
import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel




def   UpperCamelCase__     (    SCREAMING_SNAKE_CASE_  :  Tuple		,      SCREAMING_SNAKE_CASE_  :  List[Any]					)     ->    Union[str, Any]:
						_lowercase									=				UNetaDModel(**SCREAMING_SNAKE_CASE_					)
						_lowercase									=				checkpoint["""all_modules.0.W"""].data
						_lowercase									=				checkpoint["""all_modules.0.W"""].data
						_lowercase									=				checkpoint["""all_modules.1.weight"""].data
						_lowercase									=				checkpoint["""all_modules.1.bias"""].data

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

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

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

						_lowercase									=				4

						def set_attention_weights(SCREAMING_SNAKE_CASE_  :  Optional[Any]		,      SCREAMING_SNAKE_CASE_  :  Dict		,      SCREAMING_SNAKE_CASE_  :  Optional[Any]					):
												_lowercase									=				old_checkpoint[f"""all_modules.{index}.NIN_0.W"""].data.T
												_lowercase									=				old_checkpoint[f"""all_modules.{index}.NIN_1.W"""].data.T
												_lowercase									=				old_checkpoint[f"""all_modules.{index}.NIN_2.W"""].data.T

												_lowercase									=				old_checkpoint[f"""all_modules.{index}.NIN_0.b"""].data
												_lowercase									=				old_checkpoint[f"""all_modules.{index}.NIN_1.b"""].data
												_lowercase									=				old_checkpoint[f"""all_modules.{index}.NIN_2.b"""].data

												_lowercase									=				old_checkpoint[f"""all_modules.{index}.NIN_3.W"""].data.T
												_lowercase									=				old_checkpoint[f"""all_modules.{index}.NIN_3.b"""].data

												_lowercase									=				old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
												_lowercase									=				old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

						def set_resnet_weights(SCREAMING_SNAKE_CASE_  :  Any		,      SCREAMING_SNAKE_CASE_  :  Union[str, Any]		,      SCREAMING_SNAKE_CASE_  :  Tuple					):
												_lowercase									=				old_checkpoint[f"""all_modules.{index}.Conv_0.weight"""].data
												_lowercase									=				old_checkpoint[f"""all_modules.{index}.Conv_0.bias"""].data
												_lowercase									=				old_checkpoint[f"""all_modules.{index}.GroupNorm_0.weight"""].data
												_lowercase									=				old_checkpoint[f"""all_modules.{index}.GroupNorm_0.bias"""].data

												_lowercase									=				old_checkpoint[f"""all_modules.{index}.Conv_1.weight"""].data
												_lowercase									=				old_checkpoint[f"""all_modules.{index}.Conv_1.bias"""].data
												_lowercase									=				old_checkpoint[f"""all_modules.{index}.GroupNorm_1.weight"""].data
												_lowercase									=				old_checkpoint[f"""all_modules.{index}.GroupNorm_1.bias"""].data

												_lowercase									=				old_checkpoint[f"""all_modules.{index}.Dense_0.weight"""].data
												_lowercase									=				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									=				old_checkpoint[f"""all_modules.{index}.Conv_2.weight"""].data
																		_lowercase									=				old_checkpoint[f"""all_modules.{index}.Conv_2.bias"""].data

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

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

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

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

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

						_lowercase									=				checkpoint[f"""all_modules.{module_index}.weight"""].data
						_lowercase									=				checkpoint[f"""all_modules.{module_index}.bias"""].data
						module_index += 1
						_lowercase									=				checkpoint[f"""all_modules.{module_index}.weight"""].data
						_lowercase									=				checkpoint[f"""all_modules.{module_index}.bias"""].data

						return new_model_architecture.state_dict()


if __name__ == "__main__":
		A		:   str         =    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.''',
		)

		A		:   List[str]         =    parser.parse_args()

		A		:   Dict         =    torch.load(args.checkpoint_path, map_location='''cpu''')

		with open(args.config_file) as f:
				A		:   List[Any]         =    json.loads(f.read())

		A		:   Any         =    convert_ncsnpp_checkpoint(
		    checkpoint,
		    config,
		)

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

		A		:   List[str]         =    UNetaDModel(**config)
		model.load_state_dict(converted_checkpoint)

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

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

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

from ..features import Features


A		:   Optional[int]         =    TypeVar('''T''', bound='''TaskTemplate''')



@dataclass(frozen=_a						)
class       a_   ( abc.ABC						):
			# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
			a  : str
			a  : ClassVar[Features]
			a  : ClassVar[Features]






			def       UpperCamelCase_							(			self							, __UpperCamelCase     ):
									return copy.deepcopy(self     )






			@property
			def       UpperCamelCase_							(			self     ):
									return Features(**self.input_schema							, **self.label_schema     )






			@property
			@abc.abstractmethod
			def       UpperCamelCase_							(			self     ):
									raise NotImplementedError






			@classmethod
			def       UpperCamelCase_							(			cls							, __UpperCamelCase     ):
									_lowercase									=				{f.name for f in dataclasses.fields(cls     )}
									return cls(**{k: v for k, v in template_dict.items() if k in field_names}     )