code: 
  
  
  
  
  import os
  import re
  import unicodedata
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple
  
  import sentencepiece as spm
  
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
  
  
  __A :		str      							=    logging.get_logger(__name__)
  __A :		Union[str, Any]      							=    {"""vocab_file""": """spiece.model"""}
  
  __A :		List[Any]      							=    {
      """vocab_file""": {
          """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
          """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
      },
  }
  
  __A :		Any      							=    {
      """google/fnet-base""": 512,
      """google/fnet-large""": 512,
  }
  
  __A :		Optional[int]      							=    """▁"""
  class 							lowerCamelCase		( _SCREAMING_SNAKE_CASE				):
     lowercase       :  int      =				VOCAB_FILES_NAMES
     lowercase       :  Tuple      =				PRETRAINED_VOCAB_FILES_MAP
     lowercase       :  List[str]      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase       :  Any      =				['input_ids', 'token_type_ids']
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=False    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_="<unk>"    ,      SCREAMING_SNAKE_CASE_="[SEP]"    ,      SCREAMING_SNAKE_CASE_="<pad>"    ,      SCREAMING_SNAKE_CASE_="[CLS]"    ,      SCREAMING_SNAKE_CASE_="[MASK]"    ,      SCREAMING_SNAKE_CASE_ = None    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         # Mask token behave like a normal word, i.e. include the space before it and
         # is included in the raw text, there should be a match in a non-normalized sentence.
         UpperCamelCase				:   List[Any]          =							(
             AddedToken(__A    ,      lstrip=__A    ,      rstrip=__A    ,      normalized=__A )
             if isinstance(__A    ,      __A )
             else mask_token
         )
  
         UpperCamelCase				:   int          =							{} if sp_model_kwargs is None else sp_model_kwargs
  
         super().__init__(
             do_lower_case=__A    ,      remove_space=__A    ,      keep_accents=__A    ,      unk_token=__A    ,      sep_token=__A    ,      pad_token=__A    ,      cls_token=__A    ,      mask_token=__A    ,      sp_model_kwargs=self.sp_model_kwargs    ,      **__A    ,      )
  
         UpperCamelCase				:   Union[str, Any]          =							do_lower_case
         UpperCamelCase				:   Dict          =							remove_space
         UpperCamelCase				:   int          =							keep_accents
         UpperCamelCase				:   str          =							vocab_file
  
         UpperCamelCase				:   Dict          =							spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(__A )
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return len(self.sp_model )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Union[str, Any]          =							{self.convert_ids_to_tokens(__A ): i for i in range(self.vocab_size )}
         vocab.update(self.added_tokens_encoder )
         return vocab
  
  
  
  
  
  
  
     def __getstate__(							self ):
         UpperCamelCase				:   int          =							self.__dict__.copy()
         UpperCamelCase				:   Optional[int]          =							None
         return state
  
  
  
  
  
  
  
     def __setstate__(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   List[str]          =							d
  
         # for backward compatibility
         if not hasattr(self    ,      """sp_model_kwargs""" ):
             UpperCamelCase				:   Tuple          =							{}
  
         UpperCamelCase				:   List[Any]          =							spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(self.vocab_file )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         if self.remove_space:
             UpperCamelCase				:   Tuple          =							""" """.join(inputs.strip().split() )
         else:
             UpperCamelCase				:   str          =							inputs
         UpperCamelCase				:   Dict          =							outputs.replace("""``"""    ,      """\"""" ).replace("""\'\'"""    ,      """\"""" )
  
         if not self.keep_accents:
             UpperCamelCase				:   Optional[int]          =							unicodedata.normalize("""NFKD"""    ,      __A )
             UpperCamelCase				:   List[Any]          =							"""""".join([c for c in outputs if not unicodedata.combining(__A )] )
         if self.do_lower_case:
             UpperCamelCase				:   Union[str, Any]          =							outputs.lower()
  
         return outputs
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Optional[int]          =							self.preprocess_text(__A )
         UpperCamelCase				:   int          =							self.sp_model.encode(__A    ,      out_type=__A )
         UpperCamelCase				:   str          =							[]
         for piece in pieces:
             if len(__A ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit():
                 UpperCamelCase				:   Optional[Any]          =							self.sp_model.EncodeAsPieces(piece[:-1].replace(__A    ,      """""" ) )
                 if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                     if len(cur_pieces[0] ) == 1:
                         UpperCamelCase				:   Union[str, Any]          =							cur_pieces[1:]
                     else:
                         UpperCamelCase				:   Dict          =							cur_pieces[0][1:]
                 cur_pieces.append(piece[-1] )
                 new_pieces.extend(__A )
             else:
                 new_pieces.append(__A )
  
