code: 
  
  
  
  
  
  import argparse
  import json
  
  import torch
  
  from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
  
  def     a						(      _lowercase   :     Tuple ,						_lowercase   :     Union[str, Any]       )     ->Union[str, Any]:
  		UpperCamelCase__         =	UNetaDModel(**_lowercase       )
  		UpperCamelCase__         =	checkpoint["all_modules.0.W"].data
  		UpperCamelCase__         =	checkpoint["all_modules.0.W"].data
  		UpperCamelCase__         =	checkpoint["all_modules.1.weight"].data
  		UpperCamelCase__         =	checkpoint["all_modules.1.bias"].data
  
  		UpperCamelCase__         =	checkpoint["all_modules.2.weight"].data
  		UpperCamelCase__         =	checkpoint["all_modules.2.bias"].data
  
  		UpperCamelCase__         =	checkpoint["all_modules.3.weight"].data
  		UpperCamelCase__         =	checkpoint["all_modules.3.bias"].data
  
  		UpperCamelCase__         =	checkpoint[list(checkpoint.keys()       )[-4]].data
  		UpperCamelCase__         =	checkpoint[list(checkpoint.keys()       )[-3]].data
  		UpperCamelCase__         =	checkpoint[list(checkpoint.keys()       )[-2]].data
  		UpperCamelCase__         =	checkpoint[list(checkpoint.keys()       )[-1]].data
  
  		UpperCamelCase__         =	4
  
  		def set_attention_weights(_lowercase   :     List[str] ,						_lowercase   :     Dict ,						_lowercase   :     Dict       ):
  				UpperCamelCase__         =	old_checkpoint[F"""all_modules.{index}.NIN_0.W"""].data.T
  				UpperCamelCase__         =	old_checkpoint[F"""all_modules.{index}.NIN_1.W"""].data.T
  				UpperCamelCase__         =	old_checkpoint[F"""all_modules.{index}.NIN_2.W"""].data.T
  
  				UpperCamelCase__         =	old_checkpoint[F"""all_modules.{index}.NIN_0.b"""].data
  				UpperCamelCase__         =	old_checkpoint[F"""all_modules.{index}.NIN_1.b"""].data
  				UpperCamelCase__         =	old_checkpoint[F"""all_modules.{index}.NIN_2.b"""].data
  
  				UpperCamelCase__         =	old_checkpoint[F"""all_modules.{index}.NIN_3.W"""].data.T
  				UpperCamelCase__         =	old_checkpoint[F"""all_modules.{index}.NIN_3.b"""].data
  
  				UpperCamelCase__         =	old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
  				UpperCamelCase__         =	old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data
  
  		def set_resnet_weights(_lowercase   :     Optional[Any] ,						_lowercase   :     Optional[Any] ,						_lowercase   :     int       ):
  				UpperCamelCase__         =	old_checkpoint[F"""all_modules.{index}.Conv_0.weight"""].data
  				UpperCamelCase__         =	old_checkpoint[F"""all_modules.{index}.Conv_0.bias"""].data
  				UpperCamelCase__         =	old_checkpoint[F"""all_modules.{index}.GroupNorm_0.weight"""].data
  				UpperCamelCase__         =	old_checkpoint[F"""all_modules.{index}.GroupNorm_0.bias"""].data
  
  				UpperCamelCase__         =	old_checkpoint[F"""all_modules.{index}.Conv_1.weight"""].data
  				UpperCamelCase__         =	old_checkpoint[F"""all_modules.{index}.Conv_1.bias"""].data
  				UpperCamelCase__         =	old_checkpoint[F"""all_modules.{index}.GroupNorm_1.weight"""].data
  				UpperCamelCase__         =	old_checkpoint[F"""all_modules.{index}.GroupNorm_1.bias"""].data
  
  				UpperCamelCase__         =	old_checkpoint[F"""all_modules.{index}.Dense_0.weight"""].data
  				UpperCamelCase__         =	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:
  						UpperCamelCase__         =	old_checkpoint[F"""all_modules.{index}.Conv_2.weight"""].data
  						UpperCamelCase__         =	old_checkpoint[F"""all_modules.{index}.Conv_2.bias"""].data
  
  		for i, block in enumerate(new_model_architecture.downsample_blocks       ):
  				UpperCamelCase__         =	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
  						UpperCamelCase__         =	checkpoint[F"""all_modules.{module_index}.Conv_0.weight"""].data
  						UpperCamelCase__         =	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       ):
  				UpperCamelCase__         =	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:
  						UpperCamelCase__         =	checkpoint[F"""all_modules.{module_index}.weight"""].data
  						UpperCamelCase__         =	checkpoint[F"""all_modules.{module_index}.bias"""].data
  						module_index += 1
  						UpperCamelCase__         =	checkpoint[F"""all_modules.{module_index}.weight"""].data
  						UpperCamelCase__         =	checkpoint[F"""all_modules.{module_index}.bias"""].data
  						module_index += 1
  						set_resnet_weights(block.resnet_up ,						_lowercase ,						_lowercase       )
  						module_index += 1
  
