code: 
  
  
  
  '''simple docstring'''
  
  
  
  import json
  from typing import List, Optional, Tuple
  
  from tokenizers import pre_tokenizers, processors
  
  from ...tokenization_utils_base import AddedToken, BatchEncoding
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_longformer import LongformerTokenizer
  
  
  a__						:			Optional[int]										=			logging.get_logger(__name__)
  
  a__						:			int										=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}
  
  a__						:			str										=			{
      'vocab_file': {
          'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
          'allenai/longformer-large-4096': (
              'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
          ),
          'allenai/longformer-large-4096-finetuned-triviaqa': (
              'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
          ),
          'allenai/longformer-base-4096-extra.pos.embd.only': (
              'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
          ),
          'allenai/longformer-large-4096-extra.pos.embd.only': (
              'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
          ),
      },
      'merges_file': {
          'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
          'allenai/longformer-large-4096': (
              'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
          ),
          'allenai/longformer-large-4096-finetuned-triviaqa': (
              'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
          ),
          'allenai/longformer-base-4096-extra.pos.embd.only': (
              'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
          ),
          'allenai/longformer-large-4096-extra.pos.embd.only': (
              'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
          ),
      },
      'tokenizer_file': {
          'allenai/longformer-base-4096': (
              'https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'
          ),
          'allenai/longformer-large-4096': (
              'https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'
          ),
          'allenai/longformer-large-4096-finetuned-triviaqa': (
              'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'
          ),
          'allenai/longformer-base-4096-extra.pos.embd.only': (
              'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
          ),
          'allenai/longformer-large-4096-extra.pos.embd.only': (
              'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'
          ),
      },
  }
  
  a__						:			Union[str, Any]										=			{
      'allenai/longformer-base-4096': 4_0_9_6,
      'allenai/longformer-large-4096': 4_0_9_6,
      'allenai/longformer-large-4096-finetuned-triviaqa': 4_0_9_6,
      'allenai/longformer-base-4096-extra.pos.embd.only': 4_0_9_6,
      'allenai/longformer-large-4096-extra.pos.embd.only': 4_0_9_6,
  }
  class        UpperCAmelCase__  (    a__):
   __SCREAMING_SNAKE_CASE					      =				VOCAB_FILES_NAMES
   __SCREAMING_SNAKE_CASE					      =				PRETRAINED_VOCAB_FILES_MAP
   __SCREAMING_SNAKE_CASE					      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __SCREAMING_SNAKE_CASE					      =				["input_ids", "attention_mask"]
   __SCREAMING_SNAKE_CASE					      =				LongformerTokenizer
  
  
  
   def __init__(  self		,	lowercase=None		,	lowercase=None		,	lowercase=None		,	lowercase="replace"		,	lowercase="<s>"		,	lowercase="</s>"		,	lowercase="</s>"		,	lowercase="<s>"		,	lowercase="<unk>"		,	lowercase="<pad>"		,	lowercase="<mask>"		,	lowercase=False		,	lowercase=True		,	**lowercase		,	)  ->							Dict:
    super().__init__(
        SCREAMING_SNAKE_CASE_		,	SCREAMING_SNAKE_CASE_		,	tokenizer_file=SCREAMING_SNAKE_CASE_		,	errors=SCREAMING_SNAKE_CASE_		,	bos_token=SCREAMING_SNAKE_CASE_		,	eos_token=SCREAMING_SNAKE_CASE_		,	sep_token=SCREAMING_SNAKE_CASE_		,	cls_token=SCREAMING_SNAKE_CASE_		,	unk_token=SCREAMING_SNAKE_CASE_		,	pad_token=SCREAMING_SNAKE_CASE_		,	mask_token=SCREAMING_SNAKE_CASE_		,	add_prefix_space=SCREAMING_SNAKE_CASE_		,	trim_offsets=SCREAMING_SNAKE_CASE_		,	**SCREAMING_SNAKE_CASE_		,	)
  
    __UpperCamelCase				      =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
    if pre_tok_state.get("""add_prefix_space"""		,	SCREAMING_SNAKE_CASE_			) != add_prefix_space:
     __UpperCamelCase				      =  getattr(SCREAMING_SNAKE_CASE_		,	pre_tok_state.pop("""type"""			)			)
     __UpperCamelCase				      =  add_prefix_space
     __UpperCamelCase				      =  pre_tok_class(**SCREAMING_SNAKE_CASE_			)
  
