




"""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


SCREAMING_SNAKE_CASE_      =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      =					{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

SCREAMING_SNAKE_CASE_      =					{
    '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'
        ),
    },
}

SCREAMING_SNAKE_CASE_      =					{
    'allenai/longformer-base-4096': 4096,
    'allenai/longformer-large-4096': 4096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4096,
}

class 							snake_case_ (				lowerCamelCase_ ):







   """simple docstring"""




   A_										=					VOCAB_FILES_NAMES
   A_										=					PRETRAINED_VOCAB_FILES_MAP
   A_										=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A_										=					['''input_ids''', '''attention_mask''']
   A_										=					LongformerTokenizer







   def __init__(		self					,				lowerCamelCase_=None					,				lowerCamelCase_=None					,				lowerCamelCase_=None					,				lowerCamelCase_="replace"					,				lowerCamelCase_="<s>"					,				lowerCamelCase_="</s>"					,				lowerCamelCase_="</s>"					,				lowerCamelCase_="<s>"					,				lowerCamelCase_="<unk>"					,				lowerCamelCase_="<pad>"					,				lowerCamelCase_="<mask>"					,				lowerCamelCase_=False					,				lowerCamelCase_=True					,				**lowerCamelCase_					,				)							->      str:
       super().__init__(
           lowerCamelCase_					,				lowerCamelCase_					,				tokenizer_file=lowerCamelCase_					,				errors=lowerCamelCase_					,				bos_token=lowerCamelCase_					,				eos_token=lowerCamelCase_					,				sep_token=lowerCamelCase_					,				cls_token=lowerCamelCase_					,				unk_token=lowerCamelCase_					,				pad_token=lowerCamelCase_					,				mask_token=lowerCamelCase_					,				add_prefix_space=lowerCamelCase_					,				trim_offsets=lowerCamelCase_					,				**lowerCamelCase_					,				)

       UpperCamelCase										=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
       if pre_tok_state.get('''add_prefix_space'''					,				lowerCamelCase_) != add_prefix_space:
           UpperCamelCase										=  getattr(lowerCamelCase_					,				pre_tok_state.pop('''type'''))
           UpperCamelCase										=  add_prefix_space
           UpperCamelCase										=  pre_tok_class(**lowerCamelCase_)

       UpperCamelCase										=  add_prefix_space

       UpperCamelCase										=  '''post_processor'''
       UpperCamelCase										=  getattr(self.backend_tokenizer					,				lowerCamelCase_					,				lowerCamelCase_)
       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'''					,				lowerCamelCase_) != add_prefix_space:
               UpperCamelCase										=  add_prefix_space
               UpperCamelCase										=  True

           if state.get('''trim_offsets'''					,				lowerCamelCase_) != trim_offsets:
               UpperCamelCase										=  trim_offsets
               UpperCamelCase										=  True

           if changes_to_apply:
               UpperCamelCase										=  getattr(lowerCamelCase_					,				state.pop('''type'''))
               UpperCamelCase										=  component_class(**lowerCamelCase_)
               setattr(self.backend_tokenizer					,				lowerCamelCase_					,				lowerCamelCase_)







   @property
   def      UpperCAmelCase__ (		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      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Dict:
       UpperCamelCase										=  AddedToken(lowerCamelCase_					,				lstrip=lowerCamelCase_					,				rstrip=lowerCamelCase_) if isinstance(lowerCamelCase_					,				lowerCamelCase_) else value
       UpperCamelCase										=  value







   def      UpperCAmelCase__ (		self					,				*lowerCamelCase_					,				**lowerCamelCase_)							->      BatchEncoding:
       UpperCamelCase										=  kwargs.get('''is_split_into_words'''					,				lowerCamelCase_)
       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(*lowerCamelCase_					,				**lowerCamelCase_)







   def      UpperCAmelCase__ (		self					,				*lowerCamelCase_					,				**lowerCamelCase_)							->      BatchEncoding:
       UpperCamelCase										=  kwargs.get('''is_split_into_words'''					,				lowerCamelCase_)

       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(*lowerCamelCase_					,				**lowerCamelCase_)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_ = None)							->      Tuple[str]:
       UpperCamelCase										=  self._tokenizer.model.save(lowerCamelCase_					,				name=lowerCamelCase_)
       return tuple(lowerCamelCase_)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_=None)							->      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      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_ = 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 . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()





def     __snake_case   (							_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase						):


    """simple docstring"""







    UpperCamelCase										=  BertConfig.from_pretrained(
        '''bert-large-cased''' ,vocab_size=_lowercase ,max_position_embeddings=512 ,is_decoder=_lowercase ,add_cross_attention=_lowercase ,)
    UpperCamelCase										=  bert_config.to_dict()
    del bert_config_dict["type_vocab_size"]
    UpperCamelCase										=  BertGenerationConfig(**_lowercase						)
    if is_encoder:
        UpperCamelCase										=  BertGenerationEncoder(_lowercase						)
    else:
        UpperCamelCase										=  BertGenerationDecoder(_lowercase						)
    print(f'Building PyTorch model from configuration: {config}'						)

    # Load weights from tf checkpoint
    load_tf_weights_in_bert_generation(
        _lowercase ,_lowercase ,model_class='''bert''' ,is_encoder_named_decoder=_lowercase ,is_encoder=_lowercase ,)

    # Save pytorch-model
    print(f'Save PyTorch model and config to {pytorch_dump_path}'						)
    model.save_pretrained(_lowercase						)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE_      =					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=50358, type=int, help='Vocab size of model')
  SCREAMING_SNAKE_CASE_      =					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,
  )