code: 
  
  
  
  
  import argparse
  import json
  
  import torch
  
  from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
  def 						lowerCAmelCase_       (	__a	,	__a )       ->			Optional[Any]:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: List[str]	     =UNetaDModel(**__a )
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =checkpoint['''all_modules.0.W'''].data
  							SCREAMING_SNAKE_CASE			: Dict	     =checkpoint['''all_modules.0.W'''].data
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =checkpoint['''all_modules.1.weight'''].data
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =checkpoint['''all_modules.1.bias'''].data
  
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =checkpoint['''all_modules.2.weight'''].data
  							SCREAMING_SNAKE_CASE			: Union[str, Any]	     =checkpoint['''all_modules.2.bias'''].data
  
  							SCREAMING_SNAKE_CASE			: List[Any]	     =checkpoint['''all_modules.3.weight'''].data
  							SCREAMING_SNAKE_CASE			: int	     =checkpoint['''all_modules.3.bias'''].data
  
  							SCREAMING_SNAKE_CASE			: int	     =checkpoint[list(checkpoint.keys() )[-4]].data
  							SCREAMING_SNAKE_CASE			: int	     =checkpoint[list(checkpoint.keys() )[-3]].data
  							SCREAMING_SNAKE_CASE			: Tuple	     =checkpoint[list(checkpoint.keys() )[-2]].data
  							SCREAMING_SNAKE_CASE			: int	     =checkpoint[list(checkpoint.keys() )[-1]].data
  
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =4
  
  							def set_attention_weights(__a	,	__a	,	__a ):
  														SCREAMING_SNAKE_CASE			: int	     =old_checkpoint[f'all_modules.{index}.NIN_0.W'].data.T
  														SCREAMING_SNAKE_CASE			: Any	     =old_checkpoint[f'all_modules.{index}.NIN_1.W'].data.T
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =old_checkpoint[f'all_modules.{index}.NIN_2.W'].data.T
  
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =old_checkpoint[f'all_modules.{index}.NIN_0.b'].data
  														SCREAMING_SNAKE_CASE			: int	     =old_checkpoint[f'all_modules.{index}.NIN_1.b'].data
  														SCREAMING_SNAKE_CASE			: Dict	     =old_checkpoint[f'all_modules.{index}.NIN_2.b'].data
  
  														SCREAMING_SNAKE_CASE			: Optional[Any]	     =old_checkpoint[f'all_modules.{index}.NIN_3.W'].data.T
  														SCREAMING_SNAKE_CASE			: int	     =old_checkpoint[f'all_modules.{index}.NIN_3.b'].data
  
  														SCREAMING_SNAKE_CASE			: Optional[Any]	     =old_checkpoint[f'all_modules.{index}.GroupNorm_0.weight'].data
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data
  
  							def set_resnet_weights(__a	,	__a	,	__a ):
  														SCREAMING_SNAKE_CASE			: str	     =old_checkpoint[f'all_modules.{index}.Conv_0.weight'].data
  														SCREAMING_SNAKE_CASE			: Any	     =old_checkpoint[f'all_modules.{index}.Conv_0.bias'].data
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =old_checkpoint[f'all_modules.{index}.GroupNorm_0.weight'].data
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =old_checkpoint[f'all_modules.{index}.GroupNorm_0.bias'].data
  
  														SCREAMING_SNAKE_CASE			: Tuple	     =old_checkpoint[f'all_modules.{index}.Conv_1.weight'].data
  														SCREAMING_SNAKE_CASE			: Any	     =old_checkpoint[f'all_modules.{index}.Conv_1.bias'].data
  														SCREAMING_SNAKE_CASE			: Any	     =old_checkpoint[f'all_modules.{index}.GroupNorm_1.weight'].data
  														SCREAMING_SNAKE_CASE			: List[Any]	     =old_checkpoint[f'all_modules.{index}.GroupNorm_1.bias'].data
  
