

'''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_roberta import RobertaTokenizer


__lowerCamelCase						    =      logging.get_logger(__name__)

__lowerCamelCase						    =      {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase						    =      {
    """vocab_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/vocab.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/vocab.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/vocab.json""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/merges.txt""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/merges.txt""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/merges.txt""",
        """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt""",
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/tokenizer.json""",
        """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/tokenizer.json""",
        """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json""",
        """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json""",
        """roberta-base-openai-detector""": (
            """https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"""
        ),
        """roberta-large-openai-detector""": (
            """https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"""
        ),
    },
}

__lowerCamelCase						    =      {
    """roberta-base""": 512,
    """roberta-large""": 512,
    """roberta-large-mnli""": 512,
    """distilroberta-base""": 512,
    """roberta-base-openai-detector""": 512,
    """roberta-large-openai-detector""": 512,
}


class    A__ ( SCREAMING_SNAKE_CASE_     ):
    lowercase		   =						VOCAB_FILES_NAMES
    lowercase		   =						PRETRAINED_VOCAB_FILES_MAP
    lowercase		   =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase		   =						["input_ids", "attention_mask"]
    lowercase		   =						RobertaTokenizer



    def __init__(   self						,			UpperCamelCase__=None						,			UpperCamelCase__=None						,			UpperCamelCase__=None						,			UpperCamelCase__="replace"						,			UpperCamelCase__="<s>"						,			UpperCamelCase__="</s>"						,			UpperCamelCase__="</s>"						,			UpperCamelCase__="<s>"						,			UpperCamelCase__="<unk>"						,			UpperCamelCase__="<pad>"						,			UpperCamelCase__="<mask>"						,			UpperCamelCase__=False						,			UpperCamelCase__=True						,			**UpperCamelCase__						,			)  -> Optional[int]:





           '''simple docstring'''
           super().__init__(
               snake_case__						,			snake_case__						,			tokenizer_file=snake_case__						,			errors=snake_case__						,			bos_token=snake_case__						,			eos_token=snake_case__						,			sep_token=snake_case__						,			cls_token=snake_case__						,			unk_token=snake_case__						,			pad_token=snake_case__						,			mask_token=snake_case__						,			add_prefix_space=snake_case__						,			trim_offsets=snake_case__						,			**snake_case__						,			)

           A_		   = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
           if pre_tok_state.get("""add_prefix_space"""						,			snake_case__    ) != add_prefix_space:
                  A_		   = getattr(snake_case__						,			pre_tok_state.pop("""type"""    )    )
                  A_		   = add_prefix_space
                  A_		   = pre_tok_class(**snake_case__    )

           A_		   = add_prefix_space

           A_		   = 'post_processor'
           A_		   = getattr(self.backend_tokenizer						,			snake_case__						,			snake_case__    )
           if tokenizer_component_instance:
                  A_		   = 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:
                         A_		   = tuple(state["""sep"""]    )
                  if "cls" in state:
                         A_		   = tuple(state["""cls"""]    )

                  A_		   = False

                  if state.get("""add_prefix_space"""						,			snake_case__    ) != add_prefix_space:
                         A_		   = add_prefix_space
                         A_		   = True

                  if state.get("""trim_offsets"""						,			snake_case__    ) != trim_offsets:
                         A_		   = trim_offsets
                         A_		   = True

                  if changes_to_apply:
                         A_		   = getattr(snake_case__						,			state.pop("""type"""    )    )
                         A_		   = component_class(**snake_case__    )
                         setattr(self.backend_tokenizer						,			snake_case__						,			snake_case__    )



    @property
    def    snake_case_     (   self    )  -> Dict:





           '''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    snake_case_     (   self						,			UpperCamelCase__    )  -> Any:





           '''simple docstring'''
           A_		   = AddedToken(snake_case__						,			lstrip=snake_case__						,			rstrip=snake_case__    ) if isinstance(snake_case__						,			snake_case__    ) else value
           A_		   = value



    def    snake_case_     (   self						,			*UpperCamelCase__						,			**UpperCamelCase__    )  -> List[Any]:





           '''simple docstring'''
           A_		   = kwargs.get("""is_split_into_words"""						,			snake_case__    )
           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(*snake_case__						,			**snake_case__    )



    def    snake_case_     (   self						,			*UpperCamelCase__						,			**UpperCamelCase__    )  -> Tuple:





           '''simple docstring'''
           A_		   = kwargs.get("""is_split_into_words"""						,			snake_case__    )

           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(*snake_case__						,			**snake_case__    )



    def    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = None    )  -> Dict:





           '''simple docstring'''
           A_		   = self._tokenizer.model.save(snake_case__						,			name=snake_case__    )
           return tuple(snake_case__    )



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





           '''simple docstring'''
           A_		   = [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    snake_case_     (   self						,			UpperCamelCase__						,			UpperCamelCase__ = None    )  -> Tuple:





           '''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 + sep + token_ids_a + sep    ) * [0]



'''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__)
