



'''simple docstring'''






import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Optional[Any] ,       snake_case_    :     Dict  )		->Dict:






							'''simple docstring'''
							_lowercase							:		List[str]				=   UNetaDModel(**snake_case_  )
							_lowercase							:		Union[str, Any]				=   checkpoint['''all_modules.0.W'''].data
							_lowercase							:		Any				=   checkpoint['''all_modules.0.W'''].data
							_lowercase							:		Optional[int]				=   checkpoint['''all_modules.1.weight'''].data
							_lowercase							:		Union[str, Any]				=   checkpoint['''all_modules.1.bias'''].data

							_lowercase							:		int				=   checkpoint['''all_modules.2.weight'''].data
							_lowercase							:		int				=   checkpoint['''all_modules.2.bias'''].data

							_lowercase							:		Any				=   checkpoint['''all_modules.3.weight'''].data
							_lowercase							:		Optional[int]				=   checkpoint['''all_modules.3.bias'''].data

							_lowercase							:		Dict				=   checkpoint[list(checkpoint.keys()  )[-4]].data
							_lowercase							:		int				=   checkpoint[list(checkpoint.keys()  )[-3]].data
							_lowercase							:		Optional[Any]				=   checkpoint[list(checkpoint.keys()  )[-2]].data
							_lowercase							:		Dict				=   checkpoint[list(checkpoint.keys()  )[-1]].data

							_lowercase							:		List[Any]				=   4

							def set_attention_weights(snake_case_    :     Union[str, Any] ,       snake_case_    :     Optional[int] ,       snake_case_    :     Dict  ):
														_lowercase							:		List[Any]				=   old_checkpoint[F"all_modules.{index}.NIN_0.W"].data.T
														_lowercase							:		List[str]				=   old_checkpoint[F"all_modules.{index}.NIN_1.W"].data.T
														_lowercase							:		Optional[Any]				=   old_checkpoint[F"all_modules.{index}.NIN_2.W"].data.T

														_lowercase							:		List[Any]				=   old_checkpoint[F"all_modules.{index}.NIN_0.b"].data
														_lowercase							:		Tuple				=   old_checkpoint[F"all_modules.{index}.NIN_1.b"].data
														_lowercase							:		Optional[Any]				=   old_checkpoint[F"all_modules.{index}.NIN_2.b"].data

														_lowercase							:		Optional[int]				=   old_checkpoint[F"all_modules.{index}.NIN_3.W"].data.T
														_lowercase							:		Optional[Any]				=   old_checkpoint[F"all_modules.{index}.NIN_3.b"].data

														_lowercase							:		Dict				=   old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
														_lowercase							:		int				=   old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data

							def set_resnet_weights(snake_case_    :     Dict ,       snake_case_    :     Tuple ,       snake_case_    :     int  ):
														_lowercase							:		Union[str, Any]				=   old_checkpoint[F"all_modules.{index}.Conv_0.weight"].data
														_lowercase							:		Tuple				=   old_checkpoint[F"all_modules.{index}.Conv_0.bias"].data
														_lowercase							:		Optional[Any]				=   old_checkpoint[F"all_modules.{index}.GroupNorm_0.weight"].data
														_lowercase							:		Union[str, Any]				=   old_checkpoint[F"all_modules.{index}.GroupNorm_0.bias"].data

														_lowercase							:		Tuple				=   old_checkpoint[F"all_modules.{index}.Conv_1.weight"].data
														_lowercase							:		List[Any]				=   old_checkpoint[F"all_modules.{index}.Conv_1.bias"].data
														_lowercase							:		List[Any]				=   old_checkpoint[F"all_modules.{index}.GroupNorm_1.weight"].data
														_lowercase							:		Dict				=   old_checkpoint[F"all_modules.{index}.GroupNorm_1.bias"].data

														_lowercase							:		Union[str, Any]				=   old_checkpoint[F"all_modules.{index}.Dense_0.weight"].data
														_lowercase							:		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:
																					_lowercase							:		Dict				=   old_checkpoint[F"all_modules.{index}.Conv_2.weight"].data
																					_lowercase							:		List[str]				=   old_checkpoint[F"all_modules.{index}.Conv_2.bias"].data

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

														if hasattr(snake_case_ ,       '''downsamplers'''  ) and block.downsamplers is not None:
																					set_resnet_weights(block.resnet_down ,       snake_case_ ,       snake_case_  )
																					module_index += 1
																					_lowercase							:		Union[str, Any]				=   checkpoint[F"all_modules.{module_index}.Conv_0.weight"].data
																					_lowercase							:		Union[str, Any]				=   checkpoint[F"all_modules.{module_index}.Conv_0.bias"].data
																					module_index += 1

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

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

														if hasattr(snake_case_ ,       '''resnet_up'''  ) and block.resnet_up is not None:
																					_lowercase							:		List[Any]				=   checkpoint[F"all_modules.{module_index}.weight"].data
																					_lowercase							:		List[Any]				=   checkpoint[F"all_modules.{module_index}.bias"].data
																					module_index += 1
																					_lowercase							:		Union[str, Any]				=   checkpoint[F"all_modules.{module_index}.weight"].data
																					_lowercase							:		List[str]				=   checkpoint[F"all_modules.{module_index}.bias"].data
																					module_index += 1
																					set_resnet_weights(block.resnet_up ,       snake_case_ ,       snake_case_  )
																					module_index += 1

							_lowercase							:		List[Any]				=   checkpoint[F"all_modules.{module_index}.weight"].data
							_lowercase							:		List[str]				=   checkpoint[F"all_modules.{module_index}.bias"].data
							module_index += 1
							_lowercase							:		Optional[Any]				=   checkpoint[F"all_modules.{module_index}.weight"].data
							_lowercase							:		Dict				=   checkpoint[F"all_modules.{module_index}.bias"].data

							return new_model_architecture.state_dict()


if __name__ == "__main__":
						lowerCamelCase__	  =		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.',
						)

						lowerCamelCase__	  =		parser.parse_args()

						lowerCamelCase__	  =		torch.load(args.checkpoint_path, map_location='cpu')

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

						lowerCamelCase__	  =		convert_ncsnpp_checkpoint(
						    checkpoint,
						    config,
						)

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

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

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

												lowerCamelCase__	  =		ScoreSdeVePipeline(unet=model, scheduler=scheduler)
												pipe.save_pretrained(args.dump_path)
						except:  # noqa: E722
												model.save_pretrained(args.dump_path)








'''simple docstring'''






import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


lowerCamelCase__	  =		TypeVar('T', bound='TaskTemplate')

@dataclass(frozen=__A       )
class 	_lowerCAmelCase     (    abc.ABC       ):






		'''simple docstring'''
		# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
		snake_case_							= 42
		snake_case_							= 42
		snake_case_							= 42
		def 						__lowercase      (						self							:       T      ,		UpperCamelCase_							:       Features		)      ->			T:







									'''simple docstring'''





									return copy.deepcopy(self		)
		@property
		def 						__lowercase      (						self							:       int		)      ->			Features:







									'''simple docstring'''





									return Features(**self.input_schema      ,		**self.label_schema		)
		@property
		@abc.abstractmethod
		def 						__lowercase      (						self							:       List[str]		)      ->			Dict[str, str]:







									'''simple docstring'''





									raise NotImplementedError





		@classmethod
		def 						__lowercase      (						cls							:       Type[T]      ,		UpperCamelCase_							:       dict		)      ->			T:







									'''simple docstring'''





									_lowercase							:		List[str]				=   {f.name for f in dataclasses.fields(cls		)}
									return cls(**{k: v for k, v in template_dict.items() if k in field_names}		)



