs3wU-XmqrCSC}WSW&s7z2baUzt^o^;f zHxzC?&RUY}Sgpo|B7A&sJcEGxCVC3hS!VU}1u^r&=obgm_Q4O5Xtl+TTW?5N9UTX4XWZD~ z0dc3aTCbuSJ4t3V(Qx=a>PFenR zoF72Ky9ocfS8U5u@+bXL(@y&E$W-Z!6&WoF1|kwe09J)EA@y2~a^nz0BnlMEcar-N zlEi3rw#lJQw+YbU4I3Syn!4GE@P*E&ye5hdI`%WGL!27=4s&yb?EZRHIAdkG9=vA%sRp<~7S#@)i#nBio!^plc`+TIi?R5`d1Vy&=X&rQ*~rQeym|Z{u%PLMskx zYa+O`EGPPDzLp65IgkI8{kk?}I=j+kg;+E+TOB%@7la=-M9e$TYd65n5D(qj=>b?_ zTT#1Nk~%VX%UA8WL~uT;F;pguSeqcM%kMAs$8>Wp6*w9X_oC6`RxiUY{T4dgl+@ct#eZ|wI3>v+&B2V<5Re|;+ciel{l3QOErr@LE(YL6 zu!jej)bT5-ve@u?EJN}koh{uxI?C1kSwEwedYti{e%>^Fmfw~x5y}C2 zvvRwJMbQ1G9RqTt$&pIh?VheKwDt~xCEgyLc?=AL7jM!GvDvqa85vc}ZusyoasmJ>LNG?FU!MxGR< zdpYkY)IYGlIZG!5GGOmrj7LOEus3oAjRmGuUhoi~sD%hzyfDTP?~m0zAV<^fnp?W0 zHsA&;SP-wuBMc|nlU43?KN~XYrn+HnnQ0<2)*zTDi!mCeY0a_2XpGF+y*s)}gys^M z1|2D8W-1rF&Vir5im!rg?-|SBWu;RVu#r#0$&v{ zGxEc1wBAj3@TZNpb($U&UK1g{oNR0v4c})(WcFsuq`&SOuim;!d#VT1K4lxfFK`TN z4g@w~v)gMHVa}(B3oNgNPK|P`?;i>-Tr4Ys8C0M+cT)AK%3Uv>&S;ew)j#X6MlOxY zK!qF0fdXK&MNbW;%iD7ImY5ofLg5#<`bz zh?Ug>9+@`HD4dflCQ0daKcjM!Td`LksX&j%*=nHLB}(@E9OjECeahu$w8Ysr-MVwy zlyRpk2oL$L5pHrGk@4Qcy7`-s!0@kt1BKED-})mh(Bb92+3M9Aa{2sBtB#}|Ko$nZHg`y56>Uto zpmui(N;cR`ipY6Xt;ba5OBp<6W-zCN_>v%}h`~ub`buHzRz9UR+Y{w_a3M3L$lsLe z5c;o#%~HUL-qMO8eaLe268UtWKz~*r*HfN+BFrn2{ZE|)NVOO=n%H)P9{6<+tvWy; z?YD`x(z5rX5vAuS6oyc;JM62WyQkvc<2W=MD>Y%n+~1n6YN*!{T}Z9&Iyw%>r%mdf z8TvA|sq9ifAYO?*Sy}07J_fXpGv}bvZ7g(MOr}CZ5|G03<0#x$@=ju;uR>)ifrS_s z5|x!R@@A8dWg^$uAy*$)H81ASe^xW{Cuu7nIMEcI8^km(;%ZrB4gXg-Z`~Hkt>}RFLhNXCayTQ}^2SQkTVG_#ZcQHE42Zz4zjzynK%}o)XocOpiCW-`k(93H6XJqMyRK zEXJcWFlUqDcA{%EJyOX`rmiNeahoJbsM9r5wK{#R*3E?clF+qPOA zEgTGoI~DyQY|CyK=L{KU0-0LgN;qk~E%Itc6MQW5^X@W!cm5$pC-*82nr(GfvENV)EG;&j zU9yB9k+b_{O$vImTzqgc3Uxq?S-qv~q^bN`BAlB_kdth+m9lZ^_?#U-;oSC-&StgM zjA{Rn_%OrPkrv8Z;DdByV~P3*J2k*wj>qMY7>q^X4fS~kv>LTa5ZnB@8YG+K)ipZy$teZv(cvp3cYVRBs#gq$ z)*ukuOs;fqG8UJ(s<{5|INnXIU&{=y$#T z&WJk)2<5&MC@x58*UmBhng$rJ4YF2Q@2ItqFqyZ;2Mkk9*je9SHf_3^vL#Z|yg}gm ztMd5t@OF$OgOO~KrdM3~i*%_y!_Ss$g&uQdpPp4*>6UP_>!s4cRR?QwC1!C_??OXd zgR^2uNu7f`NowVomamrDK<1=(`S8Xo8Ry4CPF3dV@kyP0QhAMpe-*kHCyk|KpYHTb zdFxouP^>)E6|8HVyz-qXxBQ7C@_qMruNBgX?b!?))hO#0&SQntt!>WFe|tqL7G&H~ zS@y8WA>PsU+t)C#7hUq&fBg49=8IbPZ_|b76G{O@p6fYKY)fU0ymdm{Lj%S#e_byF ztG|6KKn04JFhnNn)^OW~mS@)T(R+U%i97`8pJwksDkI9l6_*@dSAV>jD)LKbXCf#h zb$rO+Z>#V>-dl$r)_S5(!V_xctAA+H$O*+ZWqk(z8D{XeUU|J*NF!-A+gcQK;}an3 zyh9Jo5SX<}l9AHcDMqRb^Y<4RCA%S!S8^fct1x6zRMZMICxSpU$#@iXKn{y!1? z-!1~)=Ma!;wS7Pcs0;M>S^w{;#Y=$xYq*osaZ*Y5A0~sYUx%P7GO2||GRB@3!9TsH zLl5;`DR6ElY0-Lu1VK^uuNvdun{CIZV>I~_JUL6{|^l;$srwxVV3_5^!T^9JWmlIi}ztZk?Efr zh(S6~ueK!ls}BCpLUz^k?BoNs_m%!lN&lyT8a$+fXg|t-TKo^c7ymPpKGHumkcD)R zK&AFKTlYWX0mS-cIdM}#XY%7eUH(;#BtSZ7*4+^Ohv{(J_FH*)geeRBLxVR+o$Rb0bW;=H>qp|O^6sNzoV2FOVat)0b^=Y4k zYUN>j-8)1o&-l^gEXlIufdpF3e93A)L>Lqg>eVVpL3H88YQ2ekd9lm=Ir%Y+MDvW6 z>dk{a&8{^!7rMjM2AL9}D^u!gAcOu!iA7(g8=&6tpvj>6N=;$c8L9v2lA-J7L~xQw zknSXfwoJk`*K-_>O0x;)%bFWX!}XzG!2QaW&6SSdUxyN2tq6(@6%P^EfYRy=BFm7N8`lAPy_4Iai0eu%Ca~I%JV!*PIjH3Zq~meo1u1|yHhx-H!@UoXr^-+- zsNPFS(LlTASMTz!nB7DDT75ZjZ2hD+b-K%B|B1-Evy-Y-dnEL)>%L#GaJSlV-yl_j7Mc*AGF&=EHdkc`vsKdWZ_i@JQ|I zlf&<49T*zSKcC{P_0%VkV2iBDru4?jS!zE52AFTAkSp2&b_!(Ox>c1&H5_C^&9>CESs5EZC*p0=C{9DTA3ZaZ>@88 z)>GMRBt)KX2Dy3psEpAFVH00y!v3FBXz5hDj7I< zS@~*FrhX;eppHWRoA&L5n)=ACFZ9aGKu%)Aug@B;C(4L~jNcR!68v)hYNSNsLR%BF zCR*=?7MWOKt@<#nUxD^3EZKJ$#|GTl!KPGfG*)VH zQaG<@sgxQ&B4E+_x3}})%vg9nOI-_ulq4s+m1*8^J$~KX)TXn)#7bm#!_*&34`VW! z&sLt!-=Dg}d6ZH)&|@@TPkwo3P+qk^qQ36PEH_-Kd~H0$>WtH(T6M_d;%PD1PI=b5 zUy;R^(@)?&ftQk`sf9uLYm8^w(hd-w|2C_>&DHLV^18HkC$#{^c_OyGKxb^_PN~|I zd@P+S#2*P~Gd`Wm(xn38g_XTU5`Vsb(52F-Mn9UX5XkWlo5+_ixopcLGyYkk57PIT znTuGBH^H%UVVEo~i;5!6{o0V++#2_Em}fl7^rboHjI{y0p<**Xv5j^{}Q(wV$o zwSbhpih`I-IrmDm@3;vdc4?^zAIu&j@zBrhDsIb zc8yM#bJ8fHE*@ja48kh$qGD6tZ|M#S2wTP!RaxEccjlE^Y_aP_Bkxw4Yzk$YqH85v z(NNYL8j5t5Y?_@VR_vt+iH950+rwW7miXuCxNaBFSnV&ra^u{UKiw-=Ah=09JsuH{ zs8_y1(nFk3gl#uPzS_k(;Ck7a*QsJO%JEi(y(-`kiVkzZd=IstlJyu<82$jsY+MaM~c1OBhoAwk- z)q6@YctBKvF7fdAGL3itQt$GdZHam>kz(=Ua13X^(mFC(d8=?y?F zZ(@Z>#@hmN?Q!H-a&??;peuq!%T>5?!8lqt84>wG?4U;|^y}012bvlQ55L*cFdtZy zVXPZ-{T8ZGN@CWJGX3F6ZvHLw?<?S#mi`=8q_%AA#)K##l8cC$I#473~uIHKbux z#*5ky?rR@i!8?kM*M12jRh^wAmZIK24)Y+p+`Z1svPH7q69x%iZuq>NPdIE5-}F!% zg%Fl+3Qf=Otaa}!;ERM?c%DBufn2ATkr!K8s%#*Fe~|9U_9;Fg@I(k zx~A&tDzvMLLnBN8(HDux%%c*f?D7L#2eb+v!=vg^<7`Nu2 zoUj0#k}KALX%_o5d{ZR5jX1LWj%YuL?4r1dgl=2_9%K}Hwn5w`ld5yYI8g-#px9x+_p8#l+57qx%s9UgPfc^ zA5SlDry`uX*zRZ{3twOIi9x;NM*C`VzP8~etH`sPHtlhL5lfSPXTpUD{DaswRZ1cp z`F>^kDDOeVT)q4tuu0ZIBK&5tveW1IwZdwAQp>bPOvIW8hu8_41(2ieF?`TDY}IWq z?e+j*F+ysh?sI=7{z)SYpQ~8DBpAXpxlOTXH!1hqs^@S4hG+DL)n#l}c<8T>xjZC# zd_$-D=<8f(qSLw}b}y9!)*Idg3SLNL%Aia@j256-B<9LJfr|QAPB*9FRL6Qd!d{Zz z)6OWA=O5wKP?5`2x&PuHp+RKA3NjAqUz9ns?!UG`El9w zyq}=@1XZNTC?>+IdwANu(UA^f{n&Dwlq*dq1N5k-TP?AlmKA z&1omJkyc)3>4(!6vNDuaSYEs84{1g$W(Wyv7nsSOiS934&1iAr-q#s~`4bq&`^fSH>}&G@iPn zeQe=VaR)6oA-U9>_hhvKEXUOyk}~BZRLvVAcJf|ls-Cv=v#!iM3E_n;gyg_pYoE8U zebs33g_~GpkW_CJXnQpIRe9QtqIEMTp-5qsHASGEP8*-8)isH* zQ+oo3gvd?`+Ep*mml(M<310NLSehj@jEhYCT9)iyb9xw-_4G;E(!oQ;i_ZeAPS%Nt zgm}o63&X)&-tCpPoHZ{J6W&nNcQR(wN;)!@^vLA!3}xu~*uEonz7{wyPP3+#L#SJ? zkA!&Lbfa<6tkR&3ZEva)z-#on*iLnQvu;Pkt)&TN8d9qAWgO|F;W6e&y4UDSSx*)h z$9cz-+emqa*sunrlTB->&ZP{H;gU=EsQ6|FVaiWFrXHtWR{uiMMWXtUd(hOW^j`O} z(dYs*$*Jx0?r5ae$Lxqik9UTY>s&5fqde9GPWyalv0T`=dLE~D5-PYR?-xXYFSjvH z7L#=9S6rem*w)*;tV?REQ!NIMR4>F;Qv(s)bY7hrTo!#BXK6~5Hx=Ql#is}C-+R}d zU`NW=W}E_Qp&Fokjufd`oDLTnmyD%VFWYFh*6IIdshtUW;lUxtQ>wz)fz;1g$bPBIo!03;#I6lK5&8GFV&g*d3wH@&XZ@s z3+MC}M7^94k!_1!*XjlDj6CDSK0W3{=|`iR$uyd`midLV8#WlHrS7EAax+oS)iP-= zD^Sy&7o2~kD7(Uof$_d-HktUi#^&0F->I#<$J#JNac20qB0qlMLKafnRz{!l!)H7{4kL^voYU3 zteE^O{8=E|$JT2$*Un|z>tPg(da!9sVo{!87s~Cf3YYA2QG>vQH;aIs`sjaJpK*P1Zz15@z`HNKnGy;>^lB?EKhF=I~lHE&8X3lJw zM!#FDf`9Ntt&%_b#I{5l?aJ^S=%%_Yfal>7ZC7JXp-TL3ScI$0`c#f0LGgZq4Ss>B zhizyx`Sf!iA?p4#O(288IA4khyUxj_$5a?)9P`5{u9;WSyMq&^+d7t1=Dl&v4o+a} zauZW_dCNf`ZtC?cSz5(`Zr`WndN-G5vlE4VRz;RoXINxHHmE25$G*zW1N+hAZQ=v^ zXbkp~WYQ-n)3>;-bV`(4l*Y|X{b9S4=kwW3HFz0AN})$17cf(4lKpTt;_UWn0A@1@ zti4%!m|U2eQsvm?0b&XxfYf}EJX&3DN@+bz?UcWS>WDVn;8D(ad!<53zY?6g@e)br zHza+1M&#VZ$CerLg3vqFUb1~xV zyTikS>N6++2D7ujigm!(I0ub!JTLc@eeN$l*y9&M*bb+pci2QVOU<}EQ|GSb@vW^W z)4N(}ETlARMslXcA)DUx+_`ZrO&W7__X~`5f)#a=nuJSs>Z*l(B9@i9L8t@&GZEzb zLkUb~3X{xPJ>lumee%oI0r?z^;{I_Hm_1hKSmjanQS-TFa0cV5o;s70yH}Os-u}s` z5t@!qn3ApjRYhJ(H9n8QK|ypOezFZNat3bWQ&~utTqRB^*Nyo)E^4$o$!pd&C=Aaf zcF5d+L&DCOyzmf^*}D#~Or9p;m}~VQg)YDG^}kKM>_eg0sU=Evs)+tF@N^g8##GkrrlWiJ26iPMQ2Qd-LBA0@t zE-w3c`;Vu7)|(bOjqJ~39z>TEMcW41$5HtCCC#SjPQ8*)D5;;Apx?dgbqB4~4IgK3 zakV|2JkU7skWh~@e#-ZXFBN_L`oO}*0#aq_q}f!NT)#H%b6?6-w(XlXY=*hV{e}2$y_TNmh?|S%?X|T4BdJVk?YgTH zd%BnJeao(e<8J!)n=*tRruMD*a%2ym*VJeq_{=fE1x@4C6sM)lQq{$r=EWK2bsjL( z1s4V3Uyd9nWR1@6lGJFQ3ZiD=6_)k^>JTJ*{3Ag(iiCsQ(Y$GlXnzuM|=3;|vZv>${_ zgPS<}j>?3t;HFxlp$H4^7e@7vZ%8y$DlOvrg^yh$_fA#QCLzqArDn@9swBF`w&7K~ z76vIyYBK<$r$1z=d)e5fe8NXl*)3UcYIc70+xvTC}c5aCyLM0-A5ED^L!zcrV#yh>Y ztS&z&#|ZV})f~tv-}s!2ZG>e3IMvE-NP!W03JPh3!u{i}4`K5puO`=Qu{Nv z)a%vLzU0`^7+?+UdonK5+k}}nSEbaBfb_k4J;oD*$UUC)nhTDISv#k8+9Je%Y)F8w zxy4f&!cp!!A5OD@F;Xc(j-`W+wmfM+w(CAuETEk4IxnJtEkBol$BG|_ONO?zu3DEQ zRIjcN-2#lr7gYUS8#Vo*CV~X{byw!j+aH5r37uxh+Y@v@2F(k%=y`i7))|`s^UH)h zqD3T2g92L0YU+*WXeKg3oyVbJ?3DcBte>3_Ftbb|QmV|JJg#l5y#9&|c~-o;)pHQ6 zHCgo4bf;TIRxG!F>J>FGoc-CQbmwj*>gg{+<*~GNbuW$fg%17Lv5foFfUbF|Z zZjt0lY!)2RK0z|NRPQ{BQ=`#XyIORHJE z#Lr#1KAHt)w7hu;ub#V(qdqQxoBL`dq;N8muJ8FQM%gXSwrILmx$k-k(=}WKaQ~dJ z1Zq^4Th66Kl3SeGoFBv1Lk@*_0-^5|}SlI@Y9s{R0md0`9 znCP}B)yGgfm1o~@1yPp;WD#rI4}%36NmL&9?xhDBrK^)n-wNl*$qlB`##T-E0l$tG zqLOV#IQny`DHF)T_UID!8tlaN-pD6ES zFzXgAzjI#uymrju-c>>RZ6T`_QT^Mqi|wg>E8N*ZYg+2@<68Dz8|S5bOsoAHcf0-f zHeq#oK++^vp_dI^5{tvqvIRn?M-f-rnB#rcVhG35fH2%w(m3blFgy)#T$We%c=tEp zx&sTknv1ni-9eSvLcbIS8Qc5J4GSi7K8`&l*M4KlHplgIt*oE*&gdqjczGI>6#mk3 zb4V-27SdtQTZJjBWf&xUe?l_x$hn|{lB*x>WV(nYA59<({f%2Vo|HX>Z(VJZT3UU_ zO^q5Vp@tJXgYFR<350$63vrI%nf6QuIuz$Cy8ty#&6PR>6w)WQ|Bt=D3X5wA*2m#M zkl+CV1eajJ-8Hyta7}P`cM>#6aJOK=-3NDf*C2xp?!&-;viHt8XYc3vU3~Z7?aW%e zx~r?JtKWL78s7cJa}e(1^~kwqwm)@zsGjXaFX79PC&tu7?aPKZgHZc^KXWtea?PR! zZT(g=w0&`yWzg*$roC=!niMFj*+3`I%6$xX%ob*##VNxte7?G;{lTov*B=d4X!_Lh zc-eQ$r$vPZ5vf#*#CvzuzMXx#y@Sbz{`34n{R)}B8szaYvq4*l!=W(#hsAUNsDKh` zq0jqClB>on<24?0P-_{6$<>N%@x z0Rk~|Qd_g6S0*mA_9xh9F9=aQr_2|Bzl8}W);YCniqyjf+Ub9g|5UGjF zZuZ5(kT?!!iKc@2=Ge8YcUGj}L`hpM)ORnr&?{R{T_RELywM4hV^_={M}}N%b{*7Y zuv$|;7Fb_ErcL!W-g&Rh5|5>Gvc{9~t^?O+@AKiNItaN*Dp3i3T}^!N{))1s+XkH2 z^i$N7uJ{vG`X{y#D+N~r(5>dP7>V{K@tAbsv~l=|!ck^Onwy)u2%uSomTUCo2y+!X zJWhIOiyHMv7xwoXHEepzdB~Z-d>3^N(lks-Wx``L2r{I83Xx>2NLqT&FIV~#Hih54 z0%>!u*Z^(8{Ul$ThYF;lTi_;`Dp8whsgl3e=fc2jyS1}8-Fr@kR&jOcTjcv#+I$K8 zAzF(f%4fZy!xjt4o*vVh`F)n|H<;1(%uX`Oo4cN0<_7&p%GJ@=jpWN5>~sw9gG__J zjuNci27)utXHr81IX%iwgHxEkHT!Pwcfy^9Gf#mH0;{y!BvwYsxq&q0Bc2&qc-K`ms~chU!oV>#Nw$HOuBzuYu%h8C&UivPZx^5 zgkF{?MH<|Gny)aYYCht3yoCs8X(tew_mAEe=GSRB3bQuV6Z2KdAZMJKkR370q%G7* zq_b2S0RG2szi1PQhC{48bwbl%)(rw8wg=&0iA;)ab6SqX@J**3N3YlqYz8uuk#&?` zhuO^_$w|sZHoHqjb1+V#ixYLfw4_A;g7Spqlc=C3T$t{t;w@;djEyqzU(uyq%73`Za;bCH|K zDx`N<;VNfCMcq>plpLvGJSfYr%}PqHSOhKCi;^O>@ie@xyP1+wLYs97`gVHzX~_wz zE&Xdp(FDuVK976jRSP~T;5HiY@tfVGcG&alQY90vtv1XfSN%(P5 zOo*7#@(x)!3*-)=_>{v0aU(w{`(_A8>Q5F}VKSN+4$I-72k=6xjpV!!L`ayS_V2hl zav@bho7pRxxL&UpESy-G@e%eqwPiQhp6w{y3OyJ8Kto|UR|7BEe#w>PZJC2qm>!@1 zG`!DwJ{&o*-5D4RWhvV4r5zlJ4!99f$3HzPwa8jIEU@n0Hm=oHP+Onx5uoAUlDoc9A=N*=#{K54btq~8Ce3vANdA+egPDfo8lRl2=(=U!lo@LB(U zl^s%0>S0VI=!I&%P#&+*S9;d8^P}8|zs`31{76q2yAX2E4-Gt`JxshKeiDgqpTDl% zeTP2~7ycg?i5Yn(eluaDJp{gllkHkzy^Uk!$oERC&gDExa*i&M{W$MvDEvn>Ngq<` zL4Cw%RjFuU-TN{#TXyV=1~luj6bq#A@X4ibrLt!ft#Qy3U&}|c1zw^{KuX|t`*#%; zY2HkVHE(*J3b*F6wcy*4JKIE)?xaU1SW-)j>FV`}!Q`vgq1EqZPPXs#E;j(?l}LG3 zEJse%zfToKH1~%Pr@KK+^|){Ymk(d%WQN!@@*8}nkB){oTNj%J0Fr!9o4JFumaGJ! zv9YgbAS-i6=bwPi^=m8Ov%Qtm-a7xn&rrZG)p48XQjeK&Ce2{2zwI>Ro8V#75QDc9 zV48SmD?)CJpZ*k^@uDDUpvTG^pxc|~KcCo$-TMw_}caC zq}ShisTn9;%z-6z6-_0k9r0_~&Ois8(w#~A3Lo9)JS#p5#-#W-#nhpw zGRSqdFukfSjt;xM?#k5^ba?rk&AWi1>PNl8{{-)dVUwwNQiySkvwqt^^_&U7ZQ9A< z1eym@ttl!r&pdH#^5u%V4g1<^efmDPp`T)z0G)#^P;vUO4H+8APINTa9a;>?9DAEt z&1QCFbAVpPXx_RpiROFK!X7gp+xOq^)xrvq5|ycyCVi9on6TFEbkqK8iPm8WF3aNz za|Y)M@cjpiHS~@^{76MNl?EGHuj@8^NcO&Ye_pE%{7yKtjEK3kA>{NED*jW;x7-Je zc9P5T1D&$ZKMs{~lmxHg9JYz(?BszCWsQfN$n+STfG%;@~qf^cM6e$r)wnbLKhw~9@!I$IprZWF(I`9|=sw&>Tsr-MnZc+^$pf;-4 zS973d@gfdW=b_r>Fm_X+n$#!+SV>>`tBmz;ed&yXS`0g*rsZ3>JNU`l3QN0km4O=& ztN-3cbG?UWiJk`8BmoV}kBTlG)`nN3YRTLSZ*Cs%FPjS)7+%5C#zJ84FLpbvy}ekZUaZ0)flC|J`zaukcvG-@8f-z4LE2b1V=Z zs{1;r5~T~xWojcOG@T+jas>b7O2!^R7tH8&av1#YF~_pQ5};n0>=`klwITvXIk&WF z9h%|Bv-v}P{QQ!St^}C>p2**unh5&u9VmEF00*83emq-2^s!+sY@-I22IEv}tBQbD z15B4tyfEy@EncY~R-TRKpi`s-O?`;SiOa3=cTPBR!h@CjDBY;EjN>)_9I_fX<2W$s zG5Pila%rX;o=`^+e2d`p#?PB&3gwvs1lWlzc)Pa<`57=-2k=)`AQyrnnvKyH)T4-W zi7@?wr*%k5KIk@U80z$hKH#!TuA?RPAAxAzW#$#6=?ie{r$=6d3#yy4I6RrJqk?Dp zDV)yLDT$-RWj_gA;Jpv|U`m(cwLix6;sw8rjJT+}&B{r!hnz6n8#=dw3lnX?k0kl7 z=u=6uCIggNf6?_>HtFQ;(=VtIJqmy9$z2sxLxg-Bwd+^uW1ZMwjzTwr_tEHQaBB35 z(v_c^l{TTtc{cpsY?w3@H-#%on!O_v-*=s>Kd;hq;6LJgSXn=I61Fx~-SHy=+yC?= z0Qj*62jilWX2Lyrc?q}7&$QB8$8`uhJPA}82|_tV{asy3MX-caO}7r*Kxa}OWS>iJ zhdrVyoS4F(+z1O&!hZ~m9gPcL!hjF`DFUolzPHlH;~ z=)gnqlo_#wC6e-ujqy5ogzM&Bd{)V<4oGZ0CuBPwi^hL-OfFZse`9FIyr1*UnQ>rk zkzyL|F2UO?X02B^m950&W0ev^{UAp=C(^tmNK~e%T{=NB9hT@uvHutv2W{x;YAbsbxKE_bG3?D-Ska-Lz26{H zZ!ho2G=>oRzHT<5&eIrk#3y1XH}e||yXh%`tZ$3qA76U?QXge4^SxBjgLJ9C zsazY)GGG2$l@(KdXG-}d3wUTAjXfP> z($3$lbI;aJ2A{$$)Nkti+30+wjk1FHym%ohhb})QM?7s4dsZ$ho+S4-)OX9t#Xd z2{6QMMn_jPFe}|524&K>s{g{}M}M&^3v((&={b156`G%Zfshk_dgI_Cxyg=f65lz+ z*)Zr`aB|BBk3g)1H(iG<%ViOgqA=at52+if))pQzC`IQ(+yf=oW9a(xyMB+jizf93aQETTDh&i zK)0Ql^LZjpgG3&8Ae0L|uG9(L?l@ca=wiI{3+1p?RG@z_q-VT_D7Tp6&7RygXdhX6 zx|jn@ z=|Fy6o&ErQcVUst-_m%#-TlzL#N0YTa8-!8JGg^>npgd5*s}4KgC0;K5G>F_-2Oi6GTof)Qiza=Q zoXvU>)Ai!$jU6fT?FNz^O8+098Tqa(AbVo|8_o|A63oS0FwNvi@1@tmhhOWN(-f7T zTIM~n)C;ttLd+V~s#z}p((P|eDGH>|D$TNiQ^u>ZE>E`nvYd$)-||z&ray_P1_PzG z(!zBo&qL=Xjj{@ZYiUxXAULg#)q*UGkLB4@=fJU=XRcXqr!PxVt@7ClHSlkhNEYod zPz|mR-S=7ii1|j^b2Z($z><#;fak2p-m>N?cVJfys3oswad)yhN2i?|>Fe-Uaw>9M z{*HQIQ4Yj2%?h0Tc~n}%WiGePJJ0dHx~#@xl-UHkJ`Fyq^UV&8#VkRWa?p9#?BcyJ z*5?IK#dL;iXJ_?+zd@4gIgK81m+PCR3%Fm6<<%c`f{Y|e3J*s7F{4M@xsQYcBGk)Z zeK$JXWO;P_xzwmP<8XJeCTHfqXeX zCDS@{5MT*Au71usXAiX=+WJz_ru|YkQ_9*Y?7$Xy&wTqQEsg9Hny8j?TY)ck{23Pf zL}cCmLYr~#Q0N*RS+fM>%7~&Vn+YYM85L0Wd-%4IuCuh_Z6q=vp2LR;p{Y5$jd7iD4VCRUZHGx_ulbV5?|@Om|<6}{234)gj14w{X2X4 zQ)BCx(`*U+-uDit#(->}UlvEg3LT6Mf`|5*LmpOEFV3 zr^*nNrLqPF9}G=fOY5{+h&pJZRA-*r=#RQ%n)rf_XdQNuh&CGM)cI(6;QZk8Pdmi; z8mw^V(GNfuQZ!w|$KK9c7m1UBIax&YHV%S!s}g|6QhS0ThoRcT479= zH$DmJUR@SFycCP=aKOYi;crk)!{DbRu7TcWSgTUFZTpJO7R(&Xoh^s1U7&kaC{Uel zYj3g3e=V}afBv=pN5j2`ng^h({UoiaA{~9>(ghe&-?}GT^e84{`?XYzan}suZ_9ex@G- z20+EHm|EcZOTjbxv^Ejoazr@21DjyA-nagdZ%vY$-f0GE&>{xs{&?p*j?g@>%R6|J z^UHRT=A+Z#CkwtB3%2>F{m@|NXK||3k4;(#wdh6Vob6Ts6a z#_l=ZI8&QKMpEHmXsUl^%}BEF3*ZEgdjYcDUsa9GzV(2zF+>V?eD9w1TC^_qoQL!c))S;!b+aRQ&1#StQaPW)2gm$rn%K9!{&;d1NdaK{ zC(lw621fXcZ=GGK!eo9URtYw<^XqGabUvx*jor_|z_GHPaBnUzA|mvF!~7yR@Xd$2fPe^YyWh^LFzqB@j$RHotI!3-a=3ctS? zn0BHx1M6^XlX`yn%@zwQb(N5l+hpSH)uT+0*v)yx1S{r}0qrKlxJ>NMsykaDfpD z=8@6BD@q>s2ja8oZqKqFD5k${AA+)Df)OSJ{;2@uf>ImBUc3!!5S`CRPq8O`$s~i8 z<%jq2#7DG0&gxUUht@ z8rZ&_=V1<)#fDnt**l9rv;?*Kd*igpiOmhU_%EQl<77qO^av4Q!PZoEr7q!5{-?xu~I=e9JjQYDAA%cHm~+}m;{fa5yJ_jN_e5W?SO`?dKGH7Ia|$ziK9X9NmJLZPk@k#oK= zd5P`(f(>Z3B8NkZFP>RG$7DB;7S@bKYo2mk2b;L!x|KYAz$VhOzSo(m?j>C6>HP;z@kJIDyG3h2u77s~!mIUqG|P z;0W3{2t>(q+4eT^%lt1fiBi}IWDsrjLngBLjD@TB4`x2**0-R(aZJiyIwl-uf3@5C>89!^~b%JSkF8eowRS4K}En$?a3Av%*UpsX?m0{p37 zSQLa-)F#=)d^TGqS>brmy~>eSPTAwhS^2Uo0ZDUU^hKJWD|w7kKH|foHeMD#6K6U4 zBkQaqTJn2`UF7|;_OXq$uwCX=lXbO5pIqkX+*b@F-*&!u+z&*7E+x%h*`1GzzbPSO zCX;y*{TiEjI`;9z50jNJd24am^zQb+j#l+2!Q&38O!yXmzO*@}dQF0Fp|=_tUPl{5 zZ#bEmH0X#^;_3ls5n3+ii_*gfGrV?{kmlI=GlK-Ea}n&|(nmx|9NAk_)G}6gj!_ zTT4tw^fF|PZw{rC9Inuxktc*e*)C+x9i3b~)HZkCMJwr)Y)f zw)&=?1OjV20EXA2HH5$zQsg_*&mcqR2X|ko`p|XCX~*P^49LusIJ;_F_yCz9xL<}2 z>T_AGLh6trk}SOeb*ry>&tGn_iS0g*-ozFXPu(R>sFYY*?=NaOu5hI=SHp{q{D9V& zQNCUeG!uRr=7+s?tr=4+qpG0BPHadf%So(r+^26p{v*FbF zL;$AHeLu}*<%uczY0dl@pnOE zz&Nvchs}jOXL;dJMPh!~%zEv3XfB!)C;pS5tZa^ot_gLB{lI;~%qu_(JAr`p5xEiD z*yh6SBR_fYC&&LOw<1IrtJJcuzyAZMJOn#u^Ldie-r%q*De}!=E36-zMrQg7q()=IhU9Ewh2E`UR#j^%Bq%mc`klwEDhR#|+cJ z{AgF2=xvJYg5r|3PO%pMepwpyKubZ!^9JT?q;9G6+>Zt!UK#{XM)-Mq>j)3klL*ULqiuAr#HD9lUkuS}KEZof9Mk$2ie07DfCDHjLDq@hQUoU&e7M+g z9!ftJWQ;+_RIU2(VEmztH<|e)6138Q(GB4xG%gT;5*V?t(oeL?z3YvC z=0_eRo6KEK2lcQ`N&Tz0P)JQnzJ^84IO6;-ko`XxIukS|rPi(3(6fW4*~*h%RvHq67!JIx6??1 zZI;Wi0rKYxr;QI8f%wjR%?3<{^*KLV{)yrJ3HJuTdWu2AOKpysTC&J@Z2^WNFxi#w z=>J2Wgre?)w!Z_*4fNd?zlA^eDG-`y_yd~8=kX9epq!`GkKJc-{xg*r-sx*+6}M!{ zxm>pAAyNW^3Wa7u_}Bma8gztqm}eu5@W#A<2N%8JK5hSEdA#OU2H3r9J0Ay!$w?1p zVY6x?@2Cde-HErI&+``>6v2da+~a=Q;q3Z%M0^n{xSh=38DN{pm|;2C&SP<9G8MGQ>eeP2G|)9;#lE0IHi2}9Lx;fK=$%!H zf|Ah+4@E?iTTxPUS~fSaJobJTO+F9#6}CD;sTyV@2h7}i;TN^B2k^)O>#yH$wfP;G z>Q%mHT07=6^0?YpNuc{Ue66Sl4KUti1o$at^ABlU3Ar0@5X9EJ^R^9v>Zfde7DlNK2iXlV8KK?&#Q-gNmyRI+wW zG_3EfiJlx(G=dzsI!m!NRg~40QoXTMn%K~nHld?)Dn3sl0vW`1pM|sseg-vr^Gk z`5Pv2#?&C_tGHhX|3~da(0@lhUk(bu!wcXna$Goym#3J!O#n#)@&^G~Bxye(;{_R# zkMoIpi&)pX@3K4m3oo`rJ;lhl5#Sk{rBg{ib$M-G;ae-Ld+Ig6TqHs#;z{BU3igy@ z0?Hk}eS?aGK7J3Yw`DFb^Q|vD3~+|>s!onbn+=Ty9W;>3+p#O{aHf8RsfOS?N(=28 z8y!mHpB+ym@E8{wTWtNBDhx01X7a>VN|TYpTk~iQV-DN~+tC?BLXz3Mri%5c%Biz_ zZ*x4}jV#9^eAKk}hf}?XLpYWPl%)(_3~FwvVjzOA5zX;9&wG0@EHa}SE81oAqWuel z^%Z_tXPoKl>AVrDow)1Jy?LkR5=*BmW27bo@|?FX*DHLQqW=l}21Y_z7#@!APDBHV zFr{U}`k^Jy#+vC|0L(p5Tn0-p^Qt@D*Ev7WAd9jxd1K_zY~0z~v#porR{xXE9T_peaj z0~4Mvidr{E!^Js>&UhmhY>gWv9wCf3PeKH_wk>%cATu5gFy8MHTNm(r z1M$MNp*I6QBTD1#Q5esu?9LA;H>lYcJz`8eV1f(%BDXQOc+wNTsy;T3;`Y5Iwy+>&zSB~3(xoi7R^YBo)>zc*nGpMlB$#+}6`7wXUL8DxdlC z>uR$&R7oDe9^M%Wijp(N8}FRDPI(&qkbZj|%HeUZY`x!EUHhUHQD7KtBvkIynx?um zu7!RurO#2VY=3n$wQvBp?t@T}5jOA_$ezK>>O+Hk)D+Fg_rpQLleJK0-dt)3fl=l3 zej=lR`dBX#8W40KP z<#gA_k;rQ#EcfSle{8&F2s9+B+3#w`=-e#QU7^6^$gw9&aX$Rw>nf{PmtoF)^9*@6 zuJ|B{E?|CW(zX7{|8+6@mvjphEup#&=sFa(uKT+7_60R zm760zIln*zK82@5(N8@@!XH1`xLoX2N(mwKPc$S7<~P}9bS+s2w0zWCFz+paq*n%M2LxZph$!Tbd}r8*G9b|pyU>PuoRnkaCrl=OW7x2C!~XFmn;MNaJ-?qi zI{%~<7`s{+C(*~&*?rw`@$LJH%T}w1Lg?u^VRztBC~Wn)NPv$MEi1qL z3EvXcbnNH-Rz^i)u=Z2nbugg5!wk34l~|*F5rsTTM|RRKTSpGcK?wwqW3iK8D5`8bzf+E z%RZ&1RGIU0`SOSeZDT9nHxz+(cVYkNVLPJ{SgyPAXmDADYyqW?uoWwlTH1w-^We^3 z-&{N`Al0v07kmhzX3=WU*K~Q?O4I__^^T`>z(vU1@&GnAbf%H`S!6NBsg_t0x}?_O zfZFxLt^L+0%dbk(1kfiSa~XYWyo6{^O!aS4y?MMZ;GhA5Z`8r<;sfW_uip@aK;nhD z14W-Z$vE)Y2r_Bxq>8c^#0E5G-#o1>@OU~jY6}VY*LX6ll?k>ywiALn41>80$G8I} zA)h>K?l;W%{dWZeD~ncOgC$I8$2LM<4D4upyTgoaYDBs#D4(Ld_k#qkju!+4ZiEg2 z7@LdMT^H$m*0hca z>{o)f$CGatX<(Wm+cpDNRwzVCDL&jta9K5d(0dkMcms@4P~Do5dFXb99wOneHeN=+ z$>9HD+OcNz)O8cI11aXz^xn;xbtJsEm6M{k7gFui4Sa+2GStlO z0xoNMflNg{!bD6rCw61bHb$v2`~muSmQjNz82^pX)-z}g(MSDbd*`|TC$Cp9aU!p% z;6>A1IA2Ay6fa&k3ze0ge70?E@crbTtl!$Y|F8oR%l=wKx^gImr>=L5mY7EyMnOhw znJYm?8F%&sTy2h*Ns%!N`7)aMI1Y>-9nJKf5CnSL^Nji{-j|}Grsd3a5|VzOQqx?% zDZe{*`~(}il@H+Lyx1*QG-7Gh^cQ@hUm-!wzDjB^rFz(%w$p|j{&*_uw!M8sJO*N4 z!s}=_GUI#Q);c{}mzoTId7t(a@x{t=RVyf#i^mz*Qgo+`+B0+CL02% zwJk?@-rF~K!Or)2xaGUjx@3EA7#yp1svyOJ^ z=AP(A+qeVcrHaO<;GFlItpL=vZA<<(9#*Q45#z0*qj;aGK@X0pzTfIbb@9gQ2V9Ar zb1A{g*T+Iq(koczwC4@*4+;^5Vav3esSZQym1TLf?zrDN0#%fp z>sOTZStY7!aBc6;X4o2~whXi{^4;8#r4vmFswYjnFf>l-8oN5DW-^|4(zD>K3x(gC zy;VPL6uKkG>*bD~ehy#~C(fE;P-}CLD?yC?UJKqY*y^L(lROE7-VF0=%#KD4o zE)1=Wcya``=OP}xHi?q+si;O|N8G}b%e<9e&*TVroAThcKt4q5N_%$ESw2n_VkXC9 z>K?;!$!zu-?8fV1^C7T7Ftal;-PFr~`}U{Fn|(CW*y)Qa46*ps*71Dll^kOFOyR1w zEt;k6m;Srx;IIB(;hmuCYLwT*xtM= zJuYm{a}+iQyQ}u?))9H>RqZ&2i5GIkf^pA?VU`IHtz=ERm3K)U8!h3)PGX2gZmz`N0zg=0?Gc&sSF_NTJ*SqdpqR@goDxdwwDuE?j zU0C`|tp=Bq*9OfR4?>7a3(?XuVnj7d+#i=0+J)mCLVk_kf>x+#JPN-O9J3FNvRlLv z=DT>mVq91nNLwD5wNxnxo=5oR``v{n9~8c{ireUo#v2^K?k%|mEt?Xg#@ z?Ew!^;4TBc^diUyx8cd8IwO+qjvnyaM&#qYkxM@TT#t0Te^P>tuT2J zCbj-uBEEklWMU&RLgC2d`WqfdNk>r%-(>*1Wpfb|O^<5*!{6pI&;#ogL^#!>gvwR4 zvj_Ho&pcaZNf10_j3JMvjSa2;rq!Xkr}PLQF0|ewI(A#GEYR;zl^GwOxleV;jgvaM z5kO7V?j-ac`(h3LK3Y|>(*UwP?eB7btlUB6<+8Bb6Dvr+h#{okGY1Zznrx3Q)E5m| zw}=c=XhSOTT!hM)RDHS>PAUYvq{)&pp74FMz&GLk*Eq7L>J}SI08gu9>vxL7Q3N%6flgbTLAW~d7Er7}qwMKyg^Z-aG$__@?3s?5znV6*WL+#hYriTh0& zQptxo%{X7B%g6Ngt2Njg_poKQtTB7J(_WN{ zm>{jBXZ54xeVIvus=GH~^Z4Z$GalA#u}tQq$^e*n>0QfAhSyY{jZGSBufbRPG94#q zE!%t@yZMSd^YT;;7`6q-955yDDSPB)!P5sgf0dr1YT55j9KTU@Hw4tLNl2}}lV|V= zJh7j~Y=?}teWmv8=+}PL=ehe>Q86C?Jn3W{zmy(sKb^7>C3AzLund;}S?l9GXRCj= zB$T)aa0Cgh1lh8P87M5*`<45jXuEfe8Mf2dXX=riX07!!xMMbYw}15YpT8+AP|Y_r zxXWRe%Ew2r!F78t0MVSjJ@hu&`9Zqx^S$Fj^WAF0JdqJDGb;#$p>~oGJk56O(+TNU zN;1A}H@YmXSfpCPXY2Qr^_IWr>S_YisWTYB)p(&M?Iet;(5-MD`4sF6ia6xZ+l zY_Cwy&~(cEbW`SatH=skUq2L#(^fVqN(rl5lN)i}Q)e&~Fa(}kx0!$XY#Ox(ksfJt zof7a)TP6RIuLnFCqdQ=>OfXjiDz<3 z^1l{cfJo_UBQ3|$rcw3j5m;(v?)@8@oye8(_-j&yrnmj15NCeZ%W|Cz z=S7R}&)`VnZtxcTORCZ{qowLYL~{cQVn21^RFbbCSNP2_!-=?4>Sq-o-D7J7wPy{H z(=H^=eJl~T54Y&egWDS2Q!;rV0PpM7Xbu=+7_~2A=_NHWV+#EPgPpAkQP1XpuOJ%q` zAu?Zya-TTN?W&4SoA3fP8x&|$N;k$c*Jw*DpVX5&6E z*N6vUTc%%cV{uA0UV-YLb4MLx-%Lf@m>+tTPZ6EQ#`~ty>)|B!zEk< zG?uJal)@V6jau@par+h&hN|Qx!BXIc8cR!y(jIYD6cwr8)3er1j0p5lhO< z3No`eHNFMV9=Ls!mGq^$=fCQ^LnpykoEKNt;& zX8eH|*;XvtvE}P}PBL~owxnp^A@r4EhB{2oK^}xJX$36AbJ^IwN?~J+9J(D~`~Cxw z&yaWbHZwpy=%eij8s>N{FgmZ|v$`C4XnKD9Bv< zsoF5}pWVN=LnH`Y1(lD9rJ(n|p~{K;(!1~3c9PF3rC3ZuZ1p4yc2O5nt6ru{J_O>I zbux1stkJYHFb{Pq*uAIrLTNVdu}^76Ep14au~PI?2OtFR8Z<9mJryU^DXxKh!n$r) zdnV~d=9j~#z zL#?dVfbHaZkeOe{C$vdUH~N$;AG4>fZUIo@elOpLtR7ShgIoYgB;Ns|k6{ke&iF%r z#5JD_+m8lwV=-{csf4QAZHfhnTE*;ODfRNn+4Zji+gBa^;70Q%+gRR6{0p6W)A)Q# zGCJ}M;fmK1?DXdvb~>dXk@ied=3cvLb&x^vWBO* zV>5mCARF)r{d>Mk)s-XRs&89$^=AI;8}_@n5Ge=ysQf+GkDnpbG~@A_D+fFs4C)xa z4{n<;Ufl^_GNu~>ZTQ|se0c$DfW^||#olsbQ#bgNmv65IDK2${Wvua&6Sj$YElXg3 zf82rX;w;eiIHS#f6ON6;$X$A2aD3sX?;SWjx801S+aWGo`)ZQRwCQK@`HFjX8O3|B z5MnJ%;9IO$jc9BVLbQT%)2${T_XCBPt1Bj|sJ$8Juzt{3brTcNJ4cPzTEyX_8Nr4J z8s2>@)8ET>dZ*uEQ<~jvX+6ej*1mxXA^)>e9pRAiEP7Q6a!UP!RlvOtGY(vxR-@zu zPpCHgmijy%ZSkRc#8OfgSX7P@*L*B`%PQ8ma!o?EHr_-LVl6uL=d?5b`?T|bljrk~ zb%XAO<*H=7rI|$ci!?PJ7mTVmyEJZ0#{AsX<20#LKOrW;9kGd4af>MR;zvo zeSeYMJaPepW;b zwo%XVr?2z|Xp5@E>R(-L zhk^~Me=V(nIv-RvwzVnmW<0#>@Wi=V?0oKO2`_%$K5$voU~h*9(BLYTKHIdA*DG~Q zUBM|)fd?2!i3%IN|8RU@G^ksbs&hT%N}&7EqK~uo<{YdN2O4wsJ}b(ki}|iaec(Z8 zVPtv5)iSGi!h?iZ^*C@2*|NZ42cxr?4QJg%4nDT>L~Te?gG6M9fXov;aIAb-;;@0$ zzbUk(VIjxCg9+tqx1pH=?3Dw+;lC$#V1*srjR)?vzWdX9P)p20X6II&3a0{t8*>ks zA;+-+Fn#Uh2b=IHOkq|-qRqK@j{<|d{6eiZL`phP$1NapC*R2k;(|#I2?sWS&AZ@T zu8|4}M6mS_4sZ#`*lf5rhZp2i1vb`0yb@KV|D2kd4J$4Osvy~% zDw1aun1IPbFkvG!A}(^Uu<=MoDel}UW}|2!aW?d^1d^#y?ZCd&wZge8Rl&ygaJCrN zFVrjwW&zH4AG>jMxdk*sf)eeK2|%?HyCg(dL7}vul)FXPpeOC1a8vWz-}@GQLsDr1 z^!^4#*?Zu0<*;uh7*Fjy8t{4knC;+wYy)8X2ND zM{qn#yhc}!pTzqOr;{r7Wp;6MJU6AuKz|bB!&VeaHs_{q$-y`IJ zLrT3L_AwlEd>`+eg_5&j!Ql`Bbj{+V#|lWkMrhun?@(~z_w@-vMgJ7wOTa3w+%*zC#35xh=Vj z0}&E1);*yG&lhZ8C%zOZOF&Z(DAk^1;oviqRF#C*?kGW~@IV)sb?)ton<9o9ToC<@ qYQn&lK+2R9VDkkt$Q!n^{F9%cTOk#(x~r=QQ>c35!gJD8Dj*NHyqVaY9AJGSmvDt-D*r!38EJU zv0PAufLtzg*pQM)f)^Q|S{#)q8MQEdv+2!SWROP9?*~V2uS44yv3-XK(*?fWA?&3M zr1_#OQ)132Je#Y#yN+!w7C1Md&GS66+nryF>FUL&)k?zIw-1}r?@+6iCW7BWmM}p) zS2xv=GLw@7qXxBM!N5Z;!Jt4baL|PVy1>97<3qqcfWFZ{muN1;Ur(W7av}d}L(Kl^ zD5N4HB?bCcF?KLDwRNQ zsA*&BWJu&@V{Plm?Z!v)j~?8h_Me{_NQnN?#px>_iH4jak%*mxDG@t8BRwMtKRgi; z5wC-Z8Mm^i_`kqGcYGukPEPjR3=FQWuJo=f^mY#B3`|^HTnvoN49v`QpdNIN?zT>b zZgjSepZ__?U*m|HIvP7z+B;d=*%JL3*U-q$*@=&YJ8{}#=^fdBsRUx2&}f2RI7R{WFC|M(TeXnuHJhW`>8 zKYX(?VHg;gAefY>kg6N_X%@5_U>MDhhO~Q)uDKQT;c>m63gqEDwU6#yTU$d0$pncs zh!Ta^9}8|*?G~7_Hg3(FVV!t&xyETt9VNZtqjc4~9?k^gOqe{~k*C5bLRh>Z2urR5H>i^_p8JI zMIZiRmLCZ?1|qf5mh68ZJGL5xjnVKzp8)?qNJx>iD?mt$Hgottkd4L;!bX1jI$B`- zAEXivrNLP8J(wl!|5f9^$QJA&?+WM+YWH<_`VUeOj01`HQ;j|Oe;`|O398)4$n8tz zMv$)OUH3KS2N9{Uqf)~a!#5LsQ{I`q;{39X|XtOzRnaxKE&Cm->13IBa2G84xI4QAU?w$!* zzy6xCSRUtb>B(+?CQgY$qSfY9sQUH227PmHG$ocpLGN^tw%yrb>q1Ipb2y>cLFt=T zhmK|{@3~#S^8oYNdPlHxMq^incGG570lL@o8~)Y7ION_)QlQZLGm^E#K4asLA&#O8 zGHF04whB3Rg>oQh7!B=Xn9HfxQR}QMWU;2LC8+*RBr85BX=!KRtFZ#xhwjM|RqcubP>} zTg@jttEa`?p#QsEzC-w1U?NWFRl^R*Wg>2Lc%!e?S673Dx?XI~?3GnJjB8;rohqcV zn?h)4TD5HNoguBS^T)E=;7De0I|D+@I^^)PGV|P5?=E+bSW0wigbSeqi{*3t6*xP- zE8iZ^kC9+G-CqWY$s`L)O2Rr>EW&WtTjDFtZdDe0i#p@;`9PpiNTF34*e_ot**~B2 zJ$P>lwsU9c=!oSUy5$;gO%g*A>u2+Nm|AQRz<0~%@DY;hb9^T1(syO*dHg)6Aly2k zOU%isQB|Im)o$_it*4*Se4iDI+um>8d2nEkR+UOgdjUD8K6D=|$w4*vVEWV&+2MjHqh zrF!ojjLCoN>H;gcO?@V?t|!*({_pQV+NY&?WdoS2- zUiZMe6be^crM(;9_v!uVCwIXj>zxG;+I2=h4ZjtP7S_x* zd?6$TSkIIYmus|Ot@HXK>e{J6EalQeK|^;}qZSyBPUwc;rmnb_?%oS1(x5cR z6~w#b-jNlh%>sLh?Zpi2TaQw9Ok}V-XF$QyS@Fgu%l_g zVPuEl-!F)8`&GihecnCIRXeHQA!>fB!|``T7Q`dVRho?ePUtp!K|?@5`qyV-Ok_c8 zvY2Dgsr^s|KfK@S{jlU`mQ|=Z-zGI?A@l?Pn1YaSD6T42{kxcOThqsnRvcewzm!WF zw!g4ivf{)o*O`jV9_M?Uj5SQPYJYK5BRjU7t>r3|PlEU2_YHyk5Yxrb-V*x57c(72 zg<82ec4@K&y3uxn7-KLR8+z7!BE*Krr7MW(vvb7ZxcNvTYPb=fk`!s`*V()T#+^$2 zCU(5#PFysy+BtR2Di^sDwE}9Vi)-A|5s@ur z)4Xq^3)P0BcZ@snx3OIHvdojcqX;g!7lS^X@8Cr=YQZgX>D@Yja8z?+D$SX~DT48M zsNX zV?wDLj(a3rk=q^WQIuMNnW3s1JLZ#pOzLGmpNCYMDHF@IAbkkgA@tgPM9S2%o}FG) z-jlqX?v4Z`%V+WHcn6Xu2(1!E&^We}IyI;)ewDJx=CBZU_`FJ&eU>#*m$yY*$+hIR z*(edCA$m`c=AA$PY^8daF(EgjbH-!9m`JOynYrX&!fG1GjfE{X;njfgus*sGQ8X(Wm*GNZkdH7|Q4+@=G9p8jYW}AN)h%6pg zw)^^zF4`Wl!&!|EcT9h?NhKLwNIY5ZE*R|p&1O5I+PmNHE;?A$#(sj4>lnEZ$H3jo zRPu9UxaE7hp>Np5{l$g}YQLq)WZKX#OeY?Lkyj~O3FE?NX8c*ywPHCg4Aa*T6_p9! zCrRN0X#C!}rbgYC?(`__%a)h5YOQ`#cX`~_4-$L4#V~Sv-p8lz-+s-!c0fKMlJE09 zzrpD9`}&96o>&G40PTUEZucGUA}8Tiu}zY2C2P51&DkD14dLkj&Ajju`62rn#yhXa z6W=_aAD|Zzrdx(+XllkKQWdHWqY+EIuM(mi?&N74#6dm_D^j8d_#BA3uY@EY9UNbZ zTeBFRVx4gVu7XHql2vI{aB&wa7RoS#6^wMRfpUF`$HSRS1uoCOePb4As#q7Qa`+ld z8?7~H8pQ|$S9sTKDE04_>FPUMPLlRzQmCmKnNE3j`ag_~**ZFa6$()-DC!Q63A#5iv(!eV)~-@aE^MeROMV?;rBVSRy5H{l>pho{Q7@n>6xFC!DQn`rJ9LstCrZ*zX0eGR z7VnhX=LO_{+>X+!Sh)_ptD*O0&BE9#RjUNVQ>#&*v0rQ~hg$OFyU1}NBcHF~$FoYI zO%r5Q?Tscb=%Lq|kK=4#9=l759`NH|$AjytAZqT7CrFfcdcKCcWbqClO%kpouM{@^ zkT(j4u(Elb6VRVTUhzO&%{Of!$YU>G3YqY?)~vN?DsCZI|CzV8o(NigjQX5p@3b83 zrz(9G4#%L%KWlB%Q%>{bsI*#5ddBtE3CRUEQsxkgJfVF3`jtt+oBX4LS8ZF(PH&=j zwAo`mQVabo8@>0F)5;scN|VGYaK)k4PM61Z!B*xqnpRt-%9Dd$ALX_9dSK(Epb>eU zP)Ar1SFc_OU$Sv^JYS8BVk;-z-f;{ z%sDu_=1@~XFU7*EWiXT&S1)>nh5g#t#|!b2scR675b{sM;FyWX)H_7D%VHi&Hn3YH znKlzSY&!8_dWT}FO^#vc*(vKl!)8Za2^GqM|@u0u}%{K}Mvhc_tH9ChEno<%RllJ6p;$x%TGVFr^ z^6XUPEeVVu9K*05FZ%~8}%8G*5Ec4z`#4@67` zU7?C5QNa!zeLf$s=tc&^RUlG6-PQPa9!<4YR-f%q2%5{sLwH|9A}s0AFBKnMpP!tq zjG%^3d&WE_JKk?bq*;u-()ukr-5XAZ3cUtxqquDD6*~iUr!Gi~_@3|J5*%#|v(g+n z->7EIgCAd6jM+=(#hYEehl*T|oyic};XE++_V^;;1y@Xx@Z6*sr{L(&yJ z#ZWRXC%kP2Ntle?-V{G&wcG=2RBkRfCUd%0QzrX=iuxO!=dl$@7=M0LRYCG{+cFDyFxKYzDOeVdm6zv4 zCLd;-fOi1CHb=L_Sh4qahV5+~9l}zj5*s{o0$wjD_p9BI(Sr%tHz%aN6lZ(ycLE<= zEcU~wncs#Z{1j(>pw&{u*VEorj!k~OCPhCA5=-Af*iA>_hPS!oS^S7CFO%hTkF_$@ z4!kMnSC$rqX1BfcM%?0w?7%KXBo%mS=igT`&gPdbVkgJ*Kah@Qf%CSX%}H7|h8*^urO^JPIRspro~kA$R+Eg^5c3%_>A_KY{e}c)1<7-eN^V zlnGeUUl96AEUP@6g4-U64LnjQh7}42_&Hf_d|auwqB?GIBlGIcHPqdrW6)>S#}(`Y z_#3%7&$Kj@CHD%T4+y0xlLh$ha)5mMkdGlxzuxKWuDRO zAxDu&pPvYQ>vw^~ce!8F#aR^agpp)dlZ!u$DQY|}{0aB_>V1bK2#e^mSWo&P-tc)n zWKW;t$-+f60bPpuOmT|6qbKMuuDc~eEH6CYj#*_NUA0m-c7MG0@I)$?lJj}ohyrL* z1@u$5P58NQs-_Kn-toDBbhyhMD$t|={U(p1%R2YR6}Hq*{!>z`7EmmewijY1b{>Yh zKTS|~$Fk+Dg@kWR5e^`%e!%8!1%^H`RF&*O9LVDZCXc5Je!Q`HqTF@m^$dQ}|PQZ&O6ml|Fpc6)Lvjyx<&$jl@c#Jw_Gk8%QR(Aonag(!e@3T#V2Cz<{&*?gm0;ng2ISw>g*@1Yto?vTL{f5 zRgNAQO>+`1W8xbck``z)DnorgVh0J=YQ?hMq^fAks{$`E$r|ID&l?6jVTyL#_~MWH zey|uddM;KPc_0c150fr7Sg-4tiFsJLWf9=wG)J7fZ|N3)Ud=W1w7Ni;(*tR}Qw{-h zb&mRX>3bF<`Fp_Hb3N5y{>+0M-}_`}`r#~>_@b=z4K0)HoW5UrkhENCBIf+{FTgr! zP~r7Qww^roTdVw>-MYKQ_4dA(q%r}hRiORbH+pTWN%tp8NkaFdOys_pfXx3E{n};+ zFH1elCSMxjCfd*#l+O(4r6eWk{Ce@5%=7TFvun5?+w6^n>sKYOcR8v(XCC9w)M7%C zno-SJ28W@3q&E9k9N@Z#^GU968)V7tHS<_YiHWELSRB0R=&^H{UiRSuAqp74G6ib} zAwTp0S^t~WX9Q{RENed~6z1w73IHOtWy?_Q8n?5bI|4?J>0BSaxQYs35g&= zdbDuyosFpy(^{i0x@MD%xwk(tT>|@2bXKc>P-Dmse0agtMk~Wqx?~#%t03;(-jk#2 zY6u|zS}VMe9R2BP@!FcLNdL?0Pw4I z)ikKb7Ao_*Q=>RmI3$&MYPFz&23sFXz(4_2!&EvcVNS~;UD2O)k6xjoP#&>NddjEL z`Jfi8rDCPtkZnAE$J6hPxK^W<9E_=9@T)^Q_bc@lA0txe19vFoy8<+4`+tAKyD+o} zhf|@53O(a-?TIUpFnk-c%r72P$VSmxt6GL|SFp>p)q4PMGm- z5o&L#e(&=2xVKb^7AvaB@OTsOxRF$yMQo@=QW0s3PGC1wRaTvJRB@XfM3m0XX}fJX z{bXesN4p&J96*)1+Gq>T<8l^oi_6hFJX@TnISZUZ^W9;JZ?;yc64LuCXpe#bH5B@U ze)G91qx@3D#SAf8nsA1v(FT7XJ7Z+#`L15GE>AaueX7ZvV{P2n%jY5W+vfH*M8v^( z$XE%Y(F7o6z;fc-H{D~nzV8N#E}?n@Qn{i6t^l)J!mAH>4;MtV+BNV%dAtt`>I=0N zsYi~iX@#V0Gd+MVL}UepqEkk9WzVU*BykH=pdj8v?k0kXc&qgu_2u>T>|J6#?CUFJ z{b*cP9ztQdC__x)nS^jVAEgO?7>=Rm9X?7%qt!BmB#RVzIJ|7SZz3>{ixUoDdUdw| z8#1)Jid3LFIyCRVmuBw5fkHmw>>zzME7NLt9N8n~!9=BBS$=qMjmdx*(Y2B^8eA=1 ziN)>!X!9}taM&boB35)!E1&*RB8m!)Iu>Sx;q^mLRK$VPmki!_S|{3{Q+-NQd$l#F z%L<4AvXn_|WRr8(u|bV8U&{Mn7eeb-NKIfE8f0ZHSMf}3^gA);6$=lr>-H(trGPu6 zIuu(GhVjC?<(ec5jjRX!B>uTIqU0$+pllk8B~)P5*$q^n^lfU(q(TlSd;#Oquq{9~ zN63)zRgzMEMdU2u5Zd`Ts0bv)56^m>HXRC%iG*7QZ*1Eum0 zVfVZ+>A}IpDj`AO3tOprSfYC6j2qH5$#Iq8V+nnk&1B|1MbUHpkHP${W{@4!58?l8 zGMqVGU237pe#^@d%z+>M#x9J+=>+QzieS2hQH_idG}G>K@_`yZ4W8dcMJ#_%&~cu zk4s@fY*Kl{s7VP%DvXk~31Tv=^u62$L{lGav~cldOw{`ZIH@E_haqNwa*(7H+kjT6 z$WEK|Jbl2A>4`Tw=_PH;hANW=lYl0N-@*^b&@ealo`iA)!zl?MhC1&moNcG6D7Kh< z>o?k+6_>lZd!r5CgJt`yS|_JNRcG-?oyy|tf7!rkd@S%#tE=L*MwCDlE^m#CbHqme zMAPtkm;ZRl2=JOtL`#c00phpb!wSUS%!mjRdwfd{s|P?RKF7U?+HN#RoCqjXeqphR z2pGc41J<|fNjb(T|712b>;=-{>ON?1IQ+o`mz9emY9YD= zX$4^o%pjIv$d8|s3e7x6|2gf~C+BsRs4p}z2^b|(ZO(z;YzWEni00EjtMW{qwP*Ym z7hddJ;191h))0i>b=;q_zzu1NqI%9e&cl5^E{f<|ltqJ|k?=oj?wr(ZhKkaz55Ppe z2-}}aRyo#|ts`?hOm@rCZ1sV}}>#Hge%$pY=%geK{;8l0~Y{}rO} zUm_LczkR+r48O?U=(-!nO%&n{@U$>=zPiEIsPTw8T&WGduGhumE)i%Ek^UGptxUv@ zJ}kdbrAful&+!$o5Kmm{9TD|yLU?1R?qCA5h77nlC@NngL!{)yuJCcW$#x@}oIC@N z@%#E7lU%c&na@WTC>rotTHnjpqIbF$@%%U5xNi|vQs()(FA69|g%*AM;FfPgIgDvA z9>4!SvDRt?bgMCHS^jP0!{kGiTW_=DVW=!?=0^%3%~MxYw2zF=qk$34eRT$%0JOzc zVw#Nu@PL{HFdnZz*E_<{j2H@{ zODjxE5Lz}Lk#l352dCqtFldXrswEzPw5-W`gDfZj@ZD@NIZ6=N=%n%)eXXmDu*Yzj z%J_gkY0#o5AM3GmPEMbxO9V_$VvhXktJKl(ta(qNmY#?O&dXUwb2MVFp!i1T*C3vd zTXn%Rm7nc-7v<&*dcyu_MR_FYQ_A5L-lRCluh+k)IdBWDB`C~)&nK`V4FmY{O+dS% z4U^7~c!;=6MBs4y!-TZn?nMS4>V2hlqJutIjbfi@TUXo6>+EQ40@7u&Io*|^H1fIx z5-lEqJo48WC*9}-;4UslV**thK34jwv^fXh$?iK0a_|PsE4@xXfI3$HLA>4ms`(C*82a;e-@kS^M3pE*0qQI_Z?cun zk|(w+g>d#Sn?9)_Z_!k7_OWBprXwj7F_fQQuSZ^%Qt!?eJ`F_U5s)=04m{z5JZgDN zCSf~Nz8d2aiZ_%JxAQ(Nt$w?_9qBHsH)(o`617rW>H<(ef;|dmH1NayE#A&zsisI` z7OyFF?2dcd(Bk?M9C|R=Ob^May$?R4WGdb0)s32gObM8Dht7zzQrfwI6ISU$&lyb#7V zcuWDAqYk(+8si-QS+)A%j|vV)`H)bZt&}l+DRyVRwq2`<_+BkDN{SLi`H4wJ~DR9C1>GlRpMcP3PhVCN>Q9K#qlT2HZQWJ}a;%A|DJZ=A zPhHfgihiIJGox%e{}rsoR%-cLJ=0E_BKI+mOiU<*vZo=BL9(bO$aRb+iGyrX=O9u! z!^y{FGFz=YKNo{p?C!15DQ6w;Nl5CR-)5vHl~1+xzB}-P76%M4`+b)%d8RgrW~-o8 zJR>uC7ua9vqQJU{NXLKX1S_(DAqUHbM-mAki?*QD{yh@_!=_Jw> za85vEHw`=Bq4?{p<6BnO1{Ujy-A#;)B^CdyOwOys6i%U7YcwNd!>SW{MNU{afin(A z@Js(xzj{CQRKq>Pq2qgqEz{WxD12==bg87+DDAA&=(+LUt>^Rum|>z#vuNY@w=lj{ zj*!R8jKWPY|J5kzHSd%;qo&Di|NV0O9*xLOa)sd;up%F;-#z#Kzhdmqvp=W6zSa^f zH=c8vuA@0U->po1e9L0POmDO;#;2hY!vb#Fbypi4Ian+$6)gS9-6z z1=WHZwQ$TKEl2CN{)w`=*T}|erj5+^rNX>2;kkrpQI0UPCcUx_$TiVU;!>bg0Q*d` zXW)W`TBq6H?*&4;b#CasK^2fdWs_%8w0>Lv)+o0M%GL@^xH%B6kXg-+@JwrkrD?rFv`b9!JCX?8g+Z`S= zaD`H%@Piuj*Uz)2%&ue`ocq%6`4_xs)=GV|#E?%SjpdqSY7KgPNXHz0av4osf;W?r z6yQZ5HRA<%%`PVUXlqUHsG@QGnD@IOcdoHP|hxpSFgnyrG)?sK?J*b&^j&@D~I`mr31_w3-jPlq@!5%VzOI%4Z8N zLKVpv`vqd!v)zcl$!v6NsW+hiWVc%-@TREjGJfX;NK~})P=Zkye%5V$x;Z9pjH&Q8 zS#pQJ6b*>F+8gPuJDBWq4i6eAOQvI@Ep4_2ITWElL~G?A;x-nFogFB!*@8BcjldbZknY;)1DPA z)krl+@-r~^gb2>+`V$qn_hF*wtDG2~-K`bd<|jRx^R0HhiW4;B zm$5iIcsuw@<$6ifiVcOeyD$|;olX;e@%qV-xCuG8_56}w32$le3BLm&p_8a}YYUs% zFM7L}619vs3FQAuQQ3wB4-XXNACgkHhuN@O&A$z;E2Du?;DfWAIqYn8I$Xe)q{+^H zJ<|2_LWvhc>lY!~Xq`z98jzF5-PH?3d)y@B&{9nfaGh>FXv4-i;aXmx09-{4#GK{` z_%8Tp6x%T9bx_KNbsD^z#Jl6SxL{t)Br2#2?cS&Jo2@Rdc6W2MuwbRML-wy=KR^hEy|n%{Dimi^?5wntr+Be38@S=@rlZdY{nVc7fy@avO>qbX7Z(io{n( zR*0~MdX`yo>8zIGFc$Xdv<)ZM+Z9U$!JEDA0IHN#OpbGBIm&#}?hYB3Po$%V@;vo6 zWU??+RiFbT4^;c3ZNb-qxf*W?Cbql94ms@_s|$WP_p&KL*1|L~Ee+|;`wQJDwc=C( z$j^{f8Z8>4azzlT6lp>Uim^}p=KD@(!OM8w96%}|aP?$5&62Z%8sl(yQeki+-M%9d z>r~2>dvM;4VSNIzlKkr}r0B3Q+~7-iIakwKM81W?{UY+<#mbfISXSWO=@L2X>EWK& z@YUYt;US`L3=Vk4Ldd7X5ZCi}J|j-2lNqt<+>Erkx;m`;N0q@m$+#ZF5WqT2mAxJnOstdC)6WRQiuW zFmc`Pdfy~rK z%XLwGM#}MU%d9-?ni?jM*T&_+DDh!?Wjo+1m|IuA>70cTsEo7MgP;HG>C5f-0-N4& zvQg}L-}8JEZt8*D$Li8BNMadY)ng9hk(}G5e&s)#c%7qzc)HvHjl*FLM#$eB@!?_7 zEvo3+ibGFij)}Dj=_jd`uzVUP_|PO8++BSBKI5b3TamBt zS?rtxfq(~;Y!hq7jWk{P=x- zxNA`#YT`~F9Dr=M;rGl@8IaM73(CXFsfN!eDA0Ag>7&A8H>!8j2OiiVo3{~m)`jlL zV1zv#X)X_oy=UF6jB5*eb2Yniw0XXwnFz8M;P-qo>1MLKleKrd#ru_o-#4g6E#P>k zb15z}{;!jeCrUp`xcdb=<$GF@3uG~P48XaB7k^97?6a$A8>B;bgUz6=O_Stu2*CSr zEy;wXia-ENVl`#EJrJHwqZ}@Kru>@->{H~zmy#kW%bq1BqqM6b=97mXEEZ`@;&XN8 zgX#~PCNX2Dn*g66pkEm)`41#2YS`BZM2@vH-#d2%fL~Ber%Yy5;wIX|O{wN16!eF! zi2Bd`LF6o@e89$TWI_Q@-Vj0L2({dtQ&R(#hKA&vK5t7*K-~*TIH?%jM8wum0nPLx zv^!H+CA&%cT=L2DeJqW+Jm0MD&+OtYbfRlRa{v%@o&%A_Y&sW_kN1}R&5`bhO{b%i zu;V}`rH(Si3(KG+2{*DSu2XNpbz;3ze*91sp;#apor+^*LUVhKCr!