



'''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]










'''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)





