

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_bart import BartTokenizer


lowercase_									=   logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
lowercase_									=   {
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

lowercase_									=   {
    'facebook/bart-base': 1_0_2_4,
    'facebook/bart-large': 1_0_2_4,
    'facebook/bart-large-mnli': 1_0_2_4,
    'facebook/bart-large-cnn': 1_0_2_4,
    'facebook/bart-large-xsum': 1_0_2_4,
    'yjernite/bart_eli5': 1_0_2_4,
}





class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):
  _a									=  VOCAB_FILES_NAMES
  _a									=  PRETRAINED_VOCAB_FILES_MAP
  _a									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _a									=  ["""input_ids""", """attention_mask"""]
  _a									=  BartTokenizer




  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							,   )  -> Tuple:
        '''simple docstring'''






        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							,   )

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

        _lowercase	       =add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        _lowercase	       ='post_processor'
        _lowercase	       =getattr(self.backend_tokenizer							,   lowerCAmelCase							,   lowerCAmelCase					)
        if tokenizer_component_instance:
              _lowercase	       =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:
                    _lowercase	       =tuple(state['sep']					)
              if "cls" in state:
                    _lowercase	       =tuple(state['cls']					)

              _lowercase	       =False

              if state.get('add_prefix_space'							,   lowerCAmelCase					) != add_prefix_space:
                    _lowercase	       =add_prefix_space
                    _lowercase	       =True

              if state.get('trim_offsets'							,   lowerCAmelCase					) != trim_offsets:
                    _lowercase	       =trim_offsets
                    _lowercase	       =True

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




  @property
  def   A__     (				self					)  -> str:
        '''simple docstring'''






        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   A__     (				self							,   lowerCAmelCase					)  -> Optional[Any]:
        '''simple docstring'''






        _lowercase	       =AddedToken(lowerCAmelCase							,   lstrip=lowerCAmelCase							,   rstrip=lowerCAmelCase					) if isinstance(lowerCAmelCase							,   lowerCAmelCase					) else value
        _lowercase	       =value




  def   A__     (				self							,   *lowerCAmelCase							,   **lowerCAmelCase					)  -> BatchEncoding:
        '''simple docstring'''






        _lowercase	       =kwargs.get('is_split_into_words'							,   lowerCAmelCase					)

        if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  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   A__     (				self							,   *lowerCAmelCase							,   **lowerCAmelCase					)  -> BatchEncoding:
        '''simple docstring'''






        _lowercase	       =kwargs.get('is_split_into_words'							,   lowerCAmelCase					)

        if is_split_into_words and not self.add_prefix_space:
              raise ValueError(
                  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   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = None					)  -> Tuple[str]:
        '''simple docstring'''






        _lowercase	       =self._tokenizer.model.save(lowerCAmelCase							,   name=lowerCAmelCase					)
        return tuple(lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase=None					)  -> Optional[Any]:
        '''simple docstring'''






        _lowercase	       =[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   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = None					)  -> List[int]:
        '''simple docstring'''






        _lowercase	       =[self.sep_token_id]
        _lowercase	       =[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]





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_									=   {
    'configuration_bloom': ['BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BloomConfig', 'BloomOnnxConfig'],
}
try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   ['BloomTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase_									=   [
      'BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'BloomForCausalLM',
      'BloomModel',
      'BloomPreTrainedModel',
      'BloomForSequenceClassification',
      'BloomForTokenClassification',
      'BloomForQuestionAnswering',
  ]

if TYPE_CHECKING:
  from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_bloom_fast import BloomTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_bloom import (
        BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
        BloomForCausalLM,
        BloomForQuestionAnswering,
        BloomForSequenceClassification,
        BloomForTokenClassification,
        BloomModel,
        BloomPreTrainedModel,
    )

else:
  import sys

  lowercase_									=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