{JDHJ2y^p}bz zH@Sj0QIllcgl7v-9th**ecn`oHpfz%OA%J`=XCr+*(BzX!Il+od2$T;O51!JZu+R- zO4{^o-slO|y(bbnI_Bq&;^SB#r6i$&Pq?Nzs>ZJLSIC0w_Z0_}a!Ff*pV}u?y_#KZAU7 zeuKhhHbFtYlb$p)q&`6WYcXT>i3h!KU!4Q+C2qq55W|m`lnOx@h!s*9|z#s*PIhu#pUfG@J zlQF`tO;g>jVR8JVQ(%LN2UO9Ub*%8sh@d&5L0Ko~cbEQdEnF1&jF6_3ZY>=z1S7jV zFjvb&1}OAV(%v_+Jmy3fc1KetCG{aC{fs`IDsMH}n+dN}@OkzN^8stIeNOXmhKk9P zNF~Z)Z(uq=`&sfXDkx=N((0ZInnkf!-R9@z6TvD-;M&$zEcaXmX1t(d@uKeU3pXkm za|96s$1Hm<#U&lzZnN`3?j>GQrNj6qaDcN16A`qyfK3`QwoEAv=|y)(-IJuJ?*Gb_ z&d=y-oq9HLoBS*`HB-*}l+qF514d&oC3N+3qr_0eVzm@HlgmZ4Dgu<6f;n0NU_E5W z>(5c9RS4YqM zt4H+;-Mp{F;F4m0OvL=(S-gVfD2L1Kt(X41*1MFPQ9fagtGgIJA$lDi#4k_@t>J7I zE#qPT%6smbl=h>|9D0Qy2Z?XrX<5u1Ky2Jtzp|JU?9GarP8sII`0s~K2~-5`W=&J=kkz9n|w1# zz1kb6sU7+v0G%IM%9d{UDsb&k|AZAOS1qPkE9WmCnO4`O1p&9Za{$>cVIe8utn_OL~B z#$QXjF@?>m`LFrP4#7%gEd#%1G-xcRdkk-9#5O}ZE)L(M-=L`^2Yw=?@bpI12fp51@dr~FdH4YTHn z1jr5gg_OkbMP3x#=iz~gwN(F0Rm(zXuc9S&?>?*xeQ6P28=mH5zN4SYTiL?)6c@)^afv(!%GV-U@>p+@C+guFYSQI?<$`Vup z>V{7_S2%NaU>WRo{{C3MABKJ>E>b2XOXv3?aLS35NOnXLd$3yH1tkI3H4g@5gNbCQSLB)B(+hS$IwzQxj3TWW z5P))GLq5Ns1%68ac7>@FZ-JtgM(FUz)0H;38l2|IjqqQsU7tf57)idegK~}mcziy> z<)f)gpX+UA;V~w%f6OzhTFGdmPJr^k2Xb*AM5tmKiCu{Y;_)JiZjqH@l~<+RYvo5p z1sapw5zHsS2m!zFxxJv+?Ut!uz|?=JqV7**g7=C?g~+5-jpQ3?H9ApgG+0xOrSo9w zb=HWS31Y%_eIMA}`PN&y7fYwj&gs1$X&}BoBo+bix7FqDjr@_|PfKV5UzJmfsPLKG zz&}<5F@kL3i?j(e|NMq~$evr;|5fQBfD6tn;nIEBoJbjNE9$R6b^L8=k;uj`DrhJo zAHCHmHdcqrQFyyAtbkV(URaosWOs$j6mMLkD_ry7~WKDE_=c zv=9SOfp}h|hG*skNPo{F{2!$iiejKzky6HQ#Q(cg|50`kTMhfC4CGAV|6e0%kd<#; zTKxAH4h)?U)8AEsBVcsdJGRx7a7ewWz0_($pAV*^@-DW*Ohz)9EM?lJO@A%)fQCZ& z?&*y9%Qe6FXn*i0E6yM&-{6db8*EJt(tyHc2ceaKqHds+t7IZ+8dkKjH?x9kTe6gj z@G%4#k<7e!Y@>zPn%btz9&ED?Cs@bFMwH!WY~_{RP*$-K;kOBqZfbYx_F4yGVn*Z} z`@m4gcfSjNAH|21n%GKFwb)ZxF8LgKY-oMF4Z%mMj4oT+rI)-Q(HbQuLgBvY^3=j zUj}}deMSH-TQy+_3*i)+6z_8;DZXTG1l*T>xP1KzCdiA5*wN_$Xww%!6S3!Q7$xmC z@GIWe`DpJd(w$pqu)@mE%wiLylMeTi391-+a6ESBksW6j_ZZ5pHVUK zprw>gDwVg;QkMTrN_*?&gr8lNP8y^bO}RO`aXV=rn%vn7HZ*JNM~Qy81^-c^scZGM zY;$=;Dz?Ifrn*`hV#53`{I-xOrFEWx(+<2)%GF$1;nKMlvPtFxvEATCuMNd|1>z%2 z3KA=F9V}TRtjsHCf z>(8p>0cst89rle-mq_FY$71^kS&!j7q*Dm$>#NRb*Y&U*?A7m}da)6>*+J2;gku{q zVUBxG%o@o-ZIrtjMG`n5g z5Il3@4>)$Y*51+E+3xG#asb3qJ9?=QYsKo-oJ|K0WUtp#IK9aW;bSkduJ^!%l0G1+ zni=tag2@p4W9tjJoeO;c10^0STO51#Vgyf#^8;AC`2@ISgqrHygKDpe9Jb2yy+g0; zgZ(vP;fM;>$V)pObYYlWg|M@`BfhUyj1n9EwWCkq^FG1(yHRYT8(FSnG%Ktl>nWO< zqt)Wz9jY5&JG9h{X^(M7x*j&+v$@-P8`?o~-p|g(gp(5gM}nADoLEjd@%FPwy433> zCJGkg*F7%~V{iV{$K`xr_@8Bc1|mKN<^p= zi~pD@jqHZM&kcO|3(;I+3rAk)6u~TAD&Fg(TKZ23nkjp(PQq+j0whYVIVv;%w`Fmm zZ1z{Dt9(~>%YoB;|0;e0at=|5>OKo<*?{h<^=yMA5;oFNE66w7=u5A06UrseNJa%< zc-?{pu@8`a`}&FLmr!=M#d<}?5Ch3$VS*Hr0-LFcw^gw`MPop3-mOJ`)~69U4a_Jl zhkc&6If4Q!?B+}XqHwc{dH9*Usbr|YiGzw2?)SE>o%n;C3$8NWwx((UiXIA}QMZW*S=jh63N;wzTDB3vIRecLs8i_eLtQ+x*VxcxcLAS|ygcVd6F#Ih0M()&h-+n3VTDV0HH&tCX2{=r3`IE@WbBfQIc*24J2z@mp>2_OZ$ zgPv7<(RtzTus+TwXZqEmF9^Z|NV&3?Ld?c!8obs2D4$I*wHYsgju`l%D$z!SDMcmZ zOnzjg$;6Jr8B?hht%bN;fKY^Ja!+Pv^z-iWbr;1P(XFfFRM~d_iX|wl&d*OXz7%{g zEk3tf5z+xi_;u8@YV{)~vQvs=AuI#xheb*vGj^6|s+JHpjnD=RFlBQ_Z!7a!f zTfkKhw;KA=rw2ceEDuxsAr*^}uDd@HB^tChdB?!|wwzCV2$iNRpvfwH z!vI?_S|G|Q<+xHHyh(pmnun`|q;X{Cn12}iWgx~$w&tr?k9fwwh3eKd`(+aa( zUni4=6=}Q)O1FLhBWpS+_hy$Ui^PP!l>Q1%3Qwh*%pA;FF;`y8Gv$B^c9X1xbtSSPMbTn0s29PLc zO8-S07Dk{THxeokTd5?D)f?kdFkvXdyENa@6naRt2t4uStN%h|YUB8Hb;6Wzo4~EF z#c?J+vwFz~%`y&f_!VOB>tXgWt|YS@RdXYN7LZPhU_3>lz_%?Qm0e`TUs)uUwNKKK z;cEl%P{98CdgGDebF5FH+>y^SUR&ok2>$gBzx|?Trto^6#Eg2OskB&E^EK2-|V2G0OY?KYvo2WzoR*YnU9vNV9b!(YM%2i0EAzfZ9aMhZI5gf>z z&$JhZ&GcyP)b1pisZvj8i8#YQsnLk>DU31g8Duf*V0k8?L93@JG|&R#f74iPs16Tr z6tm8S5^#&ua^Z0t$X~ks)HCU&wVNnbV-=yuiwYKO`_@fkq09v-xn@b?)p4EIig~8{ zUbcj8zd9kJ2b;q6_eYR&Z-)9wda4Yym2}DF+O*60?Xq~b-)$c}9#V^wp9ezBmi4fv)&oEpA4=#^XqI|7 zu*&h9Ev$m^F+oMG(}Br90%e;40j zMU~V6+*6?n5{G2<7FDyah87DKXESh!nXG)2rQ?YmgSs`D^Q*Ih{74Z2Haa{o8-WnS zo@?wS^=0z12It0uI%7~luy23DP119viZG>LC=nWHl7m~+MkJ~~RhdD+C4fC+$)-Lh zN%a5Sv7?MaO>8vdj`Dpc99I!Flpe0N62jX4>wf|4qX}30#o%n^*j`YL&=n$OT}9?aRA_FJetY|x3;V1x27`%ER-0E7}W6D zs8}K>$K%uV{HLIYGclwPqLjNN%$eq25gzuPhI>aa#vxdTvM2q|jliZknIJ>l zjj+_cYD?!#1UbbHl;o!9@OyhgYJ9TtF|SyVWjTV?!!Q7EGoqWv<$FIXH0yp`F(Wz}*4!18HC6<+uG>&Rw$$CZsch}jsHDb~S8V%!1_?hxiMR{Z9lZ!j~M9&zU z&^ym$;n=Dsjp!5KBN=5dG=xhp7X(U@a$Axpp(`;7;o-!etf-mJi@40i&VAAGJHO9{ zjwFcS!=YsN8!Y=g5Y%|*nUQZiV%~mZ_zwn-=G(COa^X4$|Elm6dym%_+tm@kvyYEr8&Yb+PY)1U+o7(2%4Thp3I`*Tswxsz$Y#LV#zLhu~OBTJDyNms8 zfMDkpmoJnyu~1%RwPFXw^lesfu#))1r#>NwW2c}UgTbFNASaia0_IJZpg(3Z?u&f* z|MB#V;dMn@*TIQxHnwfsw#^1jW4l3P+qUhTHco@awrzf=_ult=p8ezeIM3b-bB!_A zm~+iVZ>8H}guYGte-1bjxHoUN=c|xGPtovY(nL;z&H=4a6Get+yGlh!cbj2 zQK=ikXOaXrbW>$7zHr(${|)%i@c z_w9t#$>~s{RiBPi)7!q22gSlTqh3&P6%4wh6TwRy(g8k!>64k%-`_V!Xx> zVfgm7OIv`0?rF0Y2aKj!Zm+A`h(-g`3$*~s;-?+LF^dV5BzLLhjiz>e+OM*_f6#*p z^ZH)3y}lhY_-S@N$&K||Ib6l^Vte=94}sVzC(Or2&%pLUh!l`wIkYOwhWdjci_Xc^ zxi0{XXaX0pL+?J^7uORAqR&ioj>6sPX&_N|?j~B0J)8Ed4b0fb9EWWwVSKF&FIrf7 ztUbI;V%cBjE7xTm&qhxUi=lqD&9^@uTkG;oe z@34a^HvBs5`R!*7VMW1Go$!l#RCCqBd0Z&0TzBJXFWD*33NWSlUq4%2TrZ2XVH_)gGo5;WxxS8`C>h7hPnyve23K6#eY~o&yYiAN?3Qjq<#SAMX=f4| zgC+@w27`Elk4j+W(+p{#vU{$LJV4e$t`ewf}Ked}Wx8S{>}ROD5Z8>L)k7wHpuHgcq&Pa_fz1-$ z2u>LW{OxEN72GC!at)KOL?X)qiz>hj9Wl{FKt2XLyy`wRB|YPINp(n}PJE_bML0kz zKaWb%NQcL3#A)Uae{CY11PXXGszSIX^;DVa42rM!UuP_4c5tfre%Sa5jFLL~J4IC` zA$iC{BPyIz*RU`$@x!>VFg_N~{}q$j5d>Ec4dhsYW8}!BjEO$ZDxe}NpCQ^kJRTuN z--LW4!F|t5(5;@qyg`BxV6_=-bBgD2^Oi~0xIHt|$yG25@$FW5O3(oqkle~=oINtZ z=>K%rcOS$b(df;ch~-w_!Hmd^la=g`q)@%!wnfmRRHh|c#;;-!$OT2^$KpkvSMsIG zuT#I!O$(B6N2;qgeF;D#ESm9n3(IF#(ymv^B3<*dRXRH0b0>kYIyh?bg?Ztj^))ST zXm#a8NHc8V)KXAxiVq8uV!e*SB_oM@t#gmN)3X@JCqf40snp92R(Sjr{H*apRV z!RZqA1pX_NMIZnsC;=My2nf(A<-#8*g%noauc zPpv_p32d@x^e3;6;Ll-vyHdg~!53b=vMsjbsMH@8%QtU?kX&qXpmqdC27P@-won?# zS8Jor^P+7m=@Tnq>%5dbt5b6u8i3v0*ASK*5k90W{f$rgBSoQP1y2zV^B>Q)$RX`lE@kf$!1WYK8p>Ho?(p`Ha7n3_WltS zNt_Qhq7=7nZ64e(JH$$K2p&|39IO=a2vy^)VJh$c!XucBy>SHFO)-M@J$cXA_Z!t9B{QgI#^x0#UFPi z_LWr(Q6*;&OdvVw2dREdYJA(!7R^HGvcbPDaUdwwhQd)0g2qam5EV4Mf3dJ#oG~cH z{yPOSxPW5RtDW;#=liL5XFrJ92y=N16-Eba#7auw{(qbzR%MX%4x}E)N2lZ*P_*I4 z#-^J=$ny9up?~H7PICtD5DT?uzlZ{VhhH)oS(|ZKMIEx+z-Thp41;+t3}l!b+1*fK zv9U~D=|KOFbj5*2@~J!vGhf|WTZlzzO3Is(7Q1gK0N!0^DtdKKFC z$nNV=T8GI&F`=mc>$e@Gl5v9(N39gB4AlOQg7iz)wvqo!%J4^L!NfkvBpH_(k(0w> zV0dfj!zfcKqkfV%%%`xZ)F7_?Tvo$$=?j2!!NWofH^GZ0P6^|;mRokFkD8SO88IBO z(*#JcGaw~tfpdE;lF}LB7BMsv_|LJ*2)=)YHm_&E4HwIYR{MWiy~r6_d7d!&ALS&D z%&^WuF}yVjGSltpi&a6*T(fu%b1qnCk1&vUEU0UMHbK5=6#?q$+Jn4m>_*&VgM3m#3h?dU{5bh1h2%7Z^RQMqu}(qORaDUq zjMeDkV+K(Di=)VEm&Sl;>$d#aiS>9sCsv1bR?Z<1CX!8_gVYq%Q-G^tyrngWh1l9< zB?brc-t99iaQ@oLz*bCsH-ngw?;jMap_RUSASaz?3JaWNCNZQ>L4O7m4`&9-x$-cR zLHIgFM4*D;0WKPBvut*MlT*hW_uujMb^=O;Qed;k^KW zG$Z&k&5|He_mqFUat2a{!faVV@rz*G3LcQtXlB@HmO>)^9(!ds$KWhoBPvHB@pg8F zj!vlr52}}hAtpMeoK5$|qH2clt164kJy0r2Ge-1-440tm?a(f%PvqU;b=9Cq^S>i? z-cGUw zR)!TjA690*h<>{?h7@ayU6!qehcOf{Fv1 zP6jCcXq0s}Sh8P(`M$f6#vb0 zz`4Bjs9onU+8DIpV8HP*K?PCt1p%QI z!d7>?5R3zb>WIxLwiDw8zPmRl&tfwn>{B^$bOwy1nSl1;p54wAnV11kY_hGkD_>?Y z8A5_&XNUL^$rcqG>fX;3f@bkJgo{iNcEEyk_g^E!#9Owh{ehqj}H>i*MYd6#0oB+M5P8bJ|txL ztap3izuWkKG#>#c16}~yk1!^OhN;Pb5>hWR%@isI2hrJ*D4g>Jb}Ay&ZAl>hKJ8C5<#L-N6h+z0&b$zo%K z^a$lYZsP_wBqjN)xCm3C0ov-+qpCzA{+IXv)BNio&^llt3kYMS!j2>d5 zBQq-GxC%LKxXJiJC`Q#EFui8QW**AiGKoU;dycmR#iPa$M3b-TbsZcuxC{#c=u4m# z4T^F(6QK0m9~jib4p``z?)a8SXl>0^g;!0qX481tso1Q1z5gz$5D2TIqtovpOdO7e z97F-Eif%F17CJ?~Ej@4t^?%yFg4wl;o zIg*BClJmi-s(%%y2^M$O9391u1rIUttoTi{ds^d0yPUuLsN3qa)jamO-5rI*&Hoa| zqyKeT@#?!D4DDBVk(0rF5{Cvl@1gtl0JL1@FBNIh2wJ1+qO*4y4Zct#T73uIAG>t>mLfo z;3`$cXC)tZ)_qpgMCITZd9{--J}d`mJ1ae;WDqE&`4}%At)@|wvN_0V`ChsQCzB-R zz+a{d-=zjAuC~WSJzGrii|Mt{qCUe=TDOarJTG9&_!~OuS}*2qgkHa?MeBSK<|guj z$S5U5BlunN%8;`PPBYvjb!+A=%#sSB$^!eOl1d@2WiDArX-xEJeC}FEW-edNip$vE zp+=RxzOPy*rxdM%5mVMqg!Eb4SeXIOHpJ3*0sA=oR zf$Jyak+}zL8UYijwJ#DJcN5dkNC5A4v021rQr%eJnLokDn*Jy+@Eg;dkI&7(D!ZJQ zETg1r#$X|z$X!y9V6_|OtoT*XBXpJ42vqPgP!~3Sv)@AZyR?orD7Zq#fRO~{4?nQA zSZBO_vEJqU{^BN+NCB5juZ=vG#vCVx=y(4|_~QwZNN_eSTTfqK{9q!3)VSC8PmhlW zjX}G!!Ub_ti`(^%WV(PCn?|(`n$>h}O-)K+E;`8eGY3$oXz)N(;QK$@TA>)2Jz(R9!a1z{`eoktU5F=V1+pX4<42)54lX@*(X*&fuCu)b|BO{nPhCHs<_fNZ zaa+TZ_r;%c$bT+FKfo1^!gF9NhJZU?F^lW! zqmZE6iN?|2Dx9xt?W*bJcHBQ<*c}}mowo;}NJuLbh=u(_Iqer1Ul*QwSK#ESx zF{rj@AxepSTI?^a_LC)&KONBh^(kO^yt3pD{q3u7Z_Wl(Gjv8$I>* z&^dcl@IbPcyxk?#@z^}}>ND^1;CVs~Y5&}pi_cXQx@lp6Equ|gNm%k)sq;^}g!orv zwnbLTe}ul?w~8?NYpupxG-2TT&{v`i?;(vrJXSdpy2Ud?Z7>1VpawHEuyF}6?%aZC z*zWMpX%P$q4-wzVg_40=xCF3_p9#nH3bs)i{V3v-TQ8`Sk=y>Kp&Q=1v8AO3;);y& zR9~|0##)zd!)vXQoAf zel(tQtx;EeXEO4GzZyd$(U%I;^=m+0+t`4boLBK`Q=)xu>>dV8CHY z_zdNG|1G1{kb;E{5qdq^|IrpNU@cnCb4X(F+C%Dl`v_S!WFG?$ofBId()Ow)`huR4 zzjRfyVpoS!4YvsaS#UNSv*m6|+%DV=A>@=RKHGd8)>kRkrx*mrE$z(HjxIoKDBeT~ zBa_PsvnTc&$i?VCw;;|=T>B-=14hean4KT?&IP6_xq`$YXeYkz)R-hJ!v%8FgIRJ} zxsIq`dm1I4&Pnyj6Q|@ZU2UK7r$0`B&4iC&Cq%8ahxzN`z~WytJgX2_AiY?D|EJha zbB1M#Kui&!&}9;I05YKrVQjF(#Cg2q3M%9qCZtC+oCJN)eArmQ<+8$4!4bJszU!j> zc(Q#tid`}P7b6s-X~!X)GSD7XU8|c4CkVoidQ0O|-X6C1kmt-0K=jBQ1oF26nT6tO zZSZ8Y8}av!IS_M>KUu2cAM+KkPQ?!F82g|1u&=zm2`;gS;>oOyXqG&_;(kWdmiHht^`@|*^-pEN3Q1@-n^^dCyAmyej4$$vosk&S5VlB(&Mb_@QsYe6I+U^?|D)u* zg<7Y>hx>-@?5*^^DDar8iM*+KSxrxV?S5#Lb=|nTvB{FsPtz0-2_07aet`BnRFX(e z5$5iLInMV>x1-m4Rw5^2mU_(bP{kD(10GWh5^xnT_+=W)+K-{+!DRcRe-ea`f^_%B z91I=8TYD1vn{azX-fM`xiu0t8 zoAsc_bSVl>#6Pfog`E$Z#WwT1ybHrye9}!j=prdOJI*PT8bLbJ>t!TaKwk-QVIOwN z`V(^cciWZFH*jISf@#U_X^{~R)z}O5f!EOy`PX)~Dy&xHPsQzLi!71h8uEChp^N!Fn< zG}iPJ&$E9G9W#k7s19GCFJVUkld3~`B?$4_5YEL(;o29VLS+K~;TIjG5Han>0La^1 zDWo!*V$t*5)dO<5GXlEHvs=*3kDdf?bl)IriVBv8dU^ z!_RXL$&TTRYTPrAPd!dsD~+#)?|ptY%jzhYSi(~A88?*4)V(z-Wz0AGg(<|juqo#M zq)E;(;}`v+VYdc-O*)nQn)&|F0ZPac>z^%3C1HnA_1Jjmi+nBlDt^wvB~dGMK}}pRj>Z=5-NH*%*P{wr91KGk`28Cu_6t9B4!?&M z9W=Mo20&X|+h(JicdfJJ+St<45~{Vebuf-pF^)(OzRtMsSDaVm$Ml9*zqG30H{V$V z3RH9wR1#O%f-tfka*{$5QPHzP8m{69jsY<=Nl9Be*eLk&089WDR0(;{BR)Dwd?*Fz zq1Y3?7eL)_Pua?JYUuB?Rs?gaNqc;wkDeifJ38~f{QI2FIO$H`Slae zP7|Vl;wpf|}&o;-dER9mZ$Sj`~dE(fQA~IPS05 zVRmUuhDUeDa}*A#E}J%Dd{yf|8Muks6g6v?Z=7v}?n?HpesiLex%Dw+C8EgJx z03NhNp4`4P+@9ITw?%74cYNjR4^nzEmxXqvveTQJ#V!sKnF5FfyzA+j9r1=3Wk{7& zT4rZ^7h?5+eh7dy{2iV=)gEm@oK9&|#YgmutK7)r?S**=NGW`*Q*iwhvdrk=C(iI{g6VGu!ly;sP@FR zpOXzjgxu?N} zJLzld4eRo<4KUT5tP4oE;AlthaZ!X>ka%!#I$g_Add113&_R;GVGqo`;IDRIzX<|^ z#T15^0@lf=`nW!v>FV|R@g$9&D|g`7jxOoM|Ah}q?oCUO~^i6ZYn2H zD{IXqNTSisB}x`>e?L&Td3>a95Ww90QFw-ND$vlF#cmxNh0b6Zy>q@BCrl+QEc~() z&0OxZ**m7DikWLRz~g?VV|h=#+V^%NUxLXnIfsbNWFUhg{8rz1mDC9;zLlu1jCBTi+|Eldt!Wo4bO(dS<2@~E@>p+h7l z2Mylvf&tuO;=&djZ(_H5!gwEIwi#lwp9#*}gOM?xflN$){o-3pKGU^{#(3akARjeg z#288B=wzUF;NqrWIUX8BnFTlRWkw;qb*3$pup_QKb8#J~F4E>de>KVMKt~@*jO_Z2 zwnp&l@#(^c_u{C^o8{S-s?c2FV3UhOlG)n0{agaDq9Z~e-_3Y*#SOdR0pauAo->So zUylPwv|cl-zPuuVV?8}PMy+w9>7bWV9L*^uccz&FtVk}^>GqduYnLfYGVUw7to2VS zVyZN5z*ms(wo;MGv>1#sM60))Sa=_JxjJ6!GqmC+a+JTrFU#3d_di7|Y(-*gI|f;+ z8~PJtEg)M!KzCGA&y6|8`f`Ma2Hoc-U#+ksZLw5-i7osW(W22(a;?_f@QTt{&AeaW z%Sa>B;*<@u&lorN?3(pghRx10HAEv6!6EET+LMxm-aKg_9dt>4?!iHsu{v9s>66Eq zysA&D%H2Rt^G8Fe1`|){SlfjtCy`&s+S||4kfX4*gT*G7dww-|xtZ3R(OTYCLJY4_ zY+`RQEY03YLEWf3`Otx%b!2}j7AT2P$Q3~=W z98DJf`TqU8dX$hVd{o4=WcpgMVgFT^n(X7T z`kL3G0`ZmurtFM)IrSuBt76bPAh(3!gKgYpOOk7s6QeI4d2Dn0IVF0e$OVbcutta; z0|&1f(l5(a?>+R2JzS94f#qcmU#%r93r+8(Vxrs#J)Jj5J7&q#e5-~oUT{D!q}Vb4FdKF2y885GCe>QKvDBkdyqxhG$Vnk5`KUR;`;d*Ip+ZC%Wa1Iv2z1}wAH z=WL5m&hpKTm8`1nrD=~^@Vwl6*zM$6qg$^=TTwSm##z5PyHD2d?Cq*CIc-R+-unGM zZ#N}?4HUU1i+@*_x;`n(%%q2ByB?>W(+3thj;AsxhwCl$WwR-~Sf12IC~RV(Rf&C1pq*Tx zfp+T)1L;9igOYD(RFsx_=`vr~4D}?^N9OIWMRD=)Z0pB3E~3XG>dcfy+8$mvu?w#u zBZjr}BX}ygQ|ol&{F2@c3^i)M z@ed`F^yEf;A*P_tt)!d@tIOlHX)(K^(jNzYS*?qrT^zZ?&ri$Q?BgaAJMRCvJ+_&k zU|Hq8*`&eM&e@SnJ1tbf11fP=y5P@AL?ByGg*_Kj4!iRB5kGOz@ckj&N#T%o=&xp` zbU1N2n_FKLE?=6Qh3>nojSC=m|qtv8#G=|HKQ8l~%yMd&?Y0jog z&Q7@k?GW)DBx!lA(LPUjBN=@iwNn{?ON@QHrYnL7S?|X7=`w3t*D|=|Z#-w=3ItIW z90cXTLXB39p500fVM=o|$H`Jd(M<-m`nMX@^kZpthD%qViHJyG?$`S|8bA}q^`SMB zw+gCi=jiW53F#(Ttmxb}HOfht$dYc}lF?*Sv<8l5so|6BqBtS7g{aZd>QPg)sxkcz z*H$ORoDuU)fQ8-4>n6q*i=jfiK2*c)We$I#%umtX)X#b&eIFlGdQ=#Err zFy<0^tCnNa0;4kGQG^l3ms2`htY7vKuB1tN@qcPD{x`eAj?L zW4@Kr*PeaZ#_&i-7JcuM<0AR_u*v3|Rh^9&HwRd9tTyAgCzJk$k`yOh7jw!^K6kkR z$yEr{C@3c{yR4QKhniL3l(!n2OXU2cecuBFt6pMwhXWK+)d;VKMu%)(!sRBP<=4c| z{PeqY5r|l$>kvdW#D(Hw3Jsk$q-S2+=KezJ>#&xmP!1?bI8s?CfX^g4y)p`YbCZpX zWO=Gj;rJHvO&%Jb((HMPnd0l-E{RhCI|V1HmhRnnCCDZUseQg}rBz8kG}a9`X!I&N z^~l_3x$Xy#c9x{1Olj10k{kDga9{X8;+WTJ>GcMo+VCm!9EEpNHe z4<8!eVNZ$O^XM`dW=`LzRZ9K8Ysxa{Kjmb9a!Ds6YM+j(;v7wQ%>8Xt*k)X8HgApT z@DfbjBRyYh#B2Wk)Pw1<6F;W!bfxpq=c?TLx_=05{is&?wAZeaL=(9kz)-9%Z)I7* zcd%rXab0%XSpSzw zU1rP^R5bsqk77~CTrT&|eOp;Lid_e9JvtcV3di)`Q;apazu!LEfCAXo=}#_lgU`FJ zF4w>9^gDhE*a5?tz2IEW*JSsNSzMpL&Iz(tzy1t~Yk#$sf?rf3Qjt@f`@&Gi+tHLK z)%ptqPMp4Q7Z{N`=GoB7Gh`oL_ z`aC+e*iE{c`1OIX9>4T93Rv)jKj8$2jI1RDgYtz>0<@XrtZ;ldTfUnX{#5z=c(?5j z1h=!h>G*!AAz@*g*Knj2_RBUkb+y^HC~-QQSKJJorWe{RflCI{8qSeyyznhLDylG* zQNI#)+*|0GGtY&=_GR9B7j`5Onbscw-Kge7K>z9Lb>QeH`Ly9Y>+__px5r%jacF=0fGR{+dRG&85Etr~K<-@y< z^#g5J>d~g3M8dbXMm3*aZ30#+f}}wgSu&N{Z$42=cQC*lfv>P*7U#Hha@D2g2QwRw z(BCCqYML*C^xg(bA=V>)s%WjF*RpIqvT5RzE3+wP1P#xl$$ z;f*!Ivp2J!$(&owg1R}a%d6hLxA`f(;4?n%$nQP$l3&K-wfa=Y!28~z;v^k#5Gc6- z8il@Kb};EU)+2r(;d7`E82dk4Vx4PG)5_Hu2fS<6WYVwR3B1%jjp+CJ3o7RDrM%pq zmSM&eRwvmEK#>}D)`y2l7Q)NtqZtm9sX@d?q~zXdO1wctO=NLTQ?Nuyn5J%#EXcgS z-2de;Q>8%u!Z&Y`h9dMNqNu&Vh;eT_bKnmEIu$if8dPdj6ebSKu~Sk4ec2bH(Sm;S z+p9*D$aB(TxY?vH^LjOO+x=)2>emR!$)GitjT2z-uaaO}vs2>`b`6)(`7nr`x!EBA zR8#)3806M?ZCG4FS1Q#mv8I9g@>D<LDzm+}EDw7|~YAIdBWOXNQPv9K7f{0RY zO$z(hNXHV2zb2^q;lI+t0BT2?Y1Q?^VE9$<)+w``ca3~@cXgS0tAT} zl`F1m0(AO)aO-9WouSc($Jo;o38(5Ks|Yd>!AE3cO{ExpMU{fYnv3M4^m3wjS&*Ig zMONzm2WkA7oE>LL>zpzU+zBb<`7=3Q3UZmw>% zPk$M=UM0Na$9yD?+fSy3I3{DdWjGoL)kQV!r?;nGn@>fT6Vl@W?QUO|cax_Qa_O5- zU>zN}D#*PGf&r zrdSvI>!sC;Num0U-*rnS8V;^<4I}_p*aCl`5Nna7L97%#yX`OK8-GS2hAT(+R5(nTdATU z&efM9>PsZ;kR(WvxXPJSg@-Z5(H{U0w?7^VEA&n?zsV~|3oZREO z&>I8y!0EUxm&b)hXVT_5s2^{@v9nyg=IRKY_n_Qmh(*qEF03Fi<~+hzf86IU6}^^w z_gN=fhJ7P-fpp!S<#oBP5Me6$sWAML9qYzLwFlYcE7KkZHA*FOl=*ie*Vovn`bGPp zFK#DX{Htu?a(ml*BM5sZr*+kBPU_Vw6#j6M)%OWiZ2H%fb5_80DLpkZUZ2#12MsP< zTAhY2O+RC(jhuGiLEz#3SpB7o1{!(zoIi3u3_g%7WC9751St)Qi z^w;1j4}%*xCnwII-;=PrIbi6#oBn(PzFXbYE(Zo1w_KD2%bIo=%uK%~v*XQ)vW>C! zyiQwgKXmHL$VA-0(ahON7Pwt)iK3U}(=2&EUeuJXoydtEhY0-z-TYWZA;;42Cy7Hf zx5e&n)UcT&$XK9_{o794j8eTs&;-|Kdpn_Wy>dYT`dI|#?)(+d^^A6wAjQeo=l(>J z-~CKQPLy_fbyeR11Ik)K0Wq==6rPBdTYfrk+tjO^{bRlB9(6ek(iajG=&^xE!~t)j zXlQ71aP6Sv43M@kc(PCIVr_EzonxiISci=tnlH!;d}apVi4pFd#@q+Awf4WwR<^ z+?MN1dM4?m!f;K~xt(dzrk4!JfPtfef%B7@jN{TRM53ST81U(k7otwsm*mgWGsms* ziuqO8-H%K%lT4=stNnPYNa3s2gs=4bx<~%1gRUPN32;d;Pt*tf+nDf@pW&M%J}jm* z#PxQW&0{Dv?OELA659(!$7^yK0QnD2uNB;R<{by{%iP-Hv0oetKadI6Gp{YLxT((< zi2YmI0srvMi^8UTb)OSXT(G?Vv3I~7rNi~{CK zz)jUexaBGIAR{7DH1JI*6*Jk!2^sQq`9kFw={V)XcjkGhxQL2ZssmfdXA+Y>V2cw$IV?ho0==iObltFhrx|3!_B2) zP%jxpqAj@`taQmb~W+Z0M+;O7E^(B>H{OeQNlT9BMEA;VfA}VloZ5x#fEw zcdDw;ouk6k(fn>}IU(9NHE8xHU2aB?Ng;aRPQRkrOGf*F)jP;{iLL%RLix#3)>1CE zLqYmLDSzb4=}3#Kt{uMzV~6)MP5mj6gM{r@R+=EBs`W<>eXNIlo9mX5ndSK!6@MbA z_ikoY?2um~KzwC}wWvlll^624c>9GtZuRQWt{hKqZ#9sCUdYhW?2k8FUh{kMm*nqN z>9tg@(?XRPqN0KY4U(b`1?5roJPQ4Vx0AV4%28@KI{`Dx0~mBamF|yEV*c6pce%dO z_<$w%a4KC=mmX;{1N%#K1s~SkRxLvZb)Lkd2L(X__Mb-u6`)nErzjdDZu6wSd*o8g z)zeFIBKZZFCjv2Di3VD5&%wqyrOCmw_S|_L*X?h?><20jf^lw&?oCruK{NIxgizXwlSVZAIaDQx_r=GJ# z#qqKJNJ|x4gexYpg$!rF?A$I7Syt0rvo-)d8?;^O%?XbqdVQ=c7XgWw>*93gxE#OmoqH0O9 zdF`=#KE~)DRur0};Uema`LELe5&s)D(BPRMfX6lZ?@h43Wj_-Qhgny5T-7xp1T3{g zHZ{FH>T6Y5s_#L#z=&3xm#_Fw&Yo&JLeSIuj=;+pviOye+H zVnLor{UyZy9f#wW3Jta#Lr|zQqfs!7GI1-#g@1~Mi|>-GeAYP=Qj};HKZOk{>>gu~ zKB1?9ujJHr^wwf@ZviQ@fU;BXHJ|29U3wc<0S5>qK(;~%x3?F-{u?4591z75)V7&b z?S1t^FFv{`ecPom+!cBd0tUO6*v}a2!k9QQO?N{Jn~?SwbeGBwZ(i=60c$-;rNkFt ze4sVdr! zl(y1&Ot!WpFc#zd5vRgDkxH9 zA26gOD(G+#pOYxUAEBw2V$teLe`TQwoCpdh%=uPT>S&J#RYlhtYsE!+6_ z5h>T?WD8HrP^#Q6S5}hEhNSon1I_*@p&(|u9Rg;T)s!Td=%7u3xS`Cwge`hR3Wt82 zd9wmziRu-N{SXk(V*LiB>C7qlDyTHUfQhn2aZw!r3R$MFG@#b7Pw7(myBN zb~~JWd`=LYL?3XxSMwf`TE4#98gfywTm59U8m;~^3HcB}lLQ_G8w*F+qlB!aI?1`3 zW_=2EJ4sm@s#DuHRogRtvY56}Z@z%N^TZXVXG)ZSS)RC5ZgXMT=_Tp?ZElT90l~iN zTWQRsL?)+N#PPRZ^n!*b+s3WgY40UU%e7+~uePg}S;g{3%cW!SmYQ*@Io_`&y+6C# ziYL{$wb8M#6#L#f$q)uX*Es#jBA5CTU(wX~RU86#jTStw(b9G{^crCKJ124Y!%Gwd zZJWaZRLrsM@5!c@AI9fF$qm6OD;QWg1l z*AwB_yJ1II->VOP_c?41!2h8l2p;qI(9++gBSIt=Esf66fO+Kw&J0~zo0X7dksM;;N5NNb&zYI%-#ULTu;dx!cq*42%- zK0Rf&PnTYy8^(QemAZk|@$oJDdS4NeYA83B=QkhATX;~^mhG9tMs|R_Ic+$No2e`b zp-ct%@Tit^c=HLhnE_Nk+Icaqa4GB6xE=k5?USABp7iiDAXLq)sC;zkc~7?`7w9Vk z0G9`dJw3yKmW6D-?`)YQMZ3QFb=I6Sn|^ATib>c&tga zHz+AV@+`=|A6ai>^SaUi$XrvIOn-2bn?){MQp>Tr%;;;xLW4>OxfDnI#0rj3GPwGE zesGT~2P-Tc%aq~2&g5hy35PR12W%0>iZElLeQC4{d9$MCCYw^9Ct_^ZyZca7L9~K_ zKL*iHn4Cy17AXFh5QWe}+EGmyB}&kbgvS;NihGHGQgia7u;^FnsT+euw$aKBo2W)X z-6t^-1iwDU>-ZkBQegFoQ?Yw06%yZ_6wFoU{3EB!OrR4#mXtc&a^2TAgCcko`foM8VZ{Fm{=PN6&?1v%o zC~JVJbv%x5EYro_&bj)MqUifSwlv`cr-81Kz{10mHTJttfd{*O+zgll#f%;Ib4Q^6 z>x9h1uI=SZ9up2rQ{*4+dX-{gh7}RvjvA#e%~I*#tjK?!)qJ_?vvraNoYUGsTq@NI zST^Ucsv4X$SsF~y)+x-@Jnqa3NG7T0ENE!8T%y`H?(62RH9t#9(3}(9#y7Sjc7MuN ze%?rTI4V1yOuJbwq5WG)>0p>4TDj1m{M|CI+XGb{9bCbutq`pXP0!_8<31&$vnJ3T z-%zrFZbWyG1iCfr;?4Io2}u~reKlenC!~d3po1%3TY`n@o`oP5GWm;K*!PHjE+nf_ ziaFE67)gb=XQG5$`Wh;U&Zr~aybn8bjUb`PYh#MR6`suzQ7)!C*L_%r%BUybe2qFY z!&2Cp63NFw;oVKo43mD4(qbrJ`IO}*k;M}Le;Tr~aOf_a&6$_$5`1br=4UwF+pc6t z245{Q-+|Wl%1WkPl;HNPRXEK<sJt%ID;~%iyQ!p~mwPkkY`Dk*S~~gY)^8TKMQlcA1G( z4)P2CKBPlt9nW?WAD;v{5DGH>6rk7of9$8{ml7SK&kckQa$)wO?BO`7>9 zLG>!|YOtOzb6DJdxAK_EaT6{ST|mZy!}g~KmjM*jLZSXQaa@p3mXbUjHX9s=N{CrG z>8&~OM3=+DZ&w4W9Oz1dhw%_DBe~&l!|>Z;@+iUUy_b$hJ_d?633>ySPIfJw>-NLh zgpMps8EtU;iuX8$ipjV1ocHfrYA(<_8V=Ow2s5(^u+I1TwF(O7nSVewc9P6HVBaf_ zAhL5~8F|WU{ne%#G-~Ag))?*SoA@hYs6WP-38<7*10Q%}bFQon5n!6?2XY;v-1tziu>2ff?67(nZiU7AcC3cnhl| zadQ~#-lm%crfr;ULK}%@`DvT))Q-P?j}P})8p3lM)7^ksGBiqy<)D^rzs=M-)2eP- zeUZa04(@t!AX3#2uddqnw)v!JX9PU>(g)+d>uQslLv2d7Lv;7|u3S#=7gC9etQg{n zE&qk>L>o~aH|(OI)`qZ+_?AkhFc)mhUh*$C%+zmaoyGnv6pO^UQZJ2DoHmk$c5QI- z8)>0=aR!jGpM~_SZD@tsk{2p2oxZ5^*&DhWcM90!Wl^$;tvmw_XXl<#6g!>rDVIn) zNjG&q#9ZD>XrK$om-A~$NgW7CP5u^D+LVa^jfV2`D``&JQPlb|h5*#LKww#+8Q%vW zH@$gx8kdrH`0-|~TVz*V8sm`Yo}@AL@eQ5n9=j4~F7miQ&jewToS@3j>)E_fz)~K^>Dr!q4(3hK8!)OLp?mO%5R56cNmx@oJ&+l_@22+l?K=}UaqM_nyhi1z>z4~c7a?4A5h)5fMguk@PGha|!5=_uuW#87 z)tMm#X2F2^8UpHDnYp6CBXL!jN)R_Z(Hp;XPlF%l_p5Fs4fT~+asAcnM%#k%Q6;aL z3$-H->C<}l5CbxO0O&&VVh*i5oufL**0`ux$9eM9(6sL+8Ht+VuRZ_xA{04nk7D8D zTkhm%U<1M0+4fkW1d&_7Qz8aPJ{Hc`nwNT3S682}1`#U*M~1@E&o+u?)UqU~ySB_WqX?7jQx~m86I>Fcioq?dB)y=K#AV_^y_6!Nk0-pU5dF zlvCMlP^7ysDdopok2_?56lK2UOo>tu8sV2;K+19c7Y z91IMjytK1IodKO=o{X^5$KX59;D;-A9X>A!tOve{#VE`WHSljSQKdOqXC+$WVoJA2 zM#If{`<(16D>v*4wvoluA*_5uo`&1jrhs^R-OG;VGL$kAM_+m*fd_JyN3Sw6V7J7q ze?SfM(H+ME|NVjmctF1lUUt%v^bSJ0ca>3qbub%FWsm2R0DS><%$Y1(3JV>f(=cdx z)RUv;tfFnS_;3}w|C`mnNC=f)agaw~hl|DMD&uph@NE4ZYsO;t%?LdKi-U1-6;j=! zJA%`L5hxo^&xc=#?=f5v8FV&ePMe<6zb3N>J$qn2vx5wYG-s&S z=USm_4QDc;<=!)Hf$_JHUIL-<>u!8$CKrokX6W%{5e-N>>Vc;68aM8CV_mDhLVD;> z16t3Cd;nvuzqwPm)VVHJ#y(0v&nsC3c`58&U-*#Sw_4qfm7SMLV}#sn_{sKe<@?+y z3@UfHA?#g(rlyT1Y%O_?4f3hlzipC=-8XVK%5{4O%KjM=V9htQK)2BiU8`nH>$jQ3 zE5+g78mV;(?@zLkf3r{0d3Af2(_bpCXUTu#(E@8co3@@L?OW?Ku@6;7zHK}fD>r`_ zUHnyaq>_tOV^tWbD7?c|PsiwKjn_T)a1yPI9QvzdFf~)<>RtBUt@?U)7mZ&54Qo(k z)jI=AX(4sZmmv1|mV0-3y|F$1+;-N`h1Q6Q^d&2*-jw}}4-Q0hOVlbBe)Kl{XWb|O zGdEZD(&^zexP_tN;hbXzQ>jOvr&@x3(H`{mu!lj8dcdP0qd8@PajAE1DOR<3yFn0O1y4^;f3%<1c^tPVX81g~-R>4@XKSWP1VDn*1Iwq84?u3b#c3a#{EYPz zwfW9?HmhZvvqRSLNf!d~xYyNkVF5>fPtQ2O;O;~TTr*^DHbT5}fqyL71dLRrP(J+& zut;A5U?p24sk=aeztC(XWdbz=yCCd&7UhYhE=a4Eva6lwg z;R?-;eJ7u2Aw<@tavvFVv{Sd-DiIo&KuPn1id!U_ z3j2GGrh~%=ZRV7CEYmt(InB3M>;gT~w~_1G3oqIC76z}zpjEKLuNk((PGQ%zzwF;u zhB;+Yp}N?+kruk%(E0E#2X}0Pl4VU3sE5<>G4FpNi)wKs#Nq^BW=`(ioxjZ4OEG#^ zk51v9otU6f_G*N@h+9Xz270o3Y2B4HL2<^b+2{io%`;abe%lBNqH%T=)Ormp$>eG) zL{M%k=g;`7Pe+1OiQK-2q4O0(RRQi*2gkkCaqt+xUh4^bj*4zaDl4--*s0uSPYt%( zve-L1j=n>zG zd($7Oubbbnw-;M`b*vhGqh0SlBJNOA2{mo2;QvZCC_@$96J|bLS#IJwyn1K}0;j`@ z5*2Gmu~(f8l7yl51Nb2;jryjg)2l=!_Qww36Tu4e8DhdN#%4>X)34-sqXiZ@Bi*Z` zxZ-k~NUk=S)1t}!Z}x*t6^Tfey+_=8#pl2M?+Vg-6JRe9Bs!u;e&*3`&%{tUlnOvw zT4ocBM}b5rkjqnm8@3;_Ej<6wlv^+2gc`>k+WS!Q-vxh>?op?h3*O8aUH^k@i_hL!O5=9 ztEq30Tq8exE+1#MtYOJ5_<<(ATmq^Bp0=}9zPn&)A^*&s zes@b4Ycr@+_QCvZX;)p3m2~tjq?%Fea9(%vl0cS*~SItK9k|u;#C}YStM80GGkY%JiBjKu^i#BV&z0{_n6yw z4{r`)gUziEhd<2X4EJ$~h)eWp!Hv!RdDdE|0C)Na`{R&!E_}5nR`KtSD5a96bxy_B zo5$q5cYRLB^2rMeW`6a&WQU z(uySO)2*$qi>#G?1+Z1et?H7~^Twq_wVgrG=z?0B;;$pGEaTESXBhGoQEm z4l_&uAXA%~o2iF{fs5Z4daJY-@=LkF3pi3W8_tSTGtbk)Uz6nlI>h*@H!F^M?cksc zE1=@wya8D0v}}RSmg`VcQ~MAbs%>7=)W8d~97TC=Cr*LPr(WxQOBgW|vHuYrW8~%2 zXV9Cn+lasneRYlR%%Sy@ z4DXNTqb{(4m6ti@Gov*F;Fr!=b;?}Fi;v9}g|E5T$mKlM2x0cjYKIq83JMuU6AJP9 z%+Y2B@WO0-pVVl~^;-XlaQm^E1qBf~In5q9%{oRBn3lTc<14l9DB^m{b^Au5v}+$^ zDm>QMg?o5EsX6EW?sghWOZ}!pZ&RB;;{Aaz4m5B^ixVxeiLctC?d5&iIA9oYyjVu7 zGr#BzB9Wd|&#cThsTH5te4L$#7?9!h@<_gIf~jW}U}As$TCLu<+{ugxkck`Az6X_} zZrXCEjAdeB*c9m~C@8A?3=9mf0B~C9?k+@f!%**YNww!^%SzdccE?=|F>&#ds0@j# zv+bjbWJ0`SMmJG6svJ8=t$CF8)#YUYFyoe(-pdpqhXn)$(GH0;lUy7_4CFrQH(2A@ zEYyXxIPMCsi6~vZBmV#oZ!00_7a`gC(~0;^yRpu3nhun`4)siR zs{SqHhKtz@tZX8fp=eBn_4XUiz%ue{y2=OhW!;ctv&amRv=6lChUt`+&2$!Z*Q=wkS}d8CgF_W(y?(r^Lhl1eSW5KAJ(@va(wG-o z3TGv1{7Dw`ZF-dLpxXsiZ{eH=A+uSSr=PdP6HOZ#H^XJ^=Zt<2Q!Rtzp8S zc;1Hb0(5X+u0)i&N@Ro=Zs@ygYZh#!-yx3z)rfHxos6Kn8tn{zuNE*xmzZcI5nqY7 zSQizxe}X7nd@xYU5ER9CnWX$IRN&V~iO6vv(^bj83657$53_5(NXRGs`3f#n@YDXU zUKdEVVorseZ1(U^eD=>5$!!T6wKDw~zfo>lYciHJ2+&il;f{EFK#vS^HKr0`F6 z^Ff`~*!%34xZhlc%7h$g5V$~+rQx1yg^C-lP1s~&IT-uDPQO!i4J$U7>e=i|9a&Mk zj3nk{75*D&^Qa2bE=!?N_^@ebqnWj+v*Bt}E}(N5rPd&w|Id;6K!eZF?eBCkY7BRWnkN3bbuP+>2zSW#I)JPcrF6K5Iki zLymHLKCE-Vh>u(3X8NvAYWL#r8RY!9p4u%bZDfx*1$(4iRHuj7ya-g zdN0gZ__%@GbaJF`vO5oCsQCV`2_q=AWx&R=i^ zD2zaH`D|s!cq!*{F`ysH)fUl{nO#k$H>fp1dDCu%P+HXbO~OSv|L8?{SXTPSqwJ(y zSn+6Vfjz zMKe;YG-S%#W=uoxkEQxFbUwod5UA)kU$GH!#g>Qf&(~P_ZuXj3`DS-`i5bUJDuwqh zD8PjsEH*00EdyQFBJlB2!oo>;`S{`mM6}|mhjON^fa0~orDheej}JS!QL6GGG<2jB z!MzKgTB8mshq}=P$~@6SmA^qZ25G*60SHHpZ_WP=9NhfdotSKtw3@hFc&olrntfFD zGPzsdFE?f?nI7};Ce-cxnPB5`3_=N3#|Dc?bnmT!?zaR#$<|rX*4iFA4$hMcZzFFP zSdZ%fwlEbVDbEI|=l$kHeD9XtO|F3>v8d|es8M!tYq=lu`8tw#vyI=yJE z=5v~RUIOI&Eb8R7T6KW>n@MI7@aa;*y~5*8a3l{OWiSw|zI#>xns9NR8y1gUH;#{y z8zXq=7v`@2oX}x;?Q~U|mr$9`YlwLM0ZINSgG`;6)QEQ5LCazf)4`DC(6zk~86}5Z zn~86}R2|cv;jo;D8O;ltQiD5px#@7Am|9W|f(@_q!s3WoTEEkh;JRqIuH zbqMJi`th%nUf}h{y5-yFYV1YLV2qYqkISKKY`UpNe+lm|;~G2?AvPEmxy zwYogtzTaP-MyP!VehP0JH0mcSwJ2Nq9)7f2vXi+$z;R%2k||qVj`PkZUnS*vlks3d zWbjyt3;v}4)$!t!elIZ72ohm7CC3S#ZY!)@V!GH1K=21k>Sw|kg(OvGB46c@L!1*^|IajfUYN;?;ai;29vyaRJ{ zi2yrQ=!v$c5~9;!UF`e@2r_sbneCS-R6=^kvNRbPv}!~*-?M)C=`NYZVMjDmX<+b@ zGn#}Fqf?ZWP-c1!cw@3vL?;Jg(p%SDVi@>yX(y!NEt{8E?UKXhTeH`YAnAbBy|S~N z-1HbT5ADWVKAk^&CkzvFJ{UIKqN=IK?r2P%Yd`0W)nfcMu;MO(9=_3DbnR8#8)soT@`S^8!)X&T z_<3L6Bz^k%rPt>miZiVU?|o-oQK^`Pz^}eMB3msO5bs7Y@z9FoeeX=isB%K7yqMMu zUFuMm^H{!>RvJ~$_uY*<^(&=N}{b)O>CnhwbE*RRBs_5 ziI*(6IQF;!GMx2rDxPIqH}&G@$nFelTv~*)dqp!{h!f_N!QdY7r>g1`pVYmyqhU3f z_3x?=097cd^?C)!T}UUJANWW&XjJGXfA*=SV@&`m)+eBwL-9^oY3I=S4lkY%AE9|O zx*Sz3CUrV|yMAWG9r%REt>U^e%(wZAmfnA`_M108=jfNcSvsYL@Zz^0On-dGV^vFe zK{{R8F~V8xfjGgeeqC=N=t!ON&XtF=>c!)$>X#6(l&C+q`IarQY+c-0C~+s1`jHe5 zgZQZ=A5r0f$S}+r)hLf>(U__@qc$YyCp)109q6uJV zc3dZY&uG+&Ly-mT56_kC|M0F!R9%!gffar_PPED+vJss!aV4%H{vaqQtqEF!rN>suJjKJw>>cVwq~2+il|NjSi|`NK@}9Wizw6y48e?b-IfbC`UYq{05K9 zTwF}v#sfRmXA}3(iDaHiU#CL?bHoJivKWZRo)6-`?LFaaHhvuy%Uf`1RT&YR;BGPm z=Lgo0}hTD>nh z^C7a?$mK1{-9}Rvq^O{T1?suzpH~S=f{h3R%au9GYf|U#A)`7#7HryVR(ld=y6;qJ z-R+EZzDDM>j5*XDmqh!c0hdv;r9jKFv?GdM*2c!6CjoL{Io&S!?`-ixdYOKK7{C^b zVKiKN3+V>bxjrH|r72^s?}A5B9Zg{%X{DXHGQ#>KC<`g(`y!L4m2<(}lKPo2H#aW3 zht)>t4dj^!>h6#HJN>VJg5v1vn`f=d_T>17T|#ciB6-CIg=cP5byl!wrP+~??RUmM zAfuU$ya>x@l>*ssBCC~YM)%BRQC)|d_J+Qjt~3zHA788wV|zmv#S2B05;X!UZTFU% zYo!;#(cdfe)?1yhmEdg`8zQtDZRLy)=L+QBZ)zw4)p#)^A8H-}9{{%$=_{ZX~>$K>fy$+sz$c#8mipmr*Mb*D#3gxVc5eo zOl=_UkAtpP_I&2f6Eu5H4?wdCq1D`n*a`-cAz`E(20p3fFX`1{ zfMtJMU2X{m{&xfUs(3P5swuL3GLbD=X*omPS+okNEqXq+^hIyjv1axKy(2~v(1b{% zW{i|XooIB9Uv(ugb>v0Pi+!_haP-Fdto^N-9Se)as{gA+l&phXGrrVOS%Ceg6tT}% zI-4zt-n8*cLmJJ86>d(;tv2LtZA^vi1}o>Z2n24v^w)4j_rI34;++1C;VoK`Ax^3r zU4R9$dfQ5MfH^958wVorcubqfnW)Q>%CTmhv_Yfd@&_uz!3k+caX#kpd)E5AFT{=> zf-XC*+Gr~KD+{kVU@-T=xD?lPd2bH?$Fo+~hA}Ot%wwgMOqzO7ltBslCJse1dhuW6 za4=-r^7gSv_6O)#r+CouB=)UkXmx5MBb(3FdnY@_O}&Nd_hX6+;vqO=)pn;PcavG< zGv3Bn?}<-dY!ilhuN04QPvcIQQ%ryQ+NDdx`dqbXcI@IwV zXB2nI+Vx~e*r7Th_4?{!108^KI=ztw!ftK4Z^t8OS4*PTD0$PDLFD?}V;#cgDr=i( z(_dU?=Y`y(Hu6Uwb}N-PBAs|^i%zm=JEW z6<>XFGx!sW6eDe=cQ$UFZ{AIu{``z`N?zC)=zxy2Hz@0-f!P#14#>Kg4)%nBri_qFl~3aodj9-5m_`v& z@d#eTN8*G6Q*CsePyw4pk)rS2{XBDEqY$tJ_kV0zWEdw_UX0E0ZizAL#$TptZGMLq zQgd;(*07oJ_EzbjC8Oyzc$<_gT=m8TBY3^?H5U0ZkamgSKVT;bHs+FUs=%^7 zT``W7UP(ABoC*XbPOQ$a?@SiVV6i$f?{-DVBFiY`zQ{z;kQSnhnw<7rgS%WM91iXM zyujlYtcnV+l4QNT0aw{Uch%Yt%*l;r}DT0fa_EptbFRW-!gb^~KdWaE#srS;ChqUU56(%@H ziS%ey+#AzwHLVfam6Yo-t!k8+g^edIGJ+s&A4JjZh%alCeStYV}7+gByVbU z43aaH>O)D%p0KlDh z^hm#bCQ#|lMy^TvD(n%|*(qr1O9k`;N6(gZN+=%un=609LH~2}ZTs*?yMdR@)2f4p zF5TQDA0oX@V!A+A*;f2KAIOO$VUEXtC$12vU_V5gTPjK39xA;M@8$=uiKv1vE6RJ< zirD@*#f(J^8964uz#umW%SpACO8e!T+;blZf7%rPR&cyYuD`=G5olrYhp9V4xNnqP z>2}wi?>wnt;6Y5`Tb#X#RHzv!mu|2+Iu48t9<{bengi}dACvw&R6HCvy8s_3=}>!# z^A;3fj7l}-?Qy=7efQiM@IBA8{b+#UK~C*V`8badPIb_b?G3<;Jd`Alrx~bgM{Oi= z|Der33*pIvhtlEg2dDb7KE0u$fG<;nbGDvPy>LB{NoE2H<5+p{UTb*Ja<9AdR1J2xI38UFfd=@1@V|6LpWA-#= z9T|M$;?mMW!bf1CZvH&(@P?`b-jV!|M`>NKD$i&FO-H)e7;w2*zA*l|wBHc5kG=~t zZ_TGg_4dhywTdMF{LLUgsaqBm<{Pm;1+^3B24ApD&5?>|pD)%UX3Nuy6v^yw1ukq( z$?%)x|1sDY_Vu&mXO8nsZ77^nv#c3Y;?REF(6i<$YW8Na`z#l!?&*A!XwByO6K#Z` z_qjCxDqRZPWUU`IeXVu-8%uVJ=$nck7k7%t(yLXttRxIAuYvxCVvVO?xBz5Z3~rX@ z`NeBf?>73{-=D~UVg)QjUnWNW+&v(9!DzEoAGIO&{ek&UEU1nGm-e-Q>-YP~e}lC< z<&kh{pXY}(s=zOVVT==O(xYM618)_j;kw=&RIS#t6TuFl@T9#cFk1G^_8hWREwOjv z=5%0vnwhu~8DxYQWtHdKEMJvE_KgasE34s70D=ofK_G&5&_MF}fOiw3XbSFTqE6!Z@KtDZyHpvFsAiW&E7v&K?M)jAd57cX_J>6XoH%BNh3jNZvWM^n-1+$41q`Q zPrl6swS|7AKo!hNyh%EG)jjA>sF*8&zaqoDn40YsbG2Utou04V0)9KPQ7_-bpiK@n zXynf_o7FD|+Kz(KpvKrBpBYb}rwI?>!yW5@L?D3X+(>q6B2mrks0(hXg_0_Q6c=D( zlWm8Z2*BEw4jl8Vo%Qb(tkdzpyQi|Jzes6ai=)%q6Bm$dA2JD9Jh63uH_-;F=Yfg* zQ&ONxYGo9{G=TQBYJox^j(7XTSk>R^p=|gr#-N#H!~>or+rIvZnM0wEJ6ji=$gndHZgwB$_!8w*uXoN_eYs}J*O@W^?rIy#*AiCCLnYId zg5ECq++{n{c>F`Um|fb#zP8Hi?le!=u#C8EP~<_;1W?STkB&??4^Kcqu%nK))kdEB zCR*n!PauiuQCCdlS|rRa3`3EIcG7O>=x2iN3lb8bP@={0NOa%(v7Dr&W{BIdRM;Y| zx@Nqsx=-b2HXUnWY^&2;B7QO9*_k)D3wbc?N50 z+@wUYm4-55HIz^`9H5g$?03yjLBT)rNITMkR{s8Rb&aMjU>q1wH-JUwn-&*{XscY+ z|DqmfG6c*U-vxy*$IG7!=vK;k)Qclg*#xxa`td8jR58e=eswBdWr+645-_={^zT$~ zfD#=<{2^&z=^+C2SS;*2AWYt-kn;^^urNFawRS&#`OCl7Nu>pEtptaRhszaLb|&Jk z^Ch$^CJ3MJ6>uI2xE40R)q1L(l_|kVivv!pKR1LOxRwp_=Q3bnE5B69_gQqqLmz!> z{xzd_asx8c?ni2tMG|EC0)Alye0OiscY0~TuJ4Vez1N=w=YS*a#RMBErd=8~Dis?( zVARMXg8pS~KLr5io^^zC;ymS>|I)Q#FmqjzKswO4ijzR>{Hc!e^>eD{wIo7te;10l zo|}{kw|@n$1fFAJ_s?IF>FMkezz6_-_FwM((-5^wk<605B1%G&NFyH?Ao72HiOb@ zD8;?7LyL%o373Odr;Zd1+|@=_?)xVhrvNfm%#5_Q)^g=B{c!gVdsif?4;aa(MJ>Tj zY{05teheb`Zd^M8pM_h8hvy7fJe4FMV-i1qEHbC*zg0sw3Y{@EZ@T25nLL*N@}0bZ zOtFbPrqlRJS1HuvA%iuJkOt62%#`=pcH%jNE=LJ!!!%fW}of3I0TI>8NtxmZucWx>}4 zzY1{{1;82}Zrm@(h*&uP67g%(Z(Msj2!Ah*=wC+R+W(5}Nwk7WlvxT$4HUvv=kLVP z#O{L=+b%_zG@+4P?kI3>(og>X<&YzTj9;T^LJ*5fsPAOHLuHP%nJ8tJ2~dY25=b|{QwUg zgSa#HKy8kelx+}toa6o@T05j2@Gtt-%gH`HnChw`$`-vz z+&>gLaNj?9;BIRLu>70XgW3hNeq(el));$ynPB?Y_y42r|9mRK*QlUy`=J9PJV@~j z^ESroZo=cg9p@V(^9*oT9tRT+|83ttp@%PqEN}#d=gEZA|8~Uxe~y0_{ImbhWZHS- z?B(*>)}?ji_kU{0KgRLzpNgVLoyJwcM4YVe|7zI(_3-%j*Q0<=8~RrtBM(8bHPwuN z-OE2t=KuN>{D65<3F$F@ZOHWH?Q4vuN&bJ_vRt3c&Xw5U9PNKO;u$<022eLfiv1MB zKe@C2z6qXEJnJ<)6+8YP zcR&5HlN30F-dKqAf4yNWJ2*uY?Bn&Ym48Jv|6OjtLy{tjcF%u$rT;@Y|A|;ufccYd zr5~pq=uGq9cmMrM*wYjn{4(T!zFR;9ifurIqS+S3=#KEeh~ya@_EQk^|Dq^g8G?@v zms*m6vZnAzA|7dTh0Fp=U*G90p-;<;jl@m|&Mrc%ALg9$^7AJr{1Gf58R;8aTjC$+ zk8-q~_GA71{YOmsAFrQYJOd!*~_=~D)pqwWUItqic+*>W5#Gz6N-W{QubuoWs{Qq1dy-$fDm zTtBaetYs#|*4BoNm}=IFIB#n>!z%7g*yykAiR!H&zWR8lX3q2WvWzZ? zrfL%ucE$;tGMir$JKu*snl?L|f+S|?Z(XMfY;@dhrjyF>j!4Ks{hC%|A3d79SDuR> zAph5Jk;e#j(7rMsNs>RV!Jww6mwS#KsC|8-xX@ye>rUe1buEbxTls8nKbc}r&+wz7 zzc<=tzGh=nuR#$V+#vV<%NtsjHO*CHx`-6k>`aY&A@6s05wQ-s`d3#5THu;Gl||=6 zs3;RICZU)Ef2!2(?(XzeS5Ps!4^-IL*m$QBqG7HxO?MjrW=VZ4*Ou#&6PAV2KYlA- zxDwV(3Nb!euFkc%99B@%SE9PynU|qA=y3IIX|R$Tl#-Ih(`lAgi-khn$w5i;3-Ts? z+oNd(s61ENs&&%MueV3DO$9tFONw-hW6IUE;sy4=@0^xmVq?p}b{SHmLicIWn|Cn> zqm>7J@iI8S)LOwe*BdQTFTEnAMQZmIDtLSeUk84_!(sRlSkm&ML~SgQT@wbO4?p8H z;7(Hr%;0iUj(8V=&klA%y>bC@vvw?5nQj^@yfG zkI3?|JgXi*o^ONR@2=I0-g-=_;ic7@cz3PmRe=Cf-+xmV;PK@dL&CCs`Of%4U5GZc3Gr zXU$)$7&-iz=8C<48i(Y~f_8vPU4QIG32PN++Acl@nS9fck{v#}1CM>n&l*$Ws<@da z_TaU{Sg%|(sXc0vqrUqkQsDv}R0wQlLj}b9JH`2f!o&x|sJXGl3EGNv@ZoK^&3_5g zFhsCJ8^w0MNuS!O2hMu0KNFwsAZ2+bWdjq>EfAYgyJ+Q?3us$k=8(3o4x&+!C7N-$ z7+#20so&^JK*&b8_etHc-tMh%e-u$ZrUaUbp1P(Mt&<;q(r5a0y&u0g^OZNj?C~bb zl4!K|2z>D(kxO>Y#uR<`cJmGGGzZGAxEF`>>GGM}Qujx@V*3r&`82+4(+wqTmXk>? zl&PvTDn_}v6-9Nv3jC(VcrKez)A*Ha6H4Z7b7e`QiVe!zCHAxKVwIh22?Tu3y-itf zk2W>+BKL>22dmvX@G(D5A6E**t0obe$atSo(0;{`&g1cefiiCc9O&PE-n+8sd-i)J zN^#;#CZE2*$e@uf6Q$pOMrFP+@+1cD{Bus;eslg-pq$@UQ$+*?TTQoZ>TPUmWb`yn z$dq10Id z#U|+dd>y>Ka8TJ{*1Jxl-Drk-INzF@63ALOy4*UEWu5^#6e-Ym0R<5;Q@zah%MzC> zQa|LD-mUl8cJ?=xy`vh<<+yZ`PVvWb`r^rF*z{A!I|b&AV$2Va$G6 zD^NJ1!(MFuk9BHVpLGF63ezAutNbaZ|ZlWDOzHnbCTj6K!vb(Bl_DiylY z>6OB>&tf@|LmojOFfr~~ji&j@j{W{(I}>XZJfxhFe|?8ku>CvJsMMJd<;Ib}Z}>dI zO1<3B%*tYDOAd^o?m1Pz|2z8jk~qs;3FIztd^8c;Qd`-xDL}vJ)u0Hk5Rt$w5s_{f zpQ93K1(fXU1oehlEbT;}k4!$|?akjq*z+~7E=mQjR_1~_^seiTQcwM;EPd`R@NBl& zi4A8_-*Zr$ew(@Kdce{wZTO}h^LT*%8fV<>!RK(HKGyF&TaNzKd4WBD2gOaehOj~= zX*K)80E~dgJ;2gFnefKv(|JVn#@d=?+C{_AFXz>}$|9+C3az+)Oo^zh_5LW8vn_g* z?HK)}4mmm{Q{;xzoA{}woWu{HN%Zw~tm&-h5d#HXin8#C4ahyQWuW1|@4Qm*(n*wwsSvHSa)5Tpbx&4s;jQ3g#cyQ=1lk zGOagfSC)LXTKt&oa0ZFLA0DGVJL4?ukj87q!)aYw_J8P~W5-K0uGG(`kh}2_IOTIn zzqLCPXK*05S*c4VNGB}TTXa6(3UC=nFQ2Z()Tu;r2OT+vCtMN;c>e6zu!1r*9HH^} zWO&|a({wlowcX;+6zR`{*w%HLo3SSu!+_9ZxU8jGcUk-=-` zi#l()IPJN(Uz<^dHBAGr5Xs!x&~Z8E z!oiC@Iyj(1>z(v}&nEw9vAK6XgfxgfboZ^d#1VJ{jidJ`cqm@5*t2uNU3)|0kGV3>Em$V8hf|p#Yg1Kr9y>L1fSLj#Vs)T6LWiG>>G zkCzFoAFFdAoZp*z^`zS2T>{rF@SfE=PJeVxOrJXWa=JhBeob|A~<9D<9@TrAY4xIX4B`RDVUO! ze=rZF9kSnWSJ>d;=9X{J;@$m|l8&1py*zS;ebtQjM}T;5i46d3hZc_N34rYJHO}uY z{QT&MC|AAr*4~NeoHu~&oK5$)i#8_4RCccp`{=A5=jLaMqhj5?8txd+&z3#QqAztk zJjpBT9~lR&9*j8Pf1D{t27a0zOGI6Cc@JHjB)b2-041Kj*>r3>uhD6+7X zBTk$f-Z*$6YFC?YkRD&Z-Lb-GHHPkeniW}lqY3(a6YA*9l+!TC7ZUDq+eB@Wn^x=Y zalySwaCLE}0vl-hv$t20poL@`V{kbIi=X0hYh{{UV9x7U%8+m;{%btNS3Lh|JganW z$DhllxK=s#ou0{d!f!tteV+D$6X7e`#v@nO{_Sx5_nSSF@vuU$_qosI=nJ~g(%0QV zk%s+nq?Z*+WlW*%eMAu<_)~YWxF*JBA$>iX#yNdNI^ld2)mOHxmp?)ck8`MsvJm~S zCLS|dhq>umt3>qvw2svd52x!uk_nwbd{=47ppg9ROUH*YVyENj3>vql35`roNgno! zL~OLR?h0A@0Q5G5=xWxl1E1XJ_y@oyqYl)%an3f&va#L|Aw5AB7^G-=Cm@kTTL}5( z*wx*Q13r^ua1}RVh~44L7KCt7Dp)q>fUm`#BN&_hQAZPh0X)z|pxdLm)=7FN1IiBk z<;(X7&QEQtYf7kcR*f;2)B72*M!IJI;P*2AK-CWYU|B@CC(nPSaJS!pV!{o=;^+#p zwP1l+o_Bl=99?b_dHp#3GBb(KBxU86p?Ne4@ipU?i+0^NO0w_{B3@DT8xc+Z00UX-2~%iBLXc}2}LnN;uFS4zxp?$n6Fy1{i!J^LLap65YqYGgc+R7 z_cc^@b2E;|XJ``xqXs#oJcaUE677DzY2QuHXs0?k(%Y4YU0kobt<0ZXi1d;OWi)W4 z|CGdV3&|($i;VSVi?