         return new_pieces
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         return self.sp_model.PieceToId(__A )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         return self.sp_model.IdToPiece(__A )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   List[str]          =							[]
         UpperCamelCase				:   List[str]          =							""""""
         UpperCamelCase				:   str          =							False
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 if not prev_is_special:
                     out_string += " "
                 out_string += self.sp_model.decode(__A ) + token
                 UpperCamelCase				:   List[Any]          =							True
                 UpperCamelCase				:   Optional[int]          =							[]
             else:
                 current_sub_tokens.append(__A )
                 UpperCamelCase				:   Any          =							False
         out_string += self.sp_model.decode(__A )
         return out_string.strip()
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = False    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = True    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         UpperCamelCase				:   List[Any]          =							kwargs.pop("""use_source_tokenizer"""    ,      __A )
  
         UpperCamelCase				:   str          =							self.convert_ids_to_tokens(__A    ,      skip_special_tokens=__A )
  
         # To avoid mixing byte-level and unicode for byte-level BPT
         # we need to build string separately for added tokens and byte-level tokens
         # cf. https://github.com/huggingface/transformers/issues/1133
         UpperCamelCase				:   Tuple          =							[]
         UpperCamelCase				:   str          =							[]
         for token in filtered_tokens:
             if skip_special_tokens and token in self.all_special_ids:
                 continue
             if token in self.added_tokens_encoder:
                 if current_sub_text:
                     sub_texts.append(self.convert_tokens_to_string(__A ) )
                     UpperCamelCase				:   List[Any]          =							[]
                 sub_texts.append(__A )
             else:
                 current_sub_text.append(__A )
         if current_sub_text:
             sub_texts.append(self.convert_tokens_to_string(__A ) )
  
         # Mimic the behavior of the Rust tokenizer:
         # No space after <unk>
         if spaces_between_special_tokens:
             UpperCamelCase				:   List[str]          =							re.sub(r"""(<unk>) """    ,      r"""\1"""    ,      """ """.join(__A ) )
         else:
             UpperCamelCase				:   Union[str, Any]          =							"""""".join(__A )
  
         UpperCamelCase				:   List[Any]          =							(
             clean_up_tokenization_spaces
             if clean_up_tokenization_spaces is not None
             else self.clean_up_tokenization_spaces
         )
         if clean_up_tokenization_spaces:
             UpperCamelCase				:   str          =							self.clean_up_tokenization(__A )
             return clean_text
         else:
             return text
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
         UpperCamelCase				:   Optional[int]          =							[self.sep_token_id]
         UpperCamelCase				:   List[str]          =							[self.cls_token_id]
         if token_ids_a is None:
             return cls + token_ids_a + sep
         return cls + token_ids_a + sep + token_ids_a + sep
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = False ):
  
         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=__A    ,      token_ids_a=__A    ,      already_has_special_tokens=__A )
  
         if token_ids_a is not None:
             return [1] + ([0] * len(__A )) + [1] + ([0] * len(__A )) + [1]
         return [1] + ([0] * len(__A )) + [1]
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
         UpperCamelCase				:   Tuple          =							[self.sep_token_id]
         UpperCamelCase				:   str          =							[self.cls_token_id]
  
         if token_ids_a is None:
             return len(cls + token_ids_a + sep ) * [0]
         return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
         if not os.path.isdir(__A ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
             return
         UpperCamelCase				:   List[str]          =							os.path.join(
             __A    ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
  
         if os.path.abspath(self.vocab_file ) != os.path.abspath(__A ) and os.path.isfile(self.vocab_file ):
             copyfile(self.vocab_file    ,      __A )
         elif not os.path.isfile(self.vocab_file ):
             with open(__A    ,      """wb""" ) as fi:
                 UpperCamelCase				:   str          =							self.sp_model.serialized_model_proto()
                 fi.write(__A )
  
         return (out_vocab_file,)
  
  
  
  
  
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 :		int      							=    {512: '''DiT-XL-2-512x512.pt''', 256: '''DiT-XL-2-256x256.pt'''}
  def 					A_			(		snake_case_     :       Dict    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   Any          =							f'pretrained_models/{model_name}'
      if not os.path.isfile(snake_case_    ):
          os.makedirs("""pretrained_models"""						,exist_ok=snake_case_    )
          UpperCamelCase				:   Dict          =							f'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
          download_url(snake_case_						,"""pretrained_models"""    )
      UpperCamelCase				:   List[Any]          =							torch.load(snake_case_						,map_location=lambda snake_case_						,snake_case_    : storage    )
      return model
  def 					A_			(		snake_case_     :       List[str]    ):
  
