





'''simple docstring'''


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_big_bird import BigBirdTokenizer
else:
  lowercase							       =			None

lowercase							       =			logging.get_logger(__name__)
lowercase							       =			{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase							       =			{
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    },
    '''tokenizer_file''': {
        '''google/bigbird-roberta-base''': (
            '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json'''
        ),
    },
}

lowercase							       =			{
    '''google/bigbird-roberta-base''': 4_096,
    '''google/bigbird-roberta-large''': 4_096,
    '''google/bigbird-base-trivia-itc''': 4_096,
}


lowercase							       =			'''▁'''







class 	UpperCAmelCase	(		__a):


 '''simple docstring'''




 __magic_name__					: Optional[int]  =      VOCAB_FILES_NAMES
 __magic_name__					: List[str]  =      PRETRAINED_VOCAB_FILES_MAP
 __magic_name__					: Dict  =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __magic_name__					: int  =      BigBirdTokenizer
 __magic_name__					: List[Any]  =      ["input_ids", "attention_mask"]
 __magic_name__					: List[int]  =      []




 def __init__(   self							,  lowerCAmelCase_=None							,  lowerCAmelCase_=None							,  lowerCAmelCase_="<unk>"							,  lowerCAmelCase_="<s>"							,  lowerCAmelCase_="</s>"							,  lowerCAmelCase_="<pad>"							,  lowerCAmelCase_="[SEP]"							,  lowerCAmelCase_="[MASK]"							,  lowerCAmelCase_="[CLS]"							,  **lowerCAmelCase_							,  ) ->      int:



      """simple docstring"""







      a_       		=AddedToken(lowerCAmelCase_							,  lstrip=lowerCAmelCase_							,  rstrip=lowerCAmelCase_) if isinstance(lowerCAmelCase_							,  lowerCAmelCase_) else bos_token
      a_       		=AddedToken(lowerCAmelCase_							,  lstrip=lowerCAmelCase_							,  rstrip=lowerCAmelCase_) if isinstance(lowerCAmelCase_							,  lowerCAmelCase_) else eos_token
      a_       		=AddedToken(lowerCAmelCase_							,  lstrip=lowerCAmelCase_							,  rstrip=lowerCAmelCase_) if isinstance(lowerCAmelCase_							,  lowerCAmelCase_) else unk_token
      a_       		=AddedToken(lowerCAmelCase_							,  lstrip=lowerCAmelCase_							,  rstrip=lowerCAmelCase_) if isinstance(lowerCAmelCase_							,  lowerCAmelCase_) else pad_token
      a_       		=AddedToken(lowerCAmelCase_							,  lstrip=lowerCAmelCase_							,  rstrip=lowerCAmelCase_) if isinstance(lowerCAmelCase_							,  lowerCAmelCase_) else cls_token
      a_       		=AddedToken(lowerCAmelCase_							,  lstrip=lowerCAmelCase_							,  rstrip=lowerCAmelCase_) if isinstance(lowerCAmelCase_							,  lowerCAmelCase_) else sep_token

      # Mask token behave like a normal word, i.e. include the space before it
      a_       		=AddedToken(lowerCAmelCase_							,  lstrip=lowerCAmelCase_							,  rstrip=lowerCAmelCase_) if isinstance(lowerCAmelCase_							,  lowerCAmelCase_) else mask_token

      super().__init__(
          lowerCAmelCase_							,  tokenizer_file=lowerCAmelCase_							,  bos_token=lowerCAmelCase_							,  eos_token=lowerCAmelCase_							,  unk_token=lowerCAmelCase_							,  sep_token=lowerCAmelCase_							,  pad_token=lowerCAmelCase_							,  cls_token=lowerCAmelCase_							,  mask_token=lowerCAmelCase_							,  **lowerCAmelCase_							,  )

      a_       		=vocab_file
      a_       		=False if not self.vocab_file else True




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None) ->      List[int]:



      """simple docstring"""







      a_       		=[self.sep_token_id]
      a_       		=[self.cls_token_id]
      if token_ids_a is None:
           return cls + token_ids_a + sep
      return cls + token_ids_a + sep + token_ids_a + sep




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None							,  lowerCAmelCase_ = False) ->      List[int]:



      """simple docstring"""







      if already_has_special_tokens:
           if token_ids_a is not None:
                raise ValueError(
                    "You should not supply a second sequence if the provided sequence of "
                    "ids is already formatted with special tokens for the model.")
           return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

      if token_ids_a is None:
           return [1] + ([0] * len(lowerCAmelCase_)) + [1]
      return [1] + ([0] * len(lowerCAmelCase_)) + [1] + ([0] * len(lowerCAmelCase_)) + [1]




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None) ->      List[int]:



      """simple docstring"""







      a_       		=[self.sep_token_id]
      a_       		=[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) * [0] + len(token_ids_a + sep) * [1]




 def       lowercase_  (   self							,  lowerCAmelCase_							,  lowerCAmelCase_ = None) ->      Tuple[str]:



      """simple docstring"""







      if not self.can_save_slow_tokenizer:
           raise ValueError(
               "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
               "tokenizer.")

      if not os.path.isdir(lowerCAmelCase_):
           logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
           return
      a_       		=os.path.join(
          lowerCAmelCase_							,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

      if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase_):
           copyfile(self.vocab_file							,  lowerCAmelCase_)

      return (out_vocab_file,)







'''simple docstring'''


from typing import TYPE_CHECKING

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


lowercase							       =			{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			[
      '''RagModel''',
      '''RagPreTrainedModel''',
      '''RagSequenceForGeneration''',
      '''RagTokenForGeneration''',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase							       =			[
      '''TFRagModel''',
      '''TFRagPreTrainedModel''',
      '''TFRagSequenceForGeneration''',
      '''TFRagTokenForGeneration''',
  ]


if TYPE_CHECKING:
  from .configuration_rag import RagConfig
  from .retrieval_rag import RagRetriever
  from .tokenization_rag import RagTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_rag import (
        TFRagModel,
        TFRagPreTrainedModel,
        TFRagSequenceForGeneration,
        TFRagTokenForGeneration,
    )

else:
  import sys

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