}#7igF2fx52k23DDmaGQ>YU4b-pB$qbCLd8{lab!Tkck%vmx z$fcECT{#KgT@}RqMrNQ8$2kvZE*>MqI+ozyo~h&7r2TpAmAK>}w(GC=f~-SctVi?L zmu2bnN16Px8n&4R`zT8{vt(EY`%ED;O|>r}rhK*b$Kt1}+k&n8-m$|~kynG;v$K4# zoq^;tM6u!k$~!UD3{<)yB==(lWyXe1(Pq_os9-KGr)m_jS*=+cN%NH3#YztO`ONsLJy)^+ z7l-lSW7GPde+=EB*U?;ZWLgErrsv`j5%)@EKvPP>7^ zOjkY_(G%UFg*pc{HT7Zck+vGX>4)wwe%lR1e2zVY=7X=l;w+F7T1po^Tm=-xAI-Au zZV%ix_Vjf#%(A^Ehv%6kQNn&~_dt#1+w#(;3cUmhXAgP6UfvLtJ7+Q^S`4?c2M3+- z%*3hM69M}JSDGy)s zidSWy{lGzD!<9q}iL3+~VT_x0Uv5U@sa5JVU(u#J_2F3*ajMI4{4oyp30MA1{WLRC z`LUkqopwXtR?6hIvE1sd$;=P>=`1OgUE4nqz;i~krtnC}u|KuL)==>M&_!)0IRPV?@E>k@tbuhiRj zu{V+$#QtCuEvjoTEiYvB*ZDq;cDaW->{FvQI@H&zUij=^pDn*e%sTE@Pz-&)oWbLv zzXfT>6aOcC4&0>yuVSF(qvA_;XcF-Q-yPvuQ0Z8q@h- zDs<^_>dHpHn1_;HCMeIQ9rCR%M#jco*YpT$n-$c&qlwmBBQO7*(xxw=nZ%Z>{;OXN zHflHVgD~W!X?OK)8b5t)3gUfYpwaAbetN9j+*28@if5E~9y9GVhnQwydq|qV zA-8jOGiW4FZ?SQz$-rs0k|%JcQgc@FW3PwpjDSzrc0V>gk41@fx(>b zA2@HuNx89nGdVut*y;ke?%+MYRh=$mx(%eMx%K(lB)}fMdgqtakZX^!s#efrl7~&b zE@S?wG-*~`wwjx^o(%o^#O;b&U!|$lU!R&lYasuim~#Z-)7P|F7N>ke!MT0(q5Sw) zMnFmJ)Mv<(#+h&1y&6!l9&t4svps#})O=9jXDE~63gnZ!du-}(wQa>>ARVLix$YOh z7MzEJ^~KXp`cwmc)1v=tX@10cN7Uq6OX|?#(l+wh2H$(wWWvS%?p1Sb{&?T)^Mc8Dt3|0(xAmoR@1lwOXD zfZ5vLQ^7OnzZx0x(ZtfNHBGIGv1ir$Ksd%57MwM>-Y;!7M*8uB?=*3n>H~HGJ2k15 z<>Kn?_MN9GOYa)!BwsAG*urGJVwqJN(bY<$Jm=mRi3X%OZW7kT{Me+viOfX%m3c#T6NPiYs3=7jvhLsk61O_!wv})%?D_Z#tEFhjX?6 z(yb#Pn&}J)-3TCMs=qXo`gwe%(akGbqPW^#U|WU-ZS%RU{Xf)wWmHvN7p@?U(jn5_ zA<_-f-7VcI-5}lF-Q6A1(g*1f>28p2xEp=zd%y3F`}>Y@1~QI^GxpwVt{KmK)-&ma zL(fpYz?}4|B0Gg)$Q!mFZ3=a7V|@JL>R+MVOyVgC6RREPKgwrIFDFiyZbU5<0fR?O z!|TFs??L#9tY355f9?xqo3uEzdWPEWL%VWco?+?c?G^?vMc(3C=_5U>G}TT+cwUV4VX@u?hvQ5ZBF6pue2|o~ z?zpJhTjLtpd)S7sgRx>_TD7Lnj_{>=m#hQD^+937#otn`hc-c+jg|>=+3b}c`lVZ| zoALmEFmr59dkn9zQm3hHMVx<3-_VrGU`XY$cx-p;IKX!!-$w#YGX7JpCI42vKetZz zNEvh?Uh=K3B>@Op7j3Ci*Y#<&aoahr!=^UJrSm`mUf%BJxKHF2=U%Gew0H6dt@veW zf8yJ~53k3(c6_fm#S6i0fy|wz2ZgrXVQO`jqdcaIT^MF!H%8}WY7!%> zX)up^N%w1sSZ*^Vv_7dx4X(^(06n)`XtC&NMR%%BY2KhzB@+Lv*UsW2W_%XcGQF|8 zJt)^@A&sg-S;~stBuLj4r$pmz*+?Z>Lc2{1fxAg{Z{jUC*42HpyNoBVeN9DH!>Ha4*xoU$Wrb}eSF z+)U2D@US%FQD4Rs`z8arkotDFzl7_B>~2tH&)(TibLYh!)sFvO8R|f-+2%d_<_K|M zu_53SKvV3rvmiz6w8~9&^|{tt%;ALky*xuMbwDG*Q@+-ka#SeegZk24U()yQd!8cU zL1AN=S`V|C@@N~Kgz%4AVzkQ^GWWUU^oEfUGmw%Kn97G-?@rg@D(D*gqrDQ>y8xHAByZt8lhNDRCZf&HKr=Q1BbXexLM{hLlPqd;Li1mz@-ZdOkU* zw>xLM%iP}Fimk@`(_Ciu2B&wE$EUz}1sv?hCX4jx!nL%cv#Az`Lh*sjy0SFrrlo?t%6yZ3X#MY=FzpQdcn`#SfRU5Vot zFRD-2y`w$$(AQj8%hYaPtA%|U-83NgQL|Yy+1;~0U-3NY5WeyfO|LvSM&Z>fd0BZ< zvz{s9MPHtEbA|+6vX-}r$8ZrgJt1tgC<2$b(V*^}dZ#%)#Lhh0vauC-_IJl6N)5uE z*Sk>RI9#JHt736E+o4otJPJ&E;=DMvSfE$@RAPmlP~0!rknMorwv@k8FtFtUTNFO~ z1CfB)w~)<^#2&RX%%K6lK>s&YMw@VEvb7w{=Hv376i?R2c$7;gi2!xdEa|)TwE3pE z8~`y=G$MJkg;3Ie+@cu7`8cDh$7{p=#c})&{>Rlao|&qGu~a^tc7r>ldb^SDaLPGI zW`n$)q3WqtrsF>3dkrCHqw(+=LT<;RM8-*f-n@%Zh{c_j&j`pIF z-^z|!b=dHl4(T4f2o0QcD!0fr{GH%O(r=dkTRy~*HjS#%IDiUiM*UutNNXjZzVH3dT2K4)pM)+?)1_KM zG^~^c{m)%acd=x2BZLV25E_9W2 zk8HrW>aLvhjP)d`U$EYD9G;tf&WvRC>n#@Qg75Kf9Qz%o`0g|JD2{ez+HtaWy)tI5 ztmM~cZAW4L znP;-3IA{IzF|4^`vQ7*&zvF#`1qkdF+qa~~&mk=>6YXtoo0yahF#4dJk&j;{&{iuM z5^L>2DVf-Yy~7fsjB)q;npuq<5qyk7&Zbu*0gqGmjTsO8Xw zu&I@w)XoNM)v^%Q?Yz2 zkSb^2X&eU&3kN*R=J)S2be>}9XDS+$Tjiy#(O9B&?P)IKT2yyM|&SjM0sQ(dYOao*CB#Y7iVCR z5wIPnudm@>ogVIDEw{-d*H=+Dh}`w0RhzLjk|HJo3@HS|QgO_fhb z1gC#6W)6SDBDf%Om(S2Y8@GL>eC1T!uMzBV<1$k$0#Yt3=!M^wh>r8OF;Z&Kbb0&u zl>*B|)YL0RwdH|^L;TiB(eJ}%+G<1kPwXNX-|`>zGM`fVD=?uroy0zkpgAdwqAP4R z%WZ1#z6y;cXA%5paEU$tfPKQ`Gh?+#`7oB^)0^y7%|k-toHu4d@d@MC6T%vD8u>^1J@qf*1rP2N^Y!f0KAFSOlJ6thXh-Fp7a`;ajMoxz zbW$JMary7Pl6@13V<(OZAKD3wPd;qM2*Kw$aKEplE}MXUjU~>!-(FFncgfuuFUhU{ zt*vr$i)Z3tRGwER&IQ}8!=(Ah0v7-_fPG?QO!o|IG~tu{E;2zRD{D~|@RYQ66K|9P zP=BHbzrc2SN?d(NnaqwyV$~|24yn}D7H-UA*omPlDPw<~tPqe0wR~|!4lN>L>o5x` z%utw7%}9ud53}C{gH=40l%-f678s`LdU{Vv^|8X|J_n2HejJ@%?T4zJX-xk(MR;(m z=|mGh@xrWc79SNE(a4Xv-TOl)T(z_F`5ZwF;0x6WiyCV? zH=k`WgS@JnulVHGnH$50TO(uMcG%LfFHw3_e7f6WoVYDh=!>BJmAQT1MAp$A3!>3; zdD$CO!vr={E|8;bu!o~by$gQRxA0Sf0pV;o8bwj;1}v0y>-y#ULg>V9p<&7b`mY^+ zXVlyZ!ofaV&_~qess-e+VJ4i-JFNlwD>_gIUi8etxWmTvJ$gFwp#15x)u0IVV|pvz zdd+G50js;M-(cN^M>LMLU1jCWmCDbT>5;MxKi^3n7{{QByk?f%is-M(Xzt(Atjzl% z4l*BnNaB1(nqyM}6YLUBH6{1mmT{mcAF6AUM8xb;5)FsjpkvI&7FZb1c*M=_hnhE> z3(uRo4^u&+`?bIU_>e!rP4ku`gFEU{d60H+&?1TK+4L+MrB z=6=J7i^OB#lDs**{4}t@pW_5DsA+^=jKC5GVHyYmlY;{FIRFh}j))h8$d+T0iNnZYDF5z_bh5YqpAd7qQM zI6nHnf6Hez2(7-}iC78p-9i6#G%V-TH0~j}rI30GulQToy};{(!qD0P(t-7t8iiaN zs#QT0+JOt|=LCTQN5MmHj23vSA}Z(}lS5qV3s0Z0^_}1L&!xTa>$33le#iYkF9}4B zgpDQK&U=}I)PWidwLeowtc1%HYk`x#+1xbnDFmh|z6i&d3%T;H|8guoUtz#*gNtgS zDt6pfeCy$Z2F2QvWy{sE8H_Jn98R4-Ly|8YOTj;=Zh(=^MSl)`$wgw=zaPS$kLV4h zrB&xL>V0mCQ(h4AdASMEAtx)Ixw%z25KggJBs)g5 zgF-{{khIhDb@F98a$^>ucKSCsk38Tw>SFPnpUf5C41c_3uBoYs*@SmyiJl+$T)M$s z_Fjk^jw~~iCwI2cD#sn&e1Bf?n9W5oFC_CRXGDDVA(38tuixBCWYp(=cPhwgqNduQ z5V1)9X)w5?Aa7AHe53Ofj4%7o9Q~occkDlCuL&PM)2^n4V4wzg%_x>l-@!s9RQeR6 zLFs?}in2lOu%g(?HT~c~jC)gq-pd`D_xT&VB%Cjp2IfS0eTPDq*+tRNidu~tY2~Ly zBgG<5&XzCwSlm2LK6=EN@)hdOZ2c+B21njVa zxs_LcZp{DOgL#}_DfIc@2*#Lt~H=%PLC+gaOqEKxUUwG}cDJB}olmK_s8S>=ZZ zgVnNfZ4?6z%SpA`JCPh$$hwI#e+Q{|=xjjjG z+Avf0`-MbcL)+jK-R`xg%+4%Jm|cTSzKRFgypu<>zc7=yFt$M5K9coEbdZbrTJbRp z=1JnV{pj!`^%eZI1t0B2fjzwr-8fxsDl)D)9@3U3*hZ;L7)#*<{zbPJ(r!ocnJ2a@-375@QuAMsSr-U6 zs7Keg<-+JqM4YOW(2a6#Q1dZ+s*)w5Y0PuV7H51I?CX-t2yJ!xV5QdSw&$e;@v6E| z{uqG-yRemUnMa$@)^bYhh_}r_&gy1#{fvCm^Q56|kgnaLeW+4liLs?QFk@PcB0!s$ z`ktHz&9JroXN=>!xeAuti!(aXiPE6AHydI2FE*9!W#m$HDB|ur0+vPcfV;F*=VyyfE!8Zch@!WK(&msQ zQNC59BlvlQC>jy`%6eRh<&>2LygLh-2DV00`x`{>r~k`k5k%oZ935#2%h09e`AlQS zn1?!{+RN41sMI8Ls0w2ublA*T=rSm)g1DRWYRjmv)zcv?ji$P43A|2I8I!*A65PEx z-}xSRs!}fFcmp?yiy(gLCScw%(H0Q%l)$^I_6aHdruev2HzcTk8cHfhvm)O>tAYs6 zHnq}Ssf5#Xz0?-UZD_*K*8-;mhVPAHS0~;7c1#9L-UFZa?T!24kWDu9h;$k5x7aD+ zPLmGKFj>q^Xy7t#8{ma_$7#D9tk2rjE+$%JI~#fdD3Xu3p4>uo?Eb7U8#dx*<$kKt zAVXnXPjRX={gv&SW(^9u`?pZ%(!5r5uFa##&#AVh7%zyAwF+xvI|#5>XRBtKlnf4| zB#Hydk-=ej^dR8=JkI#n>5$H-PGG=Yk#>jYsl&+{wed~BCZOy<2$D==8~+(M*U!Vl zQxjfq?l8{vts*G^5;$BhwB8q<`}A<^HI@AV`W8@^GZ{{LXAfvPD_)!~dp!*{Kb-XA z7n_NgZ%!77jG~sdA;qG9(U5!PrOnYwk#WIr`J-5tv z4i3;(Zl<5Ox9l#p&YfP%Hn3q0KlQw?EIoq0sL>_aB8sQv{xswXd>R^gNz z-HLHb+XKD}RnJvZdr*D)OP9KRvH12RL6!*VjhhDn4(1wiNZjwjggoejb8Qepuv&7F zK8CoF*E>D8P*wVId~2mTb#MN`Ao-*UeOB2M1lob4_;Ikb-|S87Q?BjQO`P^JOr`o{RO`Vb|G zH7mioQ6hRf5>Iln(18FT7JnC>ACf6E)@XFNlC_Mf>n3EAPGPd16q>fcHBfF!Uh-ZS zJ>J!6pwyP^vJ4k@6U+Qg#qupkp=7>jh|*<987r~gx8hstrn&qDp~B#dnjkhtw7>5t zPV9^aDW{jRsD@wwab-h6k2DfkbsrFV-M|7BKu5rkEM@U+>F zI?CyakDTAO4^~$RX-_9wb>F|`^1Npg5)qLwA2@DDl|X{ucTbrvQ;WU6xIWSUQE!fU zxZIHms4f`^hf)BQj1B@AcJ)ESLEth-MRMn*E8b%g4jd-tj7`gC(;f@lw5gD#Baf_c zzH^5`BUlf;17kvAS|x`C-U&$6jNw=|$AYxm)?PGgj9xoRU)RY4t1*H5{O$_TDr+p% zcwlfky%i&qhUXgW zpsd)y%7*y2OWc92GT8=B3LQv9dHj61nAP4mNV^XG((YqM4vc%H>ovspX|!6*K_L~7 zDl86V^xVxO*77-L2c#Obd7l_2;CW6*w?pfcRF2$TKj%C=R zpm$howkyQh2F5uIs=E(v@ho{gdAJ_d&z@VpJ*vXzmUeV>6yBcx;$*GXq0{99TXKrB z3e0MauB@bAZ1+eFL8BCS9_%B-`w;gEM1}tF@L&q4huCioV^OkU&}*eVj|nePtSl8N+ua~^>SGPm=#KjCt;LNjX5$IHi)|WWn6!-O|W?Q zE!1r|xlU$B5Db$%iN?vNPr=%!7JAC`IYb*+4KxPGa-ESU@VUh=PNskkXixtj>7~t8FQ)cbI;WF? zoQjIdZe@hWjO9X;9NnB@c2w+I;Hd1UXGtcs@$ZfJ@LCpa{ousV5W3ODz!0Pwo~Km; zY)~S7Vn0BTMFWO5v05!eNqhz-@oMZ%yrh zD$)L6Uz=y4)+6!@X+QZ{ymE-hcLY(o5C^9lg-~aR(QdWU28kfC zTVi+TJtTjvFMQHMi^ZCe^u=3E@dk#zZcMcF;w`GL@Rk}AIqFT56V?Sa{<6?Ne$kPG zP^Ho5b#uEqieJN~zhkb~j4%KCT|5l3R4!LoZahb5QxguIilqGY4Gz2^CY@%K=Y5!z zVzr5W7abXyFote)RQav*ZUx8CF(ADU3=D^l*z~BejYekdr#6WMRA8I#^wnx&Z)v#)w z90OT*I~F;^FQ@A~xoe!N$gwrBYtV_Z!;y5$EX#Ullv|z63qk8=C3Y4UgIC1X%@JkR z`SW{cYxCo{c5dSHIca5>+RItEa}7J%#f8|BB^90)l|!H0X7s{n?1xT%CT%p?8(K6+ zutgfEK>$f+f!SBg0sgkIP*fh+^?@IBHs?;Dw+=ISQY|pPYS^&Ch<|rten0gsWF2Y_ zj0;thJ7D6+Mvb06^JqFJdF|rjVzFwq9&AXJKc2=6N#ePtxHcDnE)fZp901GB|<*^Zv?KQ+xF z>%-(0RqD;ArvkAWo@h?dwZBT^^T03;4>j~(ULvK@+^p3Y5|q81e1gj_W!Ran!?Ijx zCVw#Y#`_yR{FSBH(tu$G4xdH!VE9;EFgyR$G36eR(3vY4WT;N+ACzTN7&?hjmp*W! zFFwCJ?wG9Xc%Ys6Hy;lPcGf*aF)%3jt^fFZ@88P$AP{h<0rCpDx9||Ck&v6PTE@#@HO|BYf znNA9AHtdEp1{3CUd5j0y8{hYGN9MBWmZOuGHE8v;A#Bus_BxdhLQk@z?FfGuo;I zIc+KlN6;S{tBL$V;(rL9)JV4kIV!WOtHcf_)JO<)+k8*@MYN#ZolzLy% z1kXf^7I1zmNPAvzEE*P|NZ-_Y*1)qOV{ynVxtlJg_ckOZ^U|o&puM6IK^DDNP>x99 zA^S@`=+DBjn7q?lh)tZ~ZY`DD%`pcmo-~)H2@C4YYA0ccknVJe66&-PqvO#6<-mZ?_&U=*RTZWs$;0BsmTFaRg@Cy5{Y!Qr5`&y)7fgOZ-K;wqIVh3L4osm40kSg6MvHg-+ic)996(7E=A2w!oJF%&Q*c>7oDbZ8 z4Hkw>a(#?5odvfk4J9Z-jX`O`7aDN~MBkCOvZ9mRyGBdCRT_77auV%wF9LdV5l>?h zcXm;bm;qG!c2j z7^llA2`<|wJ~~UIP@yB=M4VbfoI9co9h^Rtt976{IiR<`J>RX<>xEOxpv9e|il#ko zqRl9c`3@faGWw^j8OHE;+sJYPUmu@5)s&QhFX~&T9ZwH3x2_u$-|*dkdcWH1P6*?| zXsBTVyyZ2J@t6W(ixHeL^fL}&N;wTXn}0UC0igGv|NZtG>#}V4TV|6kAIRc0OAE>5 z`2Og?=;!Bv0jBzDt871f?qA9bc~PIs%cYs*%1*77tuZ(yxpZ72pkVp zD3JvuJU13pDfCxkgoKuCAD#!M0R{1eVfZ&G_Tn^!i1_kmJKcAYZK& z|B+;f1&LemypQ{rGKDA>7y{nPVLC&N*^v5BlR8ctGXq1^!oq?i9j~+*6VA1-kdX93 zKyI$Sd~beJKR^4z;^GA;BDO4xW6<-`nVsP(8$wPlUAhjmMx40KM>QBuEob_qX|+EWTQFM#q2D5t{7JC)ayAKLB86+c9f zZ3WPcMcLZuke5Yu{uc(x81w>OW~gR4&VF#lOuQ4`Zm$-xGmcD)Ww;&Un^fz4L)=f^ zB(1@P=Bj0p@^uy?!lW3NB+kCLx^NEm&`#eEshfUw=(hPO`cHXWh7N1euJTRIdQ^}H z>QbaXIz-~3B_&2H#u^}zI_34?n_%Aj(y{tCe)0n54N8#by{yq@spfkrA%2Ovm?=gw zTEo}LYixvoag%L$6aQ!H<1Z#s8LPV(>JpmW|-U~Mp)#}?{7~`az4&mZ|4_M2|kXzJdjqwfD@ZpK@nD)sK@tZXN zYNXIU91;evyD;-tFK0@2vlK+>UC8(9~#^uyllwJDK4C@m^l__usGCyDeH!!u?=l zi`l;FP&iXcEZ*g9S1h>OKE3}oeHQE+M6tWHRBziT`wePJ@0*aR!*`2Kc3oXnbvQe3 zH)VNw4-OYa*%a|{7LEhh_=9`owv%~;)y7XPXz(6D_u|g+!t6shCT{MQd4+77n7QB1 z_8;8~m2RUIuYH3Ke{eo5zr|FoEfP z?nLw|Fg`V_lBH!(YUlreO+oBf1@eiQ3c9zkE1LE{xm|{peQlz3WtNh7JA z_}|TVzPG5($f&s1mLlCKKU3VX_SbGE89W@LRH@wUzR|#au~Ew;Cr%!dn6~LV6o%Ll zlsJ0#=?Nniw_kY2kN=95mk2vh)BdoM5)JuNsKCASz-<1_`e5$i7d@9#G`;0u(I2n| zrn*sup-9s|=sC=Q{Rp)Dsi~2vUj5=4{E;>PPR6`n(D&AZH27{;OK-m7Z^)M>k871# z92!3UZa%2e(Hr6W;^E%OK9*bz(rN3qeB~GOlrgC`u6yS@JZ1K-ImCGY<1D^X+=1MZ z$>d==yXGL-PBXQGS{wNha~BAE-TiAu$geh07U!ycY|(8BCyqVtN{B9p$zH^A*2-lQ z{2P$??N1DNyxk3{hWP!o&tfGk)%X;&SRXSa$weFs-M%D(7=bmEkc)a5K9y1B{R}w+ zZC500CN8hHDJG1{GaO!?)yS)`Bh~mHNPD?vAo<7fLk$P%^)mbM$*d`&8+{6C?wD2z z7O3>MEzj2jZSQ$l>)P%whmw#r5W4{h8f4K&=}p=lhK5io48*@p9#}*+6yjP^&*UYhWDR@DGXbwi+^LsfCUdyakuX4<3ZZ-#9hGq!n7hL=qu!Oe@ zUT5ts(xCEEmtBwm+29Z+ZjU_<@RtHz zJR&)MBG<8xfQS~g>4|3KVLJMs|cW$#JogX?#^989d3n4?J3~CYvL_br;HbZH*;aVpv53iy5q7{U-EMH&TA%Ufv`6dW zfl=GMC#gB9qgVJigV2)a7s*ojE`Ms(!>w2$HDTj7C?PTk5aUHiSs3q`j-g*a=k}j5 zzz<4aCcvu)W&h1UawO`U@zI#6OjIa2+7)w)O!t}Aact?Fv!I(3QVBwUMIq8+V#lD3 z-ngUXAdyu?YbWUMJ}PdBIk=^ z-KDb=mqX3BO-l`S1L-HQSF8AprENcr-ykv-TF4uI_Zff3Q}aoFbC|ndB04=JQKo@S zL%hJSW6&|_B^=N?M`#*I`Y(Ys0|;+z^7}XwrP+;ORK^_h^WQ*r6gW@>E4+BE^iQAr z`*#$SpPU|y-t}3wGqc-h?yspBs($Ig!V`ba@4;~7uleywxlENGzyjTBzK*1_0_xOF z(Fk~4qPfE1IZjJ1fwvd?i5A>w|3SI{bd}#ic&0NfZSc*R{rpDvQ7WS!_Lnt#4-*0i zyIqrUs+RRqtJvM;p@bvP$jC^|Fwh`0xXH@Oq9J}|`#Dvk(Q5Dnz!Ev;nvDd#@2-v& z08AmCRwLmQ=m;Wz%t8E*y&)RY8ODBQdTWtB&UZMpNy){PCY{kC=;9asg8WhQ8^ZN- zMF_lT0$=#?gPJkaY)M{1y~=Vt{IT2Lt|f?%{od#mI?$|Wu$apkRpEK@3ZEy!_90ac>{LbVi_Hvkn)cs)Ic0PtzfthN_7ByASl|Bh5hgx4@fKCeittQI+*f%5bB zMfPPIQfX|_0AwWkjN=0XrZP(uiv@rVM&W#;H7LjJ@*tbO{aOGng4ui~54aNr1B1MY zFi*G5A#?z$p<%XON)wNx;0NmK9DL7fZ$Rwq+|W5Nk>$A+qFAZLtyZDe8!}a`F9OKl zRJP-22sT(Ps(TIVZ_nn5#h$xtT^0_XZD)EF06@@SGw=uLT5gBTw|k|PJHyHEZ&|_* ze>wxLD{-I}&MlP51Z2%(4K9EN*JKpohcG36y#IZpNhB{q`D0B!5ply*4fKPwr#(;N zS^+J7$|i2t^B@aL%iWu^ZF!1wVBjM#7CE&*I-TkvhGJV)biJiDPg%@;Ax{id4?kdL zX69Lc50EK}q1W0Pwye&_CD#J~w*9@OTC>^qe5LDU^SujDjMo9ZM#@fH9`~gC`}?>p zLl-L*HTAN5Uc(HF6IQz|Nj+X~e>n6UV3?ArX|wBv8Hdf99~$KaZi!}o#P86?mlvCmc#^%Yii`{#}R zEr#G{+T)=_dKpfqqr9LX7)n=mHnyma4qjl2hrjFj4xvAH1ByX(6l$n5O%)|D;Zj;2 z4VXAh4!~|7p>|e82P{0CAMagupQ1oDy%|Jsx7XKW{_vOvoWOz-R^xZUqLK}e0W<|t zf6jal3~GIS{a;uMXyGyLwso^zwQ3`amPWWHXizznD^jb?wK$aQyKzUDrV8xmZskz- z;Ywiq1qnsyOW>|Rym!TW}E(5q|)KteRU|h7Q0X;l>u4?TP_+U=Pd=rsS$&&Z-6` z`MjruIBf<-XJb+Oa6Qv?g;&M~Cwu5qIJJ3Cvmbb3@2p$3FcTzQslI~&OR5UN<+_j> z;gT5_zv(9B_Pz*ky@21PU!#R8NA_ofJeMWSh3%|i&JEsfeQbTpTK!$@!<)MA60v7c zeIUDd!}g(rP;}rh`h1;fuEB024O9)oHPE(BcDk**)}xhQYIWIt2JG&JG6A|oGd2^Iq`2#b8I5lilSTT)(Nx6uRU#p*fi_ z!eDgSWf1Wc1{k#iw-6z$M^xk|=UeP>ZFWLOGRlJ8*X1hd>WUp!xGo5Kx-%VHN&L4+ zvSn6<-%_YK8A!pmu8KsPL5lgUhNYAKLb;R13?}Hm2wVgpB$(hCtZ;*5F#Rlos6GgS zR|W56nGzcyzGr3?_h5ML_4vFczg+Fd@a;pP|FA`XM}v1NuZN)14T_G$VJFhY3G;iI zd4D#_t5~8)sV}v2R(DxgX`&ZM3muCJ<)X$^$l+iQ?j2kZ1^C%0e;72cGhc|@*I!%% zx+`|wQ>pZ_mr9}}1ZX5X;i1`MKVu+A!J?Z_EBFj)EWI}d|60aRfC|%k`VQt;Dfu)0 z$6tBF9E4;koe`_I4}lIAgmW+*d4%w=M1T}21|+-9i@1)*+t8ex9D_P-1ic#5Niq({ zgCq-}V~t&q5_O|~CsFgxCWiR6oj6q#Ok_B!1{)DjT^Y!L(`dd4!K6fn5A#dHJw?a3EExa zS*_y0b(Y&b3?bHl&mlt->6*8uvRV(lAO!&BnDOxVXzG~&cfc7%7?kCa%Vbb|xuu0x zT{Zd2#Ump#IrAzco`~M-*GjWP#Gt~zL@0}8m`d;;vNgD1Gzju%-_95udarZ`DQU*e zFe;~pVnI=0ym_O4KoIp2rnTlx-4LBZH`jCIB+vke*h#eX<|Ao}z^)APKv`wvSqMXIiS|T9MAVo7U5$LCfiAJQ7X&!NelL?hL9D)n zMnDpQ-pb48RU>>v6d`^DJih7oDqY6FySE(n6gEBU0N_i0T3a-=iKx;#l@zOY(PRh$ zL=haza1t4z^SPec_j9SA)oBzHh)ZeVV^N;<_uZDU)l41DkfZw3+-Au;$c$vxS|) zYMt?lv0E;l9vBZqBTlDU)mc^anDjC!j#@<;ihT7Jok}qaAdrbmhyu(h$?ss^I8vQS zs&A@1k_~dMsW)EsrXW4S*_|K8tj5mPkfJbj^FKg0Y?ZkHAu09b!>dy4{ZmJbwbKvP$X z{#8qJGqT3~Ydo$bwk+sSyR@c`2Fv*gfK0SmeQG=yLpoKeLJtHo2wY%<8JiCyx!s+@ zk5$^x5H38t?SR+V2AM#@5T_impe1kG`5CMSO#bpDQ_B7!szq8puc~DU{VmO4Opw8v@gbnaURzE|=8P}2P$a&CPz^%}_Kbd)o}EY|(OED5n*H*&!(!l^{S7L7_-nXFxNtds zEoBU_$nYH^dL41zv$Z{-`c5K~rO8AU3MKHm5+EKR^v|r6YK(aQJH)BOQY`c8SzVJB z!G(4~uziY3hp7@GygQJf+Z>GTFS8x2*SGT8b9^T3@GzNY-pSG1v6P~~J>xWdC=gQ; zQSo|a)eMwg{z&lXdhGh8L7zn;-6r21$BNxT7!XxK3}anE8o`41!L+((*YZ}^@c%^C zcaQodWSk)IqB|>J6E+N>;s4HkgMM$BRB8;OreMgA1BwX#z`QSrjRGVbl6XEj+&@0| zoF<0>X)^p5f;ZBaq_Rb_gr>VHtUfi3n0C{Ke3+o9y01FY^p$0{S=aviLEO;Eft1nR|n1}V-qb^cKEjb%ThM$&* zT6S)uV=mK{S%kQpOn&zBfj*>NyYBpBTz}__1}MNDNru2%{N4L8@B)8VO>Y;*SJa*o zpqK3m%Sc36DrPv$BiI?X8m9I8`TX*8)X#nnz4C3}D1qlMlg(}6Vs zC#eB;uDsOBCg^&FSQ!8g&5}%ht;a+(K=`uv3oZC-XNRH6hWdy2>)PtAZa7y1jzIgJ zA>o)NGJ{icNnE3a!!?AvKcd^;$y3xiEZ>{qlg1Y>o}o2NZ**-HvC4w+3V&2puQkGQ zu3K4NaW_tCGjwapq;#L?9r7e$&b}T2yL)uoD|UVw#WFBt9FlM=Hl~-r%~k-3SW<=a zINS46%)pu`fJ^^qf2-H*2!Dv0Hq6+hf=lz_9?wbni<>c215T(aR2mi#O zk|Y4zYtEDXQ#E~XQEi_pBqmf~5q>R$t@0}?aAeu9y&`hY`PGLP7`|#- z^14~TEwZQaC1668uMD#UE_WyKz^LMZRrX2ISj)bV3qUbZsnrR%Oq}FY0G2^DPmpFf zXoV~U#-u`1x@20#i92(K~%nfSJV!G0zz2+)eAJR0Ur$Z?;|3l?vjc#95Ahh zNcQKm6XJt_9$aI^4FG_rsL!3$q5S(0H*CL4+bWu8XtjZ5ueMhbNXyJI=et#rhbdwef^iwGsb^*g{@&k!holz zc}t5}dX6Qf3H6@|xF9NxlGspA3ZZZ)?|48O)8 z{r*$YcitVd738irjJ+3fzjf&e^kJTnWn@aSJuF}Nw&$n7cM4Hfwqn5PBK`+?FKQZg zMdY^36Y`xW(tta-R(R@I-{OHri(6$UG|rl2G+B<0uM{qLW3fa55Ne>3#YZSdFP{JK6C0$|Pm|M`-t zX?3v8$-qtk0duw8CE;%Vlt%Q@I<{7XeMqAkW3y78mp_n@=WchcOc(a_x^spSksX7E zXlRzO5rcamJIXDG5i7mRj^p8h6AksPBy+n4pEMr&dkdK7lP$NWnUwb>_`QCC^2vC< z1LcO1ulDhA|J|Jc7WEn3%SQ#y0D`GdurP+XdmRW7dXl4r_m!K9lyI1k&9QNS>};Y} zfd&M%9Q%kfk=9k@VK%iyVbt~ja7;{>Ja?0k>P&6S5{}11)e&>ejp2iTmtE}LxbM1~ z;j%J|<2lWaa*H(Q>5)SS0xXG0qFx-RO|s?E&@{XwC_I}S^Z)Sm)?rnK+1{`M!lpNp zlAG=hX*L~#bazQfgLJ1LUDDkp-60)Hm!x!e_xCV!X3m^*-s}6z3yJ;Yy4U@y<$P`P zRAL*Q5D}p%`t77c3Lp{{md`i`s_g7*_OihLDg>`UMTlkk9T}fS(xl~p5P3kaQ!1Z- z?+wURceZ|~a=0An0LV-1))UaH6al)`>SaJtGB7go(UD+KRq7Bg&T1v?9FTA40TR7& zzT;NW$Gc1RLYYX$IE?E+)p`(-#Io2Ubdge_R;cS=+@BUd6GY z6dk=%(KjVixnskHv^T$4kro@0({w*5{fUyK#J2z}f3XUNJGXHL?hex-U4hOzC%d9p z^b8yS!}7dfWBz(~#41+EBZYe;JWOzSZ-4 zb=iDh-O^&Q`|F}JUyI%OqPva(*6|!qRW|6z1Z>;p^dO>atgi!CEV=sV}+Mby2Bac;(g7zP?H^6;2iwX3S8FxHfX`+ElFQ(K*9ugoYS!1`s!PNn{CQt~r;Poz4VheMb2roWi%!bq z#b%`H;k2Tg*?WX*&}v7@P{R9$ivA%wbIMQo^i1V?Ho|FRSPko%Ar^*<{FiIDEjT`Ar8?U&fa*x-lSF5%?NkLgU%*J7XDR6pxLtr{h3>ctMQPg8^6VL zu)QZ;da!!Cv{Vy(re5@MI+$5%jF?2{`(6IdJzNv#?hEfL_|vT;^L{Ljo`HLJVpp*( z@XADvlGjs;viXV{$H><9yJ*x1VNO5p$GFBSn*UZcRH#hak`Vrp0qltRg)TM<7wZPq zztcm`l1gPv-O)&Kj73LD@*R~s61N*C+8$GBHon>1Ivr-J$BF*Taq=`E7(Zrr(YgA))U%pVL1iLCK+6az#Q3pO)H7S5m(47= zD~f8V@{)v=-PpMO=jiUlLA$bddCKmmeKkPS3U%PJjyu^N^SbG5M5x_)I)2e0G*OID zKA~NT)l$C9Kr&=rJ7VM&tVPz<6-*ZyasPlpPj@5u=0Hyr*9=8QA>pyhEo$eZ%a^j~ zb~K_h?C2bfB!OF9c_e{C%-(nznS17(%t3}L$$p%O*DbR$ierDuxnt@$0e74%z$lVG z*LB1=4u(0%x?Edl7lpVa6l^5+miP+nZ{B*UI*Ft-L+9!o$y`793WmR7^cD}_oJkvb zoKLD?$Pfdl!91E9GOe5bmIKN5j*G(5@cy0s(-FXhJ7D&!-yzvWb`JB!sT%5v;qdi( z@Yo|fe?bt7@_|NTv5oO8bEtVV6Nx1H^dAzC94kNqVy5rju(NqS2X}Zb+_RK=ws>9y zdgKZEQ*7HZQH~l;yZDT+`DcFc9I&!VkHkI;_|X29fM3EE*@Nf3upnREW|3MMSvveM zU^`?`_3NP(yzqHI`p2gaN=(DEzDa4RG7MS7k7XE3f1aWaRs`h*Ur4)A;>Fz5QXjy$ z=9HL4kW0(9C7zf8 z3;z#V)j|#e&?Mdur;;V=mBa}&ivDzJWl{i~o7d*y?43RRczULD^=__2m0q(RupnK$ zSRDX4;#BY31N|n4p!WMCJ^&<%`JBo)HIl{?mxV%dLKpS|nh8GL7;9Dloh= zoJu15#|(aHeXmPwKxFqe>2V`Ye&=j+SYo)|Vlo&|vB_YpvF94@Ew{Dso0U{Fd%6Q^ zFZAO|08@&zTI>00G7w!k3ZxmKln$&#m9+<*2f z_}B{93Q!Hz?I9ru)8*Pp-Z!fV?q?fco4+q^+eZ-bB?9ma(=kGOpAHul6_ui<@jI>l zA(5@q_5RI?ZG28kiss|3U&-Xjr+DA;S&jP!&JTQI<7W0P%H&4aU5I78^7&U99=wg0 z0o#W9$NdJ|w#!69X6D~7g3NnBog2FisJiK+Qp4`GlCKA1$S<9z`GU{YGP?zVE@t!X z54q87aYXv!mImAWqHsylsEbQ1KfApi#6QdaV?Kwn7TK348?R}{c$#piqtG&UG{3jR zH7r^geLZ6(Z5Mc(sK*>=n&=#>YTV83VCw_Ki{SI&(0)w%_@49F+Cughnmc^TvF2`CKO6$D zylKDHI3jK2;eI}*#I)eCO=T)0Ba1a5VNlRA3Y|`efB>$a{)75x9zp3g;!i8VriLRq zh8YLIWM00AQ}V}`Uy_j=uoPpF!$};I9tOQxl{2hsP}1m?gUsR_Pu$WzM%>}a%6P^_ zP@ht3g~q(t%X-B)HW%|tCIHLW*}KQrZD9Z0QgwooF|%(dPG@u1zF<`Z=?Zn+C#mW7 zTits6o6L_IOgb$X(|4NhlTK2%ccnUTl>QIAsTvu52F);T zAmGpkNC=1&1}vdixMq5x7ANq~TgCL&nuI z!FvkBLCY6T**aI!LII2x2YAT;*gxa?0np-jwrGe+bcDm^kaQK$^fA@~5@@YDOKd=( zA@}8X`woDdN&tpGYHe+82YdT$a>>X6+;D825OH4Tv-5Lu9Q1&IULwh7D%_!kD ze!cBVhE%-KX-Gh*-P#i>CbArZYj6-{2R=<(pE);`V7u2n*NZLgJ0J5q&b||1WcL{_ z%_X($cPP2Ux9E_AfDtXv*&6_s-wY4R-<>R^7r#6#eVk$y!YS66Tes?-y_uptg>3{I zCX*sCRNNqIK?;mfJ_CO>jbiFH(UGra61!c~nJe%JuksJ0a(Ep(9Z6IVYBUkkO`bOaBu@&ILo(shCkD<)6<<_v)!6}{>*TCAL4}* zN$~z-Vm}E0GL#8-atVaEoy}D}*SHYX5z$PNGo%3N*PN>(CCMeq!KOc$Hj~L-T&U`KW}YRLT+SC^SZA!cqPhvxN$7Ie;pBRj31 zE<%97r83lHj>dXrYsnWP#T3QwE<=1BPX-^8%W`q5{ymge(oI7c-dS6rrGIyBv2uqn z1dHJ7PdoNM*seYe9D^T+#kS(Y(@HUL#>&VO%$McT13JaH&x$vN=vK&|wZ!wAQI#lk z76-e-z^I3;Vln@}m( z5b+D2vJ@sIa;K%-2qM~NICOS40Ef}MT32o4#%55Cka2PIuOPfl;%|-h_N#JY%hcHt zfP1HtCu1#+w8rI4i?sWIb@=Q~7}tXc7h7dFEit1z*&1AeF(j%Hv-f-Ab0#K1ZE|DW zo!s(hN9$JCY!LrDzC768I)o5~^-zz$`>F(bdDQwvT#cE-Q+?fw64%!~u8V4y-L890 z2>Om58KB3w`7_7q#QyW1)~zN6>m$7Rl+0Wr^o%L4;)q_{GT~eEGuS7Zj=$)4Yef%T z`1X-5{fqE|256!Lf3xqkSvm>lU2IY@QE4|Sd#NW{%W*Awk3LM*WsnGT!e!$^F)-oa zUJL&v76ZsgSR5sAk0L;rV4a>!Da+F)ypuwaL-B{g(SmZ&d(cC30ei2)^V1_X=_%kF zdW)omcL=N-Dh)QeDXhlub-?NvNyJw$ARdhcc*6^0$R$g*kZ~9w7tCs9cwXnf-@hQ* z{;?QHtTYqQFHnJ?ZO9!|&kw8TyTV65yu}}eu%s7nDL#DC_;L%P%;gLQ+$2hHfX}=A zYTnAS36cm6UVXlw3&{}hID6iA90zC@#F7EPI;RK_+B-qGdOwgyYb_?RXhQ}1-3=im z*(0Jqv7!TbsaHkO1@feR0&MWO$8novc6Rojt_Y9DF0g$Oro|Q3T5H>xq~47>?07f}??Ry)0=U)UOP;%tciSu(d@X36wkJ&6!%6Zn z&NoNU-n+wwm5CpQ-BTDIp!|Zm)pUeGgoLtk!T`wSsTVB913(pOH#2M3%f|T6dC>6o z0#_Zf4T+}h$%>NKLt?TZiEQQEKPIc*kM!H}Il zP;}c{;|{zaLDNLFIcF*DjE?(l--2O3Kfx{UCz9ysLEWAJheE4P(p8Pwq15z#kqiR2Ff8v$m5_sZ3+GEli3H@V!OW z5oGbH3!#WG{=N`9W>ViSf0m-oEFJ&VYxklNsMKyr7X}6fUHmw(x_Cd`nu#OjFoMVf zbNmTm>`knJPEkEDU5&-h6>b+!6-x@m8qnCN2I>Q14AmQ!BhkU&AEwdzX16OO3M^y{ zIFFGeLKOqpfyB6UCU@!hdz@fq0LZC&cU7`2bIjV}SsOwMQi=XD561uY=ou!hOHMii z=w8G-jWVHE`4T95*RS_J(j1)V`6}RZm`5uGFE<+A2@st7WA)v?!p@=Efj0W35doy@ zJhDfRfxDNuD~HIiaM>R=aJDNhhIfQZcPK1~UE_GUZF)TUQHAL9gWVS*g_$*%31R_v zu#_-fJAr5jn(7l7|3j3}(+_@SRKkZ-ppT~O!pi#*gEvJw)G00t1_Q1am}C)@8LO%9 zU6E-6;J1pn%1FviYgUrOOjMOLGTVV^nyP^QNzuzTSiOM?D89% zD=RUlgN!4eO8W+sf7msj)fbN$Ka?gthvc9pm>3H7!d#S_^tbi~OkGaLA;d1d*Zb>} zgW91Xia^yNgR$_GwU0YGxtriP-QPc&X&Lnx8E(g+V%ccqj8AX0e-Ne_hKHj6KI&0g z#qifmPo6!R_V&OZbEO3gKfB)k#HFwgNQa94ZHYY|=So=bM|!|tzngFSf0k@1_$^8QM$7(V5E&W9z0+R5_~~G z{9F={B~auD;5~?lh|*m3-ihPJUvABjMawWg{kliUL4%)8s}o*AFaqDFr3h<+unDEu z15wGH2=Dpq*Tg=6SnrM(o0L&u^Qki4G1Ae==#u@yroi(?sHwlWL#oRZP*NCu-Mc19 z;w1(cUzJOg0#$(iuQV+g#D`#yu3DIynEc+r2X zZA2VRn8SZZ9POjo@6pufyVNjFTj!3YLb(*K52t0);fZ^CHEQL`{BZ<8(TINQCrb*i z{4wr{&xMcF^~)x@lj+KeO6@1*gwl~vm9IEa(c6XHD$c8O#?QR6C{#a;4u)P&KK}@x zDY7MG6WtbkD)1RtHktm;7#Y__0rL#}WuVgNkMeXzCl3YKDx3NHPToszIZf;qh_8a{JyyDhwXt3NawaQp!PJLg1mV!2PpEzx@5+U3CSJ3YdJn#S7>Y!};(h<` zxJL}by7-lpIdttA6Ei`Tt;9eP zRJpyj$wTLDp1)XW2`Y;90Ew zXoRIQm8u>7&l+z_>YHY#5F;E@=m?*KhbY3CjfR4Xs=CLLm6KrjP8dWHR7dKAk0SWj zc$i}~50S{&TSWU%*K0K0vo>fI*v3!C0=ukKqg}|90caK8u<)z+ez3x%FlFfDN`q~t zQkAe9!Y0kxY1`A?eo6Q;>^HMt+UA7E_>xZ$Lc)#gx~4Ameb!6oWHwWkA{LS1>_pKW z#+g?>VE2mIqhH>8Rpn756|mViKe8aR&x)^C8P?9NdtlRsdVr>#d!R^9>}Q4*T~=hr{tMU6d8wk-NXgjY>P>r< zBbtSLIeT^otdsf+&U}x*T*Y2TBWou*+B8Y1R6J`W?4#d4`SV$4A9fR~?YBAnwWV6c zkMoIR+R)9z>(!6U!*mqNrw`#~)+4q>l0skwksu|M=g}ec6C;v8f6|Sx8DF)qouE{! ze$(J3i`~s#;~6}ZtDa@@uH^cOd2oI?Ed1k_h;SX^f({mH9nK)#efWXN!o#giyAOKM zMNth0qb|ntb)DwqrChY@NIv5`(_d1r^!PmDpB`OV+|E3mSCR`oS1t>r`}D1EOwj(6 zi1CPwukB5|1g>qWCO zXG)fm-0$cXj4kb|$ozL8jg1s6Ot=HmS4zJx7}0X;zgBrjAP00`& zy?k&81}?RazT0SxmAr|ba3?X$e!)eK-8qTMVclTl{r7Yweg;8Dkf~m$9tVCAw*qOZRua%k zy;;4KaIJGf<<%Aui-9kaVJ5p-BpE!gzb{YOJJHqQuF1UGvj0N#9n&Ll%ynNx;A+cm zOHd@VN?*Z~EDgU$5XG=>w~A(DI4#9mt6}D7osnRs(6!$5oXUAr3V+Yh>!DMYm`SrK zSWNRfj7XDx6zSjk=Mp-tGHx%{!}}KA&v?fvBjmWhJEHy5n(a@&xxZ1|y`GCADLL24 zXeu^8#MxV%L04&ym~ToPtdTeG|ACMUT_{aab$933j^8&g{m^`b&7dCVIJ>Eyop&$j zkt|)FYDuU3BD5!9Qbt*+f|X^~pWIjn2Wy z(nHdv%7a0p0*91&<#2zt5$SZ8&P3$BAggWc83}f#0JkSzp5d>r-EUnA~lmZ9@WFTIZ7;@$`E0d$V$;;C6b;=^3uA(|sG;tw(-?u&EEG znDr;`^f<8;ElZ;oG2tA@U~UqHIp?$06z^Jn+}tbVqNDwJ?8$r0grQ)&cQ6py=wp=I zJ%yjxx6c5XSPbIba0((rf>T`YD_9{{gMkVj&+CN=n%oZt% zI@e*xmWckOSFb25;a*6AwU7pjMCMQ`Bxf@snL=Dl00F)Vt_ou?r}-F^2%{RQdc0A8 z4)XxFL~1`RsHAFDQo@T9#q=D@3My_Iy#V^!hml7ml3&LIJf+KsX0v`LF5k-qU@X2K zkE`o@37is*qQKgl9m8&BUT!BP_<#i=gqH|# ze+S<8X$j7cMp3ty%5Tzu7HdF_jI6Ge2cbTbAw{lUn7c0D2CFaSr*cuftR|ZUow*Or zUEYZ9hTO%NE&J0#G^bgk)xLlfveSK{_M{OCeL542&`Z7~z(?kd^#|we#KjOm9QN$c zpfoMfBzD%RJC*ZIw2oYCxiMuwVC;sI~fKT)Y39 z_+m8mQRUq8H7Oj7lJ-CV?Cn$;c&+B|6&Hm~vps(Qm>#)`1`FtSIkR)t?@=zpXYGk# z>SN`NOnD%UuJts)5~~B=dKx3UfB$~U2zyIZR?l89?KwhLVqljx+>VC(q1F8g?%0rM z{z2B*KMWmbEU%3>L$@Vx#)&>x(5LMVq#G_pwxklk;q7ZuCIEl4U2uXrygS-YH-4o) zU#+j2u!ptu!NJ+cTDY7?bXGd1|p$bhBWuOwoD3%D4P<5X5GKG#bW-rzsFqgL2P;ul?85ZiJ>>CE4W33Q>wVD8A_$c3Q ze{+LD#-hmz<2U5mFol(dtHbPa19ShEjx6-2?#5&moiyNc#a z(k_|ud1|yQiWdtXJEi=qeeLufbVIA>njnwq#7-Ty%A5jRC!!{Qw?7g8Tf8 z%R&Dsk%s{^g?}%QaQs+J_A6TB`R+|%#7548aDMSn)~U+w?KPvdz^`o7J^1R2_3o{~ zgsk&7iFuuB=!%cm+luBAO6j z*RMiz`?J!OkLiV1u&aGxvKw|z=udBy2|$%8wojcKn({*=csWSXJ%mg0O^v8$Ag+{$ zkRhMYt9I-?h2N{0N=FZGXaw(Kk%T3rq;@MF-O`^YMYSTuE$g!AM%xBe6JfE5?F%C( z4pZMxouA2f$UUKIo$=smvXpN6>IYnJE9nsg>6Ee7$aYbCJ8LPGE9zHwL?q0!+(Ex9 zS#DjT=aewFrZSirzQnFFYYW zXU1yXq7S()jj^4cqcqd9_uY#%fXfz?{?u9WW{ig z<>5r4dhkB21SQ$zHoVE%qY3AwYO4mED`tB(Ip*xI)j%M?L!Ip38GtTWB*hY#De@J8 z?KQSJpYxs=U}sUHgVIDBfZoF!a(mr5iHz{p3Wu_uHTv<(%G<-X(O?n2_In+RMw-YS z;$9`2%m!qC700Wj^QAty0`!#Lho8tBtya?|2}&}4je#W!rLdQC+?KrrNT!`GSf8NW zpMgk4Lw>8t)nA}u@dRUyPxt1B0X4J0evcgMU6a#pELjrNUGSt6HZUy!asovnJJzu* zNHC>Yetr4wMy)6bP79Kkn`)@hQ71V=hc>K5oA^?+QdLIRs$K!;hOt z`iE#X-o8eJEOHMbKg~JJt}q`zw_ySe@N|mbK9Kl^tp@51#>W+R%+h(YTRtd}TiNHR zveI{rO2LE`JZENn!#xw>qE)KPe%8h@Lcjg88@mJzbZAu*pJTDRJIq-&QTG?2cUjr? z>QNo9k;+~rcWFitg|V{Ec;_F2b&UBWM;93QjT$T#^sVFgp5*v21qYB_*4KE>u8?a3 zW3|vfS$V1u*_`~5L6eSHRZvB%F>sh%cE?QCX(9HOKqZ)}ZxP*4Bwiu=rg(OOdWef< zr5fU0!KCQ+o|QDivr!}LY50dtNNxp~h{QQVSC%4k?Pp@YpS6e9$fb0#q#nex?Dz0& z)Sm43(Y(LT&2E&jr$|2n85(YAe>L;q>Q{A8n}19DcJu0ae_LK5gX}4ux`S5d4>z7j z6}{jinkwE67byqP?Q6$vdZ?baFxRZr0dnTP&rRukZYpciKnu-r70*S(Zmo4+!5-o9 zt9d@7&LWdpvxMVY5jyS(@p;e-=JoQJ`9skpApqNv$fPR+;RZVt(?91lzA0JozCUb8 z#0FH`YQ=-lhX){QspO0n6h^07LbI7sh0k;ZWRwj4a7MR|K}Rz|RdFA14i$PLdnWA* znvgG@8M^|)U(~{c14YPn-gdAjGV5mmlu}NZ;m(+5ZO~7%Xk!ifE`3%esbrTGuOz6! zt`UC0j2Qc;Ul6RdSGPIzgKL_f?uxd7eL5=~yn;{r*%18&VDu_MXMtmaT({q>g`0%& zXs?S_Ax_xN*IK-Is4o44Ac~UT@Pq;Us_~0FMgb%UfPp_?<0Gg26{U$&HSYVS*r9|4j_I1RS40zd)lHq2d3gc893&sh(+Oz;-U1h%*+O95B;7>BnR zxDU0LbU>R11xVd0EJ5EF93cJbLa;&_{nr8`qB)cB&1>P#NAN)JGlqQ3;;+&&+W1K# zOjZI?F-=E;BZAZcH#CV?6!XA#XFOT;96M&lXsceOJKpg`srRx4<@G~OemCY(b^f4( zPAC*=s(e%;f$3wvTEwQJM)t!xp4K$rz)&scXuj!!xOx4!wfROWyd=oNt_3p7v?!jN zf9+zg5-sk^h_VsMNSXcxV~;9O2)LWP(_fm~Y9xzVqeW4}gvk|+tOx_qLI?|oTpqK9Zuj4S_2 zSqhr;%ue+=ZzZ$eTiEIV-B%6^2w|qz>mXwP;E~sPZ|rEP#p2=Sl;g{RhFMGL)t_4r z_9p`PxRnKLl(ThWDD?S$eXD8%hEfq_88$T&GpX_U+|bGLPZZAV2Z6Z{Qo_ji4rDKn zj^W6v}x|nNZwxH@^RmLGdFOT(XzmCfn@>c|I*=?Ih)fN60 z*>C_ig}AN=SvNNOg&_z4fmpu_zsmR6f`IRC6uKl|P*dO=(EssIrvMn5Zv(_9mZ+8E zJ0Hx&NJJ4I5=VZ;%@fUi6XpTyw^e>rsIT_48rzG+y7Ly#6#T#bz<>R@zef@VHz%us zQQtktAe?xMj{0ofrBi2BynLCo3k5Z8d2RoE$^U)i|9Iv9a``$uoOU7h;1 zs$q8u-EVJ;3o_+P&h;WY{Ms)j+6sUmt2UWwp!N?68iLz_5`{#ma5GLdhu^uJmS#unO_(-h2#VuFYZrQwMQ2 ztSpL_HZv7FbpxpC1H*E=XrkNOV+dtBI6wj=2#+I&^kpsnzc=T<{__jQoc${PZ(h5v zpN=Caa87wl#>?ileb&M(@5S<`5`yaCJ)QoriX&}Z4Tz(w)5!$23Yh;Hr^h1pi1S`1 z5dZn6)!eD=ZGH1l8wdDAYQ2dc=YPCiKSUr~CpN=G>rI;lY5#tDfzP(XC9_KtbzK-0 z5)*o|B74)u@5oa-;QZB$k zzoTRd(wNoUab@z&CgW%wNM!xK;}pHSp*gmU;aHnaZfq(+posDbzJ#sdzfXOU6J)-2 zytwzrQf|Buol<+DeRqAzc{Mhn2r#YhEjH-Los}!_Y4SDwXMupUfm$yv;--o$*!RX8 zgf>UgsoT0u8Z+gObl}*2z-%KDstjl5e6RJe%B7VzunZVA4I(`lml0R8`gH#1qvH+B z)}bDk+cR2rd+qvXhQkI7qtA{krKu1Yp(U@Fnas#s-Bo+pD~xF*8mOG;*<|Sa(}uOU z=abzDn;dG3R6p}dmK7nAE`txTrCv28!Wsk=r>9}R zZVy)eCz0Wo8nV;Ni^>wisDg7SuQU7aUudKTbAAw}5yGjn(az-E-Z8|QA>gZTq zUgdjnHXhgL!QoY(w1V$9V(z_PVVEtuWFKO3zY70dJBs}!yU6!W3n)vASbnu77EQ$q zYl#p$d9qhzEL&1|y+kX+d3p|%S@1nlFkCiszA0SI@EY6kpB0=6iJ@-9ASSp$BNJ93ii4i58-gSDV-{P?qr|_ankM-T?MSMbmvnW6*1G3#ROmk zk{(Mx&l2gL5tY*pEBrjZR6X>)pHgpA55H^MBpQfPgV;yWcLcy8C8*r>-i)4oJ1u_v>=vcm~Qbop(GOOCchA1$$RS=3<3e&H7^ ztUjpdz+eW5xoI47mqiMt8wcewm!d8WE)Em8h)8&1{HuUXeC<5bO`-{Vw!BezG*Y3- ze5m9~XZci=%1-(-1+FJBWo~$EBX~rjT4CkMbs}kRN(#r)Kl*bKQV_$`z@Xy4uB)8@ zOb8mG-D8#}HxQ^mwz^(XFR+H6ZgfdF(9%Cd8DsnI@_G~1%;L~KPRmZG#vnDUt9I+= zc#U&9PmXp4OODUsNCX@psfiIfW)R5gQmPQjl^LnXgIfA~QPLR(S)re;ox@+c$6$oB z=unWU;%(1r_qNg6?mIQ&$W4~*#C+rl@=c`4J)2F&?7?QQFC?4#^nco5**gAN7&~=C zpq^dUT74UT_u7(*buY<8z@N_0_(Wm45`ep*MBJ1D=j4>EC0DdyrAk`CscDWPj8m;Z zPAU1Xj-vAt^m(Bz$$@n@SJc_Qr=MJ1iHXk(y=K2&`cUpLwxWp{H-$ydvC?F=zE6~Z zvpt32Fs@Jh{s;&qOpG8A-iFqFUly>6RfG$z|gf<(-OKjeCSB!4}r5$XQ=ZvXn)e?L31FOUK$7$7vEj>zp2j~QNS82&bU6%NR5mx)BIo16Q_ zplP|v(f2mgO2;em6U~_F-?=H>D@~=ErG$S)S$2zaW8JWYz)9K}f~}A{+!q{5Ye|`n zj*)^8Gre>YZf4h6FZN@GoqW4Pw+|w6z*KmWv+O|}xaX&Nn&|OcL%oz1y}KHB>C+Lz z@4}IF${X)v$(}sI55nRS=pwE5_((6SY{U>Q79)@|d_vUu zk?_ca`TZ0{R&K9wwKn3;P@7CRL;M<5g_!nBr~c0-dXdDh(-)d@GfsW6!9E)2vfO%f z%;M4bE!Jr zTUNCa;-^7<#C7tCWv|{@Pr4~T{T%BOEP3mIa_#Z?)_&SVW*=Ol)g$SpSGg}wlt`H0 zG14!gL_EH2h->ku3eS@8duAUeN|xj>j#B3D>4&1>u5MWzDhI#V{V9shTYp*! z5?yDeo~+X7c>90fRAGoH$QLw40fBdLwvR^P*@jP#Fv&)f8lvwGzHnp7#(F7pyV$@tpnO zIDv@^*Yy#Qkj$x2e$BMVdjFGc>YMa?$@0*KLOK-x9MX=eAzO{ZwGyk`zYla~f6-+( zoSF3l@l(Gx!7mzmik)qtOjFS9r_pI6`^XfC~o^M18t8R6R#ghueJ4TDj z{mJ<_keTpxCUJH^O-tz4niVO04W3={N1zTJ5y5S>#q0m1^H8{f7P@f zA-*G;=EqH?NAgS9!Z&l(rcr9O(Ps4-1joXw4H=@|hVm*O`Lw7u7Fdj3hTYa<+v78( zd7}M)G~Q@m%vDMh+vz;(GuajrB31ykiTTsnGoZ zsO+-Bu<0jWa({tnfJ_Yx_N{7IRgKQwViMKOkdRXNQ0Tw@5apuC;yyZJftfB6QtRbRYcLs76Oo*W?-7ZXbG9G;ugpwsvPd67ZD9q_swrBQOb zy1I@j%L|ADo(3F4A+ulKV<^vuC@Lf&^vgs=Ou|3zZW^QF7cQlSLNz0xg}mZ5?n9rG z<>P#LnGGygJM9$Weg9O;aZz&yL-ye%s#)qhgIxGDi%z3+|6{wQ4eT&LmK^*VAf+LN zVvUssXE7TQeP&zjsKu=ea6oW*qunaZD9&nH9q1{9-xC&1RNqegVdR|HYHoYY|rW;+Z!N@gYIqhOE{LNpVH}1 z1QHcvxlBRbO>Pdfm-#8~YhS5B6kC8(W^jRzhrP{woa_z|y<%s79otA|H+$=FR<75U z^6>bmY7WG2)F7@-Hb&WlVIF{dEhV5TMJN}a1FnN{U}lg7Fi1hA)?6{ay2t%PJWcjI zEQx>_kffwCP-hR8JRLW70w69xLtpS9@x&9|Gyuy32)Ro1+L}$x0Sqi(p+^gcNvG%x zszqF2Z`6Z#6Bry!wg0-S#&)HBkVVjWFa8X1gQY8fRT&`pruF_3!Y@`u0)Qh0xlE;Q ziv*B2MokzKUIo}5N*)1AggIarss&QYuxIoD?~dgQ*CD)jVmLkalSIKzWHsy&5gom- zRcDR39hlNF#YP=Gp&^QWQGcOxegeX|<9>&^5cJ@m8Ir+~0Aq@U zg{KPTNyMwy1z7M|;QYRgxdt95F=)mDACd21u_iE8;Kka`cbAcS4Aa-_dP?R6VCXM% zMe42q^AEUP=mhzEPhjGF=%rN;t;JUe3s_vR4Pbm}<`x%@1IzUOwcG(KC=-C;a2#yM zQhu1i1|dkIZ1m&mduB@^6rqrK&&3x?Tzfq~UNgEO049yD{00H@nKI420Dmfl>UXGt z$Qj+x&wgMSL-l+a><{dLe0o4w7e)kxmnCF;jAR1^L};Z1K7<;qy2;9-v^`!ez)i=; z)V_WV$|pjB`wa+NvB%$>9RsoO6dRtC@MIu2Y(LnrN?&j@K>xTuEZBAw8__n7IRf`g z!$non>}1^7cMPTp8yGS(aOk=}!wBOC@4pKS)J&sm>>>I$4H;hkrZ3;|Vn&_ay4cV4o>grk*Ad`vPRrU>ApZz41darJQnP ziQpXIh)&kfuQKYL;;V5v)`Lk#{tP(B^5>18h1u4LQN-xFPOGSI2nF0#4wKyngnoo{ zB41a9xXucTte@{!cF^T$Vn)s5uD9eK^V~7{GDt=$1U`xKAa%XXgP@e?d*b%O`FRk% zHj{!HP?G+ls?FnV#*qEGzk3EfezYNSm1J?VZ0nwrbXxGMQW+t#W z<%M!B5mS&z2^66TxBA||WU~VKI9PA^ICw&GC?KZag4hXMB*{<)|w$N}xp4_P)Ye6Ony-RyK+6bek#Kf%H2No!H;UWsl?Hb|4BcM+xNr_kJtY7RxZ`5djetV2RxWq^-4K`31YYMj9+p zEqzHNER@oHd08R7P&$9Cp5g2n>j)jV%sEXLPzFz!KywVQ-wIWK&xPKQS60HU>!|u0B90}A0@-RLR1(WelnS)&5 zR$;IqU9@$=5uhNv0WPrpKoCLz8#oxO8ko=po!Qm#(vl9nhnT)ZAHHM*?jdx+!so!{ zp^LaH_HuIuAYrWjR^YXq6eHh~1ks|%FcvNTu!*LFaNS?-7pmB5=mY79s^%Q?BH7%) zw3YB2l~Hpy!)^=b~^Hi!_VpDganpSfp5rV z@%ID$q+s45CTIiEa9C83Yv|6?NjEwj1J9m6pa3R2P8)}k{meoM+&ztH z7%|sUEXpLvd^eijD|sE1q8!I}M=auE2ny%IpR$3}WKjjuq0UC)Ph~39`?bRSEc`00 zyNc%#%1LHMgkr*mM*xNoyiFNYD}8p#Tk^}3u^FW~`kk}pHZ4BOk=>0EM} zS)N0~!j#Muz-vTKQ&}8vU&CA~0K2Zt?QFZ3hj@=4q~27chU(3wqZhjCnwKyQla!?= zcnvKFlTJFiD#ctLN| zo#cjCE}oU*w`PZhYPQn$U@Mai-Wm+4a0c1(x7Aqh2M?BFP649zzc`G)dH`)Fd_EGe zYe5hI{TC1T7L?+UMg6%83#c~&7}o=pyC?~8fBql7(yoXT#M zlaJik+T)KA(c*A?T|aTifvm9ePLt_JtlN-ju3)RNHTgz;Zo|%r9Y@W zC)ePTE+fP65Wc?uCWEH`z2+$3z?U>{PE#}n87x{<8@6Z)ffs&&AP_s$XcAzlvVf_J zmXSq0qG`s*$M<;4Nfdni#{Ax*@}Ss9Mk`=7u>?C7a7Lc;4B&(LK;=MMN&9KcCZVM` z-c&Hi(R(vNU4@%YM5)g3kp>)_!V@lUY5d0n{xZ2wfBkc1dHKD<*=u<)V4e#yj8e>h z*UM$l{3?X%c1o@ry*y>|@C0PoNX?C+Lm*O@%{7hmu{=-I*$4~2$_x^KF|uDo%J5K_ z>8(`4uM#JbA%S(@$rsoJK064tIFqkL#Liay4&Ou!bQvEM$Y97@=^=fbh~KK5N!pkr ze+K^AII&v zn>qlmod5WIFG}?|mOoXGbmYF#aT6m7eDYyfBn=RZW=DDLk&~9zh;gQd++Y0LZeL}W zR`sn7ct4~nMp8Z!iNq6T9N0?4lF3s2$kKnK7C5emRz;Vh ze1Ltjfh#P=2j(k=lt(tEg3Y#OJ9}#g5Wj7Yzm+p&LguxYt56~w?$?3Qk<*0SM(fJV z))ZXjM*HR~AQ@tV(?^%V84*)enY#zdI~}5cVU1Njpx+LGs0yUBdWxu|Hz9#h!?Jyg zVy4NuNW6?2e5!>NYO-xAl))QY2*5NI-H75?ks(cWK`DA55`O$cI_u@`7GJIa*w7I)Cz`daK<3Hq=Ev6|rt+j!i%*`~+rqE3ULEU+OM!m*zf^9g(eIfKe*Y5#mX7Z($kQjK zUmVU184G02=kH501;DyJVECT^=2kS6yf9JnH+9X3Bq=O`Y(!MIfNiHdW9?M*v5S|# zbAch&7iL3XaKyfm}T0|qHL96sKGALrmybvyb)-!|4cG{8KtRl?~RWDh^c z9_!g(2)zl(e&8#t4k!q-D3e2WinXpXbUlaNVY!Vl7|4)CYUZ~YvLbIVu>`)#$%=e{ zHP;;hg~*w1MbNg7$4ukGz=`zNDP!fhGaqj};jmaH5vlXb6RMEVg)AZpGyD7df8#(o z2}d03?F5V}<&EJ^brrhaN6=yHi9BXh6`LLp|Na@!Y|s=>1xw_kzO-%^Yh=JCNlMHPYd%-Y|#fTu}T`d78 zsY)_@R}P8~mU#`1v7kxSlf!k17LwPb+;_21=X#hvoQkfLeHhGloYT_j+)7eet)@uhDp z`OJTu<{r)4U!pmHlU;e1X<-wUZD%B%5t4*0!MD; zBK70N@?1aKVGwNHMrRz3tCn|8J^jXR1W{gNvlBUCHF;Ze`Qt)XfCTjVMNNj4LSIft zGkG&*K~Q;kXuk6{u|Bu2;EE8kx8FvazC{PY#%9sNZ2jha*7m+L5~dT>gYm~U@F#)j zP5NK}E}1$t0LpUN$h%woxq)rN-JPNO;c*Ir6gE@WhYyBG2uFxu)eG(7JIi{&4ct#= z*s)iPv0)&FI=}6p4p$#XJN#SU6R1V-xWNkk#xOaPKb^KBB)}}(Di7x`1i_w+Vy`i# z$K}sOz+{T-_bmv!l>MzwYXnRfCV*&w^YfbO` zzoR7UyD&mMWIzw~&5RbJ^^J;XqYxbsrLND@l}hzN-4|Fxkie%i{vB;(FOX5z^4lM8 z-V1*aBOtFuu74*06}SYLT=DYnvh&Da>A~khUc* zk`>TMYOc^Z#+_Py$7luFoP!+1Z%BxQ=PA%n)|KrCCq|7WAhSURzmWve%6j!S7{tJD zdi#M zG^8Kw@OKy47=N_s6ZGC6X|6I^jZ?w0D!({aKarp7%3=|@7y=C2GhS%{iXzYa$Q7WP zT0cVwy)YLs%$xDCOB6%W65?*M*fE$F$c>O#+h677`i!nO$eZx~NMIG@04MOYTwsPW zpZ+7jdrk2kRp<^Nk}~ZHM<0}Fz36o^*ZE%1&r6pYu=OdYJx*VVs7}{tDvw z2`Dzu6U;hi;K_TQTknm46P$L3lt7u6{YiztA_EY9X)$~>q%HD4z1-u=iwP`y z2Z?423jbc&n-pdpdh6&~k{CFra2kNGGF$YWhx&m4YzB4f2u~cs-+Qu6EtY}kzO>aRW!813U;^y~a$fY{)kz}AZqcw9l%ppHO;m<;N? z_nKfbVt&c4GS|adrXkldFmxtUd7;Tzcpkh303hq$Fot_4BhA&#kE_StCqb^?