



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel




def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase     ):
			A				:       Union[str, Any]														=    UNetaDModel(**_lowerCamelCase     )
			A				:       List[str]														=    checkpoint["all_modules.0.W"].data
			A				:       Tuple														=    checkpoint["all_modules.0.W"].data
			A				:       str														=    checkpoint["all_modules.1.weight"].data
			A				:       Dict														=    checkpoint["all_modules.1.bias"].data

			A				:       Tuple														=    checkpoint["all_modules.2.weight"].data
			A				:       Optional[int]														=    checkpoint["all_modules.2.bias"].data

			A				:       List[str]														=    checkpoint["all_modules.3.weight"].data
			A				:       Union[str, Any]														=    checkpoint["all_modules.3.bias"].data

			A				:       List[Any]														=    checkpoint[list(checkpoint.keys()     )[-4]].data
			A				:       Any														=    checkpoint[list(checkpoint.keys()     )[-3]].data
			A				:       List[str]														=    checkpoint[list(checkpoint.keys()     )[-2]].data
			A				:       int														=    checkpoint[list(checkpoint.keys()     )[-1]].data

			A				:       List[str]														=    4

			def set_attention_weights(_lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase     ):
						A				:       Optional[Any]														=    old_checkpoint[f"""all_modules.{index}.NIN_0.W"""].data.T
						A				:       List[str]														=    old_checkpoint[f"""all_modules.{index}.NIN_1.W"""].data.T
						A				:       Optional[int]														=    old_checkpoint[f"""all_modules.{index}.NIN_2.W"""].data.T

						A				:       Optional[int]														=    old_checkpoint[f"""all_modules.{index}.NIN_0.b"""].data
						A				:       List[str]														=    old_checkpoint[f"""all_modules.{index}.NIN_1.b"""].data
						A				:       List[Any]														=    old_checkpoint[f"""all_modules.{index}.NIN_2.b"""].data

						A				:       List[Any]														=    old_checkpoint[f"""all_modules.{index}.NIN_3.W"""].data.T
						A				:       Tuple														=    old_checkpoint[f"""all_modules.{index}.NIN_3.b"""].data

						A				:       str														=    old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
						A				:       List[str]														=    old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

			def set_resnet_weights(_lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase     ):
						A				:       Optional[int]														=    old_checkpoint[f"""all_modules.{index}.Conv_0.weight"""].data
						A				:       Dict														=    old_checkpoint[f"""all_modules.{index}.Conv_0.bias"""].data
						A				:       int														=    old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
						A				:       List[Any]														=    old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

						A				:       Optional[Any]														=    old_checkpoint[f"""all_modules.{index}.Conv_1.weight"""].data
						A				:       List[Any]														=    old_checkpoint[f"""all_modules.{index}.Conv_1.bias"""].data
						A				:       int														=    old_checkpoint[f"""all_modules.{index}.GroupNorm_1.weight"""].data
						A				:       Optional[Any]														=    old_checkpoint[f"""all_modules.{index}.GroupNorm_1.bias"""].data

						A				:       List[Any]														=    old_checkpoint[f"""all_modules.{index}.Dense_0.weight"""].data
						A				:       str														=    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				:       Any														=    old_checkpoint[f"""all_modules.{index}.Conv_2.weight"""].data
									A				:       Optional[Any]														=    old_checkpoint[f"""all_modules.{index}.Conv_2.bias"""].data

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

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

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

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

						if hasattr(_lowerCamelCase			,     "resnet_up"     ) and block.resnet_up is not None:
									A				:       Tuple														=    checkpoint[f"""all_modules.{module_index}.weight"""].data
									A				:       str														=    checkpoint[f"""all_modules.{module_index}.bias"""].data
									module_index += 1
									A				:       Any														=    checkpoint[f"""all_modules.{module_index}.weight"""].data
									A				:       Optional[int]														=    checkpoint[f"""all_modules.{module_index}.bias"""].data
									module_index += 1
									set_resnet_weights(block.resnet_up			,     _lowerCamelCase			,     _lowerCamelCase     )
									module_index += 1

			A				:       str														=    checkpoint[f"""all_modules.{module_index}.weight"""].data
			A				:       str														=    checkpoint[f"""all_modules.{module_index}.bias"""].data
			module_index += 1
			A				:       Any														=    checkpoint[f"""all_modules.{module_index}.weight"""].data
			A				:       Optional[int]														=    checkpoint[f"""all_modules.{module_index}.bias"""].data

			return new_model_architecture.state_dict()


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE     =	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.""",
							)

							__SCREAMING_SNAKE_CASE     =	parser.parse_args()

							__SCREAMING_SNAKE_CASE     =	torch.load(args.checkpoint_path, map_location="""cpu""")

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

							__SCREAMING_SNAKE_CASE     =	convert_ncsnpp_checkpoint(
							    checkpoint,
							    config,
							)

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

							__SCREAMING_SNAKE_CASE     =	UNetaDModel(**config)
							model.load_state_dict(converted_checkpoint)

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

														__SCREAMING_SNAKE_CASE     =	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


__SCREAMING_SNAKE_CASE     =	logging.get_logger(__name__)

class 						lowerCamelCase_     (			_A    ):






						'''simple docstring'''







						def __init__(						self   :      Dict						,						*__lowerCamelCase   :      Tuple						,						**__lowerCamelCase   :      Dict			)   ->					None:
									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			)