



'''simple docstring'''





import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel






def 					a(   A :					List[Any]				,  A :					Union[str, Any] )	-> Union[str, Any]:
					"""simple docstring"""





					a        =     UNetaDModel(**A )
					a        =     checkpoint["all_modules.0.W"].data
					a        =     checkpoint["all_modules.0.W"].data
					a        =     checkpoint["all_modules.1.weight"].data
					a        =     checkpoint["all_modules.1.bias"].data

					a        =     checkpoint["all_modules.2.weight"].data
					a        =     checkpoint["all_modules.2.bias"].data

					a        =     checkpoint["all_modules.3.weight"].data
					a        =     checkpoint["all_modules.3.bias"].data

					a        =     checkpoint[list(checkpoint.keys() )[-4]].data
					a        =     checkpoint[list(checkpoint.keys() )[-3]].data
					a        =     checkpoint[list(checkpoint.keys() )[-2]].data
					a        =     checkpoint[list(checkpoint.keys() )[-1]].data

					a        =     4

					def set_attention_weights(A :					List[str]				,  A :					Any				,  A :					List[str] ):
										a        =     old_checkpoint[f'''all_modules.{index}.NIN_0.W'''].data.T
										a        =     old_checkpoint[f'''all_modules.{index}.NIN_1.W'''].data.T
										a        =     old_checkpoint[f'''all_modules.{index}.NIN_2.W'''].data.T

										a        =     old_checkpoint[f'''all_modules.{index}.NIN_0.b'''].data
										a        =     old_checkpoint[f'''all_modules.{index}.NIN_1.b'''].data
										a        =     old_checkpoint[f'''all_modules.{index}.NIN_2.b'''].data

										a        =     old_checkpoint[f'''all_modules.{index}.NIN_3.W'''].data.T
										a        =     old_checkpoint[f'''all_modules.{index}.NIN_3.b'''].data

										a        =     old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
										a        =     old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

					def set_resnet_weights(A :					str				,  A :					List[str]				,  A :					Optional[Any] ):
										a        =     old_checkpoint[f'''all_modules.{index}.Conv_0.weight'''].data
										a        =     old_checkpoint[f'''all_modules.{index}.Conv_0.bias'''].data
										a        =     old_checkpoint[f'''all_modules.{index}.GroupNorm_0.weight'''].data
										a        =     old_checkpoint[f'''all_modules.{index}.GroupNorm_0.bias'''].data

										a        =     old_checkpoint[f'''all_modules.{index}.Conv_1.weight'''].data
										a        =     old_checkpoint[f'''all_modules.{index}.Conv_1.bias'''].data
										a        =     old_checkpoint[f'''all_modules.{index}.GroupNorm_1.weight'''].data
										a        =     old_checkpoint[f'''all_modules.{index}.GroupNorm_1.bias'''].data

										a        =     old_checkpoint[f'''all_modules.{index}.Dense_0.weight'''].data
										a        =     old_checkpoint[f'''all_modules.{index}.Dense_0.bias'''].data

										if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
															a        =     old_checkpoint[f'''all_modules.{index}.Conv_2.weight'''].data
															a        =     old_checkpoint[f'''all_modules.{index}.Conv_2.bias'''].data

					for i, block in enumerate(new_model_architecture.downsample_blocks ):
										a        =     hasattr(A				,  "attentions" )
										for j in range(len(block.resnets ) ):
															set_resnet_weights(block.resnets[j]				,  A				,  A )
															module_index += 1
															if has_attentions:
																				set_attention_weights(block.attentions[j]				,  A				,  A )
																				module_index += 1

										if hasattr(A				,  "downsamplers" ) and block.downsamplers is not None:
															set_resnet_weights(block.resnet_down				,  A				,  A )
															module_index += 1
															a        =     checkpoint[f'''all_modules.{module_index}.Conv_0.weight'''].data
															a        =     checkpoint[f'''all_modules.{module_index}.Conv_0.bias'''].data
															module_index += 1

					set_resnet_weights(new_model_architecture.mid_block.resnets[0]				,  A				,  A )
					module_index += 1
					set_attention_weights(new_model_architecture.mid_block.attentions[0]				,  A				,  A )
					module_index += 1
					set_resnet_weights(new_model_architecture.mid_block.resnets[1]				,  A				,  A )
					module_index += 1

					for i, block in enumerate(new_model_architecture.up_blocks ):
										a        =     hasattr(A				,  "attentions" )
										for j in range(len(block.resnets ) ):
															set_resnet_weights(block.resnets[j]				,  A				,  A )
															module_index += 1
										if has_attentions:
															set_attention_weights(
															    block.attentions[0]				,  A				,  A )  # why can there only be a single attention layer for up?
															module_index += 1

										if hasattr(A				,  "resnet_up" ) and block.resnet_up is not None:
															a        =     checkpoint[f'''all_modules.{module_index}.weight'''].data
															a        =     checkpoint[f'''all_modules.{module_index}.bias'''].data
															module_index += 1
															a        =     checkpoint[f'''all_modules.{module_index}.weight'''].data
															a        =     checkpoint[f'''all_modules.{module_index}.bias'''].data
															module_index += 1
															set_resnet_weights(block.resnet_up				,  A				,  A )
															module_index += 1

					a        =     checkpoint[f'''all_modules.{module_index}.weight'''].data
					a        =     checkpoint[f'''all_modules.{module_index}.bias'''].data
					module_index += 1
					a        =     checkpoint[f'''all_modules.{module_index}.weight'''].data
					a        =     checkpoint[f'''all_modules.{module_index}.bias'''].data

					return new_model_architecture.state_dict()


if __name__ == "__main__":
				_lowercase:				int       =	argparse.ArgumentParser()

				parser.add_argument(
				    "--checkpoint_path",
				    default="/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin",
				    type=str,
				    required=False,
				    help="Path to the checkpoint to convert.",
				)

				parser.add_argument(
				    "--config_file",
				    default="/Users/arthurzucker/Work/diffusers/ArthurZ/config.json",
				    type=str,
				    required=False,
				    help="The config json file corresponding to the architecture.",
				)

				parser.add_argument(
				    "--dump_path",
				    default="/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt",
				    type=str,
				    required=False,
				    help="Path to the output model.",
				)

				_lowercase:				int       =	parser.parse_args()

				_lowercase:				Optional[Any]       =	torch.load(args.checkpoint_path, map_location="cpu")

				with open(args.config_file) as f:
								_lowercase:				Dict       =	json.loads(f.read())

				_lowercase:				Any       =	convert_ncsnpp_checkpoint(
				    checkpoint,
				    config,
				)

				if "sde" in config:
								del config["sde"]

				_lowercase:				int       =	UNetaDModel(**config)
				model.load_state_dict(converted_checkpoint)

				try:
								_lowercase:				Any       =	ScoreSdeVeScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))

								_lowercase:				Optional[Any]       =	ScoreSdeVePipeline(unet=model, scheduler=scheduler)
								pipe.save_pretrained(args.dump_path)
				except:  # noqa: E722
								model.save_pretrained(args.dump_path)





import warnings

from ...utils import logging
from .image_processing_detr import DetrImageProcessor


_lowercase:				Optional[int]       =	logging.get_logger(__name__)



class 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""




	def __init__(self	, *lowerCamelCase_	, **lowerCamelCase_	):
						"""simple docstring"""

						warnings.warn(
						    "The class DetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
						    " Please use DetrImageProcessor instead."	, lowerCamelCase_	, )
						super().__init__(*lowerCamelCase_	, **lowerCamelCase_	)
