code: 
  
  
  
  
  """simple docstring"""
  
  import argparse
  import json
  
  import torch
  
  from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
  
  
  
  def   UpperCamelCase__     (		lowercase__  :     Optional[int]       ,     lowercase__  :     Tuple   ):
  		snake_case		:						Optional[Any]								=				UNetaDModel(**lowercase__   )
  		snake_case		:						str								=				checkpoint["all_modules.0.W"].data
  		snake_case		:						List[str]								=				checkpoint["all_modules.0.W"].data
  		snake_case		:						Dict								=				checkpoint["all_modules.1.weight"].data
  		snake_case		:						Optional[Any]								=				checkpoint["all_modules.1.bias"].data
  
  		snake_case		:						Tuple								=				checkpoint["all_modules.2.weight"].data
  		snake_case		:						Optional[int]								=				checkpoint["all_modules.2.bias"].data
  
  		snake_case		:						Tuple								=				checkpoint["all_modules.3.weight"].data
  		snake_case		:						List[str]								=				checkpoint["all_modules.3.bias"].data
  
  		snake_case		:						Tuple								=				checkpoint[list(checkpoint.keys()   )[-4]].data
  		snake_case		:						List[str]								=				checkpoint[list(checkpoint.keys()   )[-3]].data
  		snake_case		:						Any								=				checkpoint[list(checkpoint.keys()   )[-2]].data
  		snake_case		:						Tuple								=				checkpoint[list(checkpoint.keys()   )[-1]].data
  
  		snake_case		:						List[Any]								=				4
  
  		def set_attention_weights(lowercase__  :     str       ,     lowercase__  :     Union[str, Any]       ,     lowercase__  :     List[Any]   ):
  				snake_case		:						Optional[Any]								=				old_checkpoint[F'''all_modules.{index}.NIN_0.W'''].data.T
  				snake_case		:						Optional[int]								=				old_checkpoint[F'''all_modules.{index}.NIN_1.W'''].data.T
  				snake_case		:						List[str]								=				old_checkpoint[F'''all_modules.{index}.NIN_2.W'''].data.T
  
  				snake_case		:						Any								=				old_checkpoint[F'''all_modules.{index}.NIN_0.b'''].data
  				snake_case		:						Dict								=				old_checkpoint[F'''all_modules.{index}.NIN_1.b'''].data
  				snake_case		:						Union[str, Any]								=				old_checkpoint[F'''all_modules.{index}.NIN_2.b'''].data
  
  				snake_case		:						Tuple								=				old_checkpoint[F'''all_modules.{index}.NIN_3.W'''].data.T
  				snake_case		:						Union[str, Any]								=				old_checkpoint[F'''all_modules.{index}.NIN_3.b'''].data
  
  				snake_case		:						str								=				old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
  				snake_case		:						List[str]								=				old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data
  
  		def set_resnet_weights(lowercase__  :     Tuple       ,     lowercase__  :     Union[str, Any]       ,     lowercase__  :     Any   ):
  				snake_case		:						Dict								=				old_checkpoint[F'''all_modules.{index}.Conv_0.weight'''].data
  				snake_case		:						int								=				old_checkpoint[F'''all_modules.{index}.Conv_0.bias'''].data
  				snake_case		:						Any								=				old_checkpoint[F'''all_modules.{index}.GroupNorm_0.weight'''].data
  				snake_case		:						Optional[int]								=				old_checkpoint[F'''all_modules.{index}.GroupNorm_0.bias'''].data
  
  				snake_case		:						Optional[Any]								=				old_checkpoint[F'''all_modules.{index}.Conv_1.weight'''].data
  				snake_case		:						Union[str, Any]								=				old_checkpoint[F'''all_modules.{index}.Conv_1.bias'''].data
  				snake_case		:						Any								=				old_checkpoint[F'''all_modules.{index}.GroupNorm_1.weight'''].data
  				snake_case		:						List[Any]								=				old_checkpoint[F'''all_modules.{index}.GroupNorm_1.bias'''].data
  
  				snake_case		:						Dict								=				old_checkpoint[F'''all_modules.{index}.Dense_0.weight'''].data
  				snake_case		:						Union[str, Any]								=				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:
  						snake_case		:						Optional[Any]								=				old_checkpoint[F'''all_modules.{index}.Conv_2.weight'''].data
  						snake_case		:						Any								=				old_checkpoint[F'''all_modules.{index}.Conv_2.bias'''].data
  
