






'''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_bart import BartTokenizer


a_					= logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
a_					= {
    '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',
    },
}

a_					= {
    '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      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):
 snake_case_  							=							VOCAB_FILES_NAMES
 snake_case_  							=							PRETRAINED_VOCAB_FILES_MAP
 snake_case_  							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 snake_case_  							=							["""input_ids""", """attention_mask"""]
 snake_case_  							=							BartTokenizer




 def __init__(      self	:							int					,      __lowercase	:							Tuple=None					,      __lowercase	:							int=None					,      __lowercase	:							Any=None					,      __lowercase	:							Tuple="replace"					,      __lowercase	:							str="<s>"					,      __lowercase	:							int="</s>"					,      __lowercase	:							Optional[int]="</s>"					,      __lowercase	:							Optional[Any]="<s>"					,      __lowercase	:							Dict="<unk>"					,      __lowercase	:							List[Any]="<pad>"					,      __lowercase	:							Any="<mask>"					,      __lowercase	:							Optional[int]=False					,      __lowercase	:							Any=True					,      **__lowercase	:							Any					,      )	->						Optional[Any]:
       super().__init__(
           __lowercase					,      __lowercase					,      tokenizer_file=__lowercase					,      errors=__lowercase					,      bos_token=__lowercase					,      eos_token=__lowercase					,      sep_token=__lowercase					,      cls_token=__lowercase					,      unk_token=__lowercase					,      pad_token=__lowercase					,      mask_token=__lowercase					,      add_prefix_space=__lowercase					,      trim_offsets=__lowercase					,      **__lowercase					,      )

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
       if pre_tok_state.get('''add_prefix_space'''					,      __lowercase      ) != add_prefix_space:
             SCREAMING_SNAKE_CASE__     :				str									=getattr(__lowercase					,      pre_tok_state.pop('''type'''      )      )
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=add_prefix_space
             SCREAMING_SNAKE_CASE__     :				List[str]									=pre_tok_class(**__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Any									=add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									='''post_processor'''
       SCREAMING_SNAKE_CASE__     :				List[Any]									=getattr(self.backend_tokenizer					,      __lowercase					,      __lowercase      )
       if tokenizer_component_instance:
             SCREAMING_SNAKE_CASE__     :				List[Any]									=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:
                   SCREAMING_SNAKE_CASE__     :				str									=tuple(state['''sep''']      )
             if "cls" in state:
                   SCREAMING_SNAKE_CASE__     :				str									=tuple(state['''cls''']      )

             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=False

             if state.get('''add_prefix_space'''					,      __lowercase      ) != add_prefix_space:
                   SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=add_prefix_space
                   SCREAMING_SNAKE_CASE__     :				Tuple									=True

             if state.get('''trim_offsets'''					,      __lowercase      ) != trim_offsets:
                   SCREAMING_SNAKE_CASE__     :				Optional[Any]									=trim_offsets
                   SCREAMING_SNAKE_CASE__     :				str									=True

             if changes_to_apply:
                   SCREAMING_SNAKE_CASE__     :				Tuple									=getattr(__lowercase					,      state.pop('''type'''      )      )
                   SCREAMING_SNAKE_CASE__     :				Tuple									=component_class(**__lowercase      )
                   setattr(self.backend_tokenizer					,      __lowercase					,      __lowercase      )




 @property
 def      __magic_name__	(      self	:							str      )	->						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      __magic_name__	(      self	:							str					,      __lowercase	:							List[Any]      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				List[str]									=AddedToken(__lowercase					,      lstrip=__lowercase					,      rstrip=__lowercase      ) if isinstance(__lowercase					,      __lowercase      ) else value
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=value




 def      __magic_name__	(      self	:							str					,      *__lowercase	:							int					,      **__lowercase	:							str      )	->						BatchEncoding:
       SCREAMING_SNAKE_CASE__     :				Tuple									=kwargs.get('''is_split_into_words'''					,      __lowercase      )

       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(*__lowercase					,      **__lowercase      )




 def      __magic_name__	(      self	:							Optional[int]					,      *__lowercase	:							Union[str, Any]					,      **__lowercase	:							Optional[int]      )	->						BatchEncoding:
       SCREAMING_SNAKE_CASE__     :				Any									=kwargs.get('''is_split_into_words'''					,      __lowercase      )

       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(*__lowercase					,      **__lowercase      )




 def      __magic_name__	(      self	:							Tuple					,      __lowercase	:							str					,      __lowercase	:							Optional[str] = None      )	->						Tuple[str]:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self._tokenizer.model.save(__lowercase					,      name=__lowercase      )
       return tuple(__lowercase      )




 def      __magic_name__	(      self	:							Tuple					,      __lowercase	:							List[Any]					,      __lowercase	:							List[str]=None      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				str									=[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      __magic_name__	(      self	:							Union[str, Any]					,      __lowercase	:							List[int]					,      __lowercase	:							Optional[List[int]] = None      )	->						List[int]:
       SCREAMING_SNAKE_CASE__     :				int									=[self.sep_token_id]
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=[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'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_					= {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= ['DebertaTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'DebertaForMaskedLM',
      'DebertaForQuestionAnswering',
      'DebertaForSequenceClassification',
      'DebertaForTokenClassification',
      'DebertaModel',
      'DebertaPreTrainedModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a_					= [
      'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFDebertaForMaskedLM',
      'TFDebertaForQuestionAnswering',
      'TFDebertaForSequenceClassification',
      'TFDebertaForTokenClassification',
      'TFDebertaModel',
      'TFDebertaPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
  from .tokenization_deberta import DebertaTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_deberta_fast import DebertaTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_deberta import (
        DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        DebertaForMaskedLM,
        DebertaForQuestionAnswering,
        DebertaForSequenceClassification,
        DebertaForTokenClassification,
        DebertaModel,
        DebertaPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_deberta import (
        TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFDebertaForMaskedLM,
        TFDebertaForQuestionAnswering,
        TFDebertaForSequenceClassification,
        TFDebertaForTokenClassification,
        TFDebertaModel,
        TFDebertaPreTrainedModel,
    )


else:
  import sys

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