*kK?P zEApY*-KeM*AabynI$s+ROdHC2VGDjlke>7k=W>aT^-p@$!;sY_3B1Cs2;Eqgy7>!Q zAVJY~L`n6mA#BEhKY4v9GdY6ZsNx%EC<10z99~%}$UM~Z0c>%8WPNg9KTp?wynXYx z*f0Wknu|`Uzz&t3w0>a9ZX3w9`m>86&YfMbF^}pa+eCjY_(fpKwu|9|!k$^M_wMM2( z!Zb8<*dWoo$YEaBf$Y(Y^Rr{$ipBqCk85=YMQ4Qy;d3c!1;erS)wqr^ zQ&1Kka<;#<2nE}RzHbgU!mpap`S1ONpi#nQ&08^H0z|z9kwf|$_Q9>e#}T2f|0hHa z1Vcgy`lVJg}oIKC#+pT8a z8SVl4q<0@E1)D#s*SYhd zVPEJH;e0S%@%t~KU($=793t$E$Cj4Op+8ckXslxz3wllEi3r@>Nuo|l;+M`>lngjI zqA9=IJ65!0uiTZg&OA6jFOE=Yj8^tlj2Ba$F`aaNgriSFKQR%(C=%z^F!0@HOd)-` zwdf*9lTC&qys!5AphV`>`ApoEacvak z?BOpC`Bc>GQvzm$(f#V`+78mIU(&GEGO556)W!PXyaQlM*T83w^RS!?{Y_o}_E;ND zP_Q~qNoGDN_%6;-Nw~FfX-0QXd&ynSR76ZyE53Gbv_!dQ_(k;O#1egcDitLFJKk9$ zTpYkZlegqTr4b5tU^})%40?Q6R#Km7d!uAny`?TdAsABu!f zu_MDxf%bXV$MMn1{UpaP+_g!Be1;axvhc3+gjkoSf zFE6qsYj+%R)1$)*v;0>bb!5F$=1F27FLtYjLr_h;WM-3TFV(%lXc7_8+H$T(ZMN~A zZ4L?-Umy>@8vjrnH}81aaPn@T{ZgOaR3e}wSrvZXp7mzBK|;=a^bRYkppRAGDMiq@ z&jzUwp-}%5SpAKghr4Vs(dwn;h(1$Rb!JtI!b4J7?i~?#`zRP$g7HRl0?M19{w}LHnNs+@Bymd#*QjtkzR$BbcTKqvT-Cxv?DxJ(7^!3_}Jh!abGL3mvqT zRmSJdyx<|M& zewgy!UGjW*hf~P2k%0Ah3F_rq{wtg{IzH}z`;xwJ)Y^ax$S%6Qsv*{CvIe}5%kR9S z&njwS7OzECvaBVeh1FqLK7C17ns zMf&hQHIuh~J5-%_5$$|H+9VlQb2yV%rguCcBc2?T8HOtrbKi+nZ()Ql@J(AX zY`&&^G$pyWh)ta13 zB}&HS>Nngv<^8N6K<^)?I@mmF9=$2o&<;{x0O-*l!7SySyQ9%t}mQsk@m9VEtuy>69|MCS#$0O1A1z z*3#^@GHVfQ0c^y6c9_Oz86S3&qs`*~(oE~KFeKk4j^s53>OHN=NGU8gD^g*)5{>Z- zbe^O04q2sdv>=z2@R1BB{3~|$FBzFl*L=+tzE6(#+-61nq#6|#_cFhyAfr;qi(i?i7|xm`^vFRak5E@CW5nE!kI4>| z3pG~&Aa4pjG@urbog0{!A2mt*iY6-=KPxPm+6%s!qeTDdCV^q&Edx@p?YXEAcYzo`vQEprQaxBg?TzrMw@GQ5BM+=iM&gIr z#f54!jhFrCU8@$|cQzr?hK{t#G8`?L?zmeM3r=hoG&p+`d&O;;PbJB#Vd$c?rF929 zY&6`S!dllQn$xZtqrmYCf+hlm|G;fD+pPW%ado!&bt*sivGN@2rhukl1!A~Jd*npC zDAQP3sej2Uqj+M$W!Dg}z-5Q{u$RH)lar=r{hY-3NwTSqNItQ)roCI6ah?L0P8RF(9|d|2i-Gs=*sF8mVJKz9|UAS}VSo7W*n8R1b4MTkno zoV?dM;f4LBq(KWVR+0)?Y7;7juG%>29S{h2TO@evvO~3>R8vruZiMTOimgX1&EP z-CQ0jSGh>_8M$?3IMchZYgek|Mf$_gw;}=WnKID>kcBf0XNY0L9$nZoh&HX?2Yy>S z=a5WQGX8OQF+M#@sCd$+8gyigxN@xC8wdnX2^<7TP3sk;>{(CA;&W|P@q9S?42T2O zQW+nL9`+4HJiQ{i3w278^VFvezy`de9jkk7z9qKkA#-5l8)jU0_^VvZQe_YNiGyF> z;IpXT`>4caVn2Y#Cr4i{<@;UP;ufS-IFHy{+wjS+c-NCS>>QtFQXwzGDy86RpE;RnxvhZOA#jQy8^gww2YpH;3{{-x#gry72kmhQO~B@&o4jdA@$p{t zCQW$Wur>Agx$gB?;R|#|yXCFfWQ*G2ilmmr%o80%FPK>wWIRX`!UbQkSI3Cx*k+*U zeE25j-ci=q!z{L&P!{{RrONc=d0HKW^^?6!YoQCA?&!>`WKv2nTG6d^k`w;{{QhJ) zU6c(yeU{|-6is>7SD+2?UIz1rI3S20J#@G$iru;AJqRPj|nJvaQRP5ra>r6 zJV+o=#(D8nP4s|@a;sJec2IQX14bcEzC4+n<3l9ASK9?0z3bQLpCn{Z<$X~mVX4Y1 zczW+q2sALdg-MXX#qr)B&+}h01s-UVcgf9_VHZ=z;ygszL{sbYL!?N)wzJ?@k}T^x zDO)GrTI0^SBKji^@)j~(KdiV3o&Wi$8X#!p(( z?3H!*0ragSzpx~t*rXN!?Z6yE=~>5I(a!1{vEhrQ>F@}v#rk!m^cE+Wh>FhSdTMTn zrC_u|&>(ZdMUJ|&H>R+Oh&7ZSxwBAJ9JX#BRvD@5K6%b6H+A`Uxejh1-~EeCQUJ!M ziofH;|1k6U?veY=1!O(2J*E_~a=+opOEg^U(tQpj7F4^@DrA@@ZOroi_A9jCfS@8c zVtfXRCJ!ZDLc7%v&r0*#HFNPHvT-U3rB-KNS7~p&b)p>#k_Xfd#xQ#OQfl|CSKBDL zgC61`3+4z`0!>mLORaT)1^IET!kM~&xcZOm#tz1&`ZNat?RE2>m8Mfzdqj?!nEGAY z_{#d;-ZsQCQtIA3tk9jFKIx4W5X{@#C(AyiTD{TZLa^ag_kz>@HMSRK`SN$ooP9CK z(YrA4${InV6oSO|&dDe>#i(jlV{{Ur0=GRO@@~aO*96u4{zD-G{;fR(1=MaXYjb=- z0|DHKV!STN*U!EFh^j!c*IY>%uQXOg1Frm+32ekl_Y+?ZHZQ>=D*s9&UoX<`_px(f zX~SjX(XYluzx2`|t>Vq9Y%6iLLq&1)hpm zQMj=ROW$(bG%cZmWl!*0ZGZ(J=S25t8&ny*A)h87x6t-Q9GmUr)F{OQvC!+=2|xNf z2WMXHktf+?wbYtJ+iR`q3#^#yRhgAtaR-zyZe_S>2G981U56~eeuwq;fHmfq7V^6a z$!6@Q!GCTXe$W((YlW3=!FzxA*CDE5m+wQ8!4i%Ag{L0 zzB7f?5bH{$O*~6(aSn{{RX$(K3$xHE+hs17$i5v-%oL*_m0ubQfvxsO*`8l_Dm;kJ z-aIlf?vSx#-f1=7)7s$lw4FA;;J9F%Na=*bYdtPAijie^xlO2%;O--mGJhY-rIUSi z%Q@ORiME;wr%X~}otUVU`Fjd)W<*u)O8RP?gxKgUyTd4JCp6IteB$#wzlB+}_4#Na~u;l}s|zTdm0{s#GH z`3!)F_iNL;yq;&3hu7|(OO$|sknI+cE;)j$9 zhtDx?+sneY59JWVx)(Q zNFP(YH<2hp$eOsGOIInA}Hs!|r);jeV;q(-ngu5{4V!{}}NsSOWpl+`wH z!>bqlAoppNQKMH}gGo=C`H&JQg$Nv&m$C6A6k^t*C+(H9<9FzmCN;OWa8Xk@FgHoKHH3JP%d_?t_*;?nD zv7+2Tzw_N7Q6eC+6TQr0_q&+ok8Di8C&cdtc%=!nHJlTM_mSZb(BobCLq+Q490QBg zDXgMb+#dIi4EnZ@`*D{$c`wEFAT$EgHU4zbG)~F#RF<6E53JL?SpfpL#BaYygMaqy z6p7t~FT^F%r>^3Tr;~XX^h6?Ma5*IoU4B)I*LF2vE41e{)%eI;LqS3rhquIG(iC|` z#K=&vZ43*^avr$!7J1qm!<%`W5RT{N(Z0uMFUiML3|5jy&lW(Mli529qAOW(k;Q{}MDb{X`nRYK`+PN8%*X zIp1_0tWc&MNxU7vIbcWvZ}FHS-g_VweY|U;w^4w+=j*S1Y0vHB-mXp`!FzDFGP&TX zZ$aH0#aaWf7Jdw>+w|4|m{>z(Q{H0?wD(NBRyK5}M+Dv8SNT`CK%#Dx^nJ(Yot}%> zb^0V}z>=AtUbd{+XU=IQ?RN4T8~gqXc$f!FAK3>`Pv8!%x@A)IQ2o@k`B1_R)ZPDF z=Nu8-mn046r_foKSf>L_!QmFu6Awe6bv)Z8aAXidSpf~$zW&BqZgWA{pV3+s@S0(J z-cV>LXay0>8#GcD{#Eh^5Gj6l+HwakUwaRx(h`)fL^gjFQpurJm_OF4Wwk^q*RK0A zn#nEc-4j0XWlcpP9;c4#G>ykQ(mU-8Z{>y0!+g3dXvaH0mM^>&i{GZ>hKKuMk`m*+ zW$&EMWT=XE&Y)81#PEVcu|RxD0#C9N!%N}G0q(=ga)e-i$XpU$0sX8)$K5D|`%c}& z{iJsd75P)w+$os21 zDc3R7&VWG+xM1q7By@vN=hzal+J0%x=g-^AJ_J^rG9wZXla=X1B~_zZZ|`>by%7VE z3R!e+>mVvjJws&C1owPuW_S|16Hr!h21(Ki=_gXkdyAI^g;yA*enHsB>lgJ-TD-%} z@$jGVhdHA9mF^BxSL*4~t8(j-a6t!U$E;62lg1nvMr?=?A0w#Rlgx`yhPxuF;cYmr zqG7<{F_!#P%UTZ{gflIsM`J1;#Ad_yj1+mdoxV5}1O-m1mzrA-*hc+TNg4EcF@VI_ zf`OXysS_}3Owh+ez?m|%?GO6PbWkN;AJ}k3PyRkNnL9?0e#I7bp70{ThW}seDq;x{@(BjM`0<3thL;xcq^JZb^ng+K{n#Pu2?yUVY*3L zGUal^1MS0CwD#cOfP3ETUZ@{tR+Nb}*>Zx~@&-)h3IW<4W^~laynW5s2TtBMGnkI& z`zZInp59HjIf$AoH;{5KHDKbccF+nFXs}8I2g{$ddmY@@SzPw^qYR`x)>u)Fq_79X zCqGZybaon!MPf$ZNAiHoe3I_#&a15B&!=zBtS_e`ixZycqf5uC+COD_NwqaQWpl(2 zO#r^AN+T)B^0YJ{nLmztTTDV<*hO!hqd&Z6dx;#F(S4|o$2sVOzf0YPijnv28jjpU;bC8)tF2Re*)&QK;OYzPER&!@83E& zxAWCvuY=7>ZQQeJO$r8$hS@utl}5cb*r0ZI=FdpQ(Nm;8pNT^N7V{ujOj0CeDes%3 zIWQv^RisGAMMi#`HktjWc3wR$t?+OnnNeY9peADRWldYX+hWOS9R{63zr@h&z;w%S z`1WSMK__8+vM0i9B;07aek%YraV`W&DR5=}%w{T1FvRkb#J<-Ap3Z%r(wv)(R=$?J ze6vlX-SFK^Q9K%Aw8}U<#K3I!t3$-*qV~Am@)Pp?)-N z`0fl^7*@c`W#5v`>ehqxXxT+9h}uTGBcr}dClPFGp;D10SBa>wJy*tlad6rM$2AIh zZ`4C^uD1z??qM>PmY^T$N zy&yid^f`MUka_S_i1BZ;EIaIz2jQ{vm$F)Cr_@Esik z%{IV3#YE8l!P@&k=2sHKxeD_{4=C^V>`z1~LIjD3^Jaf2lj! ze2Rj{fJ9m?{ru%}OJN~OBmf874UzrnXPx4LJ(J$*I$hYCzS|6flfV3fhV*YK zuH)a)cSje9zvIep3Xj(3<&hulq*wAN_7RMuzi@irF-r=N*5>l7_`LIRCJ7&vE zEJ+wU^XH?*CIxQm0imqAIZQ7cnR1206P z^Cy{?!V-R;t2>gbMw^${ij{H(hXn~1CSK2p-Pw9poQ~Cz*)r-4RwnQ0XK${bEX;Mr z8|b_)?p`jfoLW8asyT=taNmJ{Q)~Z8P4EZDumJpS+0%QQe}$4DUN;Ep+9Z8?WC|73 zt69oAPi;HJOksPbhFxk)HI_;(;R%XB%a$`(%_-FJWLLtxIaw;rNTgDt5N?^&G{X)a z97IW5L(c9f##W3tsOlZjC=np3F&QC>EjL(oW1Z1UySf|y5n$fB`5gT*mbXi(P*3%y zk3dRsZo}B3~KroxP0BRLn? zbi7PhPrZTkQmQj(LD>GU*uWAYh#hLhTJk61zo!{w0(vEX+EaLs3Tu-kZ#71C-?VH; zYol=0J(MVqLEMCs*qTCj4u|;R5+1?p*^IO;r&#JP{PNe3;6VfPVUb5n#$ZETsY)T( z;E2O%=AzBRxzH5T7?H6;s{ub>1tCdamXkF+5$G8i+$K@J==6Px=%BuaN2OR!%SmJc z*`fv43KoH7wNksLzInM%DM9W#1Vq;Ja=TN6Nc%Za6gIu$j`8K`sUkbd#D=+LnH?58 z=6@tvYy|KMxF2N!)2Bm@0haCc-|)+g#gkK&0}ZV&|>ui z0*l%4{Qd|4p86$XZ?u3_w431C-Ni7r=d$xYiRpQCp|MAD!@w}?C@)u8Syb2my^8;TFbZPi zRS!pDt|b3PR5hZ%0RZy(i}6x-SNb`*2e3Z1Kq*oL&%-$-c;&^+Ybd{u<%mYPdmNU zt4*`b=ZYog43ElbGd!J^W1J+HI`qRmwpOd%~P%03VWv)=Jq|BpQd z^c)3fS_&FUFMqR2U;==GtO(z4djA%>UkidCSCe!HDPJ6PT@s7VngALW?$1DZfH|YE zQaT^h&!h_U1?n*0KEISLVbBorFDxu>;{i8?V^DscG9D=a>dkX5OvWao%QcE5>cGE) z7ij^N6jvu!uqd-fA<;O3_*`S(Xi294qhJPl3^2eZL*ptH9%zsNB?mj`1}`kIzJxht zEX$`0ditT)SIQb>!Mq zQ)_sL;cp)@_oj+eLL82Ju1dlguh9+nQ=Fy|F=%+YzyV1 zc{aLa<`b=7p_!f2 z^VnbM`cCq=g^BY6j%IYY;!hCFV4D8ut>@Mvv$8=(?iWov>_I+J`0Qn}7`%@!zI^c( z<8eHbB7Z&n_fhPD0#l?Z$!>e0s>&b=kkdegX#Q!@{A;8yCMQ(uxonGc&qVzGkneDo zS60$D3 zvSwsu?7Z9I;j!#cN%xwx6C&vqs_q*p-*-Q$-y{U`A9~|NYf!ne0RQ-ptAG2W^Wfcg zy8X+BS8xCL-QTYK^&4A}H&pN(TS|NEjDLT}f85K){Tc%*{TV~|)jz*%RLnPs-=r1H zW%9R?{>M}3UKf&ec4bYiy@CJh_5VGvPVC=9j#NqC8~*cq|LZBF>z5F!SdRA^zOVoL z%3pKDM*MroXC)VD$^V+Q|9x*-#w)mh&pvQ;ul{++CM3X+8MU1<82@MP|Mydpe5X)# z^~MiuUPJx$`v1OdJ@WUEdrL^kp5)8;9vCuqCC6Cu|2-B!EdM_j&pYB2$KE$R#2mwY|2%Bq zN@zaBygprmHXA<6Kh7j@7pxx)HPln(uPBMN<%EA68!&@@F;XDuXLDoGPK*HKs?*Q^ zEJ9M`24GELHB{1$MgKpZnDh~lv>H|G*Z;>oKdk@}^&TA#)foJv7R^ik%_02K%^x%6h6NxyYJJA5WkuU-tSE0U`j4TaS8@Y^Dw<6Ur{coQfpe#~w zX6_!wMrsWw-sgJlvd9;R311|R(!2EUdi6!8WmV<&%3mZ>_RU_2)oI<@?_62(#}X$_`lSIJjo0u z16akyzB}O`DGz`;<4x>1f8&u%{9^A$6jWOXzjj_kozbsTo1e7Xyrm;{ zsk6WK$$ZJoq;)!&4FXSB8dZ5qaeUW5Fkfu+7W~G;TmRG&usfO|iB74Mqy6N*=;WS| z(S6M^5GUAY`j~X4SmJ1g!&aNwV-0=vLE`3QrLgL2Z)}T)dc@W3f9=lDPY^juSgl?U zG<9ZY>{@I-KTMysRVpSkKd?-M8&R75n7mW0R-|{s^?qDk<+0}m*cC*qay+(}uvJkcq|m21jENb!^XXqI%Cg&{(Zhj}*~5d=URaO5eaK|1ev*02*_Xu9Z~Z+Vm$C3yeaH_XA)!M!jgO-DlgN7Y z5AehHAAx?naXQ}w1q8~{qvhIwUlhCNr=U@ID|T}4Y{V_>HxjcDrNj`A_IYsx^ErA2 zSNGo28-Id&Km zy^f>E*T{SLK0Q9^FfZ?=ar3})9&g}-azClB@Y461$?Dv(OEsmzTx3L@rAjps=|7dL zUy=YwT`Nq5N}ERbhvC4NiB@5Ufx}s&X1f!d5go3Wa12_Bxu2LZtGw3}^MT8c?Qd-BA7M*!1_uYF(e@Q$?oQ4Lms9??bQe zuWob}0L&U2p~jdA_BFdBc`98W(d~c|_`CSgG@kOAhuQ^`b4iO8 zcc*6thgI26YZkRXDU7lmEPCd z9uqA#xMVus6swhz*4^bqg*qhZl-e}E$!19R;5hFG^HT?U#I`FIY0AL{+#Ih?ITO!L znCjs_$sdf5+5|Y9$CY}0Qz?bo(wAOx+gffWCy4vVozz+H zx4#BYZxvdjz(Pqd9Zg(#5X;H(?lBli%3rC^n2l9%*V->o5W+tf;I+HlwA;$9h(ROa zW+@bYR;bb!HiV~W)ZZCQ7c31yx9AH`a_ojiBCDjFGklmSot+%`O15+V`0;{VspOlw z&b)SuIrq_OT`FwEgJ@4owUK7E0i4e{WY<{k`9eR#CgGLlu1(7U1GoSGeIkRYgaO2Q-O>-L#-Q{<- zyXQ_kDOGB7gBKn=!U=d=&#KMVmKu&oxS+p~I_2k=Vhn7kb&HL>JsD!3+6%uKuz2>4VTQf6p_eA_W2IvZIk#ElQW)^6_F@#H{i9`tzcli^~n&Mn%aLtq@K5>NNJ(o#)Mu z;X(OhN!k^K(y63UjgUi5XFY7c&Z8pKy)<@Lio^vM`eHW%>aGu;HHmNZmRCAX3XWom z)^iyqB=Bt?DdGw{L9FMBEP5d%yrLNYoaHxiR(8ZUC_!^H*QBbm<*>) zm}-|~vSj)gJ4Q>XF$`P_G=7z=ov9&Ua?FzRdg401Uq$Rz9p7C)d%`%(9^vlbwCBQV z01GyL3E3ISRH+x`U2rg*x$kZq)dp0J@CS>&=8Z}xdqi_f^Uq0`qs1qs)=!Iztut4^ zws{`&s=JQK$2jZ(rT`?Z*iEjspG)v+cbjPc^;ghz&>i&eXcjo z2){ZGcp11@$3KVjE=oUae_5eJIg!Gb+)1E-zmT)?#P=97aIJLZZACYBM^ba}b{e7M z)r5AUo^H0anFFFJ5&8M;K`qV7Yd9kNlyuX)WfE6|i+2t?qxmb6SkU)j`)S~IeqQtC z#>}g&9{1=p_U*H`qBmxJ(~l!7Ca%ZpUg11vkwYoW5wpCjL+yS@wch4)KrfVyyH5hN zf$y>fEJ9484^%Igw#cGqR#gD?j`UWL8RKZQv`833@u0?!`2X z>06v&OOEM9t}XJ&HYn`9n4KVK8faP!e>h>MvY4*WWAf<{rJkwd^1e<|9?O{P!B~xU zT{Fm#a|1dUx7MD#R3?*MY~sjSu?(w!fU%P&FeZRo3Ck$Jl;mh&d6 zS7%2unC@??y7XzN!)SZ9o%Y^q-CXDBh&)q05R99=g1qIW2cz1pR`2zH=DfDd1y=61 zWGH0dHqx5Y^JtN^(1%pv-4&gXsJ!YPO60Q6a2U9TdY!tNlxW}qa5Ng#0=+s9WzrVSI}QQYcni`E$2yo5Ej#;uVy>?vP%MW0 z+Mr1~7!yB>U&G|ZFqrJ(MrfsRcuS~jhtno~tK|D>ogw!88V**Kz2sbJ>AI`rq$IRY zF(gn-V6Sa-@~TDU;KJF;uq#|9wT_g8g`S!K$6^JlZs&>Ur|E*CC;hjj#tQBL4k&Om-aJ*UaNhJA%vdNhr!w^>ToACkPVYa1XZFb0gYO(L zO${b0Rl!RS8`5ec>LNv+$HS1x5YP!>E!RhRz-PwJjYQPi7H=#otbK(4?)cf^Cd#{P zwk)+}tR3x!ceQZsvGT1}qkA+mFUl4wuYSKF##b__crBd{X9e{O*f@X0OfDhlm?QKv zwaW0a`gE9CWuM4QQ#%+}#Op(DzIGXImnX@-QQQKHRh9kH%8oBqyZi4c7P=JEkq7e5 z!}lf&Mn3E~2*MDZH${4wU4=83;0PDkFtO|yAJx_FnS|c1=hbJ{6Az0&VhJ@6EN3lk zvPX=)Z&0+pysheBk#V3*==C2H@}f_tj27Nib3E%3Mrmk(d1~=u{PBYO7_L)IYV@%t2829(rDlqT@Ud(VfAw7oM=lA6_@o%$_?>Kk!hD z<%scbiToKc;bVJ~?R~TVC4L?b{nE|g;3uE zb3PnuJ8_4>61kLT;;rmEkBjin@Rs}`%}V)un;sB}jiMm+pdawqQ&YT4&tJkgW5r(S z)FUgL?&)>Np80<)7T!{xG#bPiPGxzfUTS!#PIuIiCJ+1sBg1=rz~2QH@sH2aNSAQqR?z18-ig{b2dtyWXgrP|jG4o@W# zWt3U#>GrL}Sw}M^JQCA8mp)_j@uGH&fV2L%T!t#q7;SE*29e=I#<?Cox0^?gXJBjAg*S1W_>K-ORARoY5WZO_*V_~>yXm88E0;1}Jf=k>krb^T9OSC+R4_pn_NG@e^UwaqZ`zw;P zYk1xw++Yf!H*zehTk1LPcM{(5n5|Z0$5ZP_aLyE3smFeIMy;^>5lGt_O;vD%?G69q z0?B{myG}^BEM)S|0e!m-=Zj2*jcS7&4dHV(iKDn!r8ah;J=aC^%XoUpOqmEaBhD&k z^_PLn+>jXYGi2UD#S(dbd|}U)>ou$lY1?w;9*lw#=>hqV`!a;Evn3hvPQY^F&C*(};I%CX^^vlB`Tns+5a49nQrGc6-qo?FwP<%?QJctU2DN1kRMSidr93 zB-UMo4x1qJL?l+KkqTgbi!H3WRI?mnu4dBKxaY${`1Vh@O>3O6v0C1&_JdiAmg_(+C+4 z07|p`tBiJgfkYwui{S6k^=k<{xSJhpeQqw_YN+5C-ggw zke^dhU4GJc`AJNe^4qQmo(!&cOcwMBJ;V`~=ldJPpQbm5s_S+x2N53jzBy3*EV_@k4|LR~N zW|ubIpaMsD_nBoWZeX-m`oqL!F{9~WM+TGmqs@7W)e87iMfnGWJGW3NqPLXuYkkiY z8k<;UzjQyM&*;04$mfYY|9GIxaainvu@gUY2SRSqcMK*Mad z8p`5f&0ucPyz!-9_*lSG%**Y)E}N)4;c%%woWcyLPP^w~%zJYkpnJ)-x$gSIo{ZtW z2wB1MFDGg%?6JF=;IY(mBInZL9{LLDW3DEk{q04}uSv&>I4_KykyMrN+Iu@gZx?Cs zOl9F3^&I!l7_(b=cuyRx}@f<11 z^5(ic=IjG+jrt=yt+Jh?q8l&84B`6ko6UczW!E<{G`Kla0?25_-rv}qI0j`WPJI$Y zMO`N0Izk-Hg+xo*j$wOb9-m)s_Iq-*Ee2<+IVYCpE`MDcxr3cjGBnx6WoR;pUM~6$ zwqs`%&9^ziGy6ST&E}|@ie+zf@|E;^)#*7WaLPUidRl+6nDyLveU2Xik)wq*9RrbU zc7{XlpYc+6MgS&Vb~{;Xk@2Zm6KZ>SDXPS=UOCjl&@qxjt?-|J?Khl4q>np5OFReU zd?l~~cQCbu1oN@xYW3i7=p7;I?#qIVuM9@qj^y_GvkjO$?r{k#p7Wam1p4JVzyN6Y zyKHz72pe{=#$+syv(c;?*$eQOB=-ymH4D%%qP}-EI-XT?!CMGAKn`fju6r9v-vLYy z2XCEzann4xt;iR?PcAQ%yXd7h8v)mKQRKh=NcYvXJL9I|`4PQ@Z{D`hVpF>V?34~# zUXN%D<@{8xEtyF9v26Y`43stl3i4_UI;F}umEx7xf&n+h+%Sg7SVquz(pv8%K$UfH zcX87Z8rGP0NwgirU7+tkFjt|SXr&tm2WRN;Q4uB?<`N0T>EgP>OzcIENU&M#Ta815 zCjvg5syh5=2J6&jO`(21>?8#y#%N8!NH#5}Ct)@6T7fufVZXv`k(w4~9ko=Qh_Ukf zW)hf&Ic=|{L-j=le5Z1Tf-z)k?dWB_F%!8rdU&SABvQI}N%<`19o)PDQoIpXO(N!8 z1(F)~jpzR#S#KGZ)wg{OE1l9U9nwg5-E?<%cT1OaH&O~ncXuNo-QC?O-SBLFao+Qu z|5q=)-1lC4tu^NuV~%n9ig7U6^4(>t(nhNyqOKMHzCGr+rr!X$=|2abENFcqi%t>! zi$rQ#F$2HQIVv{7yXuv8;YV(@f(8aXn37*QiS3G{q$FXAd3orZ@I&5Ij#veeb&LJZ z6`TP6u7jc& zoB-!5lHesDI<%4|%z#J4yx;K2{qv^GyRs;%{!msz9eqB##S!i}n>ti}-yp6qtrYz3?*D^03?G{L~K+QEPL>1X_8P*sk^2 z5iJuJ`&ygj+_3ft>>6A69E~Z{sUFyb+#~+YSXDGb^>QB0Tu7%dkV)=$^DlC=PD#TT ziJ2@ffx8TBcAVjG$dC&98ZcPn4henZr}&=cEqgASb&iN9&vp!~7UPk!I-g&ch4UAx z(0a#jSA2f9w>=CcAIm=p=Km4whANaff2v+{QaQR>32%F-pR21i;lW`xNvv_hHMe%Q z;1ct+<9^UUpE+NAWbVJJnNPAZwxF-SisN)LZFYEuf`nYO<+M0>x>>e^w-akWBYmW; z{sey-0h>blM$7w;6%vtyV1kn@u(2pplIm^5-f9#49F+R!t zv>&?sNcn1hyB%8JzpJ&6lA7S1`KnEJ3$^SAo#{w!+I}8oEQ`7Gw*v_k-Nm{mk|h6{ zl0Tv0w2ljPZ_IcC;(a=gk}1Qqa3%Gch<6@u%skFJzuVfSiM*I0!6ol5ZKpB9n^?Wj znb*tG7;%gftUZs%()dDJC1GljxosEXKE z&Bx@lX@eyzj!j=C92Xf3p+N8aO46x!+oxArJTp%{9z!2V-(yf~3#hPaX7GE)1tZ}I zl2{pW7y#st-o#6JDFYKWZk^LrFEt7!{`mrOt@k;c5GtheX!52Eo(?3YnEgHeyl-Ww zh05 zCH__b&@WY2L`OFQJ%__@+v;&0{uKDg*G^P&p?D1MZr~PNJNd4EYFETHIb zUIJ%Ge{;qwuZ#B-doh+zw|#%c2FRtD>K==uB1<$o40KQkPw|SC^TTW3i9`xJ5o7=H zI+|*WdBfk-Jw7Z=ip6CI<$Vq% z1RGMJGzwfFNZ7{+ijsY??FK*zkG1X=(&{l1C#Ows>CRP%Xnf{Z($u!G?q^27dD)-V z3)NNLaI%~)U@Lv8>9<00UJ%*(JjB6GYL?dZ(Rt~qNA<)o0bS^EUNP2)pXQ5Peu1U~ zJ{QG^72e<qdL<4q?>c^L$;;tp1I_w6V?7b%uiYd`Pot--us zR1rj|G6)Es0vNn*4RYI+>Zsk}l!{_W*QGVl$iyUtAmJN*h*5Sm$z;aRl>Ws=%X>{a zDf+;#sY+&2aqp_`23sQnLKwZ4+Tpzw8yJbBvvVY~U8}k?!tZ<^90Af3m&d-)Cs}B& zPmVweK)906i`s4H{9J6-${4%CDsMh&_p8SSLs|83HC5l|1W*D`S2C0vx50q3tC099X+pmaP)1pIoHRN}ufrmj64W`o6 zuddY(*NCPcGS#n*;=fLWZG9?IoRQRg>PIDy7~JL+hjX%jB~Ha>C%Ls}bph-)7KNQH z;7OBRXS?mq)F98+=oPPA4nlj|eR`7~-wzFo7Gt`yF6oL$B)*4}BGs{9^p0NFpu z3&uwe6Qu6k_dWCdDty?49NK0+1_~6WUZYTZSKVrJDny*m)B9~ZhljOmURM4RrIbgGG#vqe-8Ps?d{C9<|a`P;jg2iy%inu|xwxdOkun9gCAPqBt| z3uSP!*WdUy!}$8CXqtMyh-0@yd!3@&o`b0Z*fW6F^npr!q`bdT;=GR%&Q(eN8k!A>DdTe-(<<}Qq&=5cEJQriBXn7@CmSMnZihO`V ze}(S?$t9AsQOJ+5Vii5aZ$S_v?X2ElIEL}R-@CEM)=yniQ@s9$CHy?3yx$U6{UNZ8 zoft}v-B?65TfYbezG!iBN?B@JMz1}o)YJ8nq^T#98!ul<;j`c-nX+NOwZa+PjJkWU zW8EqB(XuvoNXA-A`Hbr>#$|C}KqvwZS16a)jDl%4ol+?+2EDd8j0nztzWs{e6HVeI ziK$LHR9zhkpp}i9Ez^;sORJ5arIY#2mQ}vynv{xefDy`JBmSaL;z9?iJW20@g9j;< zFE?!`>y_oW(%7%`_&!K|qbFy+$v`>nK+en`W$9UBDvPmcMCz7ty`RLG+wzSmu{%Q#7Q<6LQJ+k*Sc(>dt#d-Y>8dv#Y zI-E*j^ieZS-?&$$P7=eNq26{vwAbs=>?kv-dG3ZZOLFa{&0@NragndiUplLP8Q4H* zh7#SPndZ0V&kxr#7CY4jTfv*WOlsxqww5alvh_dK_Kg+hD}xl$xtz@pHuI)0TL>+V zFuNH<-BfunU^)N1%=O{-#FC`R0oR^I*F67z@z0qa??W9c8VFyFyzfJ z-zA;~2}VfOy8d9>axaqV6rn~n2iR-pKLn4$Jxw3^C5+#X{UDwkQ}NDBnzd=tO?v3` z(W=Pfg+3q7T+cQb$iTUP5*oGMyY=>5LqdrKZh0; z-wNL4QfY@@xUj-lSZ1GqGeSibnT0y(w;FJRbH7`ar(J(C2xma;&!<=SpG;yi7rgVW6x({8$kQN&ffez$ z)g@{GVFcE#Q7u$h<(GDCR>o=feXO3w3Kkc0QRv*6TIBGG!Kr33E4_dhi-=bmS~iYDL&;;+g4$sTv?x&D;>Kh=B(kZO4&{<<+5fc8gH?`gu(k zJJTF8S0+^rHSpGa!VM;2K}Zp@mMnk-UJ`&|43VYh9kgb^#Z zHQPO?p2wfX?oAN&=8?~k4V5^FLj|R0u%wov;T(f!`l-C~c7`r!X^W~z4%7MSB-{mld?$%ZKQu+BH+5VI2rt@GAHr={n*DI*pD2>@4V-BJgF>?9>dLF+)ior+NAjs zZjM)JwzxNiKD%ZtvzYG-SU$T;EvR_JAsBS)9hvvUqL9?D%hC}eW#GB3My2XoJCyLh zrZ26Mz}HkP2kl{O#sLGQQ{5p*WHBAWs&{6aLg!;6c*givshZ7L<6%u!LC*lo+nm(6 z;6ZnC4aA7Nwvz-#t2eu)+UG6P)3Fjaj-n=YFq;kM^S0dR&tt+br=9ckT(i;vWS0j( zc9Aqw9J(8O*jihithUV!&j`-y^GNlD33G;ayB^KjSl(+~ihDx=N~u)sgr>>t`-n)s z2+!*mM(q?7ty3JQF@xdXi+spqP6x$#ER{fx?Oa0As%2V}1tV*|qu-4J zy?4v=dgqqSN+C-euJYQ^~bGtR~mUm_YSX}h77&xfka6$oG|K|yy($00CH~l3@F+pHf<}h10O4? z?e~7uY`@5OR9Zz1`+R(ujv-Rsd>uEi6K{mWJM6;>#-^EIb z+k+Gd?}T@Z+eucjN_}Dxy00SUvfZT$#cngEPlxJRJbxj$3*#)StbM{j~6aYGC*j%6$141G^Qz{}TNHs%qoo>}QbZQV0@3xF;<S#0%JV5e|6UHCSeny<@AH-KUS<0A06{_D|}|n8D|)7{c-21H3`M*L*d3OMB3QnGGrs ziLD1CHAO6>X5zCqPV0XQi|4W@o+Q*8w(3ybjbdD+z0&Y@oXKcdvgVLH?}b+dPu@_R zK}zOCJL73sHvA-cHa)uWYE{`+kM_~aAJCJL{j(EQBZ?NK8X=g&9D&$_-!nw;du4@9 z&%-M<#@g?QRu=_>40ZeyT4-T8Z?ws1`~<+#BcqMh`P!gDK(lW8lLEoygQCFjN zTgS-=5!;m+Vh$i?OOuI4OT8Do8p~Mmnl10Y3VB3e+~HGx6UC_T&csxlkv4hkMK>(w z(V-xr|L*R7;wd$t<__)2EWEK9vkKc-Vj_qs4c6l#Wjvu|4TBvckwObSHjAaB1#r2f zUJDz-P;e_5FBPdhhpQK)^XTZ?wz4r2G3Jezf3?>=CSVLfkSleNP!0?o_BgV!EJ185 zcVVbX`G+I9pRsAiT9Yy#iWD~^)Y~Me5N-7nzk&o$q-TWj8 zYT+CCg!-LlA~>OcB)yv5My}ySTijK661`PfBDDZ{=ZsC&);~;YaGFHQ>D{-FTxy}= zyS$uR9V>`CM>&r}&qr=Uu6!9kgVXTn2}2Q;Jba zrO$3p>}%y1jW%wecj*I)7!!nU=~PlX^8HA5=r>un-@M%V68&1J^b`S$!7?F zoCn3;W})Ce`6J)u8vxZ|9yNyHtFSv1fWezX+<8}Itz-OKIXU>hqg{_T8zYUk*UlQ6 zf>!XewF|`fA^7o%zwJ8#{tC@rYP~Q<#3iEVV^Zu4`}h$VZ_^iTvUTKH7Xv#gja_SH z#~#OslaKE2PP;qv-SRX$M_IuMxtgA zo;k15gVQQjPT*emU=gko*R>wyTr<$-h=$@h5v#WOo7Aqk$)H`@a#NurU3D#0?FR_k zdbS#N<2SHS?XGg&`YM*sh)oYOi)eI z8I5|?OE0s)3uM#dy#dSm7a_DxH}=)6Df=ks!J_7?n=IC{V4 z`#R7xlFrR8`8>aPw>^+>(F*vjRsmDruh&O&)kw?G0EgPbY5C192$tjPSFMe3W!onx zM;fx~4}B2~5WfVd;3&Sx$e!kv$OjcIQEAjaEv6cn6k-_QP>Kjn&{t_`N`C7|>9^$h zIw-dw8a8DzQLLei7g_0RhHe|B z9+eYTqLWMpYqBMEa;1wG9xnAj(J5Js&=*=ZYgiMT zKb+)4+k(FuDh4+5gVAP`6zNu#aJWC(MiZ>uk+NTozowV_5$B-8~6ams1v zdY1ndfHCre=*yLWAQ(ym&{BtDHle+ADpTGemu8ZF*rlk*xR8scGg8mC zbln74q8#c81ks12%zdhw)8L0yv{X+t#dw5;;pm*FZG6*PF;laX>$xsg<_4s1@r`C# z4;jG_q4`nBM-}Y_4y_%+R8xwnRlcwd8v-6U`*LAZ#;BXwf*hqhXO#+AYFJU|O9slh zb8dr*Yx7)_XFVUd7z@)Y$y#MXC35##^r(u!D659PDHgpG1`cvRfP0*pE>bFTJzM>d>I*3Ss4OOPmFp~3LaqoE3XoZ_7<6exeo?Cwk04TP3N`%?67NHPBh;F0cT1r_?FCspD!7Kb0%B#U6;lrZSU+Cg`@MW23aEE z*4#}D1^3IYKSomH*Owc`yr{PQl=M`~!o$CEH=;kuerH_fTQwl+>>^r&{SR;WuiDQ~ z99g}_6mh0dAwV*j9zSdrg?%86wWPK`qA|%Rnt;lcVr2e}RyDOj@$sTxjE!#56SYc2Ul_fc(8%C3587(?{~7(L6n~ek15mFpRS+lv zc0jgMBCQ5f#Pv0_%kjJ*kS&xAOm9m>w33Jblg%wa2HHUh%H|(IH(G^k2PO1w440Edg;XY!S`T%76bWF#~}Go2Vo4kNBhK`z&HM`Xh_m^;G`h>VV2_wG~*}PfTScxU22n zfZf5Wbb(xEl7CJ}2(~R#DuaN9W{M@&`D0_UEgNAckcB6seZsHZ?WjTodwl$c#4~6l9+p`T0{zjq2{y z2Iu={2Woa4Ge)f;+r)Bd9nbVpiw6UPaSM7@x&Jdd{dIJpR!0XFJ6@n`S%}?AzGVOb$GWK_3-oER*a&skTpMyZslr=^AS+MGI?B?14&Lv zYzSlv>h;#C%d+3u=y-i!e%LNI3Ibao=jHkFEy=B}`QaNNYv`Z%Lne;1-)=c;&jF&j zB(jjN;V@|_^&Z%Ke0-RwLK+$x(20m4Ktw(8djb2V)Hgb99xCC%Dd56VJym|E1;sYh!&Pe`m!J`UM%n;9RB6!}&nf8Wwul7WgFSe|PHn+y`}J zrf>c01#mcBL^_8=$R}MXsa0o*0p#e(P+V6|Lklyj&kCXkt|xQ5+WI>a27Zow-<5+3 zg#wT4OjMrb+e01rIrNgKsy`BkdE0G7W8$s1AGNvI9sqn7WDchaV$kRH<3eAoU~Vnc z(GG{?_@9N5AtSIDWPw1nguUyFi+Id$$h>Oh+7hIqk>l)f*ThU8@VS&VWkAM2gW6IX z`1E+z0qcONV4XVD8-h6c;c2Pku5pqA zl1iynygli~qOnD$3PxFIVCpR|sL1UlDCuP5_KA;WQg~Q}m7n3c*TTIi9bdJ&9lCFp zTu%`Dl+zo|4u7^U79<%M*sV68T(7orXO>dF?2kSBNZaigeC$@gdLDepCRAB%@Wr!T zF3gEOntQxD%1gUsfeV#q2 zaEqbphartbezg+8WeeCJ&6a)t!CcCWuV9Un!^KaFy*HN0kT6{6JyG|A)hSv-0eO$# zeN*`hB57~tUKBlzYa}5pQY@@+{8!*@h(`x~|GRek_o#m>CvGUKo(|#AyMh@xL%vbb#7;_9sCgpJ@N<$po={^ zl=FABbqzhkQcxHW1p^1qtAus91Zp5fm_SxyZEIkA(`=Dv6T$mTm?nfo(9wwhgkB4>< zyp^W$21~2`sY<%X@x$MJ#2RvXdeJ*8O~yg4&Im@4c09Ia6}NjryjRdzx3ET0Yy8Fh zx=enJpw6r={TW;p^P&2VEjXGC=>H%l|IDW{0(MlwXGRkuw}14BL*2ZA7ZX1lVDA@{ zqOW2WNyi7U)y4ElhGm^U7wk`JaZ=2%nPQ1w3f2k z>5L+!U!yI-{VK=4W)=c-JuH664)2=Y8WbLguGhpJ9;dxjrvEc-eGGbs6SRRFuI?qy zWT=VEj|5qDCp~n3xwljPk<(Ef{Yd^7z&!UJEmazTlgs{4??no{CSbzvzD7|E8_Z(` zZu9Cf;1SW7@Q(Df>aB^%1pKTjoBR>#LR|}mMW?tJbX&(=h+CMn(-ep(iswU$!A6lL zYHgP4Et5y2w08X==qYy%anovIA=t@d)8M|R43(%=MD~_6K#4G6sQ~-d5-lN#N|CH! z_1QPqN!N_B%rh;8g@xd)$@)M649^C|} z$L*(zp9vllL=I7ooiL>TGC)ndC7n(h%Fl>ge1OrW9K^uF2~+@5ImH11izB}8ICM3t z(4CS^ZNQl-P+^kI#lCk3YADeVVlR@T&ypXAjZMcgbm$EjLyW|`vKfF)O5C`hDLI&3 zVP&zB)I>DI0N10`Xjs;?WYPkur}7FEK*d}OOrm89 z2-*@EJUHY`1049^Ap=5_C~g?o0?B4qc}Dy*1>kE*1&`dS2hkVQXm_9AUuJ3&+V-*j ziw*qOSfPN}y8$k~*C!`Upm4CQFsRdMae*MV7|YXRSSfE9xQ6-ilbQIKqnii_Yz|g z$2+9ZaL9e5R)IOc0CF^iOH|9^uQ%EeN`xb^R9#X^s4E48Nl+V=j4~>hh2qgee}|PKfl%*_o(ZeSd@2Sc)eBZ!0njoj6?DhC|PQ&r4uO? zIB5ET$Qa40I=v2`i{_E<>re-N%k5#TS7m-I=kgNTuAHI`TuuYoc{ zLXH8a+P4LxT>N~MoH<z@Ah zNU$R@b$mnm;zSy?Xwm48R6$JY3xdi$s3)JKQv`r(RbGX)MD%%iT9DPQt}JPG6q0_b zQZrB-9$pltsxn2`#W*y1SF*u&r4T6J6$^uY!fez4DhL+mL*el{l*v*PP?-JU9wK&}IAVtNi50KUq?22UJDj z9wE!!VxN%Zmc=(nA}9t`q(2P54z7wL+OM60Pgm+1Io6l<5HjOw=F+3H8-mFS|FmR{ zBlm_jlW@(J8dWm+dtCh!$=BBqA@c|sNL9}}>fl_mI4W^tFjuuiphH!NCzBvKhT0^E zcLX5A-`Vh0c^{C?kWM=}#DWoUXjP_eguH~k$;j`=8*4?`O4YfshOr`vPN^8_v#0cz z3u7F_RBCHnB2l9H6#9rfpxO}Zm2Vx&^?3A#`zaU=GZ+9)&^Hq!-l&Mnj=#xt#LPiE zB_Qw5`}`=9V!VgD_b0YyGf=|x#O83nXwmnc$YV!JG&%Cn0k>KBECvX0hkX$;$fB6DEU)#o0=@50s z1b@nj;65vwr@*smF|p%_@?w?cP4p#)L(TfLbq!Z*fAvyZl3h)Xyus^tVgFb6>W-H0 zdsAiCHB{bt4~f*i78eWc;b8O`KjF~WrQ$9}BKE%Qjp1zjdUVYDq6#Wgxz8>q=9pj& zs^`>A-09t|<{d?d#jl06U6HIak|U308EaH4)tjyH5iTcGh;=dt&(W*s{m+`oz#vP|k zgFTt@xa^0}Q;&p6`sG&hc))XpBd?zolLsF4CY~;*l$ofvm>@fY;;LQnkWr|oTd5uM zuWqe+xugN1&;TEFw=KBz%=u)1gUvlIBZ=N9MjV7y0~|ZLB1V<|cI@y=5&G#UC3H-3 zcIo$}k4FB3bXcHeQ7N25?C-7=rxQ1_|2oa?)FFy#IjA8xE^XWF^xA(akuCyq3!`rj zi4|L8i7Zm|a2z#iFedyG?@tb&Hk5PMtU0|hb&htJbOt1q4d+oM*VE$H^Sw%4Neu+u zl+^Ldle+YU_afm!*vB$qr8<8k?v?l?7Ry6QZe|3XTBiUL>kwfH@)PX|18pEuk7u4( z+lq7$`|N1;cdI@5BoDoKtE6T0edlE&xNt$`lvWK*!r(S{kv&n@{726q@@+kM4eZEu zu&U44??3YO`~zHcPC!`t8Xc!OKX0QoJMR|pl%c|ZQz3RqLxYFDIo{tlaD4r*U#t&S z6(}%%?R3?(rWVOYuyb^UM?PYH_iB2$In$pbsCA*qkwk#ML}vz$!gzNBBr1}C-qqeK zg!^;EQmSwbs@GBJXfdYx+v&tR<6rl&3Yk)~n{YS52r6Q%8F6o0_EKlc2N%r8)7nm#Fq9EYfz}iTnU7(i?9@BAXtg(BBCX9@|sNmJ$dz$YrvgwR0IN!Gx}~6 zts(#Px1D07u`P*s8+i;KRew660*1te4T-fd4iC<`{;Qz;5g{;+h~fD;vYh^I!Tcf% zL@DGB6aaRCkOZaRM^EI-u_CZnToSGgv_y?}?datla`fr3k9r~Pu~K5s4kJIh#nF4* z{q5Wbrg+<2WP6pSGas)n_Z%jH*E;wHwU^Th3r@nB_l}z(>+!QGOL?0&&co;?iUWMbKyuSm#|O5+ z4Z-Yz3nka_9SiS+jg55lP0!3$UbP{$_}YV_p7Hy{Vtscfoj-hnfUitD;=hGX4+w0x zrz;)aZYY%!%h;MSPM(fa*MPQlp2zs#qXT&Qc>yAehtg`7;?|&|<9QiOEf6|_yZ!f3 z*?p1Ffe`EfjqhK`8oDt0$-i;ClTr;>8$}33q#qy!ipa=EW>PXXD%(MKB*LOt&r~yRlne2QzM_F-Q(8`nHC*dK_W44O~t=2F*F@QahHY|n!xKeUmNsJdFQ_E z=!>nFt0a*jT+)Mcls&#KfN7JxQu02?smRdlLGv>%_KLPboUJ(O@q2>$T}t6L(X#{j z7>z|$AFI4JUi&MlN*_z9iX3;6HR1yPrf_gro4D*!`jfl@tEVkw2%_4*g-H_NFN2CA zlz%Sc2M?wMhDxR6Z3uErmTrh+Z6-~03;@EeMbtZ^xOS(7<6Lz_oLV#+N4E-XQ@ zi)>)~WxPNxSD>);@bsl6w!G{IMa;o#XFM|4`jQ&M!0mVk(|rh623cE(_BFq+zrSrp z0Xk8or)`Q*Pd&g3O>!l*4 zsV%3z{?|#1!Npd1a!09(6E&K1G;)=aRu*EQb%2d+Y;`|d<>j(pe+NKf-vH1|9I%EX zMMCPvQyXak>a;I6#~O81$@KK}iG1E3gWqVdynzlck>A%RXR8(E5*9Em2(o_$UT0x{Tso}~^K*`a`j)+j%|sGU0=Rak733|3RnH>Rgyx3Yc>cb$VuL6yD^yA29)^6no^o*?Qn2*&V>)*=BYV0 zgctm-u}`x2|KWjrKv={a9t!hI?1^MDT#gm<;w~GyMT|N+FCoaF0P{6=3`&`eNnw(W z&{s&I3-q~2fZJjr-hee~veGd*b&d~9c{p?}C z*#k>OfU7cX^8_G5(?GeeS!*6QE9#n}Mk+`|MD#U-#~lQShbh<_ZcXRwT`(wo+?xRO z(>jUSOt!44qJjY+h?4ES06gSsCt1(358#4;P4f=e|Db5K{#z*Ev#W{{021GgmSWvo zrPp(i3$^nm#W)!kO|w1$EO2CXLyL4gyg z!|hr>9nP;;3BT_8Fxu4Dp*42e2i_Bux?9AJ_oo6hZ; z&u+Im4uEQ~{u@9_q{ZvYb2UMapi-6sLFjv6_xV$bgrhU+lg%P#iNz4G*etRfuC%ZN z(DrJxsHtU8U|?)XNeK@aqk3w%QQr?vr~aj-?@%8;7S1qEiTqfFBoJf(-@N+h?pPHe zkoGjQ{WauShf@_n+fJo$6C(|;k*^81h3GqJ&*NX`%&6c`lLR>`O3IY_v(>|oXuBG{ z8eojqp6zs1 z{Uo4C0`i*nony7R+;t}kdf-Qg{kEoP=!zuyXH;deR#$o{I{K{S@Ud?QuU9wQnh|^I z6pA|ry7_E(3>DJ+`0M#Ze1Ju6}XNs#com zahPq^;*@c*qLwut#+Rwj^bgPg6;z+yS703_Z;-_inS6ACEZ3czU_`uJ`}=`UnetQl zG8P+ML2sA=90EdaR}lPEAS@asfDF|-?a69(__Wm~MluY4qfsdq1^{(*bW zI0-)Y)PeZzzxm<00rs8pL2qo(Lgq5EZztj&fL0R&WOB@Oiv38Zir#Fwj+DOZ{y4D( zv1S$svRJ2u&HMR!?!hv5FYIMqw%u-x&*N$zWoIZk2I!+j-adEBZaiRt0QWUQRCrQJ z+pcrCt(&%AZzgP=O|X&ptg7IjMN4Zw7jFEmUl52S1w>0^-pT5cHEI{ z!ZZ9vmnRhRLGd`CvmPLGEvNFM7Iu-pr?K0~R<1g^x)#)0Oj*(o0DNFVc0B6S>2i}0 zGBvT6;E$n1s)R3E!ADHO)kb~M3X{3Pv>>D2$T<3{%HZMe>=o!J&(F_0*e@@Sc2sEw z;ND$24*)U5;&QAG!tYV>dVXNlY5pQ{bAM2fIZ5dIM6A;}!wBPG9?`B`W#VN&)yqjN z3bGbb@FwK-P=?FJ9s^vx@~~lDZP0t517jn=uOWltwnYHo4lLh$p`x?j>IA3`b{xNk;yvqQK)2wSEDZ z7!{1b-=;`f2OJH*ZH7JapIGoJj%d+p`>nU&V?W;hi1cRzX3sG&2M)4k-S@hEKL%Wz ze)B>^KF2uJG1CMPz)K}9nH4{dI^4MhL+a8c-}C9I!rJZ=(fJ1kLFE`cbNd^2E*d8(_~sv~V?bPoZD{lmxLI z5N@wXc6omO#-k5yRf4h5A(wsDgLwna5@0z{KgrStaZbMi|0GWw<{XiP7!G1A1W82O z-}U$6dC`M<*8@`dTne+Wa*vI$-wp|!@DyM=JK?(K{iy-P2zklvS2Zf6gwW@504?i3 z|H1F8OAnxhRPIIX0NLbJ2zHM4Osifga01t`Tx)@bNWh~W?l+V~M@mNJcTKqa0mTs@ zs93xouIx`f6RK;1IDT_7<^#ot1dT+alas;QL6U;{RUlL?1Xcu=5u@X->9dOM0kPh~ z1rDFwx<8L?FUh^-YSt%WD9IWI-=7KzSp9RWLIu(-J2rop&7pn}SF`?>Zk4#(U4Qpt zj0u40BjaT=hqZDj5)94f#zWAspJyRoKpE?&U=WJ9_KLNG{ z@0ZRsaEUms&6KDe0Jt_p)dwRbZ&H--@F?gKL3H(Z*?K*^fh{y}i&PVc{y7$Yv2Rik zg*T2w$`R0=8^>cz3-JRe|4+cbDmJ2j6Jky`)R7~ky0+HD>%j?F3N5%a`PBWKatx{&u93c;2Rfr<^ zbA$ohWsX9skIZJHv}yplUg2UPMW*X_O|l7*Ju zLv-5l3Y~aKUdV<93kB6e-yLJWl|6woVnQ+!%D~f-8Jxga$xOuqtf)+`avu*5&o)4I z%FWR0G64>$pH0W!QJ*NJT5em4!)}q=dk_$U{d5&nb-o^u;n2;>?)ppHE-*~4KdLDo z<#N35@X<>C*ZN2 zsUuw$?mYr#Kt|uLOS|nB6!)%Q=C4|0LRx>ci*pg@(sYOcZOMJ5SsGS{_H|9ms*~pL zChm^{%pbIe9Q~N~1QJgMR5^jqC0Y&T6Jc>xm;(~A%3!L2vv2{SGNE8c9F04o%CXDf z6+_7kQVl!&uTM%D@T%eVCNDQjc01VPVr80j#@Q%R&%oXQ0lWflULNU#0^~v{arQ#X zSCsI&t(v)e18-=T%HWO=BfsahIly9UDvO5@3 zfCf<{Pum~5ZvutU`(LfQsU9mKX}El zSmz3_P2=b?b8b!`1%GQuNe}nm4d8kXu+)EYvSKpOx)CuQAPsQLsS!ZHG#70+t8H0f z$h>1{cZgz{CToNt3GE_s!-vz38q<+@2|#)Y!O&Pl;s9BVDRc}-a!QYpl6;Y~uT-7w z$%EfPMX(qv1c!#Xc_PDpXSJfx31qwhD%PQtfL-k{lRbqyqoF(f5i=`=OA$kK?^I^9 zXmm&iADGsM>`msw+k-L0BT4ey!=wE<6i<{e-B_~U)9LCaN}`UqJ-U(bd8beQ(~cH- zjh`_xglNh2sgZjluqZO(&J7Q&4M!`lRfB*GQj|A%sW9#Ge-sHEh=8B()?J=xJQ8{% zB560>awu^IF;0I9qtP~M)ftK7ub}?G(gx%Rfaw3UwMD!U*wlj#_eq#qow`d0V}Kff zu(Ll{oF60-1#gAkLt~k!zoTx@XGvkRNvmb)xgY<;gaZu?t&}JE4Wv-FeOx?Zcb0KL=#mr#$Yp&EEmNFk^}d^B$zxoG_0CL1Ndx6<@yf} z2a-CwHCM(^Ln2IGFt7WC@j*G<5WhO+q0u4+t-8GFeUOxuFl4F| z3N*6eCa`^tv(d%qbPj>SMD~!uchD1Oz$!viA>hzoHjV^-+bU9Z6|mb)}&m0d&xuA!pj`*-S}t?Yn0{aY$xoY#OW%4-Z<# zLx#L2DYJN-uWx(yO{Uz>BhHex#^a7*cR}61mh)`Ju$RH}3f@kQGDHjxikgZ(58r~_ z{|(xSZgtwQaW~xYk3`(@C{3E5Yw(1oorly=T<8CL$9F>fi22Glkb60jVfGOpqFA*A z6~BPd^8+T-h+&u(cQTz8MNlU2kq+h(1D6uS?pS6f(T4-087dk+UtixYct~f1xDa(u zI<5M|>o}cTp&ft@OwK0U+z>qOhKGE&WV^q+lNT3i}S7V8V}n2N_iGbTXaUMP`3@kTKDV`Ni@hqNO(4=?jMMm zF2JV1`Hzm0QjF-q20k!qkiH=xyo|tU`}ezhGevd~TyAgY;f~`6!MxviVnExWrdYVs zTfn0deRLuG8GMba^pA{ybVkhDIuV$K4{54djDW5l=JUFXb=(`J+XCX|)NeUu zZ*#|C)YL)&Khe#L-4VQtiwhEC9=50iwH6l}s&`#cII{r{{%+xUQppr*YHCbTa|IPx zGuNTLSTpT(bs^tbQ>Yq@q^`5832&hYpc_RUs=gBX7WVz7!E@SU`TJ5_Fl5_gxi1;` zh4lDc=f>z%g^UTSV*E>M-1BR^1%K;blG#uxh~@bF1dT9o&=beKxOQ_T57yiEqkDh- zy!EE9chZj#B>m{%3!Z9z9zF`Drmfz*VIt$_jJN!41cQuJ&~N9bg#s+s$8-_)?DN#< z0o|q9>WIPn?8o-dLG>m&G{A=WL5&a}FL=P{>D~J_`0C=~9uP)}{K?*b!*9ST9clxB zxREw$!vlR0+f??JO<{;|#6F<7+D{iMFx~e93QH4d{>F~k5;eJA z`oI>(x1am>CGr1Zn}6G^`-Q&Ay%-7kaKT}T>VJkaVB_LamqDmFMqzVk6QI5wN}YhP zz1>F&i^p!ViEi$oNbYkGM6lu@!T_ar#&lwimnrouWrNV@V-+`V`!1@^yhi}s+?a^T zVhR%Xc~xKk-oKyMKaWrq1eC5-JCf%dBdFtMBdl6}u73$1Hi`V^vh2(y3VaWC2U7aIBz_0vHOz6J(IVSak*yYx8 z(2mNnVFNR3f!}lQJ+??)+c^V13|SVm_&$E_Evi6lHjxYv(R6_x_gVwyAO2^&ARETTo7xZ@ zB{Bai%K_b&Kw+440is-X#SQ*F`kG@~wgKBiiu+T~8fK@XrjFG0-676f-QI6MVG@hh z%UjXGDQf)^>X)`>)?rG0?Fh3O`buto;f;|V3kNn6TpRHJKtd2&-$XNabj81GD2gVf zk=@vwu;+Q05pYwbFF6b=_J6(h-Iz76TQjiKv z?N0wWN+(9Wcttny=qgXOUAuUK&hnH*%Nr+}wGlI6a|Rnp^yhs^IalJuYmfIh4~>QC zOzCK5zGwyIhnm-|ttf*(S@5>;y+4Xq=N>?tGsBqpJSXi!SCaC|E(B`wwd@+E== z(Z>%3>^FvMZ{BMP`9#+%l8+%9_64Ow7+SI~@47%g(#=Ii$T&e$aG zR}+eooaMu4>0O+I&dp5Ja5&`uc$H=NR^G@@zCn;`!G{N&OyP`YUUrLZ_p}Qt@1wlE zgR(5Ou=zzwEjPBbmUiybvgUl1$aS@t&h-`QOy4K{NuRs%^zasD`u|IPlQMK1mrL^gwPE{W`t(WjGa6QazHJkek#UcFo zdVlq3=v3u-FgEkhW;3Flvr|xIIPRm9W64sw@Ad4#RE2g38EH z^ju>^62Ap!X|~OYyL(ESWesQihTDtJMXNFNU)Y&6OAU9J^m})WXZyoS(m4Ypc(!*< z6IprWFGtng7IQJ2*wtPk{_j#?*CY&uSv2l5ZrWd~>(@rLFyB?yU>8&I&2< z1et2EoASwFP=O0`2YHE`2BT?p`k?iBE0)&shrh1^kJ~TITW`N)J3lCa>%2nNa&9^j6X|Es z_)VPq+_d|fSR1njbZuGbZ-{W;8iKNXES*p(2Ma5nW64GI633?52})x;)(g7a?YyUd z_v&9Z;x{Y<{*<}bVW;ODD83{?PWi<2D=`8Rk|AIbuMdAj16W)X5}79!5EH0!%rv@j zK)p^ORW`Ga08y>;gLK^6c%yUi5xiD_q3`dvW4;G)-u}^%XgEIe{-j~_B9LD^Db6XO z5pbO>(>EJ0dU$~*a7HWxq80<%zk?kg$Ei63J(V| z?lD9QTPEPXw^_Kp?JLMzX*wC3+GFvr)gS$+`62g1Dpy?D^8Q=11dYOkEGXn2k*$#m zp0v62>W%3{qdQeQErUKCd#SbShV!|@$)(!DlF51R(uZOp`o|{{TF$c-5&u5)Er{?l z36qFYMN6&-V&?#KBm&QO*q;N>d1YWgEh#CHQdHCsSp+!ZLCamtS96VS79=c9atOla zR{(P%qB{Xew!!uJfehR6@v+!!y_5as=wpO?_w>P3fw$H0Tci2!H%ql^6+++S=jZ2X zRWmSXltcjiz*39fU9dQJf$jj##l^*NK9PX(g*RA~JiVr;&u)%Y`V8jcU`7~)5cG97 zv`5ka+nWMM_T@}OGpnQU4GUK!KgvrX+{lE|Jz*;`_{PF`$sc+_PL~Co`+hEJtQ~sM z?Om9shC_wAk%l`ucIf)1C54gF*V;q@l7?9%1Qmwm)KktDHQ9TXb z0%7W+kJ=h?S}UJGj;v;`zj*`ENUB8_%WedAV8keTUEP|ZRHgkLy}!@wUkBwe<*V}} zkH_!Wa~H zEbSs_Px>(uBRjU+o?vnL>NxmjjOFuiVO$)p#J~<3rXtU_o+n<$OsyPBcm~GJ1EOa$ zcTESV_WWwEvUNJy`G{h~BcGCZRv)C<-L60IbabYA zRI;1>X?(bpsxqGe$uwxPOAMY`sRG5*XJ3Ws#)Beun1l7@+lds7f}KDB69v@xr?$Rt zjX8lVwe3OjM;6sZVH6{Gl{!CD8nhW=SL{&^SCtu?60}cX zU}G@HAtz$?HxZ>v&VCF{pDP&1pIuX)%N3Z$vBQX!K)~cDhuq%i=x7boi?=6e8pT?HKztz{ zh;gcjnPA-$~0YoP&ekJ-Sz-H|p=mIXlTgyv2dQfATfIVao>xKz1}Pf{t)7~t-);}1Mv3Ycp+d$p zl3|5QHu(y^FyzpsNcy%#AosD<>bJz9XNNB>_Cai2><+WK9_B9lD7?PvjAm|*Udmv- z2e*pk891}%KhTzTL2#<*7hpru@gRcV}-n8PGu zdWf2q77EIWSru~F z9Z3|Nmq#xE8ZNFY2^!>C-HOjHKHxox5stxiwQM0U(<^q0`K~;Se(t%)KZ$qPV6O!w zeb7Jt$dh*GYt&koOyfUOMA&;j5o%%mGAw-u`fe)nIZr@Rs6a)-FKq?w9#QAV&t5Nw zOfz*18%*VEiuiSI!P-GUp~noIhac!778c+bq^Y^@ep-v?JFxQ2YRN3GAzsE(8EQ zWoHm#=CaTN=X8E`Rd@vz^e_Nn(p|;L`)a3_0_tCJy8ybYqZIdsO@R9+)Y=uOas@BVW zcHD^7(pKuIPm3+$aRpE4pg9DBR183rFR)hF(bRK9Fq-W-A6?JpXn~U)=ONRlxBn)P zCL<3&+G2Y>*JOsikmp9r=F!74CTa7Tb!F;~^Is*$eYuvt_pz6=^ei-C^Km5SeBU_1 zUJZ*1|Gc3(DY`(6KPF@n+`D7??k?QHWhGbWMCNF1Ze+1nCF#GD*FR}B9wOYXj1FNu z-+z+ZKfedZmX{E?g!`w$W`C6juz>!5y}-8w;SeAiM*)BK>oO%4EbI`*KYvz)jQPAa z9W~SIEC@6`)9H#Tjn1^V39CChu~FY*i~D!3mFbF;$)+igK~(%Fr53=J3X^W#Xpt?5 z?$C&PqQAjbSJz;)$-yEZJto@}6O-RR9zj(8SSm{KIfNB6{@Z=pe_In=H+hSaLAUGu zDp2qIl~0F72v2ToP4e3M_(gs`sT57-v3pYEs9xEHvSYBL|Y+_n%_sm4@dIV z+0uJnGD7vG)lQU{f3Jd(yl++qq*5Q{_wvNM!mC+oyipuLMV|72E1a8*GW8~bi$1jB z`+5b=(7DGBB<;J$!=$Ky_Ngh9gsqFrD?^voQhBa1y<)@jxo2W8qcqO`S&7G=m}uHZ zWkUR5Hxl+3?qV;1nvO2l;OX6W@~~C^=zm8LQMTJ*mFc&0A(w@3pF{Q4pivYHkT9I( zZ>TIX)havi7iRJ>rn+B*Eb@8=LrgEU^+2BD{IdK3@9~yUfkl4;LK+`oK`%Ac((9`F zq{fJ3ulqow7S1p8-QSVb3m zHlsFc;b3f%X8Lxh&|ty!>dof+DM6>P>TT|sttn^Ts%yOL(oRYvNqVjM`5_ArlmC>O zyokW&FV2aVRNfzz^EiwDHtRsyS3Ui^$b$GK0Zn7J+b!#gyX6+b9iKE4$UL~}N349*xPR#iS8wraNhGei4UQ4)K^O`d&Sn>H&F>csn;LK0rqBL5`iB5>FH6H)9;U!@(>=K6+}i+X}$V+ zh^r}N%UUlu%`kF4m%zB-{sIrGX(wdCT^gIO!>%)Ok8F2@-R*{774Uo2 ziQlxeZ0IUgJJV2$(doBKu4c6vyc!F7kdVHZ3!e1dI=jt#*==4l+y+Qmo~^tDx!?7I z&80WG1pb}--$B6>1WPIY6Wt8%x2f>r!JSpu5MG+xNz>&B#tzRqc{-+ax_f@fwp2>< z$XZcWp}U6legTHut6^1V!9>RsBWm_xn@3wme~$2mCYA0c`B`&fbrBA*tP7TG$)X|iRC!^~#-<|j)OkY3WT zLxtS$kzrilQ`>YmT=wu_I-X|MwCKV`FD_zryxgEBmw8@d%sFMSeWdLUF|?EH+=0 z>pbhmp5oXD7@1(IC1S+{R1t0L9~9o9ql+Unh)X0PwLs)c{?Dxt2Ln_5-+qH|tchKP zY|oSZq;ZAU)$Kg~EyP$b@=&eUShok~KRz{>Mp%DHp9}+o>Q^RRg79KEuAIRPj7o<1 z`9%gLJ@PB7d&cUq^UQ%BaV@%Q`g_F5zuZU5B0x?Y>m)_|e_fRfs$YB+5gu~*MLQn0 z(r}b`5_s5nKCja%(!XL&JOVr(qHLSWGBNDGwfLblo=nK{I=YqK)z;k4f#4#ed8ln&%b9YO{QfB7n6 z{}xNHwQR2en<#xbIPLP~Nq8o~`WD`>U~NbVm)QqpPJ$fDOwCvF$fM#dEbl-xQLuiF zU1kP-AjzzUU^82<)`*@{4L13{c*~bBU$VBdo=_`2LQx`Qid0N@_!ScA@!;_g(C#_p zJl{q^85bAc2oQNi8{hxW?2UdA_=d!Nr2dUxAqwvq9OeW&;j|}{3l$5(@o9*!K~ysf zxp<3+D=+as&s57I_*;(Wl45s%?N%6(-{Jrniw!LSnuini{`EIaJKYw8gB~;LKT8Kf zpszoOlIshYA*;UwqOdtlixZseR@_?SKQ|pU9DsLilmqsE8)5}@rpMh~O-Jdm@*2Jn zydFdf9RfDl2&RAD3prc^FqJAa0!A3v@z-lP!B>znGosT8?nowW=bwKrp-dFrhy8pj| zf^Z28?t8$^{ddJQ)C9K!Az;h2)P4gy34WebWd@z7p4SK6pynYk8F-gqdow-!4OdaX z4~&gi3}xM%2WolGWi1F$;_DC}wAY{_?r+Fa?F7;9t&++%FqumalbNvOQ6WQxRn7{Lar^SKI)&`BX~N9MGq~)!7?1o-c*h zUF|juWJ=uOOfYNquZm!GyHN~JaU^g;QUx3DYo=Wb0_p{x|ha4s? zv$L~BCDlY8)+A3I^W484KKsH?Nl}xtH@lYM69wAI*+3rP3J^OEj0;#n2pF!dF#M@4 zcgG8w^0YBse@o!