  
  
  
  
  
  
      '''simple docstring'''
      UpperCamelCase				:   int          =							download_model(pretrained_models[args.image_size]    )
  
      UpperCamelCase				:   str          =							state_dict["""x_embedder.proj.weight"""]
      UpperCamelCase				:   Optional[Any]          =							state_dict["""x_embedder.proj.bias"""]
      state_dict.pop("""x_embedder.proj.weight"""    )
      state_dict.pop("""x_embedder.proj.bias"""    )
  
      for depth in range(2_8    ):
          UpperCamelCase				:   Optional[int]          =							state_dict[
              """t_embedder.mlp.0.weight"""
          ]
          UpperCamelCase				:   Optional[Any]          =							state_dict[
              """t_embedder.mlp.0.bias"""
          ]
          UpperCamelCase				:   str          =							state_dict[
              """t_embedder.mlp.2.weight"""
          ]
          UpperCamelCase				:   Optional[int]          =							state_dict[
              """t_embedder.mlp.2.bias"""
          ]
          UpperCamelCase				:   int          =							state_dict[
              """y_embedder.embedding_table.weight"""
          ]
  
          UpperCamelCase				:   List[Any]          =							state_dict[
              f'blocks.{depth}.adaLN_modulation.1.weight'
          ]
          UpperCamelCase				:   Any          =							state_dict[
              f'blocks.{depth}.adaLN_modulation.1.bias'
          ]
  
          UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   Dict          =							torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.weight']						,3						,dim=0    )
          UpperCamelCase       ,							UpperCamelCase       ,							UpperCamelCase				:   Dict          =							torch.chunk(state_dict[f'blocks.{depth}.attn.qkv.bias']						,3						,dim=0    )
  
          UpperCamelCase				:   int          =							q
          UpperCamelCase				:   Tuple          =							q_bias
          UpperCamelCase				:   List[Any]          =							k
          UpperCamelCase				:   int          =							k_bias
          UpperCamelCase				:   List[str]          =							v
          UpperCamelCase				:   str          =							v_bias
  
          UpperCamelCase				:   Any          =							state_dict[
              f'blocks.{depth}.attn.proj.weight'
          ]
          UpperCamelCase				:   Optional[Any]          =							state_dict[f'blocks.{depth}.attn.proj.bias']
  
          UpperCamelCase				:   str          =							state_dict[f'blocks.{depth}.mlp.fc1.weight']
          UpperCamelCase				:   Tuple          =							state_dict[f'blocks.{depth}.mlp.fc1.bias']
          UpperCamelCase				:   str          =							state_dict[f'blocks.{depth}.mlp.fc2.weight']
          UpperCamelCase				:   Tuple          =							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				:   List[str]          =							state_dict["""final_layer.adaLN_modulation.1.weight"""]
      UpperCamelCase				:   str          =							state_dict["""final_layer.adaLN_modulation.1.bias"""]
      UpperCamelCase				:   Tuple          =							state_dict["""final_layer.linear.weight"""]
      UpperCamelCase				:   List[Any]          =							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				:   int          =							TransformeraDModel(
          sample_size=args.image_size // 8						,num_layers=2_8						,attention_head_dim=7_2						,in_channels=4						,out_channels=8						,patch_size=2						,attention_bias=snake_case_						,num_attention_heads=1_6						,activation_fn="""gelu-approximate"""						,num_embeds_ada_norm=1_0_0_0						,norm_type="""ada_norm_zero"""						,norm_elementwise_affine=snake_case_						,)
      transformer.load_state_dict(snake_case_						,strict=snake_case_    )
  
      UpperCamelCase				:   str          =							DDIMScheduler(
          num_train_timesteps=1_0_0_0						,beta_schedule="""linear"""						,prediction_type="""epsilon"""						,clip_sample=snake_case_						,)
  
      UpperCamelCase				:   Optional[Any]          =							AutoencoderKL.from_pretrained(args.vae_model    )
  
      UpperCamelCase				:   List[Any]          =							DiTPipeline(transformer=snake_case_						,vae=snake_case_						,scheduler=snake_case_    )
  
      if args.save:
          pipeline.save_pretrained(args.checkpoint_path    )
  
  
  if __name__ == "__main__":
    __A :		Any      							=    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 :		Dict      							=    parser.parse_args()
    main(args)
  
  
  
  
  