  		for i, block in enumerate(new_model_architecture.downsample_blocks   ):
  				snake_case		:						str								=				hasattr(lowercase__       ,     "attentions"   )
  				for j in range(len(block.resnets   )   ):
  						set_resnet_weights(block.resnets[j]       ,     lowercase__       ,     lowercase__   )
  						module_index += 1
  						if has_attentions:
  								set_attention_weights(block.attentions[j]       ,     lowercase__       ,     lowercase__   )
  								module_index += 1
  
  				if hasattr(lowercase__       ,     "downsamplers"   ) and block.downsamplers is not None:
  						set_resnet_weights(block.resnet_down       ,     lowercase__       ,     lowercase__   )
  						module_index += 1
  						snake_case		:						Union[str, Any]								=				checkpoint[F'''all_modules.{module_index}.Conv_0.weight'''].data
  						snake_case		:						Any								=				checkpoint[F'''all_modules.{module_index}.Conv_0.bias'''].data
  						module_index += 1
  
  		set_resnet_weights(new_model_architecture.mid_block.resnets[0]       ,     lowercase__       ,     lowercase__   )
  		module_index += 1
  		set_attention_weights(new_model_architecture.mid_block.attentions[0]       ,     lowercase__       ,     lowercase__   )
  		module_index += 1
  		set_resnet_weights(new_model_architecture.mid_block.resnets[1]       ,     lowercase__       ,     lowercase__   )
  		module_index += 1
  
  		for i, block in enumerate(new_model_architecture.up_blocks   ):
  				snake_case		:						int								=				hasattr(lowercase__       ,     "attentions"   )
  				for j in range(len(block.resnets   )   ):
  						set_resnet_weights(block.resnets[j]       ,     lowercase__       ,     lowercase__   )
  						module_index += 1
  				if has_attentions:
  						set_attention_weights(
  						    block.attentions[0]       ,     lowercase__       ,     lowercase__   )  # why can there only be a single attention layer for up?
  						module_index += 1
  
  				if hasattr(lowercase__       ,     "resnet_up"   ) and block.resnet_up is not None:
  						snake_case		:						str								=				checkpoint[F'''all_modules.{module_index}.weight'''].data
  						snake_case		:						List[Any]								=				checkpoint[F'''all_modules.{module_index}.bias'''].data
  						module_index += 1
  						snake_case		:						List[str]								=				checkpoint[F'''all_modules.{module_index}.weight'''].data
  						snake_case		:						Tuple								=				checkpoint[F'''all_modules.{module_index}.bias'''].data
  						module_index += 1
  						set_resnet_weights(block.resnet_up       ,     lowercase__       ,     lowercase__   )
  						module_index += 1
  
  		snake_case		:						List[Any]								=				checkpoint[F'''all_modules.{module_index}.weight'''].data
  		snake_case		:						int								=				checkpoint[F'''all_modules.{module_index}.bias'''].data
  		module_index += 1
  		snake_case		:						int								=				checkpoint[F'''all_modules.{module_index}.weight'''].data
  		snake_case		:						Any								=				checkpoint[F'''all_modules.{module_index}.bias'''].data
  
  		return new_model_architecture.state_dict()
  
  
  if __name__ == "__main__":
  							__A 		=			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 		=			parser.parse_args()
  
  							__A 		=			torch.load(args.checkpoint_path, map_location="cpu")
  
  							with open(args.config_file) as f:
  														__A 		=			json.loads(f.read())
  
  							__A 		=			convert_ncsnpp_checkpoint(
  							    checkpoint,
  							    config,
  							)
  
  							if "sde" in config:
  														del config["sde"]
  
  							__A 		=			UNetaDModel(**config)
  							model.load_state_dict(converted_checkpoint)
  
  							try:
  														__A 		=			ScoreSdeVeScheduler.from_config("/".join(args.checkpoint_path.split("/")[:-1]))
  
  														__A 		=			ScoreSdeVePipeline(unet=model, scheduler=scheduler)
  														pipe.save_pretrained(args.dump_path)
  							except:  # noqa: E722
  														model.save_pretrained(args.dump_path)
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  def   UpperCamelCase__     (		lowercase__  :     int   ):
  		if not isinstance(lowercase__       ,     lowercase__   ):
  				snake_case		:						Optional[Any]								=				F'''Input value of [number={number}] must be an integer'''
  				raise TypeError(lowercase__   )
  		if number < 0 or number % 2 == 1:
  				return False
  		snake_case		:						str								=				int(number**0.5   )
  		return number == number_sqrt * (number_sqrt + 1)
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  
  