  		UpperCamelCase__         =	checkpoint[F"""all_modules.{module_index}.weight"""].data
  		UpperCamelCase__         =	checkpoint[F"""all_modules.{module_index}.bias"""].data
  		module_index += 1
  		UpperCamelCase__         =	checkpoint[F"""all_modules.{module_index}.weight"""].data
  		UpperCamelCase__         =	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)
  
  
  
  
style_context: 
  
  
  
  
  
  from dataclasses import dataclass
  
  __lowerCAmelCase     =			"0.1"
  __lowerCAmelCase     =			"Lucia Harcekova"
  
  
  
  
  @dataclass
  class   _UpperCAmelCase	:
  					__A          =   42
  					__A          =   42
  					__A          =   42
  
  
  
  
  					def __repr__(						self       )						->  str:
  							return F"""({self.offset}, {self.length}, {self.indicator})"""
  
  
  
  
  class   _UpperCAmelCase	:
  
  
  
  
  					def __init__(						self    ,      __lowerCAmelCase = 13    ,      __lowerCAmelCase = 6       )						->  None:
  							UpperCamelCase__         =	window_size
  							UpperCamelCase__         =	lookahead_buffer_size
  							UpperCamelCase__         =	self.window_size - self.lookahead_buffer_size
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  list[Token]:
  							UpperCamelCase__         =	[]
  							UpperCamelCase__         =	""""""
  
  							# while there are still characters in text to compress
  							while text:
  									# find the next encoding phrase
  									# - triplet with offset, length, indicator (the next encoding character)
  									UpperCamelCase__         =	self._find_encoding_token(__lowerCAmelCase    ,      __lowerCAmelCase       )
  
  									# update the search buffer:
  									# - add new characters from text into it
  									# - check if size exceed the max search buffer size, if so, drop the
  									#   oldest elements
  									search_buffer += text[: token.length + 1]
  									if len(__lowerCAmelCase       ) > self.search_buffer_size:
  											UpperCamelCase__         =	search_buffer[-self.search_buffer_size :]
  
  									# update the text
  									UpperCamelCase__         =	text[token.length + 1 :]
  
  									# append the token to output
  									output.append(__lowerCAmelCase       )
  
  							return output
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  str:
  							UpperCamelCase__         =	""""""
  
  							for token in tokens:
  									for _ in range(token.length       ):
  											output += output[-token.offset]
  									output += token.indicator
  
  							return output
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  Token:
  
  							if not text:
  									raise ValueError("""We need some text to work with."""       )
  
  							# Initialise result parameters to default values
  							UpperCamelCase__  ,			UpperCamelCase__         =	0, 0
  
  							if not search_buffer:
  									return Token(__lowerCAmelCase    ,      __lowerCAmelCase    ,      text[length]       )
  
  							for i, character in enumerate(__lowerCAmelCase       ):
  									UpperCamelCase__         =	len(__lowerCAmelCase       ) - i
  									if character == text[0]:
  											UpperCamelCase__         =	self._match_length_from_index(__lowerCAmelCase    ,      __lowerCAmelCase    ,      0    ,      __lowerCAmelCase       )
  											# if the found length is bigger than the current or if it's equal,
  											# which means it's offset is smaller: update offset and length
  											if found_length >= length:
  													UpperCamelCase__  ,			UpperCamelCase__         =	found_offset, found_length
  
  							return Token(__lowerCAmelCase    ,      __lowerCAmelCase    ,      text[length]       )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       )						->  int:
  							if not text or text[text_index] != window[window_index]:
  									return 0
  							return 1 + self._match_length_from_index(
  							    __lowerCAmelCase    ,      window + text[text_index]    ,      text_index + 1    ,      window_index + 1       )
  
  
  if __name__ == "__main__":
  				from doctest import testmod
  
  				testmod()
  				# Initialize compressor class
  				__lowerCAmelCase     =			LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
  				# Example
  				__lowerCAmelCase     =			"cabracadabrarrarrad"
  				__lowerCAmelCase     =			lzaa_compressor.compress(TEXT)
  				print(lzaa_compressor.compress("ababcbababaa"))
  				__lowerCAmelCase     =			lzaa_compressor.decompress(compressed_text)
  				assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
  
  
  
  
  
  