

'''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_rembert import RemBertTokenizer
else:
     __lowerCamelCase						    =      None

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

__lowerCamelCase						    =      {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
    '''tokenizer_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/tokenizer.json''',
    },
}

__lowerCamelCase						    =      {
    '''google/rembert''': 256,
}

__lowerCamelCase						    =      '''▁'''


class    A__ ( _snake_case     ):
    lowercase		   =						VOCAB_FILES_NAMES
    lowercase		   =						PRETRAINED_VOCAB_FILES_MAP
    lowercase		   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase		   =						RemBertTokenizer



    def __init__(   self						,			UpperCamelCase__=None						,			UpperCamelCase__=None						,			UpperCamelCase__=True						,			UpperCamelCase__=True						,			UpperCamelCase__=False						,			UpperCamelCase__="[CLS]"						,			UpperCamelCase__="[SEP]"						,			UpperCamelCase__="<unk>"						,			UpperCamelCase__="[SEP]"						,			UpperCamelCase__="<pad>"						,			UpperCamelCase__="[CLS]"						,			UpperCamelCase__="[MASK]"						,			**UpperCamelCase__						,			)  -> Tuple:





           '''simple docstring'''
           A_		   = AddedToken(UpperCamelCase__						,			lstrip=UpperCamelCase__						,			rstrip=UpperCamelCase__    ) if isinstance(UpperCamelCase__						,			UpperCamelCase__    ) else mask_token

           super().__init__(
               UpperCamelCase__						,			tokenizer_file=UpperCamelCase__						,			do_lower_case=UpperCamelCase__						,			remove_space=UpperCamelCase__						,			keep_accents=UpperCamelCase__						,			bos_token=UpperCamelCase__						,			eos_token=UpperCamelCase__						,			unk_token=UpperCamelCase__						,			sep_token=UpperCamelCase__						,			pad_token=UpperCamelCase__						,			cls_token=UpperCamelCase__						,			mask_token=UpperCamelCase__						,			**UpperCamelCase__						,			)

           A_		   = do_lower_case
           A_		   = remove_space
           A_		   = keep_accents
           A_		   = vocab_file
           A_		   = False if not self.vocab_file else True



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = 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    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = None						,			UpperCamelCase__ = 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 not None:
                  return [1] + ([0] * len(UpperCamelCase__    )) + [1] + ([0] * len(UpperCamelCase__    )) + [1]
           return [1] + ([0] * len(UpperCamelCase__    )) + [1]



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = 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    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = None    )  -> Tuple[str]:





           '''simple docstring'''
           if not os.path.isdir(UpperCamelCase__    ):
                  logger.error("""Vocabulary path ({}) should be a directory""".format(UpperCamelCase__    )    )
                  return
           A_		   = os.path.join(
               UpperCamelCase__						,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]    )

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

           return (out_vocab_file,)



'''simple docstring'''







from typing import TYPE_CHECKING

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


__lowerCamelCase						    =      {
    '''configuration_blenderbot''': [
        '''BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlenderbotConfig''',
        '''BlenderbotOnnxConfig''',
    ],
    '''tokenization_blenderbot''': ['''BlenderbotTokenizer'''],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						    =      [
          '''BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''BlenderbotForCausalLM''',
          '''BlenderbotForConditionalGeneration''',
          '''BlenderbotModel''',
          '''BlenderbotPreTrainedModel''',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						    =      [
          '''TFBlenderbotForConditionalGeneration''',
          '''TFBlenderbotModel''',
          '''TFBlenderbotPreTrainedModel''',
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						    =      [
          '''FlaxBlenderbotForConditionalGeneration''',
          '''FlaxBlenderbotModel''',
          '''FlaxBlenderbotPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_blenderbot import (
          BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          BlenderbotConfig,
          BlenderbotOnnxConfig,
      )
      from .tokenization_blenderbot import BlenderbotTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_blenderbot import (
                BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BlenderbotForCausalLM,
                BlenderbotForConditionalGeneration,
                BlenderbotModel,
                BlenderbotPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_blenderbot import (
                TFBlenderbotForConditionalGeneration,
                TFBlenderbotModel,
                TFBlenderbotPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_blenderbot import (
                FlaxBlenderbotForConditionalGeneration,
                FlaxBlenderbotModel,
                FlaxBlenderbotPreTrainedModel,
            )

else:
      import sys

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