




import re

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     int:
     def docstring_decorator(__UpperCamelCase):
          a      =      fn.__doc__
          a      =      func_doc.split("\n")
          a      =      0
          while i < len(lowerCamelCase_) and re.search(r"^\s*Examples?:\s*$"		,					lines[i]) is None:
               i += 1
          if i < len(lowerCamelCase_):
               a      =      example_docstring
               a      =      "\n".join(lowerCamelCase_)
          else:
               raise ValueError(
                   f'''The function {fn} should have an empty \'Examples:\' in its docstring as placeholder, '''
                   f'''current docstring is:\n{func_doc}''')
          a      =      func_doc
          return fn

     return docstring_decorator








'''simple docstring'''



import argparse
import json

import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel




def       _lowerCAmelCase  ( lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     List[Any] ):
			__lowercase   =							UNetaDModel(**lowerCamelCase_ )
			__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(lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     int						,				lowerCamelCase_	:     str ):
						__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(lowerCamelCase_	:     str						,				lowerCamelCase_	:     Tuple						,				lowerCamelCase_	:     Any ):
						__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(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
									__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]						,				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 ):
						__lowercase   =							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:
									__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						,				lowerCamelCase_						,				lowerCamelCase_ )
									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__":
	_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)