_)cpo@s0&O-iy#~V`z?37?DU(U0xl>diDv=~3^F%zy_YGmpwQ## z8T6%w(7jllZSrur1`1gBW~s&T)-jy07b-l-C$7SpYsewo}_~B6~0%^vJgVn zknhIiW8?x|ZSMl6(V5!PbT$_b7~KW(+>f}e@;VxXZqM&pPL!w_wD-hNjUkA0Zja<* z0@?Fj&<|HF(Sd|=P!1L8H9Z>cda=##yq%20NJ&YlntFWfNJAT1MxPxUAiF6o)`a=& zTzEEYGA=ne1n`E(_DnOpf^wnmGNq^+wLU*@WjJF7o&)x9{Hw=rl0yP=q&vu_#KdAF zY*S8mzUQ`a{a7mUFi9O}r6-c_9x4T@@DKp4KQ31nktlwGj)I~*_}unrw_(o!5Ro_4 z$1@$Aox8xaguEz#H!-#K$z8(*X zAD}fY+9Y|%@@16+3PR4x1fny^foX1`^>TaxuT{$%U%goC(hwu<5;KPfw2w&}5Z41Hd%FAy0wkSi>OG3c9~jJO60w20+mTUO>bhPZxj8njKt} zOWPI{LPEJNg_EV&!FcciN0Ap>U>Qr12g0FPYPv0u!4lRhanN67M4x7KyVR6Vhn*;f zjvKR^o8kRJu0vD>K@U6Z+2uMF4c5jrksj@RKlpt{=SSZ}4!pAUbBkL3t{fMD&1gG%f(K?+6x=4?rkm z45Gx5p+96SZOkqRB}~*Eo<}-B9us60S9-^5pY2W-8yBbM@bC<_VrLh6Rk+)-+b1y{=9-*X+Q?<%mvt76*s**^J~!iy25iB^Y7X1BneUH=IT{ zcl7vghjj;K5*H_Jc9k4`W2FIxxvL^{G{|<;QW-;lv{f&l1_c_u2ODkmJYr|U~N0LsFq&Oj`CgT zSw=uq-%-K|`U#eSxew3=j70|6$)=d&u<}YTJ*THku6{c|GtDsPO^IxpL@Sr<3r^8X zx3^(ynE|Td!JA;3)yK9KfWb+R%|O>u#$16#ma?|x7!OaNRrS}N=pjti&U+J8*kmEs zQm)U{+25t)3>&&C^||RAFVTz!C;B<8Y4(H8r_q zo3v}T>+%XPZ|zwst$_jz*Cf%u0qxeD)h)aB35(5s5xqbby98rDC{ zGM;@?=R!?X{A85pV;&ftkh2kWPBVINVDBc&gBu@vQDd2;`NKnepXT&T3iME}t zGA{_dMgJglKvy<~6} zx|V-t?FMWFCyX^8B#4Q+D;Ij++~T82Z#Mv-`)IG}42qhBudIroH$@);8WWkW*ge-T z`wWbH?wy5o_GrD60p_5Ee2l*CSDot3$~>|na3m!hAgt|-oTyI^zlzPB3hW8*F&xU& zg9FLNTO&bs2&R$I|G ztz|Q6SnT6*1a(_|c+%MM!vTa?hV7S^L+ueeJFL0$TOKE0+y~lhrnNpK=1VGy>Ir9% zCW^zFH)SH3nrQy)8I9MH6R4ObutOsZMbShv{^F3ko!No&7DGfX&I+9}lrr=21K9Dx zbAhK!mRiq47QlJ)c8)}E3M_gE5bV;;j1>Yq0Rth~VGkIQh* z25KB0S05j9Q+><5X}GrHIic&4FE30@J;t7Wnep|@fQF?YKcFaVGB4OeHpkUjMzheBE37-e$N`v{ zMWL}z;@Ke-BxaD%?>IQjl(hnqZo$V!gGc?F>1DveZdojidO7O%ny(yHMj>K-#nXsS zK|b19Irz*{-NgNUWsT?H$0%2Hne|Vuv|+sT4O-!~xkcuhSmxgoNg&Rtx+&&Vx;mqQ z9UAj&MZxA;!4tspP>gOTDyw1_{*-o^$jBjBJp<3f?g#_}P=sC;9!Kw!)A}cz2wv+ zrG(!U$+U?^4td>pG~Td@#VXEMV>^|vv6zA&!|Z3U_e9eogP2a?FO*NI2nCkaAUIlU z7p)2ZfR(r{>hMd|)Rf5~R!gB14KB7ct5(xhC>t9aSa4DTZ>gp0%B{o>K$cPv`52bP z3S5g3;~cQDrV{l8QGrxjSjj`2%kD@qZ4rlQWK&PgYH~Na-d?PYy>nUW zqFPxz}cXu3Y$z zJ9KzxnqPlUK+kpY-UAj^0ps^%Q=yu3g8H=3LQ3~7gZC-rv^*78HP?-yA&f!shC67oRvEj&B+}Q?yPcq&U;UFMK6wh=5RH$++*$p1rap>*{I*%_j+8 zQh%Clzt>NH<)5AYcf?zl4${m3&Mkb4l?PbZx92}F5fBja(&av1ZZbZQV9;~HiL$82 zmip{{6VH9$&d-=)Z?7|(J|m!7Pc z#3m#(t7$lJ7!=GE7z5;*_iZ#hHZviB2hQjx)`8!5Jg#l44;oxD^l|$WAtA^QDwyCS zLl_fbqQUj~&!2JbpG0bWP_#v&%g9#2iwG@k@ee zc@z?SOb+a;4kFb#%nilH8BmNGb5?5C+E_SXy0IGZm`|;Xib+9y>{{R4!}`Wl7*AGX zGFA&^ZLkV2W0rOW@UrSq^opF+omw z9~0^bZD^oX1*MP=j)^UURo|mnciv{Jr*UyZW?w#(?TKH zJcsGNp5KN)TP=I6T}&=oZe={TJAcy1wSdN1YN9Fmn5==v`DFYh>*CN#lWXywPF3mD zK=NRC8y(w{;4L6v{CahG-dd-GocL)B{4GRl@kBnj12XQ>E;fiYuYonOAS-Llrq}X` z74S0_Ode|#@C^ZXL9xPL$l{Q|3Wb7EO07Ux3g!k!=$t6W&!?`;vq^U@dU%v9RA8fM zV&#oe3$J{D3Yw_5DH(7OHv&lGp8y)Y6wGnV`P5LOOeA1+y^-PDNwa)f5|Ug)dyriC z@^f?XSY@$>61NF|9!a1Z@b%HoXr zgUP|X83l6V#cHa>kB1P&7qJ2HYhBu-6ttpLAgzO;v!2+n{4UcM`P%KZ8{A31&A&jt z%zZ!wn63C~0@!41W<%uF>PCy&t=Qn-9L9md==Fe5vUT1`B` zt0nP|7Rmw}f}Zz1f)HyucTJZYBF(w}z^ERX z1cRs?th!ODmC{hqE!Zn~Qx(7e>#D$C1gIA;4E4H(BRD<=gzw;D?b=n|2r%-s;HVu_ zDDSdj#mgQW(Kv}~Do`PITXPLr+LimGySI-hYETm)P+E(LcWEIuFfxxEW|+o0Sb>++lm(i;Xu3@{_o z4V_eAQgd~k0D#I}x+^wwjVN9$;^t>6ih_XvVj=;KSyb3pzwaQsD%kLEob~T(0Sz2` zD}9C(1XP)Du*2;0Ng}BUuJAQBv(vnDFj7`E48d&HdoC~o#2cmahiPc=6QwV^6PdM@ z03Qo<+W80s%RpaL*qn0zV$KCS$!d~MDdVg``=xS{o-xDH6wAo56;wJ?6=$+}jSarO zhB?7u?ZZEo7fcfN6QL;s8aP-f*V|_e#_U>H2`V~Q*Z;h|Dz{q{v6^otvA4Iks4WDz z%bjY=i7uc?KLA4~^}$iHb9R;h1Hblx1Y6X(i(MYf-hNZ$MNjb+OiGmiGRh$kP+`-Djh|I)|Dy}WMYKPgbt!aT!gf2~*Eb~EkW_U)h)sLy1R|xA zfEN@DdF>9trosybK$6I9Hr35D5IoI*aM_-z53O9I4rn`(P*6}Tc-UqU0gQx92inwr zOQM-CaQ$m-2cxWq#QE!8EVRj&0x~p6JP8a)Ut_tN2g9G3UHxQy)&dIcWn4IHc$a&m zU2`{*WupAlfQA57Y^o&s-&v>E128sz>6+Shag*yoHmVPoE`j188X8)=$6gbR8xk^< z0EI>YENXlHGa079{vEJFxni76+W_WzTZ8kWknR&q7ld;uCQt;zoS0{vXj|GxR_r@8 zp>=c@OS2#fi3S7*7w)ftTl8C3k5?cXA`U#_h!=_z!{du$( zftw5vgw3Zy9c^&(60gLEC_Cmt*iTsr=8PHrh?bKn7UfZ>UopV<9y?N$At)rM{0+vF z`{`iYeN|)Rru2g+Cx^NKA$}X+X2m2Y=ZOKPKBi8c-E+70NK!#96c1@>X&GufT(J&Z zKY7I^d-^#a;9XmR25A(LqyqOfDAeu2G>+=qtkD~QUPfeRx*~w*Pv(2WxFZnBeON}L z3&tZVp}4sJq*TxO_;C?fni`}gDz`SNGJ@vKM_0};!pEo1o1@Z^LVzPRQeZMs!ScRp zj0g1c)OJ_S0roMlc?i0|ndeyhXoPTlp4UV~k@zu6QTow>m@=kPw(_7vAF^}NgB$0W6TPf2! z8tzDnBi-tcetv(Sq-TeJh$B-n=9Z|d6`ZO&?&ycic7!knVUqohz>@+{?*hyt7?r|i zqJ#vWRnMwQt<6iE5La0qp>uPrkcyfb0Te%hA)#d6=M-Sha?YkK8Az!)VTsrZ7kzd{ zhz|f2cxhkmD~bisW2|UD31m2w z!LfEvCrU2Man+=|i0DI=5vJI8#R(&#xIG^WI zOXUDLfO4m2z)ph0NZ?3@PaCqNz}-&K{b2u$g2uLeeByBfrd6wSo2ut5 z!!|huSBuQ-9&GQ_mN8J0u`-d(;Qf>SMoy{U4g#OB%Aw?(^clfOu(c@G^*n_ZdzAXi zBk!drX3Wxr6;?|WOIby%yynBy526}9&TM*~IIPM6rTjghx8>LzFSaa3GlVw%ct~Vo z57J3%=v)M4LNSJ0zNEOikz;T&GG6rm-UF9wCrG;c8{+Mvpgn36D$J1pO#$~4*@BQJq?&fz~?heuiQhXCR7>@p-vIm($+Vp_}4ofm{Ky*stzMU-U7#g^-ODvp)t(;6Aq(W z`#QV`zxu`H{p?lEc10`nzpX^c9r=2#^k;iuGDltQhQDj(Mf!@_Yf3xrr-WCmd&2iz zY-hfoXIT~Qq0I*Uyw_YP9?#hoF-Nv&H%aPQVOnER`RZJ&I!4Wzq$id$m&zZ@^MrT^uFRFetxgU98r-UDW8YwcIwK}o@iH7Bx$6oIY7=cBUgX`nhqfyC{D zlH39pdn!##F~j@vaU$!>lJN(4F$yAKVA^VdGh$+T2$=5iz5x6}55=#7FSHph#L9${ zD5d9q+xJ@A;O+ZNU!Vu7{@=+ehzyzw1bWmqHNH=>37B??2I60h8FqBH<>7?`l7P1FzKE-{45a> zR6LEgacuvvPnq!gXq25x;`=*YRRU>qluf-o)&>V18FamiBm1=S$w`H2bc?4rtd8mF zAIn^i#^rzT^4LDiHmx8k9Vx3KfmMBSiHT%}=L zMZ()l`+2Xkd<9@0i z$waB$9L|nc2B7#nKkQ>2qot}Se66H}B^H0Znrh2b_-N1gZX77l3kIY=z!;bZfG^Id zC>8cbLJr0m?eX)?*T~Z2;xWkDSfzCr8vj~9ks1Hsc>2v54^enEosanIX$%gP-nxXq z_$Lq0;lh0HwI?+FZmj!K;hfVg(T@i+bx-?vk8qzhn#YBP=G!ATR;i5b%-hw;Tgx#v zHE%qoL9OR)$hK|2TG&t~ZTM;{;d-rBG_swy3Ay`2OUQCMV8R)=Z8Ib$^??-MUE?G* z`KB316>Lj4caITwcc}NcXyD0d?0m5{&ov+mpKn(V+5rkFF_`0Wvm(rm;|3<%M7F&G zDFgYOGi%c^j*%!P_8RpUS1^CNL*u2_$K8`?fTP_9hg}>r%wZimXQf1T?e3AQn97ha zgXqamU~&zY2@K&#dVidx3uC^HB0dJp`=9{1!FODJ>u$-q{C_nhftkFd{Z${a9U!s)|tk;iXQX{q-hm^Wtjsc^pG=MS%&I5G^14PaXcOUg+MlyLX8M^GfKP zJuz5IiGn}3-(Sqc|C*+uuS9vg?DJLOtNKn^fBfZAW1LDwwVuKao)KSQ>zfbSs8^(1 z7I@1&3}0AUUMJInb^L_vm`ItfM@yMrO|9qk+Q`U=zO@vDudX5OkxMWLryC519%bpw z%E~H`LTIQk?t7=MG66)>$W8{&7uz|&!8y|X&GOz-(=?JJHR z1fy)D-$HFPxAkpNfl-PNdmA}wZ(Y;ooTYlH<<2u1LIGTTP08;?LqS9+9D2?MaQ9zM z`7c;@$0HeyY`SD6*wRHg}Xd|mN+-+A$U1ka|J zRAtTewjvp<%6RIunLIb`$owNYi0}JoNCM0P0G~w@7fqgrC-jq}Ok}nP?xyLNDm=8K zR^mckO?Ql8?I8x*!jyWR1*8mS)s>nM-xtbrXn#{;{oc@Ey$0jFB_g`=zF`_{7OD*8 z9i)FOMRi{0Z_;4V#waA?LnbgKNcco~e#IXM(Fsk`9~v{nny+}e(+b~kA4 z;j(jZ7KQLAR)#I2XpDLp4_cU9)XaI-c_7{jd=gP({h=jsbjPtPrtfMD8^@lQWACH08z`>aXg3Phimf&r|%FQZUN=ibV{$SEQz?d^E4H2yL| zA*^GnMr#L}#EJ{cXbYM%C$x}L|BCkyNt zj~3c~u9ViZ8W(@AeTBu!FU@ROCC&vNs>jVS9yg@tv*+IBaYIQ%^{|1}My*!9VD_Ww zcTaE@VwCYb^Vh77t?h);7T+{w`790$u<8#6T5Gzxl0vO`ASV|SQ(T)ft)+}}yPt)1 zGs2&RnyN<)jXyoMRg9)9$YXJmgjDu2@1L{J@xIfpH8 z{lFhVh5`;n5?>K?ANg6jK4RU?m4p8n)RN^ddC;1MS;cA<_pK(~%Q7F&MJmxC%N+T& z{&ql-2L`HS2{W=10@JRLauuN60~^XDM^PPa9O$U*8ymaF!ct=AY#&sJ|8b?rt&^|e zX`wvBtZGVXz4NzTV>kMvdM&8BWc5hz$A0;(_AgV?4da*Jb}_sjP1pDG4hN4*d=0uq zpD+lHD4xz|-_ff4(e?>V0-X})c7Hbd-0d5V!0GvWS3u0OyclCk) zSHykFEW4W9^!VTISg7d>Z_-wu6w%{d{hrAm|Sr35w+)E3@CciE>3B=r^i z(?i*dWGA!Kqvh22ug=ul76R8R88{~gAmcp5A);9xWAB0t6OW&x&5y5V7u)mZj3KB# z)|j0Pc12b%Rl=QEDqUb6I4ZNa;4^z!m^$u_wThu-g)Yy{ZRd*ySdRNxX7GO%Ho{+M zh5gu$j<_D6Ibn)wcZz6_Gcqwl-%uh}LAw z@~!nAOV-ybXy`XQ?2-DM-{!LNj7pbrM8xeQbkQ@{M#4R4x=tkUYHn9@dZyk@);i4X z$OLh?`suJj7yB`**Tci(=8rQes4nk&tOtq^+cV}g0U;?Rs!0zFra7<_U?wq$mszP$S|FyR+c@!MCoU{9ODpg@}9W)Ke? z?|>~Zo>)ytU?<7PT?|-*>?-cV&99srNQ!0$FotgT*3GA4S0wb6+9Jq2*Wns@SHwAO zzjaW^Cpn!sL%-))HRSr;q(*5fFsPv|rr%6_#?A{o3nPD;|Gn@N&b^N>r>rL%vFSTl)lhrOe+LjQb)$2YM%4jNn+;`(O;!Ks$jezj&a?Co6E63oXuWqrJ<0mJmIfU916s!svHmBR z(Oqb98LY9op*s}ibdG3CFANgyOq8p$db~3!NKJkeJi#qk?9AglBG$Cgw3aEE4wteC z>3cgMdmR7vQRz5h{1u|wyS$-=%>DLt-9dr=i@p831=7qJPc7({Ka7g?PYOI=k>5Sw z-n`^|(LJe-`($NM(*C#?j<}&7b10y7E6$8^=CTYwbJ9ep&@T#abDoE$)j~vC(=5l--jR# zX<4W@J&4koi!MDIXt(YD+?|c|vn{y++I3)wl)!u5M)pH#{C%}a3Ig(T%UGKN%52QJr#n;!m6t2{))(I z8ENxS`Ze{rB6`MXi38_;h9;c-XRB5j*`W@S*a?K8sqd09wmAg~2XoH__CNDJs(HBs zW!foJ%kyjS{H~u(i<)T9XuADcq+4S#$BncE?e;;1b~$mZXRlh9ze{9yiv(t#$2_Vx z&;kD3Ke;^{b*-SyriPT3rN1N?_->cez9YB&q1YkbJd5h=vN(QGnGWN-P$3cjGiR&W zK#wa-iNQI+GUxU^=c=kw27=StXaY+Zsp7sL6mI&nS1Ri6d~;ih_-$l%FwW<37n^)k zny;amq%t!r=?Q#l#%>^?uhGk@UbLE;=>UkzGv{p~gJQ<>a~?Qx;X*T1K?m?O0gL__ z?=@yee?IbG;i?&X=p-Pv>N>jrOISyoZNJQG$tPceQT|hc@vMsYLpebW7VR-=aRv)SHDaH{0t)l4uCE91oh-$7+ofHnD0|;=s%hn2*l|? zdLii}QUxeYzY05`ZZ)db(;up>jMp8=NWPcf{%^TYXQZ!#JxB8C|M=*_%D8L~;N?rh zr&*#D|IybHhChWB!kEnCH2>}c{_j5ga717NOSiXa|9{Efq^NIOreKxk-$eRf*ZGg1 zGRS@gO&3ytl|-}r#O@KyVTKC-(ng%ca?*VLgO*iLx9lxLvh{)ZNBBO%c@rO!FcYnx z2)PniwM7Ro15>1>g95e;EzagW0GH5wKN7BRYFWYB@gF)3N{OX%ONvH!mD-2s5>^w%)1fA071P5$+@ znH^w%Gz~~;=tTatZU4NH8kp{opK<)R%IN>zNIx8~Kd(aF;?t0S$J+m12%r!u_%`5t z|5_kyIoB&Cx-Eo+MO1|;txvGHF^{DuKi!(^Mo?)_g=s6gpR3Y;xZG8(b1yz$RlaZ@ zj_WciNHReyWfRXisDEQ<>;2nDxPIfM-u|y|#r*q0b6UKLoet{OP_cew}D0sjl47 zc4ioH-kl7bZE)r*NT~`(2nzt6U&>aSXJ%S2h@-v|hxZuzt=^l~jhqu5TI!5me9@(n zU`*Xf_2k$y$2}IikYwW2rYnC>NH}$(S*&{ZOuJY^Gli!_PppHYU8p`r(gm-8&DC>P zEMV_SR(|YDCk9Umly%6QPWPFAd=FcEz7DRjCEqGR=NouC4(12ttwAmmx#AkUpXw$# zRhI%^QR_WJD+N!e$|=Z@w+>VJxQFSjmDbO!q|Mr`$24?1#rJ!qmVvk+UK1p#z73TP z<=+cYlI-anOY4O^ofJy~TB}m?k(<3$PrhYa@YnHvKTZE)7Jxx8ZdIEFX;%M&seWPc z*53s`#ZkA(7JK?r*>DsZksZw`4mVWK7$_Qk-1P5UJ1P%qd0=14#eH0_weGt=VoR@xe z8@gWb-t2o+OU;>jC$xz(Ta_PUKKl?P5=XuSyo1M;F!;|!V zd#V^7F0v4h-CNY)BY3YC^%-}HtLN=e2+FfJ&Xqp&;7QX3nVsDBuW0^0*xhtWeT6L0 z9d6wt!U(Z>_fJSJ;WO}*B!pp~(fBE41@r$(&Ls;7x?08?4v3ak@N7e;w zxf)ZecurI132<1LNZlYbKeKQv{tsPe0TkD^?ET<_5ALpkV8McWkU)Uo9w0%3yW8Nw zAvgp`aCdhPPH=aJ!5zMxd+#~#-1q9Mnxdv=53^-SukO|T`!jvGrC#52=&#Kt3pBTj zsdwU(sLAL4sM{Lqk6>6tHd(1XMV@+CVChuuIV9ig`T+-Fi*5To)O?Pc!oxYOl-yio z!0xLZ_??B@6!6#Vuem%4r+mv}qC7V$iMP_;^qq)$%ICq#JPMC}6Jo|hB5c;f^|Z^A z7km{=@~IA$+=(ERmgJW6{>TbQr^w{#lz66uHPUaXmg4#F**pfli^-FhPkXek^nS(2 zZ;IJnXf9v()#$4 zk38Os1ri{=bF%Enlc~-Ww)U!A5vJN*NXlbhwh{ArTjTB{kV8hIn!h4<8Rsd4YuftE zMvY-DZSP2{ySTKbRfJ)I`AA@aP8#J9zTCAn+}4?A$6TFu6)`t!kHdBHTwMXDoHO%i z#{T3~b!KcM;y0@j1V(@P=Au_q!NdOTQOAa|M>lN<*u!pXCxpKeXIw1!1eIa8G z^Y^wAI>D8%nd*-G5TjayDpMFNoL{nV;8Pj2`6YdSlxenC#?i4c&zewMTd)KX3!fEY z>$13=c$8+Y>(!_jaJg-#16l3&K_2A^AiZ8{UH* zj~LTxc8Qt|*J{$GLgT>`5UD#v=BBQTwGaGf1*=dc~HlFV#i7@Vs6;+p{%~vyLVSJkO8y*aTtwcox}Gmpn;hr+7E<1x8<(39ja?r!||# zcV1VWFhpN&M+PBKZ@RM&CKmWQb2k}%*qKqD9xjiMS-w*3wtKL%K#nvJ3i+j-?}Q$a z*i7(95+Pc&A9PaTuGiwDqP`|;c=I$S4V2sRX2)f(uB}I=EZ1>?$n#iJzRv%MYtYmkkV zmn@&4r|Zfn^bA^YpvQGEwAq{CCc~+-ELD*AOj>hc6`WY(#8u@Xf*EwIex5-LWld4D zq8X0Z`{KyH{BR&uuPG4CvtLJ*lW9C_*aXkOLQqnv*~TKyN@gK z4O#oY+#T1)lYSs;Q_s3hLnpgCK;7v=p&RFUX4G1wf(KmV(-{I~cUCu>pIL^gzgaL- zP^$-8>u1d+Af9Y*8;Jf8Jif3iIZumgWGAAQa{!xR7PgZ171Cy$p zYKrwgEKf>*=$OAu*-iOc&)E^=F>LZH-t{(3T?A%tAc*HfDW|~AAZXaSi}Td_%gq^< z%NZp`Ge074O)H)3to6jyuYJ%=XI_&}13Gzvm)hKx)5_XSa1b}Ayro2!&K(sOL+T6fm{bawUQbNg(LA7xu-sEvL-a%sh(taOEHF=uz31k+ z4Vj)!X|A_`i7nMrZz#>ci?)6G(fG~frND_woVBYgY>$6?P0?CNSeRJVc0_n!3s>x) z1#l%b^w&6o>&E=^TF12FNBJV3&uT!^attU(*O=wRQJ?M%;K}@wks_L@lo^oeI6l`Y zf1Y3+YC1vU8V3xh`y;p5FR10+wFNR!sPL&RcQMk8NADMA$3{dAA z)wBrVP=@)I;=%5Fi*18A+rvRICpP1t-n2ftBU`)LCxE&tkW_e?SR5MQ&YyRAJJ`q& z4a9;5Jbm*#+#r1hYJG+ZA5-S8jUDr+aPnyU?bLAYFK7>%LYFFA(|*KZ^|3#W=KX%y zT7HMh*-Gr9Mb^3M)1{GZuhm82>tNh;VV-i)Ag?b#Za2Wuo4pAknYFb@pojmNjBLDP zEY5zUm~84_c%YIStDWZd4GI#UCZ%z>s zH4iE8uUJm@gQD@0Asv@_ zJ}M`6aTY89PBG&AHIg}yTKUC`;HS;wPQJTaz@)hINnX`eQ~Z#}aKTUD`{;QZg_XTi zDI_~AkDPxpWY}OwBEOW@ic_v^l3vzks5u204H9SbE{n_-RcG}^h0J2c)vY&U+NJm-QTA8y(ZOOP~zUtJ4 z`r9CSxrh(h-(BL=cQ3nOF7)p(-P!oR7LvinYdS$Aq@&$+AXPm2iv@6q%1X?KXXC^Q zxI(_IZGqq4wtE%c^-Q7GIAI}KW?0P|NO4yj4c<`Gw!Y{BU_b zae(ig55L$|MXqLie?{o)YjZ8WJ+s$hz&$={fZRlddJIZ6)IsaCpSlv_?lm1dZwlDG z+QO%c4{Il=aETC}lHb{Fb0L)Kz_5A#sxOi$N^zvcn|WcNrXjw_Y28i3yj-HMlT@HM z5M4vxucb$^bFVzMtt~788zkHE-r9A0Ko;1nY!1+QcmBsc zq%?*773m$0CF0yQ+V@)8adVOXo*0M+oWHGw`YQmRNxA&1B~kOT3;8#pX4H3YZMyQO5j)K1Do*V9j+^%_ zPb=6G7#XQonOjym_N6h6DtNa0)uSjS&QYr9mVUPuyA5;*-^H8o=N27n>;h4&BI^4ffaTf!(yYeZCY2MvLNOCAHK8t1id_O1;7y+F# zQJ7ZU6BPm#t{3=@(39N5jEYmOocvs3Ezc0>%k1wzqJm!ay3ouRH}Lf7qa)@wPtjX) zE}WC98OKF6&l2akAz)Z?AZ9MN;f}hbTTK+LRr(mirIPLNh-b89Y`QNrpzmL01W8Qs zu{lIz9Hs{gon*frTc>Hdc2Jr`QaegFL_`FG@l(bh39&ZkN3Y*)KpM{u_Hb0juZ#4 zl$q<;{$c@yw4>(>d1hm;VY5Qo=Z49gDBQ))Sub8sUmv-Nyk~ooId|lAg)Q`ly)oli zLm&QT8X?q&crr5`8Q;bvkaenJLnPtHT#Mnm($4Xw1}bDFG+5GmcX!;rj>SaMUVpDD z73j{QWZM6xJL8FD9k9mLk?t*7+LLw3@1LXJ<7x>%t+m zUn)*NN-c7x?Bbditf~bGN`}DhB`o1bme8{A%alzCh@AMbm8m9^mQK^|0L9cB2%cvmA~;K4d-&BCgcNpX88 zyBRfXp~+U|{+d(bK437Lg1~q$_Ot7!hd0fCwvbD``cNC|heNMoMP+Hn?k^qboVWTG zwN-6WDV8>MD2Bbx`^b@FxD}|ugE5)p5dF`dQ0M2e_;vH+)!F4&b@M^4RQvDIdX?5 zZ$93m4Zq{)mV0+z%&FX=ErL~;nYYje3j~Y{#VuD@3CCRFTLqvTuxv+MJcF1Emm~BR zB!x+(NH=Dwva`1KKj}R`Q|fL|l7#Appo>Y0*I%s|naUsWAx&{(seeeYpPG8yXv6sM zB`TOg^m;z|*_O>oFYWkYo+yJbSMc~#M5i)KW~bB9@+6DPsPY|;f?I`?c;W!z_#wyS z@lfeKFJY+7wNEkp*khs#p~NDl-rUb-ilyo?vq!zk9X&3R&8rB&e=TzvPhojHh-&w5 zcLQ^HFWlDD=lQK&Ga`FM$sSr~99cgj@zs&GF@%rMjP}e#4LFeIH@xrZoEN2XjXYE3W6msAD@Q8_w7@X>pFpHMARd0EgyGd7mS~OZ5 zXu40ETNoFut0+9B$HssAmh1K6<^1!X+$D&4KvF((V55?oP6#f{6y*>8E(PR>(!x?P z$KQiLT0wCs?Tnqp*k(52CDySnI<|PHQ*VMoQr~h+j!kRk_OgRA8s8L#mlQ@$=cRR( z5_+?KzB&{3ydM^Sxagu5y#0;iYBz}L7TozKF^V+O_ouJ=Z$$iRp+-^7pUNXE+zv+L zN;;DY%}??wmMbILA?3km%>o3En39Z>DlBY1oXSdPqtcn`Jycj?7Q|Ls^eOhfb=&#I#{rz*S9Smd27; z60ccBE&h+H3Io6?tQbEEZ=SViu>SseW%)Bx_T+P&3%>0KH7i1&y2SYtdChMrYOq||rEx;t>&(;a;Do{( ztLF3kqch*995tXQz~{F@5kY>@w3S1)t@pPTAPoKxAo>*);0AjpGjm)E9m+SV_qwkg zO5BRPB6}51pvgPt@S!|>1Ub<_?Rt1u%4yBA zqxKn28ly3P(`s_rb)RjW$u6AiCDTCX2h&ljHWMM)IGVC2_w!kpgU`VZ4_mGC3`Lvm zKU(dbCoLMBo~lU=gcW^0nErZ+hd8Vx^`_9=rv6ZjySm)TelAg)P_W9m6@%?9VO16T z9(zbQTmPKdd94cHL;Px36myf$M#|$E5gwE0r#0I2=P~c`r=p22<_ah0bBIE9w`<-E zi=DusP(mISc_fU2fJ~*T`6g7uSS=z3@-o9Ig*LoKar*Eow9nQRLe`jooM28@NRY zDP`X83#uAaEp@pp(W`p(aTcP_lSFaqqN-hsUye`XKX#G~SJw$qOe@YN)p`%z6k>i2 z<+#8aofn+{a^BfezH>Rwqc}P3&Gx1An&okJ+a&du(9%wqlVH2H9m`jk=^ry?ef4bD zL-QwwU%L8vMuT<5oiJV&6FFa(3g2u+HCddg2&XeunT~uLL*x6MIko%zd7e$oc7hfh z1u7={rprpEdlE;KB_r)wxg?x2+R7>5Xo!9=ze+aO=*ghxNiC80YFbBFNHLQs2ODv; z2>dZ&WtJL^AJblAiyksVb~w8Qezda=IN?=e72QOJOFGJ$lPT}9!K5;5Yf@7aN3|$Y%m_AI~4T4tk$^~OXXvP^~lY9HN-nW`BpDh!C=y>`hKEJ1i zTSP;pfX0`$uRoDdIh>~uXTYuDq>hZXc590h-MEz zv!G=>c_qhtK7LEnx4fUJ_eCsOayq(lpNKD@mFj&ppTlZ4wi8=oB$Q|s2cau%b=pok zBwl#u*1M!Km24^3x?RR-SK0WZKVBj-hjF0#T2Gi_TxvrWUpY18_~$^f;>w6>akVOu zmGN3(3$jOV%VJdBGzD%1U!7s-V8u-wQ;@xAgb#t7)TBfiPA5|oYCiP`JBwFydy>)F>Lo`#8qC%cxUvu2XXPmx9Rcf^8qAAufx z+}}kJ^f;xHs3&Z<^-bXu_kr!4j2^@{EG?|gaygbIZ+akiqm5~FC}E#MW%Qtvxu0)$ zSh$h6Ps71!inmk^-BZ3Y_g8M<)=JczatkIO|MmH zSd(f(NLz4UwUKJ)hry_UH$M%fur{4NI6j&VtBEuNC6Ztfqeqf8sR~-Acr53WZKvSc{z=or<1NmsX5adt@=c`c zM&p_6=%S31*+u6hfx6a5-X?rI!mRFC$dbR>VDvM=II&C z-8nnpowaDO9?gG;bU?d0n$EflrS9G)&q(k2Q|01yFCxjks?o;A3o<`^8fLa;CQIe% zUjqHo6ztcXIFAT3Eoyb_*DUg`u!q$qk$y6|dB{9OV- zU~EtVPTim1@BtfK(y$yXAbs_9Y!pUK2|49**lg5dv)YMB9Q)G}+ghW_^UX|qnZWI7 z*0I^Fo(n7EPor~I6W?V}-6|yo2Ff~pt}|}mU8OD1bDvGQn1z$NX3WwUCd7{F3j8c= z)>n1Is+yx%@OBSC(_EIC$hYW2owrhY$)alg@m>AlaMR3Tg0;e%lhC5Ri}SZHx(RUK z5uM))#8R8f5lurmWfNZ2C~jM4<|8rWQeT)895my&>xi??WrvXuXpLCeF!olg&04&S zYl?M5bAPwvD@opO(vumd|8D*l3*Nq$=#k4_T0(cKmm~QYfrHcW_G>0|b=TI8@+ zCI1bBhojnKw&I&ji7ONI?eu32H4~ogBmQPDg5fvQIZfA-)#mE!e96srKZ^OAC0i1o zcYj@m3$8&;r>nPOfJ}lmkl+J}mtd+3P}A_~xX$5I*Egq$v%PJM=d1`*m56CadGZG4 z!7j}8-$&;Rjy^7|Y4OnwIhStOmu#t8%)gxkFq-RFtqpRzt18Y+%NXK)Yo+U0PWPLb z@^n?|M%_GUBx3BD4q4JM=$3e&(CIAZ)^C+cZrV4!nN&6C6x1Dtb#vcB^k{bUp#BAN zx`I2EdK2;H7f!gNATzTff2ZA|&^zss{it_g<>HjA6p^b&)AqGK!zfyB6iUhF&22*0 znWXWDqnYoWXC*A-@@gN$57Ub@s?9zVXKM1OVEAwjc-By;n2zR*mXXc>lu~h^(b}(; zW2_29A%C`O+EXLhY)Qv@8MTkuX13~i{+f^asAyo+%vEM6~UwkvGk4$iTk!wv<5FEh!jbW{^ zUlyd3Ux-Q3Xm*ce=_$Rt;nLI=?`7&-ux>D)Zh-RnrJv5(FFz?J)663Z7AkpGk&ATC zmUB)own_@A%5T5f?n~lPQ0!vp>d36c>+%_{i`yTn5!)XTL{=3X7g z!~#t6{TbQz9(*k`Sm^g)QTp2k1E5cBHw2}$A4*4EWy$c*_ASOpxijM>3?kz0c}suU zkQEjA6Ydj$5>}rlmaQ7y<`?~7VJ0+Jamse7Y4~qeQ16`+s0V^R0_h<{ln#>QyHQL8wmnvtuj2ZxjvPaZl^Vw7$->BwWjG-X}-77ETwS!-pyen zv`P6|oiP6f(~Q}4nk^w*K#uY0R&8omMLS-q?^e~x$k>g-hOQ5PG}c&TM48;*CqJzF z=jQfln-e3){ATgv5A%InN$-I#pNj??SS+{NFR++0*NoI`X_Iw@s%Ns&Z($WnU;Xs2 zCRl$e2vF1S9Vk3=;Cwmm6mM=MBK}(;T_~~C+RJvH^0HJu=G><=@j+pM35L6*?CGAFR#i!QAU2agfo{yuRXR4Q}pVl~s_#{M?1WvSZr>ko7Nso4DOp(9JZCe&8rC$b}H~cxNUmAoNcVL#-796Ale@Ec%_vz zlT;J+1&<;T1uj>%_H62+&4k)u2~N$5(=u638&~qZKoQ?{CcO(xf#9L#Kp})SBy{IV zH_u^CjB!Z6;+raY>zIBr;p2CqyP%&VDH4@x*7>6}cYe0Uddgall`*((%bjTc8V%Rm zjHksBKYrolqeknZF0;w1l5AoX3!B%B_=Ic|g(Ju|EN{@dHD1{74APPLqhYqdvIyim zy=T{(kJ1{PNr+8fQBnJLZblBqk~o>i#wPH(^cv9GQl8=o6$L~53=(3_X(PhLSx1I0 z;0#475rmkw?tKckbtF7U^!dJve~HgCNlA12sd|k`W{>!7S5O>9Xhy3|gR$1${U}Ns zS3ZF+RWQ3*7LK^C&iTkzzwrc@yKkRgaD6|^OAdFDfhasazL0_?_Tm;rpt>iq@=u*&}6P-JaB2{kZdFS|is!=aJ9Suw2cgoV!Iy?nWThsly4y)bM~7(o_e(oJnT zGf?RjLhTm@Gxfu*V47)>r-4*ioZS8yZ$w@9?l&2|n486{he85%jj&?Y89YCU_-mx8 zd!VlFtQt^)03IA^tE;Hn9v#8Ovw8T*VMZ`il=SR|L2mUWTh{ZE##(uBwY2W#gVR>Y zyhK{l^a>S!#|JvEH$0uuNP@!Za@$Nf8JGzw8c_${u&=jNI!7eUEwB(z4+WyUX}8YR z$1?&5tkdGEc;By8>Ki}18`Cm=FJz6hUp>@63Qgpcu-;QX*K@1z9 zI&C!n14SqAHqy99u;$ZuZPVN4gsByEav5k(z@P&(BYr?dEZqMRwWgeB29}JKP`!Z@ zez3fElFKF}R44M8HT7AmEWAS%t(dioM4>MDhkI2NMnI5P=rQ2M?s=Bjap+`W>Ds7X zyxduEXUS*{|7@@Jjg|YZKyJ7}m8$lB&~j{Bc@J1vxe+Wq^4VW|ryyw0ER}0AJ0$gn=VER*ziT2xfih~d)~ zBGf=W>r4^4g37Im;8^!_&wLc-7KgoJeBPy!Yd`E6?-NZmYrQu#fte>vJ^U>v!(gmh zsjVeoO)g`h_6p%ye0$_rSFq$_1}TvQP*E^|59-YQG`bW4_%; z`G{IyB=~yoZt2&ldt{pKB2K?{=Qrx?yAXa$cNV5!j?pB8b1v9VG3@k695HxjtS=(A|Th zepuT1hF!?|l7qgEi5?cSC!e@*JVWCax$5V@%L0|zPNSR&0`XYIl{YW7KA5WMYZK8N zc4|08*@E*@lU8n1P1U#!F9q_?QZA;!^xk_{ZZ$KQv_NG|nA$w33F=6p=Y+k~(74aZ zG?QJ0_;u%q-}91{zw9#j(ZMl841zmqdETUXZpdGEc_H9qa}|%f5F>hloYdyCh(5P9 zrqzs2=bZGJ`Y!gh!+SL|+B}0|!mM)WZg?RTrv5zW3_)B{fXk~KP(W7){Qb91jESRS z-_N@#J?}cB@7c21Ep7T<;3z`4UFF_7=^iSlfUf!%_)@PjMwY5~fOYr36I?0nz`iok zLRPGqukc~h8NR*U8bwvg+$C;?rb)=^idVvD3;eh zHt&AdLU=sAs7N@+o`v7C-SuB_#%wKS&rs*|rEDor-6D-2$r>JJcWc1eD7^vP5TVF5 z-T0`*b6?A5X6DX3-=hPa7`bUQI1At{0d!A<-`Sw=>{^Q`kzwQa-i+@*Suf+K~ z>GFTSfwBP%?MKVDH#-0O$Y0lr4Mq0b6yBr55Ri?fl?76Cv;JgAQwsX=J6Q&|^)vq` z{W;nf8R~Kd-`t=}jNDBL^R!GFU^~Z+>?+e<7T9s%!sbyg&+{Kn_g`7jA;_=~>4Nn6 zm5Tp4cL*6Q8eJsW2PYo*YSWBKeZ5eL-hZ>Uf@@75sia6rT>2ZGNSN*+%toS z%SG=PhJWU3)2<@HKk)Z`r+{wZ{<*Dxy&lqhv7X+SJZP6~4xYA=saKzbQ{8!N1B!EA zYZG^Z#xeG>6`Baa&JX{c2Ywm>WHR5>(xhSj)m22(utMbFxp0b9dg$-eH2M^Lj^kH; z?+l&>+~q73>-@k}uV62i^*DbM^{V}1l}fByjRrdtT64Ih#7 z#}M{4JRjVj$7f5elxJEN;C!5yn zRKkieSG@UNuU##(?#_1U%;_T{|HH6XW&bIT`q$X##{F{!n60+wr|_3NhyN-;$d;r8 z`i-)CB<7!?5$VfI)$ta-d-v_^`ILMyKpt@D?q<6vP%|fSG`5x6`+_2T`X7AOy+<81}>tP zLj2=@uGN>C-T60sgZE?O#qOM5rRk^~y)gBv4fS`1U(Q=veoG zn}4=}s~9jK3Cd^xs#s{Iv)$-LM}d%w2Y}5aMFrOn)+x!~(=78;g$2@+&h2Xv#%T7h z)1Nx)@NxW4hYwMKl~p~Bw4D<8fX|x8;Iy}T_a=>RNC-vj*q!8w^$_M2{D)J@093~mAfp-sCAbK7*32Q_TAq3#yK*FoZqeIkt> z1>NC5qpZQb*|vnYI4eFoPBlJ%!i#^2*erwlwX;jDy?H++b{Ss~I<|MP{e7&Jel-FV zQi1b9ro2qceIoOep3AC4KQOl*`M3hRlB%2Lub~0BPWr$j#W`7a*aH5WLuM5)?;NuN zO;<>;5{Ta4B|?UEBl138^ESywlFn^zATSS?z&rf_5kA*W68nEK*^ztowHW(#cq*a$ zAE)|1&semR3Yb`X^a0423VN;rdi^Oxwcvx)+bHs*U!}%B%T~Pa6#zb)kQ)Hm{0UIyIfS<*Lu z8XaT)NYVoc4kORzdQwV&90XwCzUTcBkdTd`mePISw`1IQ)wFSrP%eS31fPb`0fFxc z@ug?`D7n_I$twi2F?uXbkN-BljNPFXJGRx_V~zq;Fb1pn>KH)!a1g+L zc+NT;I$pLtKPu)bX0k>Jo`nGTFp*v)FX*ZtE|aPZt9~;hz;PP$AleA%9e;6syc8Gz zQe}VqWU;|mBr0d`#&dSo+r_iy#e+)#c|dFSUfbhWGHx-Y%0_0_Ullw{+vDeJSp4XCI%?Z>C54$^>_ z!{hH_0h`B5O%quXNGvGYsa*%g0Epvm0-#dV>|TsZQw#ulvo8UZ$8U0rpYIl)5SH$A zjfDCPLO4J5Q=T-RM_whloeeM;BS=mBko^fDa$mbbKX!NnJYr7u-Yv>j1~AD9Ml&l_ zdG7o4`D@gVQK2cKC*DGj?vK4B(LG`h?j$)vZbhb91A4oK^`Q=};%J9uMuyc{75|zZ z|7`)GG_V(h)nBF?9CoT*h&nl<$b||e0__%D(M$&8WlMD%jNHWG^|gV+(=!Z;8Kg8> zl~3CoZ;X41F67&o5TddDXsLb+)2QJ2 zSNtn1cVR#}BokdaN-Q%=evB;Hry?AaJOG&MW^sey8Fn=|<-#54@cCUn0mZwTKhi$E zQtxPxE%wRl`&dk~q|R8@h%ezj!wn6g%jccy`V265kyJ10x+SK zU*=xV)!XH?a~8N??tf_W_Hw)3N=#4PuV7F}`EgEiyxgL*!~12qxuHvUwcw^3M{&3H z5BrP7TT6i7ZXNPRMI-i=)@!Xs$ATYn34`uBz?h%~#z#fJU38qy)?h*` zr|J9KleXtgKuverh_N(>0`dYt`)XMH&R-iNXnlA%AFcc}fIZjh)%0mNMa@IYs0ARh zOdNIeebuV7)+4jGI_g#t&W*}dNF4;IhjdB2XQN_6Qsn(uG;d{+Uw=N-GyU~VVKpnH z#uMQD@nimYG@)fs?W*U-U=kpAG;+_?=fVD_l_iRmvR9*M+!DwvD`8 zhPV}15H6ga9^7sL^35jzZ|8mlK!orfZabRUOXp}fS)*v9qmh;1<@=jRTr4@4Ats2^ z;7=;Ze3gLJ0P4IgSN~q#1IuMN7<`I~!!uw?yyOL8WDs^o0a}xi;TYzVxd{LfhEI^k zepAL{x#N{qB}iPi4N!A=6cdX>#-V>x9lYv)&koW7LnvnxUTIA%W2{92V8#h?8VJT$ zLvBG#J4D#XD#j3JaJlU>Aj5D55F1gObBnXl;oB>W`}SF>#sINt6;wU&^B)Ohu@PQl zuz$1$7O9dag8vvGGIDVQ(4%At-gOXgnrsgx59i$NqEk#F!Lx+u03tZxO=pNSB|H~M z9)O-tEDljJPGjR4g1_1<)>8o(s&T{q)lW5ZRi>6fm-|zbUq>^9nBQO`Bagp|$;AFz z;nQCd;sEs{6p+M=>2SeC^J_;1x|uOVxcj40aAt5J^;R{&F0}4NgBuA zrQ(Bp4|B}kMW~++Mp&ByWVAVkab8j40%G^tz(}Kpio>4(w(!6dP{_ya#u}zU;0g;x`rxwI%pt<(SauS89dvhf7Tw;f;-1!6KAlVyY0vkd~D?n@s$NaztE9 zJN%?^mU+==NPDOY!MUF+a#p!slk-&!(6I}p#D>Tm1noCN0Ck{=j0LRq{7a!gQDo(E zV4U2gi?z+tg!s8)oP1~4-fQyh6x9?Fq#PHS4m@pN2h;#2&M!X+)>6UvP`;poc31M* ze>_|82M>4|pp9qZ91w&S5b8?fA#~)~)M1=;6^GjIt-5OoUK>B!(QV&!HS$S6Q~F+` zW(3}>rgt?3yMRxqg?m@ELehW_gnDy6^rx7(||4Jd4wn~K{Va>0B zXa~0zbt5VnunQL;8Uj@+qFTiAe+Ot(IlZA1x2H;LC&3~&V!tp%%kX1|pB358D z+}RFWfIm|PQDJ7gVO&$ii(ZJyuoV(Ye}aljx^njV2a^neBI<*{ABKoBw4wH0#8iml zvyhG)w1XneifoLCUT#-L=YwhVJm(XR$DSTco7*H-FzZ)aJqU7_F~&N@S$D@OhZ#4H z_E)bO6qRY7;}NV#^pn@B%5RgZIY6>tnzgOzMkh-aF0sXc?*f2uJsEa*ZNC8DyFe-l z%>c6y9`?=3fI7-6jOAo-3RPpaQW&yn4?jDGYiAKIIe`DKvHWFO){;BA#=RS1SoRSl z_Euk>@QQ#u6J%2tbh4kikJRab<%~8e<(klW-~@=`-0^ga4v|~|w#lM`1eS5z3~ilh zS;h6Icv66vYPUdGjTfMMbj9ulIT!csb=q(l!$*=6eA-VkPiPf9sxaK#S+|0Ey!4CofOVSbw z!WriJY?f4|oHBA(oP!BcVN{0;fSPPsLHfTE zaqcgMHY(3z;;Q*|1dNeX;A8}Wl}|?i5nTn3CvNLSO%GaeO6Sj-obd09k}X$5uMx89 zZQ34dpD4S@^h3czRK3JmWTUAq9L8&WJ0hn2WXPj(io=sG9nInNLrWJn+nZK0wQd>G z*dYqAz3&F&P&$$bB>asXT;6;uLw>WvEcsj-mik6RJTj_B!E-q1FTW3P6MV}6ojf!e zlq@*J)BY3(w!>OsQPFY~FX&SMBdiU(D@6w?Mki93BV`))_Tc@PYmnF*zjio7RH}S- z#hxNyrD|zt3*eghf8bNcV$rPy;^T2T_-cGVMRPpv1F7_5A+hnf)={D1op;;B-b&;6TC8mUsJ$4ORGvJ~wMfWOoqNLgKJzct9J)1iZfXd?mP14NWk`APAp3VO@EaY3Q|&09Fi z2FJ%8_d`(Lw51v+C$bFBRpbsFH(bM6V^#CF73fJX+YKd!y~fYFT!)jL>jT*rbC}|1 z3B_yHtSkiIL@^HaZi0JOa)^xg95BLpKDul!s<##EbgIGpR5 zS~yJNIqc}2K}hJtv`|TSnr3EjZv^OT>oBK@fgllEqgA^LfSJ|?RkzM0QNW8ghjU?v zyn_Y9YLU{iy7(Fc!3m99cEB*-ZOrqT{*R*$r30c>hFlRJVa72sG^fR>V_<@>aRr}0 zf@41d%eW~7d`eiY7Bst2h>EzNUmrG_zC1V_oDxQjugco( z4yrOTB%;OZS>Sm?)Rij;^j*a$Us1^~K#tc_I6#%`wgF&sy*gyxJ}<~gC#7JW&5Hic zZGdYdKQAxvpLY3 zqYuVF!}USe7!5RQpId@dd$x!MD-xFdM*Bul%GwZL?(d-#Iu@ zB-j{^kQ)MVgcAIqA>n%WX|DLba@y8x+{$emTKMy;c&Y8v{l*{0Oj9^pDba1^WPKhj=oJ8$<}u+ zL1P=hy7XZ2+q9r}63Lw}QOx{>pG7ycnllKNKED&a0u#0#*_daoNxe)K^c_sTW@ygc zOaYY-Y&S=lO1jT^8!^$DbMBpduPh`!)=R3~jdXGXI80?N?lEu^aW3428)tmj(D)bO z0j_dkM>6dmu)zG9B-j5Ia^0#S{FiVzFjpW411h^L9_EE<;)Gn}`rj7jX?|@Q27G@J zgQ>6m$9DUg+Ruw!_$dV_DS%9&gZ?fAf}phuc;&IpjjnT02Sl2nxm#%!<_MEQxL8V# zru}>R)M|PVN+%eHYC>$5Gw@CEk)V(?44P_EAP6yv$+gQ+D1SdJn_5#qu=FF?YxUH` zeoCvaoij@4Dyek&&{}x;tm5iquK_uNo7{D~;g7PPLys_x^bcej-s=)+NJs!PM2YZh zT+0DZ+)TUtqaVH$)x6kZm$$@27t*U$E0ChFtt5#nO!f=pjKI8o08h(i(|j6q1urQ^ z0vg)Ip=$W}wPHN4fIq_RPeAF7Syz+4P*;<$P*=tq5=k9U2-I3?4d)OOUJN25vD?)Y zWkf!LDD2ussha~uOA?cZM4JN{+S}pReYiUvq)<~hD}~((^C(*rTw=eZM*vMOJDx37 z6HF;784@>+RX0pHO>;)!3`ieQ4t7$`62tj4hl4`%Vnn~-_`ZP|_y3aw*aWZZhTnUP z_U7+^3lRFHfaTH1y5Y=xlaAbtG7I4SNw*CJ3n&q(~QHZ(JaJOd7(hE1*Fu}=$(iNRK%J*As#rDqd5_~vYku#|`?ty3xQV8@#F_{%` zjS|P@I>Ec^NODzNH8(@cDxGy{R)O{hbaa6mXh?uiT-gR`_Ukjukt7a@aII~(kgwoZ zrx?lWb33JCB(AZ`Q;#li~eL!4z5&(mRSMXjy>&_n20yE(|7b@yVS0YV{TQ9%z~OkL z1D4zt99CX7offki?gc>QSiY{6ve}dd2L)MFpf4v(FZwQDtS#buiN^%Nnj|% z;ozJ*dXFl%5G?zEVt5luse+6TLz?^@9FomzS1&CIIu?a&&#-2505nGU!XMb4q{cy@A}7Y=bY;2wfZHvoG(e#C zIlTvWkrP2#7ugcZ1#weBo+S}(JeTqQu8AVauo+hs<`3>xdI5I}RZ^RqLF54Y|0t6b z8^ZMJNF5=_ro4m0ya+H4M1 z6AMyB1ph36w`v)6_#ean_vV?*E_{;ln0auF560S8m0Wo&mEqq7vduSP>$uQe2{Di> zDz&uX7h)7XQB^k}e&wVu1d@h^NRkVx>ILPIi_33xw!>%Dfi+{=MlHbKDZyF(){abX zbnZ9Z9_V}*!@!S}*%BZrM($;x^U}0YK_oVHE}ZncZr(vS|K2e9`(h~lf&nOs@TaU% zxdbMwm%aC&(02PPm;{PGRxBclzGj(-r~M=ijj#UBro;oUvD|W3UVwlR`&HoJk77 zN&V|(d9#a85D(!@fiC5|WRI8P=u9qB@d&U}kja;&gBgRa*8ay|`O~-eeFXwuL^@E2 z;YeNE@6MS&p5>PS2kd33L<#bL{o>DlH}V1=jOl)lC&I^K`%`89Q9>mJ40tnzZTO$A z!r$w!y#m~Z;9|zaU##Xo+yB*{Mv9X)d1{Gls*zt=nsko-LahRcJ9c0zpFQDs|J#oj z>VE0If%wjkuhoTGYKm4V!zEOJMC?)iM)0-foJ@zPEH=UE-f{r?>Qz(5^mois-2F>>rANKkS=) z5w4r(E5jh{0EbL$b-?`<%tVSU)nj;yJ9li-*)*|-43A43$GEF3R@wPYis9o#$naq6 zZ(f$W*}(L03?yMHzB_-X82yt*JX^8)H5EyN57@t<24?kl9#1{~l-{@PbZ~vgdVyTR zWhy%G26*?2u~(6eOu5@$xD)7d+~7Fjo96^Vn?s{-0$AnTVez+0CSHfoc)Ep#e7cVP z++GIh!uGAC4G1Jml)2m=hZL43%6gehkT%5SYhD#=>t^&*MO(7qV z?^v+lZvjhJqi3X}>AQq%vz`oATplis>FL?K=)+~?u{_ESq~AyA^A5Dy-bXI7hY`V{ zP&;$T(+Ge=LXD`rxjgu!V5TLdn9Hc(3v2tdeYmu4K^*a*)UxX{26P_8V?Hs@mk}*~ zIcm-+Tu8#^5tQ-@E?K(wea55qXd$!DFy-*V*8!7Gy9+NGg6_;@3Dy`S!-hWe7xG^- zhycjLjce?x8blTm(cHR5HM`3aC?-qjvOWW@`vij+1Qq(`MxXOweaNx&K0ES?Z~UdS z^;6%JWrMTYQg9n*maTl!7}7lo6W{e2oev?&yB!?JAZ+vH-P4u)VRe+%atZZYRkcrUA5WJZcsl7w2;B7AH-fPvoELDV{)0W{D zEb%pVny6+ktVzy6QGFKIC|k^unc3fQ6NZdF_Rdv!KI$uL{gThJ+DwZ{j{OMl2WT7G z4lAm8ME=n)lv|Zn*}LFguz|Fk8SJ;8B}|Cj{dh6=pTS)AVtw~jWQw!3qm89)gYtzm ztEiU&xEVQ72l#zX^t{jn6Rsri1QwV78Bq!$M5}`QGRrg#$KNcUW=U_}B`ga9L}T&5}g-g=rQI1!&=GHm4kQ& z;ZY&B?T>uMC_P`(8ZC;YL(?FJ!3eQ`&2#%y{kN_JG3#Zl`}(5}8`s@0 zcE;5XX^*<2mGU;D+eL!&r%5T9=2uzjSX`Ipg=VeeZUYX<)K@VM#W!zD&G2Ruz%u-F z0tW{wP)e)L?yWuNt26Tvsq5o=VoRkK4#dc8i=8Ys?PN2xSOIR%@Dw=Io6zQYj=pVQ zX+mw<=7PH^Mk1afU2B(C=SAg-6I6++insMNcDJy|8k_b+Rr<2+{X&KS^vam`BOK`| z&HHNhmVAu74a*HO>d^O=3f-vDP-s$1dqcV73sw^L_FG4-Km1gSpmOqXZePj6fA@AzSkH?(di zNJ*}>-Mxd|9jljKuF@h?Nob3Jv+AN0KXJ_p-wMn&Ewu~aFs=9T@0w@r5Ny&mm?Clhgb`A$sX7Wq!h`$^w@stSC~bN zM{?E$!M-ZhX^x1sgMgrLcvO(yi|Io^u?VHIyo&e!D^(1EC=Wc@&2%A3Mm91LiOg$>= znE`yJqD~PKmYp$D?drHOUr`p6QaLWn$hXQ4;Rym{vMzUri|lih z3T;hgk$d&qxK4*X2PuAX*k z2!dhRJC4QCdH1yFdESU`(O^`r6RTYsIL6ZD>-Ggk>J?l&gIxsVzS`QFR@zjd@GB$h zEdvwQ^Q|nqC1UukBK~o8eE1Z2VNN1r;%zch-a?dSnrlCEUQ6>SAmaBk8VEu#2JHkM zn>!bI0URvE9aHY~eJ8A8dp1P|i{^WW@ts8RYdgdgLWiAylKuhN%u82{4GW|rg9!L7Z7 z*{xChZC6P@vcREoC)^}K8619NiJDdJi{s1l5X*7UwtsD9foh?01y@K`5IPg}{OZ8r zyF2NhTU(;!Vo@hcMCMu}H1APyz}8m+IVW=&D>tWAVEe2Q+Hqx7w; z{*Tw)FkKPn8Lr_+7|A3!Ok-3yd6a4hbquup~fpHK*)X%KJ=`@U(By{cWK5!J3c&v3J8 zq2d*-;C0%7c6-5+Vf{t5q!#SmhL;!8ys|iFFte{&lSVvZ5-1Jw4pJwFMfJwPAFnN{ zMe|soz-9YEUh6ZxP_s%888b_2b9tr)i9*a{w92Jx{>)-N8&67MOQQ#fYQq9ORkE{1 zVRlzigOwa(FfMibFA5%xXl3Z;L@o^#t4!n?q{hoj7hTz=04o1U} zaUHwhojz;mdB8pTH{EGv?d7E*w+g!hl3bZHE^f_B3j&o{CRP8GEz(Ad+l%~XR2HvL zOUkV`=ty4ko=aqCSHG2&d`EA$BZGXO`6;7CIwqzzeD&6`1$H;|NVRJ=$HaeE?e=3& zkUQ3Pki|-A=Zaglpn3fw{fXmIFYx*-)iRZ`;%2) zHNvZbAfn9C09$tL@!-LDX(eGp1@KeH#<&RGEH%g3O|Z7b>X5>VH*_w0qX$sZ4H-`o z(+Ab^P>>g$RS@N0iw{>4snM2V1w}0t5$b(0W93kldmHQuFE?_XDp4M5Q%X=7=AKx} zo^l<4d~J581TAj4wOwh9&N&NP!by<+T_P<(N1;qo;1v~_2rPJu_i zq(1!bG$1pBkMZFm#BTgU+S1oOL=5%#z zm0g_{VeD3OuSp_Y71_;reX0AbJ-t8$#k43%BK27}%%>MuEUP@&>wUYrfW_2fHG@tN z+Pmo{G1`09X40vw94_=w8H!m+l5#RpTrB|hl5~EX7x2yQyr&U`NVM8?`i0q?YG-kc zfsq5;jdj&4{xY`ROBBSlbH_#)8^=-H*k>oVobMv{_HajtwQPb1^upw2%A zxjCp=imZKz%BVrzh0M#qd15X_;G%4hq<|h4kI~tL`UG(6HBHBr0P5C4FBL6M?BMdZ z<+Co_G2}9$FU{Pg>g!X5oGOmyTBYO<3_0^xe%dJ(z;o4(!(6)>6bR9vI8I zz?DkZoy?8JG0UJnUt#xv?v~)p&U`=T1N-=L>A7}mypgmISK};k-eSXMe7s1hyREmO z1&a!3oe)>@dS}w`TFBXXui<73EU9PmYb!C1-QLFR{jnJ+|DLUOiZ#S_r2O&s)!@hS z4Bo5h=Mcf2q7j9RmtzGOJ_o&svMCGW34D_F9rSbqm5=KPTTWE%BfA-*!w=8VgSbwf z2mH8>Z*DHCGi-M=d(&E&X(x#0n4?r~{fQ0)X>VT%A;lX>7>V+zgL5Y3`^ur?;bMR^Xmo;&spU0Wr zKY$I6_cUoO(f5;)XS~gC{*>Bc9eS}bThvrv>j1JMR4 z>UB4mzi7z3@}mXeXo$>_nIPC@QC;R|-H9@+X35H(g9!(ny9W}xFxjqz!MS&Kx%l54 zt|4L1_9g~su9D^cOp-lC<}Z}md}If;2lkMx66o~D7B^!>z%E+1fBT)g$3W->8}H(EGmJ9cFO65Jybi#X?XM?DY!Ov+r2Cq~L98+r)hy6)h^axY5qE}e zTI}Yc?`Qj4Oz}3FIQSmqz_2g-_D%la9HUQn+7Y~hq!dhW&8wn|HjGm4H@ebv`_+a! zXjtabUdKe~q^g`H&lERpYNy>ytD!tt_UYi9sCrW><_PF4SrdMG&dK7FG{jF@Wl9~K zvrUK*ucq^8&>^O5CEZ|on_b#;u=zLWEBFGiJ|6}OxBP^iD9^H~JDodM<+}6g+bQ+j z`p1*+USf;+O-tYY1ZG&;(b=-Z%GfGBjDM03BsP)Awb zXlU9E&B9-@TGb;*dR}k@<_GDR$4zN`S=t)GZ=8ki7bjmn3rQnGKak;N0gC~vb|A`XbE{X}9tQtLbC{?b6N85|E;O#)Z{HKnF^_xg zFg1Vpk>^z;ucuaCCRHVtuw1&^5v`4E&!=vf=^7t17g-znexPe}TsHPQ8zyL;oz12h zs~4V3Zt&hoQoF+XH)>qq2s2jy(vPpXTF_y|2vC|l!To*|XpoR@fg-WA_Gj1c9u)f} z$Y@*Do8|23zl&=lRd}h2a7wc7d>&+xvy$@dKm24rB0Bv@R-WIc1Faoa&5|EO^31v< zfOK#;4j0F!s)V;kHmS2Zrchm#2IJ&y(1~g1>Nl1iX5o^k2HQlAciU%3K+S+0dBuK> zb1j-|V|hYFEf%6E>hyI6!y7WZaR^wrFI;{k=vU05mG1cFZJ?57S=|`0G9aK~^P;QAGV$k# z03*B2eG+nhccIy3_ Date: Fri, 14 Mar 2025 17:23:45 +0000 Subject: [PATCH 42/59] readme Signed-off-by: Can-Zhao --- generation/maisi/README.md | 45 +++++++++++++++++++------------------- 1 file changed, 23 insertions(+), 22 deletions(-) diff --git a/generation/maisi/README.md b/generation/maisi/README.md index e2e1935a1..f44d7b8d9 100644 --- a/generation/maisi/README.md +++ b/generation/maisi/README.md @@ -2,7 +2,7 @@ This example demonstrates the applications of training and validating NVIDIA MAISI, a 3D Latent Diffusion Model (LDM) capable of generating large CT images accompanied by corresponding segmentation masks. It supports variable volume size and voxel spacing and allows for the precise control of organ/tumor size. ## MAISI Model Highlight -**Initial Version (August 2024):** First release `'maisi3d-ddpm'`. +**Initial Version (August 2024):** First release `maisi3d-ddpm`. - A Foundation Variational Auto-Encoder (VAE) model for latent feature compression that works for both CT and MRI with flexible volume size and voxel size. Tensor parallel is included to reduce GPU memory usage. - A Foundation Diffusion model that can generate large CT volumes up to 512 × 512 × 768 size, with flexible volume size and voxel size @@ -12,10 +12,10 @@ More details can be found in our WACV 2025 paper: [Guo, P., Zhao, C., Yang, D., Xu, Z., Nath, V., Tang, Y., ... & Xu, D. (2024). MAISI: Medical AI for Synthetic Imaging. WACV 2025](https://arxiv.org/pdf/2409.11169) -**Release Note (March 2025):** We are excited to announce the new MAISI Version `'maisi3d-rflow'`. Compared with the previous version `'maisi3d-ddpm'`, it accelerated latent diffusion model inference by 33x. The differences are: -- The maisi version `'maisi3d-ddpm'` uses basic noise scheduler DDPM. `'maisi3d-rflow'` uses Rectified Flow scheduler. The diffusion model inference can be 33 times faster. -- The maisi version `'maisi3d-ddpm'` requires training images to be labeled with body regions (`"top_region_index"` and `"bottom_region_index"`), while `'maisi3d-rflow'` does not have such requirement. In other words, it is easier to prepare training data for `'maisi3d-rflow'`. -- For the released model weights, `'maisi3d-rflow'` can generate images with better quality for head region and small output volumes, and comparable quality for other cases compared with `'maisi3d-ddpm'`. +**Release Note (March 2025):** We are excited to announce the new MAISI Version `maisi3d-rflow`. Compared with the previous version `maisi3d-ddpm`, it accelerated latent diffusion model inference by 33x. The differences are: +- The maisi version `maisi3d-ddpm` uses basic noise scheduler DDPM. `maisi3d-rflow` uses Rectified Flow scheduler. The diffusion model inference can be 33 times faster. +- The maisi version `maisi3d-ddpm` requires training images to be labeled with body regions (`"top_region_index"` and `"bottom_region_index"`), while `maisi3d-rflow` does not have such requirement. In other words, it is easier to prepare training data for `maisi3d-rflow`. +- For the released model weights, `maisi3d-rflow` can generate images with better quality for head region and small output volumes, and comparable quality for other cases compared with `maisi3d-ddpm`. **GUI demo:** Welcome to try our GUI demo at [https://build.nvidia.com/nvidia/maisi](https://build.nvidia.com/nvidia/maisi). The GUI is only a demo for toy examples. This Github repo is the full version. @@ -37,8 +37,8 @@ We retrained several state-of-the-art diffusion model-based methods using our da | [DDPM](https://proceedings.neurips.cc/paper_files/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf) | 18.524 | 23.696 | 25.604 | 22.608 | | [LDM](https://openaccess.thecvf.com/content/CVPR2022/papers/Rombach_High-Resolution_Image_Synthesis_With_Latent_Diffusion_Models_CVPR_2022_paper.pdf) | 16.853 | 10.191 | 10.093 | 12.379 | | [HA-GAN](https://ieeexplore.ieee.org/document/9770375) | 17.432 | 10.266 | 13.572 | 13.757 | -| MAISI-DDPM | 3.301 | 5.838 | 9.109 | 6.083 | -| MAISI-RFlow | 2.685 | 4.723 | 7.963 | 5.124 | +| MAISI (`maisi3d-ddpm`) | 3.301 | 5.838 | 9.109 | 6.083 | +| MAISI (`maisi3d-rflow`) | 2.685 | 4.723 | 7.963 | 5.124 | **Table 1.** Comparison of Fréchet Inception Distance scores between our foundation model and retrained baseline methods
