code: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  from dataclasses import dataclass
  
  A_			  =    "0.1"
  A_			  =    "Lucia Harcekova"
  @dataclass
  class 						__lowerCAmelCase   :
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    __lowerCamelCase       :    int
    __lowerCamelCase       :    int
    __lowerCamelCase       :    str
    def __repr__(      self:		Optional[int]					):
       return f"""({self.offset}, {self.length}, {self.indicator})"""
  
  
  
  
  class 						__lowerCAmelCase   :
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def __init__(      self:		Union[str, Any]				,			UpperCamelCase_:		int = 13				,			UpperCamelCase_:		int = 6					):
       UpperCamelCase_     =window_size
       UpperCamelCase_     =lookahead_buffer_size
       UpperCamelCase_     =self.window_size - self.lookahead_buffer_size
    def 					UpperCamelCase__   (      self:		Tuple				,			UpperCamelCase_:		str					):
       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(UpperCamelCase_				,			UpperCamelCase_					)
  
          # 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(UpperCamelCase_					) > 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(UpperCamelCase_					)
  
       return output
    def 					UpperCamelCase__   (      self:		Tuple				,			UpperCamelCase_:		list[Token]					):
       UpperCamelCase_     =""
  
       for token in tokens:
          for _ in range(token.length					):
             output += output[-token.offset]
          output += token.indicator
  
       return output
    def 					UpperCamelCase__   (      self:		Optional[int]				,			UpperCamelCase_:		str				,			UpperCamelCase_:		str					):
  
       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(UpperCamelCase_				,			UpperCamelCase_				,			text[length]					)
  
       for i, character in enumerate(UpperCamelCase_					):
          UpperCamelCase_     =len(UpperCamelCase_					) - i
          if character == text[0]:
             UpperCamelCase_     =self._match_length_from_index(UpperCamelCase_				,			UpperCamelCase_				,			0				,			UpperCamelCase_					)
             # 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(UpperCamelCase_				,			UpperCamelCase_				,			text[length]					)
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		str				,			UpperCamelCase_:		str				,			UpperCamelCase_:		str				,			UpperCamelCase_:		int				,			UpperCamelCase_:		int					):
       if not text or text[text_index] != window[window_index]:
          return 0
       return 1 + self._match_length_from_index(
           UpperCamelCase_				,			window + text[text_index]				,			text_index + 1				,			window_index + 1					)
  
  
  if __name__ == "__main__":
      from doctest import testmod
  
      testmod()
      # Initialize compressor class
      A_			  =    LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
      # Example
      A_			  =    "cabracadabrarrarrad"
      A_			  =    lzaa_compressor.compress(TEXT)
      print(lzaa_compressor.compress("ababcbababaa"))
      A_			  =    lzaa_compressor.decompress(compressed_text)
      assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
  
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  import argparse
  import os
  
  import torch
  from torchvision.datasets.utils import download_url
  
  from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel
  
  
  A_			  =    {512: "DiT-XL-2-512x512.pt", 256: "DiT-XL-2-256x256.pt"}
  
  
  
  
  def 						_UpperCamelCase			(  A			):
     UpperCamelCase_     =f"""pretrained_models/{model_name}"""
     if not os.path.isfile(A			):
        os.makedirs("pretrained_models"    ,       exist_ok=A			)
        UpperCamelCase_     =f"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
        download_url(A    ,       "pretrained_models"			)
     UpperCamelCase_     =torch.load(A    ,       map_location=lambda A    ,       A			: storage			)
     return model
  
  
  
  
  def 						_UpperCamelCase			(  A			):
     UpperCamelCase_     =download_model(pretrained_models[args.image_size]			)
  
     UpperCamelCase_     =state_dict["x_embedder.proj.weight"]
     UpperCamelCase_     =state_dict["x_embedder.proj.bias"]
     state_dict.pop("x_embedder.proj.weight"			)
     state_dict.pop("x_embedder.proj.bias"			)
  
     for depth in range(28			):
        UpperCamelCase_     =state_dict[
            "t_embedder.mlp.0.weight"
        ]
        UpperCamelCase_     =state_dict[
            "t_embedder.mlp.0.bias"
        ]
        UpperCamelCase_     =state_dict[
            "t_embedder.mlp.2.weight"
        ]
        UpperCamelCase_     =state_dict[
            "t_embedder.mlp.2.bias"
        ]
        UpperCamelCase_     =state_dict[
            "y_embedder.embedding_table.weight"
        ]
  
        UpperCamelCase_     =state_dict[
            f"""blocks.{depth}.adaLN_modulation.1.weight"""
        ]
        UpperCamelCase_     =state_dict[
            f"""blocks.{depth}.adaLN_modulation.1.bias"""
        ]
  
        UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_     =torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.weight"""]    ,       3    ,       dim=0			)
        UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_     =torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.bias"""]    ,       3    ,       dim=0			)
  
        UpperCamelCase_     =q
        UpperCamelCase_     =q_bias
        UpperCamelCase_     =k
        UpperCamelCase_     =k_bias
        UpperCamelCase_     =v
        UpperCamelCase_     =v_bias
  
        UpperCamelCase_     =state_dict[
            f"""blocks.{depth}.attn.proj.weight"""
        ]
        UpperCamelCase_     =state_dict[f"""blocks.{depth}.attn.proj.bias"""]
  
        UpperCamelCase_     =state_dict[f"""blocks.{depth}.mlp.fc1.weight"""]
        UpperCamelCase_     =state_dict[f"""blocks.{depth}.mlp.fc1.bias"""]
        UpperCamelCase_     =state_dict[f"""blocks.{depth}.mlp.fc2.weight"""]
        UpperCamelCase_     =state_dict[f"""blocks.{depth}.mlp.fc2.bias"""]
  
        state_dict.pop(f"""blocks.{depth}.attn.qkv.weight"""			)
        state_dict.pop(f"""blocks.{depth}.attn.qkv.bias"""			)
        state_dict.pop(f"""blocks.{depth}.attn.proj.weight"""			)
        state_dict.pop(f"""blocks.{depth}.attn.proj.bias"""			)
        state_dict.pop(f"""blocks.{depth}.mlp.fc1.weight"""			)
        state_dict.pop(f"""blocks.{depth}.mlp.fc1.bias"""			)
        state_dict.pop(f"""blocks.{depth}.mlp.fc2.weight"""			)
        state_dict.pop(f"""blocks.{depth}.mlp.fc2.bias"""			)
        state_dict.pop(f"""blocks.{depth}.adaLN_modulation.1.weight"""			)
        state_dict.pop(f"""blocks.{depth}.adaLN_modulation.1.bias"""			)
  
     state_dict.pop("t_embedder.mlp.0.weight"			)
     state_dict.pop("t_embedder.mlp.0.bias"			)
     state_dict.pop("t_embedder.mlp.2.weight"			)
     state_dict.pop("t_embedder.mlp.2.bias"			)
     state_dict.pop("y_embedder.embedding_table.weight"			)
  
     UpperCamelCase_     =state_dict["final_layer.adaLN_modulation.1.weight"]
     UpperCamelCase_     =state_dict["final_layer.adaLN_modulation.1.bias"]
     UpperCamelCase_     =state_dict["final_layer.linear.weight"]
     UpperCamelCase_     =state_dict["final_layer.linear.bias"]
  
     state_dict.pop("final_layer.linear.weight"			)
     state_dict.pop("final_layer.linear.bias"			)
     state_dict.pop("final_layer.adaLN_modulation.1.weight"			)
     state_dict.pop("final_layer.adaLN_modulation.1.bias"			)
  
     # DiT XL/2
     UpperCamelCase_     =TransformeraDModel(
         sample_size=args.image_size // 8    ,       num_layers=28    ,       attention_head_dim=72    ,       in_channels=4    ,       out_channels=8    ,       patch_size=2    ,       attention_bias=A    ,       num_attention_heads=16    ,       activation_fn="gelu-approximate"    ,       num_embeds_ada_norm=1_000    ,       norm_type="ada_norm_zero"    ,       norm_elementwise_affine=A    ,       )
     transformer.load_state_dict(A    ,       strict=A			)
  
     UpperCamelCase_     =DDIMScheduler(
         num_train_timesteps=1_000    ,       beta_schedule="linear"    ,       prediction_type="epsilon"    ,       clip_sample=A    ,       )
  
     UpperCamelCase_     =AutoencoderKL.from_pretrained(args.vae_model			)
  
     UpperCamelCase_     =DiTPipeline(transformer=A    ,       vae=A    ,       scheduler=A			)
  
     if args.save:
        pipeline.save_pretrained(args.checkpoint_path			)
  
  
  if __name__ == "__main__":
      A_			  =    argparse.ArgumentParser()
  
      parser.add_argument(
          "--image_size",
          default=256,
          type=int,
          required=False,
          help="Image size of pretrained model, either 256 or 512.",
      )
      parser.add_argument(
          "--vae_model",
          default="stabilityai/sd-vae-ft-ema",
          type=str,
          required=False,
          help="Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.",
      )
      parser.add_argument(
          "--save", default=True, type=bool, required=False, help="Whether to save the converted pipeline or not."
      )
      parser.add_argument(
          "--checkpoint_path", default=None, type=str, required=True, help="Path to the output pipeline."
      )
  
      A_			  =    parser.parse_args()
      main(args)
  