code: 
  
  
  
  """simple docstring"""
  
  
  
  
  import argparse
  
  from . import (
      BertConfig,
      BertGenerationConfig,
      BertGenerationDecoder,
      BertGenerationEncoder,
      load_tf_weights_in_bert_generation,
      logging,
  )
  
  
  logging.set_verbosity_info()
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str		,						SCREAMING_SNAKE_CASE__			:		List[str]		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		Union[str, Any]		,						SCREAMING_SNAKE_CASE__			:		Optional[Any]       )						->     str:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			List[str]              =					BertConfig.from_pretrained(
           "bert-large-cased"		,						vocab_size=SCREAMING_SNAKE_CASE__		,						max_position_embeddings=512		,						is_decoder=SCREAMING_SNAKE_CASE__		,						add_cross_attention=SCREAMING_SNAKE_CASE__		,						)
       _UpperCAmelCase    :			Union[str, Any]              =					bert_config.to_dict()
       del bert_config_dict["type_vocab_size"]
       _UpperCAmelCase    :			Optional[int]              =					BertGenerationConfig(**SCREAMING_SNAKE_CASE__       )
       if is_encoder:
            _UpperCAmelCase    :			Union[str, Any]              =					BertGenerationEncoder(SCREAMING_SNAKE_CASE__       )
       else:
            _UpperCAmelCase    :			int              =					BertGenerationDecoder(SCREAMING_SNAKE_CASE__       )
       print(f'Building PyTorch model from configuration: {config}'       )
  
       # Load weights from tf checkpoint
       load_tf_weights_in_bert_generation(
           SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						model_class="bert"		,						is_encoder_named_decoder=SCREAMING_SNAKE_CASE__		,						is_encoder=SCREAMING_SNAKE_CASE__		,						)
  
       # Save pytorch-model
       print(f'Save PyTorch model and config to {pytorch_dump_path}'       )
       model.save_pretrained(SCREAMING_SNAKE_CASE__       )
  
  
  if __name__ == "__main__":
   _lowerCAmelCase       :      Optional[int]							=							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
   )
   parser.add_argument(
       "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   parser.add_argument(
       "--is_encoder_named_decoder",
       action="store_true",
       help="If decoder has to be renamed to encoder in PyTorch model.",
   )
   parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
   parser.add_argument("--vocab_size", default=5_03_58, type=int, help="Vocab size of model")
   _lowerCAmelCase       :      int							=							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_hub_path,
       args.pytorch_dump_path,
       args.is_encoder_named_decoder,
       args.vocab_size,
       is_encoder=args.is_encoder,
   )
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  from arguments import TokenizerTrainingArguments
  from datasets import load_dataset
  from tqdm import tqdm
  
  from transformers import AutoTokenizer, HfArgumentParser
  from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str=10       )						->     List[Any]:
  
  
  
  
  
       '''simple docstring'''
  
       for _ in tqdm(range(0		,						args.n_examples		,						SCREAMING_SNAKE_CASE__       )       ):
            yield [next(SCREAMING_SNAKE_CASE__       )[args.text_column] for _ in range(SCREAMING_SNAKE_CASE__       )]
  
  
  # Configuration
  _lowerCAmelCase       :      Optional[Any]							=							HfArgumentParser(TokenizerTrainingArguments)
  _lowerCAmelCase       :      Dict							=							parser.parse_args()
  
  # Base tokenizer
  _lowerCAmelCase       :      int							=							AutoTokenizer.from_pretrained(args.base_tokenizer)
  _lowerCAmelCase       :      List[str]							=							list(bytes_to_unicode().values())
  
  # Load dataset
  _lowerCAmelCase       :      Dict							=							load_dataset(args.dataset_name, split="train", streaming=True)
  _lowerCAmelCase       :      List[Any]							=							iter(dataset)
  
  
  # Training and saving
  _lowerCAmelCase       :      List[str]							=							tokenizer.train_new_from_iterator(
      batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
  )
  new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
  