    __UpperCamelCase				      =  add_prefix_space
  
    __UpperCamelCase				      =  'post_processor'
    __UpperCamelCase				      =  getattr(self.backend_tokenizer		,	SCREAMING_SNAKE_CASE_		,	SCREAMING_SNAKE_CASE_			)
    if tokenizer_component_instance:
     __UpperCamelCase				      =  json.loads(tokenizer_component_instance.__getstate__()			)
  
     # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
     if "sep" in state:
      __UpperCamelCase				      =  tuple(state["""sep"""]			)
     if "cls" in state:
      __UpperCamelCase				      =  tuple(state["""cls"""]			)
  
     __UpperCamelCase				      =  False
  
     if state.get("""add_prefix_space"""		,	SCREAMING_SNAKE_CASE_			) != add_prefix_space:
      __UpperCamelCase				      =  add_prefix_space
      __UpperCamelCase				      =  True
  
     if state.get("""trim_offsets"""		,	SCREAMING_SNAKE_CASE_			) != trim_offsets:
      __UpperCamelCase				      =  trim_offsets
      __UpperCamelCase				      =  True
  
     if changes_to_apply:
      __UpperCamelCase				      =  getattr(SCREAMING_SNAKE_CASE_		,	state.pop("""type"""			)			)
      __UpperCamelCase				      =  component_class(**SCREAMING_SNAKE_CASE_			)
      setattr(self.backend_tokenizer		,	SCREAMING_SNAKE_CASE_		,	SCREAMING_SNAKE_CASE_			)
  
  
  
   @property
   def    __lowerCamelCase				(  self			)  ->							str:
    if self._mask_token is None:
     if self.verbose:
      logger.error("""Using mask_token, but it is not set yet."""			)
     return None
    return str(self._mask_token			)
  
  
  
   @mask_token.setter
   def    __lowerCamelCase				(  self		,	lowercase			)  ->							List[str]:
    __UpperCamelCase				      =  AddedToken(SCREAMING_SNAKE_CASE_		,	lstrip=SCREAMING_SNAKE_CASE_		,	rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_		,	SCREAMING_SNAKE_CASE_			) else value
    __UpperCamelCase				      =  value
  
  
  
   def    __lowerCamelCase				(  self		,	*lowercase		,	**lowercase			)  ->							BatchEncoding:
    __UpperCamelCase				      =  kwargs.get("""is_split_into_words"""		,	SCREAMING_SNAKE_CASE_			)
    assert self.add_prefix_space or not is_split_into_words, (
        f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
        "to use it with pretokenized inputs."
    )
  
    return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_		,	**SCREAMING_SNAKE_CASE_			)
  
  
  
   def    __lowerCamelCase				(  self		,	*lowercase		,	**lowercase			)  ->							BatchEncoding:
    __UpperCamelCase				      =  kwargs.get("""is_split_into_words"""		,	SCREAMING_SNAKE_CASE_			)
  
    assert self.add_prefix_space or not is_split_into_words, (
        f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
        "to use it with pretokenized inputs."
    )
  
    return super()._encode_plus(*SCREAMING_SNAKE_CASE_		,	**SCREAMING_SNAKE_CASE_			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase = None			)  ->							Tuple[str]:
    __UpperCamelCase				      =  self._tokenizer.model.save(SCREAMING_SNAKE_CASE_		,	name=SCREAMING_SNAKE_CASE_			)
    return tuple(SCREAMING_SNAKE_CASE_			)
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase=None			)  ->							Optional[Any]:
    __UpperCamelCase				      =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
    if token_ids_a is None:
     return output
  
    return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
  
  
  
  
   def    __lowerCamelCase				(  self		,	lowercase		,	lowercase = None			)  ->							List[int]:
    __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]
  
  
  
  
  
  
style_context: 
  
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  
  
  
  def _lowercase   (  __A							,__A							,__A       ):
  
  
  
   '''simple docstring'''
  
  
  
   __UpperCamelCase				      =  TaConfig.from_json_file(__A       )
   print(f"Building PyTorch model from configuration: {config}"       )
   __UpperCamelCase				      =  TaForConditionalGeneration(__A       )
  
   # Load weights from tf checkpoint
   load_tf_weights_in_ta(__A							,__A							,__A       )
  
   # Save pytorch-model
   print(f"Save PyTorch model to {pytorch_dump_path}"       )
   model.save_pretrained(__A       )
  
  
  if __name__ == "__main__":
   a__						:			int										=			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 T5 model. \nThis specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   a__						:			Optional[Any]										=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  
  
  
  