code: 
  
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from ...tokenization_utils import AddedToken
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
      from .tokenization_xlm_roberta import XLMRobertaTokenizer
  else:
      __snake_case             =	None
  
  
  __snake_case             =	logging.get_logger(__name__)
  
  __snake_case             =	{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
  
  __snake_case             =	{
      '''vocab_file''': {
          '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
          '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
          '''xlm-roberta-large-finetuned-conll02-dutch''': (
              '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
          ),
          '''xlm-roberta-large-finetuned-conll02-spanish''': (
              '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
          ),
          '''xlm-roberta-large-finetuned-conll03-english''': (
              '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
          ),
          '''xlm-roberta-large-finetuned-conll03-german''': (
              '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
          ),
      },
      '''tokenizer_file''': {
          '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
          '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
          '''xlm-roberta-large-finetuned-conll02-dutch''': (
              '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
          ),
          '''xlm-roberta-large-finetuned-conll02-spanish''': (
              '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
          ),
          '''xlm-roberta-large-finetuned-conll03-english''': (
              '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
          ),
          '''xlm-roberta-large-finetuned-conll03-german''': (
              '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
          ),
      },
  }
  
  __snake_case             =	{
      '''xlm-roberta-base''': 5_1_2,
      '''xlm-roberta-large''': 5_1_2,
      '''xlm-roberta-large-finetuned-conll02-dutch''': 5_1_2,
      '''xlm-roberta-large-finetuned-conll02-spanish''': 5_1_2,
      '''xlm-roberta-large-finetuned-conll03-english''': 5_1_2,
      '''xlm-roberta-large-finetuned-conll03-german''': 5_1_2,
  }
  
  
  
  class   __lowerCamelCase (_a	):
         _lowercase       							=    VOCAB_FILES_NAMES
         _lowercase       							=    PRETRAINED_VOCAB_FILES_MAP
         _lowercase       							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
         _lowercase       							=    ["""input_ids""", """attention_mask"""]
         _lowercase       							=    XLMRobertaTokenizer
  
  
  
  
  
  
  
         def __init__( self:   int,A_:   List[Any]=None,A_:   List[Any]=None,A_:   Any="<s>",A_:   Union[str, Any]="</s>",A_:   Any="</s>",A_:   str="<s>",A_:   Optional[Any]="<unk>",A_:   str="<pad>",A_:   Tuple="<mask>",**A_:   str,):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = AddedToken(A_,lstrip=A_,rstrip=A_				) if isinstance(A_,A_				) else mask_token
  
              super().__init__(
                  A_,tokenizer_file=A_,bos_token=A_,eos_token=A_,sep_token=A_,cls_token=A_,unk_token=A_,pad_token=A_,mask_token=A_,**A_,)
  
              __UpperCamelCase   = vocab_file
              __UpperCamelCase   = False if not self.vocab_file else True
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   int,A_:   List[int],A_:   Optional[List[int]] = None				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              if token_ids_a is None:
                   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
              __UpperCamelCase   = [self.cls_token_id]
              __UpperCamelCase   = [self.sep_token_id]
              return cls + token_ids_a + sep + sep + token_ids_a + sep
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   int,A_:   List[int],A_:   Optional[List[int]] = None				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = [self.sep_token_id]
              __UpperCamelCase   = [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 + sep + token_ids_a + sep				) * [0]
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Dict,A_:   str,A_:   Optional[str] = None				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              if not self.can_save_slow_tokenizer:
                   raise ValueError(
                       'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                       'tokenizer.'				)
  
              if not os.path.isdir(A_				):
                   logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''				)
                   return
              __UpperCamelCase   = 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_				):
                   copyfile(self.vocab_file,A_				)
  
              return (out_vocab_file,)
  
  
  
style_context: 
  
  
  import argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase   )				->   List[str]:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = json.load(open(_lowercase				,     'r'   )   )
       __UpperCamelCase   = GPTNeoConfig(
           hidden_size=config_json['n_embd']				,     num_layers=config_json['n_layer']				,     num_heads=config_json['n_head']				,     attention_types=config_json['attention_types']				,     max_position_embeddings=config_json['n_positions']				,     resid_dropout=config_json['res_dropout']				,     embed_dropout=config_json['embed_dropout']				,     attention_dropout=config_json['attn_dropout']				,     )
       print(f'''Building PyTorch model from configuration: {config}'''   )
       __UpperCamelCase   = GPTNeoForCausalLM(_lowercase   )
  
       # Load weights from tf checkpoint
       load_tf_weights_in_gpt_neo(_lowercase				,     _lowercase				,     _lowercase   )
  
       # Save pytorch-model
       print(f'''Save PyTorch model to {pytorch_dump_path}'''   )
       model.save_pretrained(_lowercase   )
  
  
  if __name__ == "__main__":
      __snake_case             =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
      )
      parser.add_argument(
          '''--config_file''',
          default=None,
          type=str,
          required=True,
          help=(
              '''The config json file corresponding to the pre-trained mesh-tf model. \n'''
              '''This specifies the model architecture.'''
          ),
      )
      parser.add_argument(
          '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
      )
      __snake_case             =	parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  