  														SCREAMING_SNAKE_CASE			: Any	     =old_checkpoint[f'all_modules.{index}.Dense_0.weight'].data
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =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:
  																					SCREAMING_SNAKE_CASE			: List[Any]	     =old_checkpoint[f'all_modules.{index}.Conv_2.weight'].data
  																					SCREAMING_SNAKE_CASE			: List[Any]	     =old_checkpoint[f'all_modules.{index}.Conv_2.bias'].data
  
  							for i, block in enumerate(new_model_architecture.downsample_blocks ):
  														SCREAMING_SNAKE_CASE			: Tuple	     =hasattr(__a	,	'''attentions''' )
  														for j in range(len(block.resnets ) ):
  																					set_resnet_weights(block.resnets[j]	,	__a	,	__a )
  																					module_index += 1
  																					if has_attentions:
  																												set_attention_weights(block.attentions[j]	,	__a	,	__a )
  																												module_index += 1
  
  														if hasattr(__a	,	'''downsamplers''' ) and block.downsamplers is not None:
  																					set_resnet_weights(block.resnet_down	,	__a	,	__a )
  																					module_index += 1
  																					SCREAMING_SNAKE_CASE			: Union[str, Any]	     =checkpoint[f'all_modules.{module_index}.Conv_0.weight'].data
  																					SCREAMING_SNAKE_CASE			: Tuple	     =checkpoint[f'all_modules.{module_index}.Conv_0.bias'].data
  																					module_index += 1
  
  							set_resnet_weights(new_model_architecture.mid_block.resnets[0]	,	__a	,	__a )
  							module_index += 1
  							set_attention_weights(new_model_architecture.mid_block.attentions[0]	,	__a	,	__a )
  							module_index += 1
  							set_resnet_weights(new_model_architecture.mid_block.resnets[1]	,	__a	,	__a )
  							module_index += 1
  
  							for i, block in enumerate(new_model_architecture.up_blocks ):
  														SCREAMING_SNAKE_CASE			: Dict	     =hasattr(__a	,	'''attentions''' )
  														for j in range(len(block.resnets ) ):
  																					set_resnet_weights(block.resnets[j]	,	__a	,	__a )
  																					module_index += 1
  														if has_attentions:
  																					set_attention_weights(
  																					    block.attentions[0]	,	__a	,	__a )  # why can there only be a single attention layer for up?
  																					module_index += 1
  
  														if hasattr(__a	,	'''resnet_up''' ) and block.resnet_up is not None:
  																					SCREAMING_SNAKE_CASE			: str	     =checkpoint[f'all_modules.{module_index}.weight'].data
  																					SCREAMING_SNAKE_CASE			: int	     =checkpoint[f'all_modules.{module_index}.bias'].data
  																					module_index += 1
  																					SCREAMING_SNAKE_CASE			: Dict	     =checkpoint[f'all_modules.{module_index}.weight'].data
  																					SCREAMING_SNAKE_CASE			: str	     =checkpoint[f'all_modules.{module_index}.bias'].data
  																					module_index += 1
  																					set_resnet_weights(block.resnet_up	,	__a	,	__a )
  																					module_index += 1
  
  							SCREAMING_SNAKE_CASE			: int	     =checkpoint[f'all_modules.{module_index}.weight'].data
  							SCREAMING_SNAKE_CASE			: Optional[Any]	     =checkpoint[f'all_modules.{module_index}.bias'].data
  							module_index += 1
  							SCREAMING_SNAKE_CASE			: List[str]	     =checkpoint[f'all_modules.{module_index}.weight'].data
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =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: 
  
  
  
  
  from __future__ import annotations
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a )       ->			None:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Any	     =m - 1, 0
  							while i >= 0 and j < n:
  														if key == mat[i][j]:
  																					print(f'Key {key} found at row- {i + 1} column- {j + 1}' )
  																					return
  														if key < mat[i][j]:
  																					i -= 1
  														else:
  																					j += 1
  							print(f'Key {key} not found' )
  def 						lowerCAmelCase_       (	)       ->			None:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: int	     =[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
  							SCREAMING_SNAKE_CASE			: List[str]	     =int(input('''Enter the element to be searched:''' ) )
  							print(__a )
  							search_in_a_sorted_matrix(__a	,	len(__a )	,	len(mat[0] )	,	__a )
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  							main()
  
  
  
  
  
  
  