using the unseen public dataset [autoPET 2023](https://www.nature.com/articles/s41597-022-01718-3) as the reference. @@ -67,21 +67,22 @@ We retrained several state-of-the-art diffusion model-based methods using our da ## Time Cost and GPU Memory Usage ### Inference Time Cost and GPU Memory Usage -| `output_size` | latent size |`autoencoder_sliding_window_infer_size` | `autoencoder_tp_num_splits` | Peak Memory | VAE Time | DM Time (DDPM) | DM Time (RFlow) | -|---------------|:--------------------------------------:|:--------------------------------------:|:---------------------------:|:-----------:|:--------:|:-------:|:---------------:| -| [256x256x128](./configs/config_infer_16g_256x256x128.json) |4x64x64x32| >=[64,64,32], not used | 2 | 15.0G | 1s | 57s | 2s | -| [256x256x256](./configs/config_infer_16g_256x256x256.json) |4x64x64x64| [48,48,64], 4 patches | 4 | 15.4G | 5s | 81s | 3s | -| [512x512x128](./configs/config_infer_16g_512x512x128.json) |4x128x128x32| [64,64,32], 9 patches | 2 | 15.7G | 8s | 138s | 5s | -| | | | | | | | | -| [256x256x256](./configs/config_infer_24g_256x256x256.json) |4x64x64x64| >=[64,64,64], not used | 4 | 22.7G | 2s | 81s | 3s | -| [512x512x128](./configs/config_infer_24g_512x512x128.json) |4x128x128x32| [80,80,32], 4 patches | 2 | 21.0G | 6s | 138s | 5s | -| [512x512x512](./configs/config_infer_24g_512x512x512.json) |4x128x128x128| [64,64,48], 36 patches | 2 | 22.8G | 29s | 569s | 19s | -| | | | | | | | | -| [512x512x512](./configs/config_infer_32g_512x512x512.json) |4x128x128x128| [80,80,48], 16 patches | 4 | 28.4G | 30s | 569s | 19s | -| | | | | | | | | -| [512x512x128](./configs/config_infer_80g_512x512x128.json) |4x128x128x32| >=[128,128,32], not used | 4 | 37.7G | 127s | 138s | 5s | -| [512x512x512](./configs/config_infer_80g_512x512x512.json) |4x128x128x128| [80,80,80], 8 patches | 2 | 45.3G | 32s | 569s | 19s | -| [512x512x768](./configs/config_infer_80g_512x512x768.json) |4x128x128x192| [80,80,112], 8 patches | 4 | 56.2G | 50s | 904s | 30s | +### Inference Time Cost and GPU Memory Usage +| `output_size` | latent size |`autoencoder_sliding_window_infer_size` | `autoencoder_tp_num_splits` | Peak Memory | VAE Time | DM Time (`maisi3d-ddpm`) | DM Time (`maisi3d-rflow`) | VAE Time + DM Time (`maisi3d-ddpm`) | VAE Time + DM Time (`maisi3d-rflow`) | +|---------------|:--------------------------------------:|:--------------------------------------:|:---------------------------:|:-----------:|:--------:|:---------------:|:---------------:|:------------------------:|:------------------------:| +| [256x256x128](./configs/config_infer_16g_256x256x128.json) |4x64x64x32| >=[64,64,32], not used | 2 | 15.0G | 1s | 57s | 2s | 58s | 3s | +| [256x256x256](./configs/config_infer_16g_256x256x256.json) |4x64x64x64| [48,48,64], 4 patches | 4 | 15.4G | 5s | 81s | 3s | 86s | 8s | +| [512x512x128](./configs/config_infer_16g_512x512x128.json) |4x128x128x32| [64,64,32], 9 patches | 2 | 15.7G | 8s | 138s | 5s | 146s | 13s | +| | | | | | | | | | | +| [256x256x256](./configs/config_infer_24g_256x256x256.json) |4x64x64x64| >=[64,64,64], not used | 4 | 22.7G | 2s | 81s | 3s | 83s | 5s | +| [512x512x128](./configs/config_infer_24g_512x512x128.json) |4x128x128x32| [80,80,32], 4 patches | 2 | 21.0G | 6s | 138s | 5s | 144s | 11s | +| [512x512x512](./configs/config_infer_24g_512x512x512.json) |4x128x128x128| [64,64,48], 36 patches | 2 | 22.8G | 29s | 569s | 19s | 598s | 48s | +| | | | | | | | | | | +| [512x512x512](./configs/config_infer_32g_512x512x512.json) |4x128x128x128| [80,80,48], 16 patches | 4 | 28.4G | 30s | 569s | 19s | 599s | 49s | +| | | | | | | | | | | +| [512x512x128](./configs/config_infer_80g_512x512x128.json) |4x128x128x32| >=[128,128,32], not used | 4 | 37.7G | 127s | 138s | 5s | 265s | 132s | +| [512x512x512](./configs/config_infer_80g_512x512x512.json) |4x128x128x128| [80,80,80], 8 patches | 2 | 45.3G | 32s | 569s | 19s | 601s | 51s | +| [512x512x768](./configs/config_infer_80g_512x512x768.json) |4x128x128x192| [80,80,112], 8 patches | 4 | 56.2G | 50s | 904s | 30s | 954s | 80s | **Table 3:** Inference Time Cost and GPU Memory Usage. `DM Time` refers to the time required for diffusion model inference. `VAE Time` refers to the time required for VAE decoder inference. The total inference time is the sum of `DM Time` and `VAE Time`. The experiment was conducted on an A100 80G GPU. From 875c78ba044c7521bca67e3ef908c5e8a7517166 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Fri, 14 Mar 2025 17:26:16 +0000 Subject: [PATCH 43/59] readme Signed-off-by: Can-Zhao --- generation/maisi/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/generation/maisi/README.md b/generation/maisi/README.md index f44d7b8d9..c9d125b00 100644 --- a/generation/maisi/README.md +++ b/generation/maisi/README.md @@ -114,13 +114,13 @@ The DM and ControlNet are trained on whole images rather than patches. The GPU m ## MAISI Model Workflow The training and inference workflows of MAISI are depicted in the figure below. It begins by training an autoencoder in pixel space to encode images into latent features. Following that, it trains a diffusion model in the latent space to denoise the noisy latent features. During inference, it first generates latent features from random noise by applying multiple denoising steps using the trained diffusion model. Finally, it decodes the denoised latent features into images using the trained autoencoder.

- MAISI training scheme + MAISI training scheme
Figure 1: MAISI training scheme

- MAISI inference scheme Figure 2: MAISI inference scheme

From ce53d1d20435802939216b50bd75aa2c7ee73be1 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Fri, 14 Mar 2025 17:32:45 +0000 Subject: [PATCH 44/59] readme Signed-off-by: Can-Zhao --- generation/maisi/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/generation/maisi/README.md b/generation/maisi/README.md index c9d125b00..157462e61 100644 --- a/generation/maisi/README.md +++ b/generation/maisi/README.md @@ -48,7 +48,7 @@ We retrained several state-of-the-art diffusion model-based methods using our da ![Generated image examples](https://developer-blogs.nvidia.com/wp-content/uploads/2024/06/generated-medical-image-method-comparison-1.png) -**Figure 1.** Qualitative comparison of generated images between baseline methods
(retrained using our large-scale dataset) and our method. +**Figure 1.** Qualitative comparison of generated images between baseline methods
(retrained using our large-scale dataset) and our method. The MAISI here refers to `maisi3d-ddpm`. From 5b2c4d7b82f31d3ff867040d7772faf080453e80 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Fri, 14 Mar 2025 19:55:08 +0000 Subject: [PATCH 45/59] readme Signed-off-by: Can-Zhao --- generation/maisi/README.md | 44 +++++++++++-------- .../configs/config_infer_80g_512x512x128.json | 29 ------------ .../configs/config_infer_80g_512x512x768.json | 4 +- .../maisi/configs/modality_mapping.json | 15 +++++++ 4 files changed, 42 insertions(+), 50 deletions(-) delete mode 100644 generation/maisi/configs/config_infer_80g_512x512x128.json create mode 100644 generation/maisi/configs/modality_mapping.json diff --git a/generation/maisi/README.md b/generation/maisi/README.md index 157462e61..da91c24d2 100644 --- a/generation/maisi/README.md +++ b/generation/maisi/README.md @@ -12,10 +12,13 @@ More details can be found in our WACV 2025 paper: [Guo, P., Zhao, C., Yang, D., Xu, Z., Nath, V., Tang, Y., ... & Xu, D. (2024). MAISI: Medical AI for Synthetic Imaging. WACV 2025](https://arxiv.org/pdf/2409.11169) -**Release Note (March 2025):** We are excited to announce the new MAISI Version `maisi3d-rflow`. Compared with the previous version `maisi3d-ddpm`, it accelerated latent diffusion model inference by 33x. The differences are: +🎉🎉🎉🎉🎉🎉**Release Note (March 2025):** 🎉🎉🎉🎉🎉🎉 + +We are excited to announce the new MAISI Version `maisi3d-rflow`. Compared with the previous version `maisi3d-ddpm`, **it accelerated latent diffusion model inference by 33x**. The MAISI VAE is not changed. The differences are: - The maisi version `maisi3d-ddpm` uses basic noise scheduler DDPM. `maisi3d-rflow` uses Rectified Flow scheduler. The diffusion model inference can be 33 times faster. - The maisi version `maisi3d-ddpm` requires training images to be labeled with body regions (`"top_region_index"` and `"bottom_region_index"`), while `maisi3d-rflow` does not have such requirement. In other words, it is easier to prepare training data for `maisi3d-rflow`. - For the released model weights, `maisi3d-rflow` can generate images with better quality for head region and small output volumes, and comparable quality for other cases compared with `maisi3d-ddpm`. +- `maisi3d-rflow` added a diffusionn model input `modality`, which gives it flexibility to extend to other modalities. Currently it is set as always equal to 1 since this version only supports CT generation. We predefined some modalities in [./configs/modality_mapping.json](./configs/modality_mapping.json). **GUI demo:** Welcome to try our GUI demo at [https://build.nvidia.com/nvidia/maisi](https://build.nvidia.com/nvidia/maisi). The GUI is only a demo for toy examples. This Github repo is the full version. @@ -67,22 +70,20 @@ We retrained several state-of-the-art diffusion model-based methods using our da ## Time Cost and GPU Memory Usage ### Inference Time Cost and GPU Memory Usage -### Inference Time Cost and GPU Memory Usage -| `output_size` | latent size |`autoencoder_sliding_window_infer_size` | `autoencoder_tp_num_splits` | Peak Memory | VAE Time | DM Time (`maisi3d-ddpm`) | DM Time (`maisi3d-rflow`) | VAE Time + DM Time (`maisi3d-ddpm`) | VAE Time + DM Time (`maisi3d-rflow`) | -|---------------|:--------------------------------------:|:--------------------------------------:|:---------------------------:|:-----------:|:--------:|:---------------:|:---------------:|:------------------------:|:------------------------:| -| [256x256x128](./configs/config_infer_16g_256x256x128.json) |4x64x64x32| >=[64,64,32], not used | 2 | 15.0G | 1s | 57s | 2s | 58s | 3s | -| [256x256x256](./configs/config_infer_16g_256x256x256.json) |4x64x64x64| [48,48,64], 4 patches | 4 | 15.4G | 5s | 81s | 3s | 86s | 8s | -| [512x512x128](./configs/config_infer_16g_512x512x128.json) |4x128x128x32| [64,64,32], 9 patches | 2 | 15.7G | 8s | 138s | 5s | 146s | 13s | -| | | | | | | | | | | -| [256x256x256](./configs/config_infer_24g_256x256x256.json) |4x64x64x64| >=[64,64,64], not used | 4 | 22.7G | 2s | 81s | 3s | 83s | 5s | -| [512x512x128](./configs/config_infer_24g_512x512x128.json) |4x128x128x32| [80,80,32], 4 patches | 2 | 21.0G | 6s | 138s | 5s | 144s | 11s | -| [512x512x512](./configs/config_infer_24g_512x512x512.json) |4x128x128x128| [64,64,48], 36 patches | 2 | 22.8G | 29s | 569s | 19s | 598s | 48s | -| | | | | | | | | | | -| [512x512x512](./configs/config_infer_32g_512x512x512.json) |4x128x128x128| [80,80,48], 16 patches | 4 | 28.4G | 30s | 569s | 19s | 599s | 49s | -| | | | | | | | | | | -| [512x512x128](./configs/config_infer_80g_512x512x128.json) |4x128x128x32| >=[128,128,32], not used | 4 | 37.7G | 127s | 138s | 5s | 265s | 132s | -| [512x512x512](./configs/config_infer_80g_512x512x512.json) |4x128x128x128| [80,80,80], 8 patches | 2 | 45.3G | 32s | 569s | 19s | 601s | 51s | -| [512x512x768](./configs/config_infer_80g_512x512x768.json) |4x128x128x192| [80,80,112], 8 patches | 4 | 56.2G | 50s | 904s | 30s | 954s | 80s | +| `output_size` | Peak Memory | VAE Time + DM Time (`maisi3d-ddpm`) | VAE Time + DM Time (`maisi3d-rflow`) | latent size | `autoencoder_sliding_window_infer_size` | `autoencoder_tp_num_splits` | VAE Time | DM Time (`maisi3d-ddpm`) | DM Time (`maisi3d-rflow`) | +|---------------|:-----------:|:------------------------:|:------------------------:|:--------------------------------------:|:--------------------------------------:|:---------------------------:|:--------:|:---------------:|:---------------:| +| [256x256x128](./configs/config_infer_16g_256x256x128.json) | 15.0G | 58s | 3s | 4x64x64x32 | >=[64,64,32], not used | 2 | 1s | 57s | 2s | +| [256x256x256](./configs/config_infer_16g_256x256x256.json) | 15.4G | 86s | 8s | 4x64x64x64 | [48,48,64], 4 patches | 4 | 5s | 81s | 3s | +| [512x512x128](./configs/config_infer_16g_512x512x128.json) | 15.7G | 146s | 13s | 4x128x128x32 | [64,64,32], 9 patches | 2 | 8s | 138s | 5s | +| | | | | | | | | | | +| [256x256x256](./configs/config_infer_24g_256x256x256.json) | 22.7G | 83s | 5s | 4x64x64x64 | >=[64,64,64], not used | 4 | 2s | 81s | 3s | +| [512x512x128](./configs/config_infer_24g_512x512x128.json) | 21.0G | 144s | 11s | 4x128x128x32 | [80,80,32], 4 patches | 2 | 6s | 138s | 5s | +| [512x512x512](./configs/config_infer_24g_512x512x512.json) | 22.8G | 598s | 48s | 4x128x128x128 | [64,64,48], 36 patches | 2 | 29s | 569s | 19s | +| | | | | | | | | | | +| [512x512x512](./configs/config_infer_32g_512x512x512.json) | 28.4G | 599s | 49s | 4x128x128x128 | [80,80,48], 16 patches | 4 | 30s | 569s | 19s | +| | | | | | | | | | | +| [512x512x512](./configs/config_infer_80g_512x512x512.json) | 45.3G | 601s | 51s | 4x128x128x128 | [80,80,80], 8 patches | 2 | 32s | 569s | 19s | +| [512x512x768](./configs/config_infer_80g_512x512x768.json) | 49.7G | 961s | 87s | 4x128x128x192 | [80,80,96], 12 patches | 4 | 57s | 904s | 30s | **Table 3:** Inference Time Cost and GPU Memory Usage. `DM Time` refers to the time required for diffusion model inference. `VAE Time` refers to the time required for VAE decoder inference. The total inference time is the sum of `DM Time` and `VAE Time`. The experiment was conducted on an A100 80G GPU. @@ -96,7 +97,7 @@ When `autoencoder_sliding_window_infer_size` is equal to or larger than the late ### Training GPU Memory Usage The VAE is trained on patches and can be trained using a 16G GPU if the patch size is set to a small value, such as [64, 64, 64]. Users can adjust the patch size to fit the available GPU memory. For the released model, we initially trained the autoencoder on 16G V100 GPUs with a small patch size of [64, 64, 64], and then continued training on 32G V100 GPUs with a larger patch size of [128, 128, 128]. -The DM and ControlNet are trained on whole images rather than patches. The GPU memory usage during training depends on the size of the input images. +The DM and ControlNet are trained on whole images rather than patches. The GPU memory usage during training depends on the size of the input images. There is no big difference on memory usage between `maisi3d-ddpm` and `maisi3d-rflow`. | image size | latent size | Peak Memory | |--------------|:------------- |:-----------:| @@ -198,7 +199,12 @@ Please refer to [maisi_inference_tutorial.ipynb](maisi_inference_tutorial.ipynb) To run the inference script with TensorRT acceleration, please run: ```bash export MONAI_DATA_DIRECTORY= -python -m scripts.inference -c ./configs/config_maisi.json -i ./configs/config_infer.json -e ./configs/environment.json -x ./configs/config_trt.json --random-seed 0 +python -m scripts.inference -c ./configs/config_maisi3d-ddpm.json -i ./configs/config_infer.json -e ./configs/environment_maisi3d-ddpm.json -x ./configs/config_trt.json --random-seed 0 --version maisi3d-ddpm +``` + +```bash +export MONAI_DATA_DIRECTORY= +python -m scripts.inference -c ./configs/config_maisi3d-rflow.json -i ./configs/config_infer.json -e ./configs/environment_maisi3d-rflow.json -x ./configs/config_trt.json --random-seed 0 --version maisi3d-rflow ``` Extra config file, [./configs/config_trt.json](./configs/config_trt.json) is using `trt_compile()` utility from MONAI to convert select modules to TensorRT by overriding their definitions from [./configs/config_infer.json](./configs/config_infer.json). diff --git a/generation/maisi/configs/config_infer_80g_512x512x128.json b/generation/maisi/configs/config_infer_80g_512x512x128.json deleted file mode 100644 index 2b7b025cb..000000000 --- a/generation/maisi/configs/config_infer_80g_512x512x128.json +++ /dev/null @@ -1,29 +0,0 @@ -{ - "num_output_samples": 1, - "body_region": ["abdomen"], - "anatomy_list": ["liver","hepatic tumor"], - "controllable_anatomy_size": [], - "num_inference_steps": 30, - "mask_generation_num_inference_steps": 1000, - "output_size": [ - 512, - 512, - 128 - ], - "image_output_ext": ".nii.gz", - "label_output_ext": ".nii.gz", - "spacing": [ - 0.75, - 0.75, - 4.0 - ], - "autoencoder_sliding_window_infer_size": [128,128,32], - "autoencoder_sliding_window_infer_overlap": 0.5, - "autoencoder_tp_num_splits": 4, - "controlnet": "$@controlnet_def", - "diffusion_unet": "$@diffusion_unet_def", - "autoencoder": "$@autoencoder_def", - "mask_generation_autoencoder": "$@mask_generation_autoencoder_def", - "mask_generation_diffusion": "$@mask_generation_diffusion_def", - "modality": 1 -} diff --git a/generation/maisi/configs/config_infer_80g_512x512x768.json b/generation/maisi/configs/config_infer_80g_512x512x768.json index 1b2e4fd08..4f1a945f2 100644 --- a/generation/maisi/configs/config_infer_80g_512x512x768.json +++ b/generation/maisi/configs/config_infer_80g_512x512x768.json @@ -17,8 +17,8 @@ 0.75, 0.66667 ], - "autoencoder_sliding_window_infer_size": [80,80,112], - "autoencoder_sliding_window_infer_overlap": 0.25, + "autoencoder_sliding_window_infer_size": [80,80,96], + "autoencoder_sliding_window_infer_overlap": 0.4, "autoencoder_tp_num_splits": 4, "controlnet": "$@controlnet_def", "diffusion_unet": "$@diffusion_unet_def", diff --git a/generation/maisi/configs/modality_mapping.json b/generation/maisi/configs/modality_mapping.json new file mode 100644 index 000000000..47cb07333 --- /dev/null +++ b/generation/maisi/configs/modality_mapping.json @@ -0,0 +1,15 @@ +{ + "unknown":0, + "ct":1, + "ct_wo_contrast":2, + "ct_contrast":3, + "mri":8, + "mri_t1":9, + "mri_t2":10, + "mri_flair":11, + "mri_pd":12, + "mri_dwi":13, + "mri_adc":14, + "mri_ssfp":15, + "mri_mra":16 +} \ No newline at end of file From 7779f0b04084f90fe0a4288343ccd60dc69f0174 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 14 Mar 2025 19:56:28 +0000 Subject: [PATCH 46/59] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- generation/maisi/README.md | 2 +- generation/maisi/configs/modality_mapping.json | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/generation/maisi/README.md b/generation/maisi/README.md index da91c24d2..37c04c3a0 100644 --- a/generation/maisi/README.md +++ b/generation/maisi/README.md @@ -12,7 +12,7 @@ More details can be found in our WACV 2025 paper: [Guo, P., Zhao, C., Yang, D., Xu, Z., Nath, V., Tang, Y., ... & Xu, D. (2024). MAISI: Medical AI for Synthetic Imaging. WACV 2025](https://arxiv.org/pdf/2409.11169) -🎉🎉🎉🎉🎉🎉**Release Note (March 2025):** 🎉🎉🎉🎉🎉🎉 +🎉🎉🎉🎉🎉🎉**Release Note (March 2025):** 🎉🎉🎉🎉🎉🎉 We are excited to announce the new MAISI Version `maisi3d-rflow`. Compared with the previous version `maisi3d-ddpm`, **it accelerated latent diffusion model inference by 33x**. The MAISI VAE is not changed. The differences are: - The maisi version `maisi3d-ddpm` uses basic noise scheduler DDPM. `maisi3d-rflow` uses Rectified Flow scheduler. The diffusion model inference can be 33 times faster. diff --git a/generation/maisi/configs/modality_mapping.json b/generation/maisi/configs/modality_mapping.json index 47cb07333..38bd3ee32 100644 --- a/generation/maisi/configs/modality_mapping.json +++ b/generation/maisi/configs/modality_mapping.json @@ -3,7 +3,7 @@ "ct":1, "ct_wo_contrast":2, "ct_contrast":3, - "mri":8, + "mri":8, "mri_t1":9, "mri_t2":10, "mri_flair":11, @@ -12,4 +12,4 @@ "mri_adc":14, "mri_ssfp":15, "mri_mra":16 -} \ No newline at end of file +} From f145874bc912ffac092f1388a2bc82f502080240 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Fri, 14 Mar 2025 20:11:31 +0000 Subject: [PATCH 47/59] readme Signed-off-by: Can-Zhao --- generation/maisi/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/generation/maisi/README.md b/generation/maisi/README.md index 37c04c3a0..530aea624 100644 --- a/generation/maisi/README.md +++ b/generation/maisi/README.md @@ -18,7 +18,7 @@ We are excited to announce the new MAISI Version `maisi3d-rflow`. Compared with - The maisi version `maisi3d-ddpm` uses basic noise scheduler DDPM. `maisi3d-rflow` uses Rectified Flow scheduler. The diffusion model inference can be 33 times faster. - The maisi version `maisi3d-ddpm` requires training images to be labeled with body regions (`"top_region_index"` and `"bottom_region_index"`), while `maisi3d-rflow` does not have such requirement. In other words, it is easier to prepare training data for `maisi3d-rflow`. - For the released model weights, `maisi3d-rflow` can generate images with better quality for head region and small output volumes, and comparable quality for other cases compared with `maisi3d-ddpm`. -- `maisi3d-rflow` added a diffusionn model input `modality`, which gives it flexibility to extend to other modalities. Currently it is set as always equal to 1 since this version only supports CT generation. We predefined some modalities in [./configs/modality_mapping.json](./configs/modality_mapping.json). +- `maisi3d-rflow` added a diffusion model input `modality`, which gives it flexibility to extend to other modalities. Currently it is set as always equal to 1 since this version only supports CT generation. We predefined some modalities in [./configs/modality_mapping.json](./configs/modality_mapping.json). **GUI demo:** Welcome to try our GUI demo at [https://build.nvidia.com/nvidia/maisi](https://build.nvidia.com/nvidia/maisi). The GUI is only a demo for toy examples. This Github repo is the full version. From 12dfa47d1e390d4c06e7c2dc340ad82c81415bdf Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Tue, 18 Mar 2025 18:16:58 +0000 Subject: [PATCH 48/59] rm MONAI_DATA_DIRECTORY Signed-off-by: Can-Zhao --- generation/maisi/maisi_inference_tutorial.ipynb | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/generation/maisi/maisi_inference_tutorial.ipynb b/generation/maisi/maisi_inference_tutorial.ipynb index 9b6a87baf..1986c9d29 100644 --- a/generation/maisi/maisi_inference_tutorial.ipynb +++ b/generation/maisi/maisi_inference_tutorial.ipynb @@ -164,9 +164,8 @@ "metadata": {}, "source": [ "## Setup data directory\n", - "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable.\n", - "This allows you to save results and reuse downloads.\n", - "If not specified a temporary directory will be used." + "You can specify a directory with the `root_dir`.\n", + "This allows you to save results and reuse downloads." ] }, { From 225e8b48b44e366b88e30b2ef5a4474d97bcd9d0 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Tue, 18 Mar 2025 18:54:32 +0000 Subject: [PATCH 49/59] add expnanation on tumor Signed-off-by: Can-Zhao --- generation/maisi/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/generation/maisi/README.md b/generation/maisi/README.md index b36392115..3ccf606bb 100644 --- a/generation/maisi/README.md +++ b/generation/maisi/README.md @@ -17,7 +17,7 @@ More details can be found in our WACV 2025 paper: We are excited to announce the new MAISI Version `maisi3d-rflow`. Compared with the previous version `maisi3d-ddpm`, **it accelerated latent diffusion model inference by 33x**. The MAISI VAE is not changed. The differences are: - The maisi version `maisi3d-ddpm` uses basic noise scheduler DDPM. `maisi3d-rflow` uses Rectified Flow scheduler. The diffusion model inference can be 33 times faster. - The maisi version `maisi3d-ddpm` requires training images to be labeled with body regions (`"top_region_index"` and `"bottom_region_index"`), while `maisi3d-rflow` does not have such requirement. In other words, it is easier to prepare training data for `maisi3d-rflow`. -- For the released model weights, `maisi3d-rflow` can generate images with better quality for head region and small output volumes, and comparable quality for other cases compared with `maisi3d-ddpm`. +- For the released model weights, `maisi3d-rflow` can generate images with better quality for head region and small output volumes; `maisi3d-ddpm` controlnet has better tumor generation quality; they have comparable quality for other cases. - `maisi3d-rflow` added a diffusion model input `modality`, which gives it flexibility to extend to other modalities. Currently it is set as always equal to 1 since this version only supports CT generation. We predefined some modalities in [./configs/modality_mapping.json](./configs/modality_mapping.json). **GUI demo:** Welcome to try our GUI demo at [https://build.nvidia.com/nvidia/maisi](https://build.nvidia.com/nvidia/maisi). From 4e136d1efaab9ed72511d5155f4acbd35727d6e5 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 18 Mar 2025 18:56:04 +0000 Subject: [PATCH 50/59] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- generation/maisi/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/generation/maisi/README.md b/generation/maisi/README.md index 3ccf606bb..a94c32a9f 100644 --- a/generation/maisi/README.md +++ b/generation/maisi/README.md @@ -17,7 +17,7 @@ More details can be found in our WACV 2025 paper: We are excited to announce the new MAISI Version `maisi3d-rflow`. Compared with the previous version `maisi3d-ddpm`, **it accelerated latent diffusion model inference by 33x**. The MAISI VAE is not changed. The differences are: - The maisi version `maisi3d-ddpm` uses basic noise scheduler DDPM. `maisi3d-rflow` uses Rectified Flow scheduler. The diffusion model inference can be 33 times faster. - The maisi version `maisi3d-ddpm` requires training images to be labeled with body regions (`"top_region_index"` and `"bottom_region_index"`), while `maisi3d-rflow` does not have such requirement. In other words, it is easier to prepare training data for `maisi3d-rflow`. -- For the released model weights, `maisi3d-rflow` can generate images with better quality for head region and small output volumes; `maisi3d-ddpm` controlnet has better tumor generation quality; they have comparable quality for other cases. +- For the released model weights, `maisi3d-rflow` can generate images with better quality for head region and small output volumes; `maisi3d-ddpm` controlnet has better tumor generation quality; they have comparable quality for other cases. - `maisi3d-rflow` added a diffusion model input `modality`, which gives it flexibility to extend to other modalities. Currently it is set as always equal to 1 since this version only supports CT generation. We predefined some modalities in [./configs/modality_mapping.json](./configs/modality_mapping.json). **GUI demo:** Welcome to try our GUI demo at [https://build.nvidia.com/nvidia/maisi](https://build.nvidia.com/nvidia/maisi). From 58a265a3827c4f63b7b5c9b0229d210f2589e54b Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Wed, 19 Mar 2025 03:45:51 +0000 Subject: [PATCH 51/59] add back monai data dir Signed-off-by: Can-Zhao --- generation/maisi/maisi_inference_tutorial.ipynb | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/generation/maisi/maisi_inference_tutorial.ipynb b/generation/maisi/maisi_inference_tutorial.ipynb index 1986c9d29..5891adc33 100644 --- a/generation/maisi/maisi_inference_tutorial.ipynb +++ b/generation/maisi/maisi_inference_tutorial.ipynb @@ -198,9 +198,10 @@ } ], "source": [ - "root_dir = \"./temp_work_dir_inference_demo\"\n", - "if not os.path.isdir(root_dir):\n", - " os.makedirs(root_dir)\n", + "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", + "if directory is not None:\n", + " os.makedirs(directory, exist_ok=True)\n", + "root_dir = tempfile.mkdtemp() if directory is None else directory\n", "\n", "# TODO: remove the `files` after the files are uploaded to the NGC\n", "files = [\n", From e01a532836bfba2af4ae33daf7b7e22fd81be789 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Wed, 19 Mar 2025 04:18:22 +0000 Subject: [PATCH 52/59] update model Signed-off-by: Can-Zhao --- generation/maisi/README.md | 2 +- generation/maisi/scripts/inference.