code: 
  
  
  
  
  
  
  """simple docstring"""
  
  
  
  
  
  
  import multiprocessing
  import os
  from typing import BinaryIO, Optional, Union
  
  from .. import Dataset, Features, NamedSplit, config
  from ..formatting import query_table
  from ..packaged_modules.csv.csv import Csv
  from ..utils import logging
  from ..utils.typing import NestedDataStructureLike, PathLike
  from .abc import AbstractDatasetReader
  class 						__lowerCAmelCase   (			UpperCAmelCase       ):
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def __init__(      self:		List[str]				,			UpperCamelCase_:		NestedDataStructureLike[PathLike]				,			UpperCamelCase_:		Optional[NamedSplit] = None				,			UpperCamelCase_:		Optional[Features] = None				,			UpperCamelCase_:		str = None				,			UpperCamelCase_:		bool = False				,			UpperCamelCase_:		bool = False				,			UpperCamelCase_:		Optional[int] = None				,			**UpperCamelCase_:		Dict				,			):
       super().__init__(
           UpperCamelCase_				,			split=UpperCamelCase_				,			features=UpperCamelCase_				,			cache_dir=UpperCamelCase_				,			keep_in_memory=UpperCamelCase_				,			streaming=UpperCamelCase_				,			num_proc=UpperCamelCase_				,			**UpperCamelCase_				,			)
       UpperCamelCase_     =path_or_paths if isinstance(UpperCamelCase_				,			UpperCamelCase_					) else {self.split: path_or_paths}
       UpperCamelCase_     =Csv(
           cache_dir=UpperCamelCase_				,			data_files=UpperCamelCase_				,			features=UpperCamelCase_				,			**UpperCamelCase_				,			)
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		int					):
       # Build iterable dataset
       if self.streaming:
          UpperCamelCase_     =self.builder.as_streaming_dataset(split=self.split					)
       # Build regular (map-style) dataset
       else:
          UpperCamelCase_     =None
          UpperCamelCase_     =None
          UpperCamelCase_     =None
          UpperCamelCase_     =None
  
          self.builder.download_and_prepare(
              download_config=UpperCamelCase_				,			download_mode=UpperCamelCase_				,			verification_mode=UpperCamelCase_				,			base_path=UpperCamelCase_				,			num_proc=self.num_proc				,			)
          UpperCamelCase_     =self.builder.as_dataset(
              split=self.split				,			verification_mode=UpperCamelCase_				,			in_memory=self.keep_in_memory					)
       return dataset
  
  
  
  
  class 						__lowerCAmelCase   :
  
  
  
  
  
  
  
    '''simple docstring'''
  
  
    def __init__(      self:		Tuple				,			UpperCamelCase_:		Dataset				,			UpperCamelCase_:		Union[PathLike, BinaryIO]				,			UpperCamelCase_:		Optional[int] = None				,			UpperCamelCase_:		Optional[int] = None				,			**UpperCamelCase_:		Dict				,			):
       if num_proc is not None and num_proc <= 0:
          raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""					)
  
       UpperCamelCase_     =dataset
       UpperCamelCase_     =path_or_buf
       UpperCamelCase_     =batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
       UpperCamelCase_     =num_proc
       UpperCamelCase_     ="utf-8"
       UpperCamelCase_     =to_csv_kwargs
    def 					UpperCamelCase__   (      self:		Optional[Any]					):
       UpperCamelCase_     =self.to_csv_kwargs.pop("path_or_buf"				,			UpperCamelCase_					)
       UpperCamelCase_     =self.to_csv_kwargs.pop("header"				,			UpperCamelCase_					)
       UpperCamelCase_     =self.to_csv_kwargs.pop("index"				,			UpperCamelCase_					)
  
       if isinstance(self.path_or_buf				,			(str, bytes, os.PathLike)					):
          with open(self.path_or_buf				,			"wb+"					) as buffer:
             UpperCamelCase_     =self._write(file_obj=UpperCamelCase_				,			header=UpperCamelCase_				,			index=UpperCamelCase_				,			**self.to_csv_kwargs					)
       else:
          UpperCamelCase_     =self._write(file_obj=self.path_or_buf				,			header=UpperCamelCase_				,			index=UpperCamelCase_				,			**self.to_csv_kwargs					)
       return written
    def 					UpperCamelCase__   (      self:		List[str]				,			UpperCamelCase_:		Optional[Any]					):
       UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_    ,							UpperCamelCase_     =args
  
       UpperCamelCase_     =query_table(
           table=self.dataset.data				,			key=slice(UpperCamelCase_				,			offset + self.batch_size					)				,			indices=self.dataset._indices				,			)
       UpperCamelCase_     =batch.to_pandas().to_csv(
           path_or_buf=UpperCamelCase_				,			header=header if (offset == 0) else False				,			index=UpperCamelCase_				,			**UpperCamelCase_					)
       return csv_str.encode(self.encoding					)
  
  
  
  
  
    def 					UpperCamelCase__   (      self:		Tuple				,			UpperCamelCase_:		BinaryIO				,			UpperCamelCase_:		Dict				,			UpperCamelCase_:		Optional[int]				,			**UpperCamelCase_:		Union[str, Any]					):
       UpperCamelCase_     =0
  
       if self.num_proc is None or self.num_proc == 1:
          for offset in logging.tqdm(
              range(0				,			len(self.dataset					)				,			self.batch_size					)				,			unit="ba"				,			disable=not logging.is_progress_bar_enabled()				,			desc="Creating CSV from Arrow format"				,			):
             UpperCamelCase_     =self._batch_csv((offset, header, index, to_csv_kwargs)					)
             written += file_obj.write(UpperCamelCase_					)
  
       else:
          UpperCamelCase_    ,							UpperCamelCase_     =len(self.dataset					), self.batch_size
          with multiprocessing.Pool(self.num_proc					) as pool:
             for csv_str in logging.tqdm(
                 pool.imap(
                     self._batch_csv				,			[(offset, header, index, to_csv_kwargs) for offset in range(0				,			UpperCamelCase_				,			UpperCamelCase_					)]				,			)				,			total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size				,			unit="ba"				,			disable=not logging.is_progress_bar_enabled()				,			desc="Creating CSV from Arrow format"				,			):
                written += file_obj.write(UpperCamelCase_					)
  
       return written
  
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)
  