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/generation/maisi/README.md b/generation/maisi/README.md index a94c32a9f..5c596b75d 100644 --- a/generation/maisi/README.md +++ b/generation/maisi/README.md @@ -17,7 +17,7 @@ More details can be found in our WACV 2025 paper: We are excited to announce the new MAISI Version `maisi3d-rflow`. Compared with the previous version `maisi3d-ddpm`, **it accelerated latent diffusion model inference by 33x**. The MAISI VAE is not changed. The differences are: - The maisi version `maisi3d-ddpm` uses basic noise scheduler DDPM. `maisi3d-rflow` uses Rectified Flow scheduler. The diffusion model inference can be 33 times faster. - The maisi version `maisi3d-ddpm` requires training images to be labeled with body regions (`"top_region_index"` and `"bottom_region_index"`), while `maisi3d-rflow` does not have such requirement. In other words, it is easier to prepare training data for `maisi3d-rflow`. -- For the released model weights, `maisi3d-rflow` can generate images with better quality for head region and small output volumes; `maisi3d-ddpm` controlnet has better tumor generation quality; they have comparable quality for other cases. +- For the released model weights, `maisi3d-rflow` can generate images with better quality for head region and small output volumes than `maisi3d-ddpm`; they have comparable quality for other cases. - `maisi3d-rflow` added a diffusion model input `modality`, which gives it flexibility to extend to other modalities. Currently it is set as always equal to 1 since this version only supports CT generation. We predefined some modalities in [./configs/modality_mapping.json](./configs/modality_mapping.json). **GUI demo:** Welcome to try our GUI demo at [https://build.nvidia.com/nvidia/maisi](https://build.nvidia.com/nvidia/maisi). diff --git a/generation/maisi/scripts/inference.py b/generation/maisi/scripts/inference.py index 49498396c..8f0782121 100644 --- a/generation/maisi/scripts/inference.py +++ b/generation/maisi/scripts/inference.py @@ -144,7 +144,7 @@ def main(): { "path": "models/controlnet_3d_rflow.pt", "url": "https://developer.download.nvidia.com/assets/Clara/monai/tutorials/" - "controlnet_rflow_epoch208.pt", + "controlnet_rflow_epoch60.pt", }, { "path": "configs/candidate_masks_flexible_size_and_spacing_4000.json", From 9ab0b214cbaf623b4b4d5c01d600afb05245cfd3 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Wed, 19 Mar 2025 04:26:38 +0000 Subject: [PATCH 53/59] update model Signed-off-by: Can-Zhao --- .../maisi/maisi_inference_tutorial.ipynb | 179 +++++++++--------- 1 file changed, 90 insertions(+), 89 deletions(-) diff --git a/generation/maisi/maisi_inference_tutorial.ipynb b/generation/maisi/maisi_inference_tutorial.ipynb index 5891adc33..a7b720e0d 100644 --- a/generation/maisi/maisi_inference_tutorial.ipynb +++ b/generation/maisi/maisi_inference_tutorial.ipynb @@ -63,11 +63,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "MONAI version: 1.4.1rc1+31.g90de55b1\n", + "MONAI version: 1.4.1rc1+32.g34f37973\n", "Numpy version: 1.26.4\n", "Pytorch version: 2.5.0+cu124\n", "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n", - "MONAI rev id: 90de55b1340ffc8eccf960d111229535bcb2f81d\n", + "MONAI rev id: 34f379735c5e18e7f809453eb1b3606c225c788b\n", "MONAI __file__: /localhome//.local/lib/python3.10/site-packages/monai/__init__.py\n", "\n", "Optional dependencies:\n", @@ -100,6 +100,7 @@ "import argparse\n", "import json\n", "import os\n", + "import tempfile\n", "\n", "import monai\n", "import torch\n", @@ -140,7 +141,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 21:06:36.662][ INFO](notebook) - MAISI version is maisi3d-rflow, whether to use body_region is False\n" + "[2025-03-19 04:16:01.407][ INFO](notebook) - MAISI version is maisi3d-rflow, whether to use body_region is False\n" ] } ], @@ -178,22 +179,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-13 21:06:36,670 - INFO - Expected md5 is None, skip md5 check for file models/autoencoder_epoch273.pt.\n", - "2025-03-13 21:06:36,672 - INFO - File exists: models/autoencoder_epoch273.pt, skipped downloading.\n", - "2025-03-13 21:06:36,672 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_autoencoder.pt.\n", - "2025-03-13 21:06:36,673 - INFO - File exists: models/mask_generation_autoencoder.pt, skipped downloading.\n", - "2025-03-13 21:06:36,674 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_diffusion_unet.pt.\n", - "2025-03-13 21:06:36,674 - INFO - File exists: models/mask_generation_diffusion_unet.pt, skipped downloading.\n", - "2025-03-13 21:06:36,675 - INFO - Expected md5 is None, skip md5 check for file configs/all_anatomy_size_condtions.json.\n", - "2025-03-13 21:06:36,675 - INFO - File exists: configs/all_anatomy_size_condtions.json, skipped downloading.\n", - "2025-03-13 21:06:36,677 - INFO - Expected md5 is None, skip md5 check for file temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip.\n", - "2025-03-13 21:06:36,677 - INFO - File exists: temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip, skipped downloading.\n", - "2025-03-13 21:06:36,678 - INFO - Expected md5 is None, skip md5 check for file models/diff_unet_3d_rflow.pt.\n", - "2025-03-13 21:06:36,678 - INFO - File exists: models/diff_unet_3d_rflow.pt, skipped downloading.\n", - "2025-03-13 21:06:36,680 - INFO - Expected md5 is None, skip md5 check for file models/controlnet_3d_rflow.pt.\n", - "2025-03-13 21:06:36,680 - INFO - File exists: models/controlnet_3d_rflow.pt, skipped downloading.\n", - "2025-03-13 21:06:36,681 - INFO - Expected md5 is None, skip md5 check for file configs/candidate_masks_flexible_size_and_spacing_4000.json.\n", - "2025-03-13 21:06:36,681 - INFO - File exists: configs/candidate_masks_flexible_size_and_spacing_4000.json, skipped downloading.\n" + "2025-03-19 04:16:01,415 - INFO - Expected md5 is None, skip md5 check for file models/autoencoder_epoch273.pt.\n", + "2025-03-19 04:16:01,416 - INFO - File exists: models/autoencoder_epoch273.pt, skipped downloading.\n", + "2025-03-19 04:16:01,416 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_autoencoder.pt.\n", + "2025-03-19 04:16:01,416 - INFO - File exists: models/mask_generation_autoencoder.pt, skipped downloading.\n", + "2025-03-19 04:16:01,417 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_diffusion_unet.pt.\n", + "2025-03-19 04:16:01,418 - INFO - File exists: models/mask_generation_diffusion_unet.pt, skipped downloading.\n", + "2025-03-19 04:16:01,418 - INFO - Expected md5 is None, skip md5 check for file configs/all_anatomy_size_condtions.json.\n", + "2025-03-19 04:16:01,419 - INFO - File exists: configs/all_anatomy_size_condtions.json, skipped downloading.\n", + "2025-03-19 04:16:01,420 - INFO - Expected md5 is None, skip md5 check for file temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip.\n", + "2025-03-19 04:16:01,420 - INFO - File exists: temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip, skipped downloading.\n", + "2025-03-19 04:16:01,421 - INFO - Expected md5 is None, skip md5 check for file models/diff_unet_3d_rflow.pt.\n", + "2025-03-19 04:16:01,421 - INFO - File exists: models/diff_unet_3d_rflow.pt, skipped downloading.\n", + "2025-03-19 04:16:01,422 - INFO - Expected md5 is None, skip md5 check for file models/controlnet_3d_rflow.pt.\n", + "2025-03-19 04:16:01,422 - INFO - File exists: models/controlnet_3d_rflow.pt, skipped downloading.\n", + "2025-03-19 04:16:01,423 - INFO - Expected md5 is None, skip md5 check for file configs/candidate_masks_flexible_size_and_spacing_4000.json.\n", + "2025-03-19 04:16:01,423 - INFO - File exists: configs/candidate_masks_flexible_size_and_spacing_4000.json, skipped downloading.\n" ] } ], @@ -257,7 +258,7 @@ " },\n", " {\n", " \"path\": \"models/controlnet_3d_rflow.pt\",\n", - " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/\" \"controlnet_rflow_epoch208.pt\",\n", + " \"url\": \"https://developer.download.nvidia.com/assets/Clara/monai/tutorials/controlnet_rflow_epoch60.pt\",\n", " },\n", " {\n", " \"path\": \"configs/candidate_masks_flexible_size_and_spacing_4000.json\",\n", @@ -295,18 +296,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 21:06:36.687][ INFO](notebook) - output_dir: output\n", - "[2025-03-13 21:06:36.688][ INFO](notebook) - trained_autoencoder_path: models/autoencoder_epoch273.pt\n", - "[2025-03-13 21:06:36.689][ INFO](notebook) - trained_diffusion_path: models/diff_unet_3d_rflow.pt\n", - "[2025-03-13 21:06:36.689][ INFO](notebook) - trained_controlnet_path: models/controlnet_3d_rflow.pt\n", - "[2025-03-13 21:06:36.690][ INFO](notebook) - trained_mask_generation_autoencoder_path: models/mask_generation_autoencoder.pt\n", - "[2025-03-13 21:06:36.691][ INFO](notebook) - trained_mask_generation_diffusion_path: models/mask_generation_diffusion_unet.pt\n", - "[2025-03-13 21:06:36.692][ INFO](notebook) - all_mask_files_base_dir: ./temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000\n", - "[2025-03-13 21:06:36.692][ INFO](notebook) - all_mask_files_json: ./configs/candidate_masks_flexible_size_and_spacing_4000.json\n", - "[2025-03-13 21:06:36.693][ INFO](notebook) - all_anatomy_size_conditions_json: ./configs/all_anatomy_size_condtions.json\n", - "[2025-03-13 21:06:36.693][ INFO](notebook) - label_dict_json: ./configs/label_dict.json\n", - "[2025-03-13 21:06:36.694][ INFO](notebook) - label_dict_remap_json: ./configs/label_dict_124_to_132.json\n", - "[2025-03-13 21:06:36.694][ INFO](notebook) - Global config variables have been loaded.\n" + "[2025-03-19 04:16:01.430][ INFO](notebook) - output_dir: output\n", + "[2025-03-19 04:16:01.431][ INFO](notebook) - trained_autoencoder_path: models/autoencoder_epoch273.pt\n", + "[2025-03-19 04:16:01.431][ INFO](notebook) - trained_diffusion_path: models/diff_unet_3d_rflow.pt\n", + "[2025-03-19 04:16:01.432][ INFO](notebook) - trained_controlnet_path: models/controlnet_3d_rflow.pt\n", + "[2025-03-19 04:16:01.432][ INFO](notebook) - trained_mask_generation_autoencoder_path: models/mask_generation_autoencoder.pt\n", + "[2025-03-19 04:16:01.433][ INFO](notebook) - trained_mask_generation_diffusion_path: models/mask_generation_diffusion_unet.pt\n", + "[2025-03-19 04:16:01.433][ INFO](notebook) - all_mask_files_base_dir: ./temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000\n", + "[2025-03-19 04:16:01.434][ INFO](notebook) - all_mask_files_json: ./configs/candidate_masks_flexible_size_and_spacing_4000.json\n", + "[2025-03-19 04:16:01.434][ INFO](notebook) - all_anatomy_size_conditions_json: ./configs/all_anatomy_size_condtions.json\n", + "[2025-03-19 04:16:01.435][ INFO](notebook) - label_dict_json: ./configs/label_dict.json\n", + "[2025-03-19 04:16:01.436][ INFO](notebook) - label_dict_remap_json: ./configs/label_dict_124_to_132.json\n", + "[2025-03-19 04:16:01.436][ INFO](notebook) - Global config variables have been loaded.\n" ] } ], @@ -350,28 +351,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 21:06:36.702][ INFO](notebook) - num_output_samples: 1\n", - "[2025-03-13 21:06:36.703][ INFO](notebook) - body_region: ['abdomen']\n", - "[2025-03-13 21:06:36.704][ INFO](notebook) - anatomy_list: ['liver', 'hepatic tumor']\n", - "[2025-03-13 21:06:36.705][ INFO](notebook) - controllable_anatomy_size: []\n", - "[2025-03-13 21:06:36.705][ INFO](notebook) - num_inference_steps: 30\n", - "[2025-03-13 21:06:36.706][ INFO](notebook) - mask_generation_num_inference_steps: 1000\n", - "[2025-03-13 21:06:36.707][ INFO](notebook) - output_size: [256, 256, 256]\n", - "[2025-03-13 21:06:36.707][ INFO](notebook) - image_output_ext: .nii.gz\n", - "[2025-03-13 21:06:36.708][ INFO](notebook) - label_output_ext: .nii.gz\n", - "[2025-03-13 21:06:36.708][ INFO](notebook) - spacing: [1.5, 1.5, 2.0]\n", - "[2025-03-13 21:06:36.709][ INFO](notebook) - autoencoder_sliding_window_infer_size: [48, 48, 48]\n", - "[2025-03-13 21:06:36.710][ INFO](notebook) - autoencoder_sliding_window_infer_overlap: 0.6666\n", - "[2025-03-13 21:06:36.710][ INFO](notebook) - controlnet: $@controlnet_def\n", - "[2025-03-13 21:06:36.711][ INFO](notebook) - diffusion_unet: $@diffusion_unet_def\n", - "[2025-03-13 21:06:36.711][ INFO](notebook) - autoencoder: $@autoencoder_def\n", - "[2025-03-13 21:06:36.712][ INFO](notebook) - mask_generation_autoencoder: $@mask_generation_autoencoder_def\n", - "[2025-03-13 21:06:36.712][ INFO](notebook) - mask_generation_diffusion: $@mask_generation_diffusion_def\n", - "[2025-03-13 21:06:36.713][ INFO](notebook) - modality: 1\n", - "[2025-03-13 21:06:36.713][ INFO](root) - `controllable_anatomy_size` is empty.\n", + "[2025-03-19 04:16:01.445][ INFO](notebook) - num_output_samples: 1\n", + "[2025-03-19 04:16:01.445][ INFO](notebook) - body_region: ['abdomen']\n", + "[2025-03-19 04:16:01.446][ INFO](notebook) - anatomy_list: ['liver', 'hepatic tumor']\n", + "[2025-03-19 04:16:01.447][ INFO](notebook) - controllable_anatomy_size: []\n", + "[2025-03-19 04:16:01.447][ INFO](notebook) - num_inference_steps: 30\n", + "[2025-03-19 04:16:01.448][ INFO](notebook) - mask_generation_num_inference_steps: 1000\n", + "[2025-03-19 04:16:01.449][ INFO](notebook) - output_size: [256, 256, 256]\n", + "[2025-03-19 04:16:01.450][ INFO](notebook) - image_output_ext: .nii.gz\n", + "[2025-03-19 04:16:01.451][ INFO](notebook) - label_output_ext: .nii.gz\n", + "[2025-03-19 04:16:01.451][ INFO](notebook) - spacing: [1.5, 1.5, 2.0]\n", + "[2025-03-19 04:16:01.452][ INFO](notebook) - autoencoder_sliding_window_infer_size: [48, 48, 48]\n", + "[2025-03-19 04:16:01.452][ INFO](notebook) - autoencoder_sliding_window_infer_overlap: 0.6666\n", + "[2025-03-19 04:16:01.453][ INFO](notebook) - controlnet: $@controlnet_def\n", + "[2025-03-19 04:16:01.453][ INFO](notebook) - diffusion_unet: $@diffusion_unet_def\n", + "[2025-03-19 04:16:01.454][ INFO](notebook) - autoencoder: $@autoencoder_def\n", + "[2025-03-19 04:16:01.454][ INFO](notebook) - mask_generation_autoencoder: $@mask_generation_autoencoder_def\n", + "[2025-03-19 04:16:01.455][ INFO](notebook) - mask_generation_diffusion: $@mask_generation_diffusion_def\n", + "[2025-03-19 04:16:01.455][ INFO](notebook) - modality: 1\n", + "[2025-03-19 04:16:01.456][ INFO](root) - `controllable_anatomy_size` is empty.\n", "We will synthesize based on `body_region`: (['abdomen']) and `anatomy_list`: (['liver', 'hepatic tumor']).\n", - "[2025-03-13 21:06:36.716][ INFO](root) - The generate results will have voxel size to be [1.5, 1.5, 2.0]mm, volume size to be [256, 256, 256].\n", - "[2025-03-13 21:06:36.717][ INFO](notebook) - Network definition and inference inputs have been loaded.\n" + "[2025-03-19 04:16:01.458][ INFO](root) - The generate results will have voxel size to be [1.5, 1.5, 2.0]mm, volume size to be [256, 256, 256].\n", + "[2025-03-19 04:16:01.458][ INFO](notebook) - Network definition and inference inputs have been loaded.\n" ] } ], @@ -442,14 +443,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-13 21:06:42,723 - INFO - 'dst' model updated: 180 of 231 variables.\n" + "2025-03-19 04:16:08,878 - INFO - 'dst' model updated: 180 of 231 variables.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 21:06:45.213][ INFO](notebook) - All the trained model weights have been loaded.\n" + "[2025-03-19 04:16:11.191][ INFO](notebook) - All the trained model weights have been loaded.\n" ] } ], @@ -503,7 +504,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 21:06:45.238][ INFO](root) - LDM sampler initialized.\n" + "[2025-03-19 04:16:11.216][ INFO](root) - LDM sampler initialized.\n" ] } ], @@ -566,28 +567,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 21:06:45.243][ INFO](notebook) - The generated image/mask pairs will be saved in output.\n", - "[2025-03-13 21:06:45.290][ INFO](root) - Resample mask file to get desired output size and spacing\n", - "[2025-03-13 21:06:46.647][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 21:06:46.652][ INFO](root) - Resize Spacing: [tensor(0.7539, dtype=torch.float64), tensor(0.7539, dtype=torch.float64), tensor(1.9141, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 21:06:46.653][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", - "[2025-03-13 21:06:49.523][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 21:06:49.526][ INFO](root) - Resize Spacing: [tensor(0.7578, dtype=torch.float64), tensor(0.7578, dtype=torch.float64), tensor(1.8848, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 21:06:49.526][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", - "[2025-03-13 21:06:52.446][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 21:06:52.448][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 21:06:52.448][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", - "[2025-03-13 21:06:56.827][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 21:06:56.830][ INFO](root) - Resize Spacing: [tensor(0.7754, dtype=torch.float64), tensor(0.7754, dtype=torch.float64), tensor(1.0098, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 21:06:56.831][ INFO](root) - Output size: [512, 512, 512] -> [256, 256, 256]\n", - "[2025-03-13 21:07:01.208][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 21:07:01.210][ INFO](root) - Resize Spacing: [tensor(0.7480, dtype=torch.float64), tensor(0.7480, dtype=torch.float64), tensor(1.0566, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 21:07:01.211][ INFO](root) - Output size: [512, 512, 512] -> [256, 256, 256]\n", - "[2025-03-13 21:07:03.013][ INFO](root) - Images will be generated based on [{'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_78_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}, {'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_61_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}, {'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_69_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}, {'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_7_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}, {'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_2_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}].\n", - "[2025-03-13 21:07:03.014][ INFO](root) - ---- Start preparing masks... ----\n", - "[2025-03-13 21:07:04.235][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-13 21:07:04.237][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-13 21:07:04.237][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n" + "[2025-03-19 04:16:11.221][ INFO](notebook) - The generated image/mask pairs will be saved in output.\n", + "[2025-03-19 04:16:11.266][ INFO](root) - Resample mask file to get desired output size and spacing\n", + "[2025-03-19 04:16:12.931][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-19 04:16:12.934][ INFO](root) - Resize Spacing: [tensor(0.7539, dtype=torch.float64), tensor(0.7539, dtype=torch.float64), tensor(1.9141, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-19 04:16:12.935][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-19 04:16:15.739][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-19 04:16:15.742][ INFO](root) - Resize Spacing: [tensor(0.7578, dtype=torch.float64), tensor(0.7578, dtype=torch.float64), tensor(1.8848, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-19 04:16:15.743][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-19 04:16:18.527][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-19 04:16:18.529][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-19 04:16:18.530][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-19 04:16:22.793][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-19 04:16:22.797][ INFO](root) - Resize Spacing: [tensor(0.7754, dtype=torch.float64), tensor(0.7754, dtype=torch.float64), tensor(1.0098, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-19 04:16:22.797][ INFO](root) - Output size: [512, 512, 512] -> [256, 256, 256]\n", + "[2025-03-19 04:16:27.652][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-19 04:16:27.654][ INFO](root) - Resize Spacing: [tensor(0.7480, dtype=torch.float64), tensor(0.7480, dtype=torch.float64), tensor(1.0566, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-19 04:16:27.655][ INFO](root) - Output size: [512, 512, 512] -> [256, 256, 256]\n", + "[2025-03-19 04:16:29.442][ INFO](root) - Images will be generated based on [{'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_78_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}, {'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_61_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}, {'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_69_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}, {'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_7_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}, {'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_2_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}].\n", + "[2025-03-19 04:16:29.443][ INFO](root) - ---- Start preparing masks... ----\n", + "[2025-03-19 04:16:30.690][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-19 04:16:30.693][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-19 04:16:30.693][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n" ] }, { @@ -602,8 +603,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 21:07:05.630][ INFO](root) - ---- Mask preparation time: 2.6154592037200928 seconds ----\n", - "[2025-03-13 21:07:05.641][ INFO](root) - ---- Start generating latent features... ----\n" + "[2025-03-19 04:16:32.857][ INFO](root) - ---- Mask preparation time: 3.4133803844451904 seconds ----\n", + "[2025-03-19 04:16:32.869][ INFO](root) - ---- Start generating latent features... ----\n" ] }, { @@ -617,11 +618,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|███████████████████████████████████████████| 30/30 [00:02<00:00, 10.15it/s]\n", - "[2025-03-13 21:07:08.634][ INFO](root) - ---- DM/ControlNet Latent features generation time: 2.9929230213165283 seconds ----\n", - "[2025-03-13 21:07:08.657][ INFO](root) - ---- Start decoding latent features into images... ----\n", + "100%|███████████████████████████████████████████| 30/30 [00:02<00:00, 10.17it/s]\n", + "[2025-03-19 04:16:35.859][ INFO](root) - ---- DM/ControlNet Latent features generation time: 2.9892637729644775 seconds ----\n", + "[2025-03-19 04:16:35.882][ INFO](root) - ---- Start decoding latent features into images... ----\n", "100%|█████████████████████████████████████████████| 8/8 [00:07<00:00, 1.06it/s]\n", - "[2025-03-13 21:07:16.261][ INFO](root) - ---- Image VAE decoding time: 7.603064298629761 seconds ----\n" + "[2025-03-19 04:16:43.453][ INFO](root) - ---- Image VAE decoding time: 7.57032322883606 seconds ----\n" ] }, { @@ -629,15 +630,15 @@ "output_type": "stream", "text": [ "1 5\n", - "2025-03-13 21:07:16,694 INFO image_writer.py:197 - writing: output/sample_20250313_210716_671235_image.nii.gz\n", - "2025-03-13 21:07:18,358 INFO image_writer.py:197 - writing: output/sample_20250313_210716_671235_label.nii.gz\n" + "2025-03-19 04:16:43,843 INFO image_writer.py:197 - writing: output/sample_20250319_041643_825051_image.nii.gz\n", + "2025-03-19 04:16:45,440 INFO image_writer.py:197 - writing: output/sample_20250319_041643_825051_label.nii.gz\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 21:07:19.322][ INFO](notebook) - MAISI image/mask generation finished\n" + "[2025-03-19 04:16:46.393][ INFO](notebook) - MAISI image/mask generation finished\n" ] } ], @@ -665,7 +666,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-13 21:07:19.330][ INFO](notebook) - Visualizing output/sample_20250313_210716_671235_image.nii.gz and output/sample_20250313_210716_671235_label.nii.gz...\n" + "[2025-03-19 04:16:46.402][ INFO](notebook) - Visualizing output/sample_20250319_041643_825051_image.nii.gz and output/sample_20250319_041643_825051_label.nii.gz...\n" ] }, { @@ -680,7 +681,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] From 210a063d705aeda238b390547c3d5c65412a1d64 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Wed, 19 Mar 2025 05:08:54 +0000 Subject: [PATCH 54/59] update test cases Signed-off-by: Can-Zhao --- generation/maisi/configs/config_infer.json | 4 +- .../configs/config_infer_16g_256x256x128.json | 4 +- .../configs/config_infer_16g_256x256x256.json | 4 +- .../configs/config_infer_16g_512x512x128.json | 4 +- .../configs/config_infer_24g_256x256x256.json | 4 +- .../configs/config_infer_24g_512x512x128.json | 4 +- .../configs/config_infer_24g_512x512x512.json | 4 +- .../configs/config_infer_32g_512x512x512.json | 4 +- .../configs/config_infer_80g_512x512x512.json | 4 +- .../configs/config_infer_80g_512x512x768.json | 2 +- .../maisi/maisi_inference_tutorial.ipynb | 199 +++++++++--------- generation/maisi/scripts/find_masks.py | 29 +-- 12 files changed, 139 insertions(+), 127 deletions(-) diff --git a/generation/maisi/configs/config_infer.json b/generation/maisi/configs/config_infer.json index 33de4d853..19b12a6d2 100644 --- a/generation/maisi/configs/config_infer.json +++ b/generation/maisi/configs/config_infer.json @@ -1,7 +1,7 @@ { "num_output_samples": 1, - "body_region": ["abdomen"], - "anatomy_list": ["liver","hepatic tumor"], + "body_region": ["chest"], + "anatomy_list": ["lung tumor"], "controllable_anatomy_size": [], "num_inference_steps": 30, "mask_generation_num_inference_steps": 1000, diff --git a/generation/maisi/configs/config_infer_16g_256x256x128.json b/generation/maisi/configs/config_infer_16g_256x256x128.json index 943f19224..1c6d424f2 100644 --- a/generation/maisi/configs/config_infer_16g_256x256x128.json +++ b/generation/maisi/configs/config_infer_16g_256x256x128.json @@ -1,7 +1,7 @@ { "num_output_samples": 1, - "body_region": ["abdomen"], - "anatomy_list": ["liver","hepatic tumor"], + "body_region": ["chest"], + "anatomy_list": ["lung tumor"], "controllable_anatomy_size": [], "num_inference_steps": 30, "mask_generation_num_inference_steps": 1000, diff --git a/generation/maisi/configs/config_infer_16g_256x256x256.json b/generation/maisi/configs/config_infer_16g_256x256x256.json index dcdf40277..8ccd0bc2c 100644 --- a/generation/maisi/configs/config_infer_16g_256x256x256.json +++ b/generation/maisi/configs/config_infer_16g_256x256x256.json @@ -1,7 +1,7 @@ { "num_output_samples": 1, - "body_region": ["abdomen"], - "anatomy_list": ["liver","hepatic tumor"], + "body_region": ["chest"], + "anatomy_list": ["lung tumor"], "controllable_anatomy_size": [], "num_inference_steps": 30, "mask_generation_num_inference_steps": 1000, diff --git a/generation/maisi/configs/config_infer_16g_512x512x128.json b/generation/maisi/configs/config_infer_16g_512x512x128.json index fa5cdbcc6..ec80d72a8 100644 --- a/generation/maisi/configs/config_infer_16g_512x512x128.json +++ b/generation/maisi/configs/config_infer_16g_512x512x128.json @@ -1,7 +1,7 @@ { "num_output_samples": 1, - "body_region": ["abdomen"], - "anatomy_list": ["liver","hepatic tumor"], + "body_region": ["chest"], + "anatomy_list": ["lung tumor"], "controllable_anatomy_size": [], "num_inference_steps": 30, "mask_generation_num_inference_steps": 1000, diff --git a/generation/maisi/configs/config_infer_24g_256x256x256.json b/generation/maisi/configs/config_infer_24g_256x256x256.json index 529bedd84..a0be706b1 100644 --- a/generation/maisi/configs/config_infer_24g_256x256x256.json +++ b/generation/maisi/configs/config_infer_24g_256x256x256.json @@ -1,7 +1,7 @@ { "num_output_samples": 1, - "body_region": ["abdomen"], - "anatomy_list": ["liver","hepatic tumor"], + "body_region": ["chest"], + "anatomy_list": ["lung tumor"], "controllable_anatomy_size": [], "num_inference_steps": 30, "mask_generation_num_inference_steps": 1000, diff --git a/generation/maisi/configs/config_infer_24g_512x512x128.json b/generation/maisi/configs/config_infer_24g_512x512x128.json index 2dbc8e036..95bd38795 100644 --- a/generation/maisi/configs/config_infer_24g_512x512x128.json +++ b/generation/maisi/configs/config_infer_24g_512x512x128.json @@ -1,7 +1,7 @@ { "num_output_samples": 1, - "body_region": ["abdomen"], - "anatomy_list": ["liver","hepatic tumor"], + "body_region": ["chest"], + "anatomy_list": ["lung tumor"], "controllable_anatomy_size": [], "num_inference_steps": 30, "mask_generation_num_inference_steps": 1000, diff --git a/generation/maisi/configs/config_infer_24g_512x512x512.json b/generation/maisi/configs/config_infer_24g_512x512x512.json index 6882d605b..0606c5e94 100644 --- a/generation/maisi/configs/config_infer_24g_512x512x512.json +++ b/generation/maisi/configs/config_infer_24g_512x512x512.json @@ -1,7 +1,7 @@ { "num_output_samples": 1, - "body_region": ["abdomen"], - "anatomy_list": ["liver","hepatic tumor"], + "body_region": ["chest"], + "anatomy_list": ["lung tumor"], "controllable_anatomy_size": [], "num_inference_steps": 30, "mask_generation_num_inference_steps": 1000, diff --git a/generation/maisi/configs/config_infer_32g_512x512x512.json b/generation/maisi/configs/config_infer_32g_512x512x512.json index 2e0682391..5044955c9 100644 --- a/generation/maisi/configs/config_infer_32g_512x512x512.json +++ b/generation/maisi/configs/config_infer_32g_512x512x512.json @@ -1,7 +1,7 @@ { "num_output_samples": 1, - "body_region": ["abdomen"], - "anatomy_list": ["liver","hepatic tumor"], + "body_region": ["chest"], + "anatomy_list": ["lung tumor"], "controllable_anatomy_size": [], "num_inference_steps": 30, "mask_generation_num_inference_steps": 1000, diff --git a/generation/maisi/configs/config_infer_80g_512x512x512.json b/generation/maisi/configs/config_infer_80g_512x512x512.json index cf3d7263d..71f5d031e 100644 --- a/generation/maisi/configs/config_infer_80g_512x512x512.json +++ b/generation/maisi/configs/config_infer_80g_512x512x512.json @@ -1,7 +1,7 @@ { "num_output_samples": 1, - "body_region": ["abdomen"], - "anatomy_list": ["liver","hepatic tumor"], + "body_region": ["chest"], + "anatomy_list": ["lung tumor"], "controllable_anatomy_size": [], "num_inference_steps": 30, "mask_generation_num_inference_steps": 1000, diff --git a/generation/maisi/configs/config_infer_80g_512x512x768.json b/generation/maisi/configs/config_infer_80g_512x512x768.json index 4f1a945f2..9d1bee4cd 100644 --- a/generation/maisi/configs/config_infer_80g_512x512x768.json +++ b/generation/maisi/configs/config_infer_80g_512x512x768.json @@ -1,7 +1,7 @@ { "num_output_samples": 1, "body_region": ["abdomen"], - "anatomy_list": ["liver","hepatic tumor"], + "anatomy_list": ["liver"], "controllable_anatomy_size": [], "num_inference_steps": 30, "mask_generation_num_inference_steps": 1000, diff --git a/generation/maisi/maisi_inference_tutorial.ipynb b/generation/maisi/maisi_inference_tutorial.ipynb index a7b720e0d..ae35a1513 100644 --- a/generation/maisi/maisi_inference_tutorial.ipynb +++ b/generation/maisi/maisi_inference_tutorial.ipynb @@ -141,7 +141,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-19 04:16:01.407][ INFO](notebook) - MAISI version is maisi3d-rflow, whether to use body_region is False\n" + "[2025-03-19 05:06:22.467][ INFO](notebook) - MAISI version is maisi3d-rflow, whether to use body_region is False\n" ] } ], @@ -165,8 +165,7 @@ "metadata": {}, "source": [ "## Setup data directory\n", - "You can specify a directory with the `root_dir`.\n", - "This allows you to save results and reuse downloads." + "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable. This allows you to save results and reuse downloads. If not specified a temporary directory will be used." ] }, { @@ -179,26 +178,27 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-19 04:16:01,415 - INFO - Expected md5 is None, skip md5 check for file models/autoencoder_epoch273.pt.\n", - "2025-03-19 04:16:01,416 - INFO - File exists: models/autoencoder_epoch273.pt, skipped downloading.\n", - "2025-03-19 04:16:01,416 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_autoencoder.pt.\n", - "2025-03-19 04:16:01,416 - INFO - File exists: models/mask_generation_autoencoder.pt, skipped downloading.\n", - "2025-03-19 04:16:01,417 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_diffusion_unet.pt.\n", - "2025-03-19 04:16:01,418 - INFO - File exists: models/mask_generation_diffusion_unet.pt, skipped downloading.\n", - "2025-03-19 04:16:01,418 - INFO - Expected md5 is None, skip md5 check for file configs/all_anatomy_size_condtions.json.\n", - "2025-03-19 04:16:01,419 - INFO - File exists: configs/all_anatomy_size_condtions.json, skipped downloading.\n", - "2025-03-19 04:16:01,420 - INFO - Expected md5 is None, skip md5 check for file temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip.\n", - "2025-03-19 04:16:01,420 - INFO - File exists: temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip, skipped downloading.\n", - "2025-03-19 04:16:01,421 - INFO - Expected md5 is None, skip md5 check for file models/diff_unet_3d_rflow.pt.\n", - "2025-03-19 04:16:01,421 - INFO - File exists: models/diff_unet_3d_rflow.pt, skipped downloading.\n", - "2025-03-19 04:16:01,422 - INFO - Expected md5 is None, skip md5 check for file models/controlnet_3d_rflow.pt.\n", - "2025-03-19 04:16:01,422 - INFO - File exists: models/controlnet_3d_rflow.pt, skipped downloading.\n", - "2025-03-19 04:16:01,423 - INFO - Expected md5 is None, skip md5 check for file configs/candidate_masks_flexible_size_and_spacing_4000.json.\n", - "2025-03-19 04:16:01,423 - INFO - File exists: configs/candidate_masks_flexible_size_and_spacing_4000.json, skipped downloading.\n" + "2025-03-19 05:06:22,476 - INFO - Expected md5 is None, skip md5 check for file models/autoencoder_epoch273.pt.\n", + "2025-03-19 05:06:22,477 - INFO - File exists: models/autoencoder_epoch273.pt, skipped downloading.\n", + "2025-03-19 05:06:22,478 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_autoencoder.pt.\n", + "2025-03-19 05:06:22,478 - INFO - File exists: models/mask_generation_autoencoder.pt, skipped downloading.\n", + "2025-03-19 05:06:22,479 - INFO - Expected md5 is None, skip md5 check for file models/mask_generation_diffusion_unet.pt.\n", + "2025-03-19 05:06:22,480 - INFO - File exists: models/mask_generation_diffusion_unet.pt, skipped downloading.\n", + "2025-03-19 05:06:22,481 - INFO - Expected md5 is None, skip md5 check for file configs/all_anatomy_size_condtions.json.\n", + "2025-03-19 05:06:22,482 - INFO - File exists: configs/all_anatomy_size_condtions.json, skipped downloading.\n", + "2025-03-19 05:06:22,482 - INFO - Expected md5 is None, skip md5 check for file temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip.\n", + "2025-03-19 05:06:22,483 - INFO - File exists: temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000.zip, skipped downloading.\n", + "2025-03-19 05:06:22,483 - INFO - Expected md5 is None, skip md5 check for file models/diff_unet_3d_rflow.pt.\n", + "2025-03-19 05:06:22,484 - INFO - File exists: models/diff_unet_3d_rflow.pt, skipped downloading.\n", + "2025-03-19 05:06:22,484 - INFO - Expected md5 is None, skip md5 check for file models/controlnet_3d_rflow.pt.\n", + "2025-03-19 05:06:22,485 - INFO - File exists: models/controlnet_3d_rflow.pt, skipped downloading.\n", + "2025-03-19 05:06:22,485 - INFO - Expected md5 is None, skip md5 check for file configs/candidate_masks_flexible_size_and_spacing_4000.json.\n", + "2025-03-19 05:06:22,486 - INFO - File exists: configs/candidate_masks_flexible_size_and_spacing_4000.json, skipped downloading.\n" ] } ], "source": [ + "os.environ[\"MONAI_DATA_DIRECTORY\"] = \"temp_work_dir_inference_demo\"\n", "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", "if directory is not None:\n", " os.makedirs(directory, exist_ok=True)\n", @@ -296,18 +296,18 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-19 04:16:01.430][ INFO](notebook) - output_dir: output\n", - "[2025-03-19 04:16:01.431][ INFO](notebook) - trained_autoencoder_path: models/autoencoder_epoch273.pt\n", - "[2025-03-19 04:16:01.431][ INFO](notebook) - trained_diffusion_path: models/diff_unet_3d_rflow.pt\n", - "[2025-03-19 04:16:01.432][ INFO](notebook) - trained_controlnet_path: models/controlnet_3d_rflow.pt\n", - "[2025-03-19 04:16:01.432][ INFO](notebook) - trained_mask_generation_autoencoder_path: models/mask_generation_autoencoder.pt\n", - "[2025-03-19 04:16:01.433][ INFO](notebook) - trained_mask_generation_diffusion_path: models/mask_generation_diffusion_unet.pt\n", - "[2025-03-19 04:16:01.433][ INFO](notebook) - all_mask_files_base_dir: ./temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000\n", - "[2025-03-19 04:16:01.434][ INFO](notebook) - all_mask_files_json: ./configs/candidate_masks_flexible_size_and_spacing_4000.json\n", - "[2025-03-19 04:16:01.434][ INFO](notebook) - all_anatomy_size_conditions_json: ./configs/all_anatomy_size_condtions.json\n", - "[2025-03-19 04:16:01.435][ INFO](notebook) - label_dict_json: ./configs/label_dict.json\n", - "[2025-03-19 04:16:01.436][ INFO](notebook) - label_dict_remap_json: ./configs/label_dict_124_to_132.json\n", - "[2025-03-19 04:16:01.436][ INFO](notebook) - Global config variables have been loaded.\n" + "[2025-03-19 05:06:22.493][ INFO](notebook) - output_dir: output\n", + "[2025-03-19 05:06:22.494][ INFO](notebook) - trained_autoencoder_path: models/autoencoder_epoch273.pt\n", + "[2025-03-19 05:06:22.494][ INFO](notebook) - trained_diffusion_path: models/diff_unet_3d_rflow.pt\n", + "[2025-03-19 05:06:22.495][ INFO](notebook) - trained_controlnet_path: models/controlnet_3d_rflow.pt\n", + "[2025-03-19 05:06:22.496][ INFO](notebook) - trained_mask_generation_autoencoder_path: models/mask_generation_autoencoder.pt\n", + "[2025-03-19 05:06:22.497][ INFO](notebook) - trained_mask_generation_diffusion_path: models/mask_generation_diffusion_unet.pt\n", + "[2025-03-19 05:06:22.497][ INFO](notebook) - all_mask_files_base_dir: temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000\n", + "[2025-03-19 05:06:22.498][ INFO](notebook) - all_mask_files_json: ./configs/candidate_masks_flexible_size_and_spacing_4000.json\n", + "[2025-03-19 05:06:22.498][ INFO](notebook) - all_anatomy_size_conditions_json: ./configs/all_anatomy_size_condtions.json\n", + "[2025-03-19 05:06:22.499][ INFO](notebook) - label_dict_json: ./configs/label_dict.json\n", + "[2025-03-19 05:06:22.500][ INFO](notebook) - label_dict_remap_json: ./configs/label_dict_124_to_132.json\n", + "[2025-03-19 05:06:22.501][ INFO](notebook) - Global config variables have been loaded.\n" ] } ], @@ -351,28 +351,28 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-19 04:16:01.445][ INFO](notebook) - num_output_samples: 1\n", - "[2025-03-19 04:16:01.445][ INFO](notebook) - body_region: ['abdomen']\n", - "[2025-03-19 04:16:01.446][ INFO](notebook) - anatomy_list: ['liver', 'hepatic tumor']\n", - "[2025-03-19 04:16:01.447][ INFO](notebook) - controllable_anatomy_size: []\n", - "[2025-03-19 04:16:01.447][ INFO](notebook) - num_inference_steps: 30\n", - "[2025-03-19 04:16:01.448][ INFO](notebook) - mask_generation_num_inference_steps: 1000\n", - "[2025-03-19 04:16:01.449][ INFO](notebook) - output_size: [256, 256, 256]\n", - "[2025-03-19 04:16:01.450][ INFO](notebook) - image_output_ext: .nii.gz\n", - "[2025-03-19 04:16:01.451][ INFO](notebook) - label_output_ext: .nii.gz\n", - "[2025-03-19 04:16:01.451][ INFO](notebook) - spacing: [1.5, 1.5, 2.0]\n", - "[2025-03-19 04:16:01.452][ INFO](notebook) - autoencoder_sliding_window_infer_size: [48, 48, 48]\n", - "[2025-03-19 04:16:01.452][ INFO](notebook) - autoencoder_sliding_window_infer_overlap: 0.6666\n", - "[2025-03-19 04:16:01.453][ INFO](notebook) - controlnet: $@controlnet_def\n", - "[2025-03-19 04:16:01.453][ INFO](notebook) - diffusion_unet: $@diffusion_unet_def\n", - "[2025-03-19 04:16:01.454][ INFO](notebook) - autoencoder: $@autoencoder_def\n", - "[2025-03-19 04:16:01.454][ INFO](notebook) - mask_generation_autoencoder: $@mask_generation_autoencoder_def\n", - "[2025-03-19 04:16:01.455][ INFO](notebook) - mask_generation_diffusion: $@mask_generation_diffusion_def\n", - "[2025-03-19 04:16:01.455][ INFO](notebook) - modality: 1\n", - "[2025-03-19 04:16:01.456][ INFO](root) - `controllable_anatomy_size` is empty.\n", - "We will synthesize based on `body_region`: (['abdomen']) and `anatomy_list`: (['liver', 'hepatic tumor']).\n", - "[2025-03-19 04:16:01.458][ INFO](root) - The generate results will have voxel size to be [1.5, 1.5, 2.0]mm, volume size to be [256, 256, 256].\n", - "[2025-03-19 04:16:01.458][ INFO](notebook) - Network definition and inference inputs have been loaded.\n" + "[2025-03-19 05:06:22.508][ INFO](notebook) - num_output_samples: 1\n", + "[2025-03-19 05:06:22.509][ INFO](notebook) - body_region: ['chest']\n", + "[2025-03-19 05:06:22.509][ INFO](notebook) - anatomy_list: ['lung tumor']\n", + "[2025-03-19 05:06:22.510][ INFO](notebook) - controllable_anatomy_size: []\n", + "[2025-03-19 05:06:22.510][ INFO](notebook) - num_inference_steps: 30\n", + "[2025-03-19 05:06:22.512][ INFO](notebook) - mask_generation_num_inference_steps: 1000\n", + "[2025-03-19 05:06:22.512][ INFO](notebook) - output_size: [256, 256, 256]\n", + "[2025-03-19 05:06:22.513][ INFO](notebook) - image_output_ext: .nii.gz\n", + "[2025-03-19 05:06:22.514][ INFO](notebook) - label_output_ext: .nii.gz\n", + "[2025-03-19 05:06:22.514][ INFO](notebook) - spacing: [1.5, 1.5, 2.0]\n", + "[2025-03-19 05:06:22.515][ INFO](notebook) - autoencoder_sliding_window_infer_size: [48, 48, 48]\n", + "[2025-03-19 05:06:22.515][ INFO](notebook) - autoencoder_sliding_window_infer_overlap: 0.6666\n", + "[2025-03-19 05:06:22.516][ INFO](notebook) - controlnet: $@controlnet_def\n", + "[2025-03-19 05:06:22.517][ INFO](notebook) - diffusion_unet: $@diffusion_unet_def\n", + "[2025-03-19 05:06:22.518][ INFO](notebook) - autoencoder: $@autoencoder_def\n", + "[2025-03-19 05:06:22.518][ INFO](notebook) - mask_generation_autoencoder: $@mask_generation_autoencoder_def\n", + "[2025-03-19 05:06:22.519][ INFO](notebook) - mask_generation_diffusion: $@mask_generation_diffusion_def\n", + "[2025-03-19 05:06:22.520][ INFO](notebook) - modality: 1\n", + "[2025-03-19 05:06:22.521][ INFO](root) - `controllable_anatomy_size` is empty.\n", + "We will synthesize based on `body_region`: (['chest']) and `anatomy_list`: (['lung tumor']).\n", + "[2025-03-19 05:06:22.522][ INFO](root) - The generate results will have voxel size to be [1.5, 1.5, 2.0]mm, volume size to be [256, 256, 256].\n", + "[2025-03-19 05:06:22.522][ INFO](notebook) - Network definition and inference inputs have been loaded.\n" ] } ], @@ -443,14 +443,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-19 04:16:08,878 - INFO - 'dst' model updated: 180 of 231 variables.\n" + "2025-03-19 05:06:28,553 - INFO - 'dst' model updated: 180 of 231 variables.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-19 04:16:11.191][ INFO](notebook) - All the trained model weights have been loaded.\n" + "[2025-03-19 05:06:30.944][ INFO](notebook) - All the trained model weights have been loaded.\n" ] } ], @@ -504,7 +504,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-19 04:16:11.216][ INFO](root) - LDM sampler initialized.\n" + "[2025-03-19 05:06:30.969][ INFO](root) - LDM sampler initialized.\n" ] } ], @@ -567,62 +567,63 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-19 04:16:11.221][ INFO](notebook) - The generated image/mask pairs will be saved in output.\n", - "[2025-03-19 04:16:11.266][ INFO](root) - Resample mask file to get desired output size and spacing\n", - "[2025-03-19 04:16:12.931][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-19 04:16:12.934][ INFO](root) - Resize Spacing: [tensor(0.7539, dtype=torch.float64), tensor(0.7539, dtype=torch.float64), tensor(1.9141, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-19 04:16:12.935][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", - "[2025-03-19 04:16:15.739][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-19 04:16:15.742][ INFO](root) - Resize Spacing: [tensor(0.7578, dtype=torch.float64), tensor(0.7578, dtype=torch.float64), tensor(1.8848, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-19 04:16:15.743][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", - "[2025-03-19 04:16:18.527][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-19 04:16:18.529][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-19 04:16:18.530][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", - "[2025-03-19 04:16:22.793][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-19 04:16:22.797][ INFO](root) - Resize Spacing: [tensor(0.7754, dtype=torch.float64), tensor(0.7754, dtype=torch.float64), tensor(1.0098, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-19 04:16:22.797][ INFO](root) - Output size: [512, 512, 512] -> [256, 256, 256]\n", - "[2025-03-19 04:16:27.652][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-19 04:16:27.654][ INFO](root) - Resize Spacing: [tensor(0.7480, dtype=torch.float64), tensor(0.7480, dtype=torch.float64), tensor(1.0566, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-19 04:16:27.655][ INFO](root) - Output size: [512, 512, 512] -> [256, 256, 256]\n", - "[2025-03-19 04:16:29.442][ INFO](root) - Images will be generated based on [{'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_78_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}, {'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_61_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}, {'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_69_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}, {'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_7_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}, {'mask_file': {'pseudo_label': './temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task03/labelsTr/liver_2_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 0, 1]}, 'if_aug': True}].\n", - "[2025-03-19 04:16:29.443][ INFO](root) - ---- Start preparing masks... ----\n", - "[2025-03-19 04:16:30.690][ INFO](root) - Resampling mask to target shape and spacing\n", - "[2025-03-19 04:16:30.693][ INFO](root) - Resize Spacing: [tensor(0.7852, dtype=torch.float64), tensor(0.7852, dtype=torch.float64), tensor(1.9336, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", - "[2025-03-19 04:16:30.693][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n" + "[2025-03-19 05:06:30.974][ INFO](notebook) - The generated image/mask pairs will be saved in output.\n", + "[2025-03-19 05:06:31.018][ INFO](root) - Resample mask file to get desired output size and spacing\n", + "[2025-03-19 05:06:32.950][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-19 05:06:32.953][ INFO](root) - Resize Spacing: [tensor(0.7988, dtype=torch.float64), tensor(0.7988, dtype=torch.float64), tensor(1.1016, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-19 05:06:32.954][ INFO](root) - Output size: [512, 512, 384] -> [256, 256, 256]\n", + "[2025-03-19 05:06:35.876][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-19 05:06:35.878][ INFO](root) - Resize Spacing: [tensor(0.7031, dtype=torch.float64), tensor(0.7031, dtype=torch.float64), tensor(1.4795, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-19 05:06:35.879][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-19 05:06:38.564][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-19 05:06:38.566][ INFO](root) - Resize Spacing: [tensor(0.7617, dtype=torch.float64), tensor(0.7617, dtype=torch.float64), tensor(1.2939, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-19 05:06:38.567][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-19 05:06:41.321][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-19 05:06:41.324][ INFO](root) - Resize Spacing: [tensor(0.7031, dtype=torch.float64), tensor(0.7031, dtype=torch.float64), tensor(1.4209, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-19 05:06:41.325][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n", + "[2025-03-19 05:06:46.041][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-19 05:06:46.043][ INFO](root) - Resize Spacing: [tensor(0.7422, dtype=torch.float64), tensor(0.7422, dtype=torch.float64), tensor(0.5752, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-19 05:06:46.044][ INFO](root) - Output size: [512, 512, 640] -> [256, 256, 256]\n", + "[2025-03-19 05:06:47.854][ INFO](root) - Images will be generated based on [{'mask_file': {'pseudo_label': 'temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task06/labelsTr/lung_070_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [1, 0, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}, 'if_aug': True}, {'mask_file': {'pseudo_label': 'temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task06/labelsTr/lung_031_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}, 'if_aug': True}, {'mask_file': {'pseudo_label': 'temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task06/labelsTr/lung_075_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [1, 0, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}, 'if_aug': True}, {'mask_file': {'pseudo_label': 'temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task06/labelsTr/lung_014_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [0, 1, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}, 'if_aug': True}, {'mask_file': {'pseudo_label': 'temp_work_dir_inference_demo/datasets/all_masks_flexible_size_and_spacing_4000/./Task06/labelsTr/lung_049_133combined_aug_wbdm.nii.gz', 'spacing': [1.5, 1.5, 2.0], 'dim': [256, 256, 256], 'top_region_index': [1, 0, 0, 0], 'bottom_region_index': [0, 0, 1, 0]}, 'if_aug': True}].\n", + "[2025-03-19 05:06:47.855][ INFO](root) - ---- Start preparing masks... ----\n", + "[2025-03-19 05:06:49.096][ INFO](root) - Resampling mask to target shape and spacing\n", + "[2025-03-19 05:06:49.100][ INFO](root) - Resize Spacing: [tensor(0.7617, dtype=torch.float64), tensor(0.7617, dtype=torch.float64), tensor(1.2939, dtype=torch.float64)] -> [1.5, 1.5, 2.0]\n", + "[2025-03-19 05:06:49.100][ INFO](root) - Output size: [512, 512, 256] -> [256, 256, 256]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "augmenting liver tumor\n", - "metatensor(3216., device='cuda:0') | metatensor(3474.4001, device='cuda:0')\n" + "augmenting lung tumor\n", + "28\n", + "metatensor(180., device='cuda:0') | metatensor(547.4000, device='cuda:0')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-19 04:16:32.857][ INFO](root) - ---- Mask preparation time: 3.4133803844451904 seconds ----\n", - "[2025-03-19 04:16:32.869][ INFO](root) - ---- Start generating latent features... ----\n" + "[2025-03-19 05:06:52.345][ INFO](root) - ---- Mask preparation time: 4.489694595336914 seconds ----\n", + "[2025-03-19 05:06:52.363][ INFO](root) - ---- Start generating latent features... ----\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "metatensor(5213., device='cuda:0') | metatensor(3474.4001, device='cuda:0')\n" + "metatensor(687., device='cuda:0') | metatensor(547.4000, device='cuda:0')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|███████████████████████████████████████████| 30/30 [00:02<00:00, 10.17it/s]\n", - "[2025-03-19 04:16:35.859][ INFO](root) - ---- DM/ControlNet Latent features generation time: 2.9892637729644775 seconds ----\n", - "[2025-03-19 04:16:35.882][ INFO](root) - ---- Start decoding latent features into images... ----\n", - "100%|█████████████████████████████████████████████| 8/8 [00:07<00:00, 1.06it/s]\n", - "[2025-03-19 04:16:43.453][ INFO](root) - ---- Image VAE decoding time: 7.57032322883606 seconds ----\n" + "100%|███████████████████████████████████████████| 30/30 [00:06<00:00, 4.58it/s]\n", + "[2025-03-19 05:06:58.962][ INFO](root) - ---- DM/ControlNet Latent features generation time: 6.598896265029907 seconds ----\n", + "[2025-03-19 05:06:59.051][ INFO](root) - ---- Start decoding latent features into images... ----\n", + "100%|█████████████████████████████████████████████| 8/8 [00:11<00:00, 1.42s/it]\n", + "[2025-03-19 05:07:10.494][ INFO](root) - ---- Image VAE decoding time: 11.442715883255005 seconds ----\n" ] }, { @@ -630,15 +631,15 @@ "output_type": "stream", "text": [ "1 5\n", - "2025-03-19 04:16:43,843 INFO image_writer.py:197 - writing: output/sample_20250319_041643_825051_image.nii.gz\n", - "2025-03-19 04:16:45,440 INFO image_writer.py:197 - writing: output/sample_20250319_041643_825051_label.nii.gz\n" + "2025-03-19 05:07:10,994 INFO image_writer.py:197 - writing: output/sample_20250319_050710_975233_image.nii.gz\n", + "2025-03-19 05:07:12,461 INFO image_writer.py:197 - writing: output/sample_20250319_050710_975233_label.nii.gz\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-19 04:16:46.393][ INFO](notebook) - MAISI image/mask generation finished\n" + "[2025-03-19 05:07:13.422][ INFO](notebook) - MAISI image/mask generation finished\n" ] } ], @@ -666,12 +667,12 @@ "name": "stderr", "output_type": "stream", "text": [ - "[2025-03-19 04:16:46.402][ INFO](notebook) - Visualizing output/sample_20250319_041643_825051_image.nii.gz and output/sample_20250319_041643_825051_label.nii.gz...\n" + "[2025-03-19 05:07:13.430][ INFO](notebook) - Visualizing output/sample_20250319_050710_975233_image.nii.gz and output/sample_20250319_050710_975233_label.nii.gz...\n" ] }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -681,7 +682,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -702,7 +703,7 @@ "mask_volume = orientation(loader(visualize_mask_filename)).to(torch.uint8)\n", "\n", "# visualize for CT HU intensity between [-200, 500]\n", - "image_volume = torch.clip(image_volume, -200, 500)\n", + "image_volume = torch.clip(image_volume, -1000, 300)\n", "image_volume = image_volume - torch.min(image_volume)\n", "image_volume = image_volume / torch.max(image_volume)\n", "\n", @@ -722,6 +723,14 @@ "vis_image = get_xyz_plot(image_volume, center_loc_axis, mask_bool=False)\n", "show_image(vis_image, title=\"image\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "06740a60-9eb4-47dc-8ac8-bea74ce7a1b1", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/generation/maisi/scripts/find_masks.py b/generation/maisi/scripts/find_masks.py index c919d3932..15fdc12b8 100644 --- a/generation/maisi/scripts/find_masks.py +++ b/generation/maisi/scripts/find_masks.py @@ -102,24 +102,26 @@ def find_masks( db = json.load(f) # select candidate_masks - candidate_masks = [] + candidate_masks = [] for _item in db: if not set(anatomy_list).issubset(_item["label_list"]): continue - # extract region indice (top_index and bottom_index) for candidate mask - top_index = [index for index, element in enumerate(_item["top_region_index"]) if element != 0] - top_index = top_index[0] - bottom_index = [index for index, element in enumerate(_item["bottom_region_index"]) if element != 0] - bottom_index = bottom_index[0] - # whether to keep this mask, default to be True. keep_mask = True - # if candiate mask does not contain all the body_region, skip it - for _idx in body_region: - if _idx > bottom_index or _idx < top_index: - keep_mask = False + # extract region indice (top_index and bottom_index) for candidate mask + include_body_region = ("top_region_index" in _item.keys()) + if include_body_region: + top_index = [index for index, element in enumerate(_item["top_region_index"]) if element != 0] + top_index = top_index[0] + bottom_index = [index for index, element in enumerate(_item["bottom_region_index"]) if element != 0] + bottom_index = bottom_index[0] + + # if candiate mask does not contain all the body_region, skip it + for _idx in body_region: + if _idx > bottom_index or _idx < top_index: + keep_mask = False for tumor_label in [23, 24, 26, 27, 128]: # we skip those mask with tumors if users do not provide tumor label in anatomy_list @@ -138,9 +140,10 @@ def find_masks( "pseudo_label": os.path.join(mask_foldername, _item["pseudo_label_filename"]), "spacing": _item["spacing"], "dim": _item["dim"], - "top_region_index": _item["top_region_index"], - "bottom_region_index": _item["bottom_region_index"], } + if include_body_region: + candidate["top_region_index"] = _item["top_region_index"] + candidate["bottom_region_index"] = _item["bottom_region_index"] # Conditionally add the label to the candidate dictionary if "label_filename" in _item: From 4b24deb2c3aa1830347cc2e0a85f727ffd38440e Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 19 Mar 2025 05:10:13 +0000 Subject: [PATCH 55/59] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- generation/maisi/scripts/find_masks.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/generation/maisi/scripts/find_masks.py b/generation/maisi/scripts/find_masks.py index 15fdc12b8..b7a730c46 100644 --- a/generation/maisi/scripts/find_masks.py +++ b/generation/maisi/scripts/find_masks.py @@ -102,7 +102,7 @@ def find_masks( db = json.load(f) # select candidate_masks - candidate_masks = [] + candidate_masks = [] for _item in db: if not set(anatomy_list).issubset(_item["label_list"]): continue @@ -111,7 +111,7 @@ def find_masks( keep_mask = True # extract region indice (top_index and bottom_index) for candidate mask - include_body_region = ("top_region_index" in _item.keys()) + include_body_region = "top_region_index" in _item.keys() if include_body_region: top_index = [index for index, element in enumerate(_item["top_region_index"]) if element != 0] top_index = top_index[0] From 4c8efcfc88c396767b5f220356dfd2337815a86d Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Wed, 19 Mar 2025 05:49:36 +0000 Subject: [PATCH 56/59] reformat Signed-off-by: Can-Zhao --- generation/maisi/maisi_inference_tutorial.ipynb | 8 -------- 1 file changed, 8 deletions(-) diff --git a/generation/maisi/maisi_inference_tutorial.ipynb b/generation/maisi/maisi_inference_tutorial.ipynb index ae35a1513..69ce91b9b 100644 --- a/generation/maisi/maisi_inference_tutorial.ipynb +++ b/generation/maisi/maisi_inference_tutorial.ipynb @@ -723,14 +723,6 @@ "vis_image = get_xyz_plot(image_volume, center_loc_axis, mask_bool=False)\n", "show_image(vis_image, title=\"image\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "06740a60-9eb4-47dc-8ac8-bea74ce7a1b1", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From c4c80bb87d35161644331d1b5fc8c15c94907988 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Wed, 19 Mar 2025 06:18:18 +0000 Subject: [PATCH 57/59] OOM Signed-off-by: Can-Zhao --- generation/maisi/scripts/sample.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/generation/maisi/scripts/sample.py b/generation/maisi/scripts/sample.py index e7441b5e0..afe444516 100644 --- a/generation/maisi/scripts/sample.py +++ b/generation/maisi/scripts/sample.py @@ -332,7 +332,7 @@ def ldm_conditional_sample_one_image( latents, _ = noise_scheduler.step(model_output, t, latents, next_t) # type: ignore end_time = time.time() logging.info(f"---- DM/ControlNet Latent features generation time: {end_time - start_time} seconds ----") - del model_output, controlnet_cond_vis + del model_output, controlnet_cond_vis, down_block_res_samples, mid_block_res_sample torch.cuda.empty_cache() # decode latents to synthesized images From 039e6fd5ca193575b24e9680c14b24807a92d6e5 Mon Sep 17 00:00:00 2001 From: Can-Zhao Date: Wed, 19 Mar 2025 06:28:13 +0000 Subject: [PATCH 58/59] OOM Signed-off-by: Can-Zhao --- generation/maisi/scripts/sample.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/generation/maisi/scripts/sample.py b/generation/maisi/scripts/sample.py index afe444516..3499e741c 100644 --- a/generation/maisi/scripts/sample.py +++ b/generation/maisi/scripts/sample.py @@ -17,6 +17,7 @@ import time from datetime import datetime import warnings +import gc import monai import torch @@ -332,7 +333,8 @@ def ldm_conditional_sample_one_image( latents, _ = noise_scheduler.step(model_output, t, latents, next_t) # type: ignore end_time = time.time() logging.info(f"---- DM/ControlNet Latent features generation time: {end_time - start_time} seconds ----") - del model_output, controlnet_cond_vis, down_block_res_samples, mid_block_res_sample + del unet_inputs, controlnet_inputs, model_output, controlnet_cond_vis, down_block_res_samples, mid_block_res_sample + gc.collect() torch.cuda.empty_cache() # decode latents to synthesized images From 44ecd9b341b96bfb451dc7015898036b339305b5 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 19 Mar 2025 06:29:26 +0000 Subject: [PATCH 59/59] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- generation/maisi/scripts/sample.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/generation/maisi/scripts/sample.py b/generation/maisi/scripts/sample.py index 3499e741c..fb1d0f425 100644 --- a/generation/maisi/scripts/sample.py +++ b/generation/maisi/scripts/sample.py @@ -333,7 +333,14 @@ def ldm_conditional_sample_one_image( latents, _ = noise_scheduler.step(model_output, t, latents, next_t) # type: ignore end_time = time.time() logging.info(f"---- DM/ControlNet Latent features generation time: {end_time - start_time} seconds ----") - del unet_inputs, controlnet_inputs, model_output, controlnet_cond_vis, down_block_res_samples, mid_block_res_sample + del ( + unet_inputs, + controlnet_inputs, + model_output, + controlnet_cond_vis, + down_block_res_samples, + mid_block_res_sample, + ) gc.collect() torch.cuda.empty_cache()