


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


UpperCamelCase					:       Any			    =     logging.get_logger(__name__)

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

UpperCamelCase					:       Union[str, Any]			    =     {
    """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"""
        ),
    },
}

UpperCamelCase					:       List[str]			    =     {
    """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__     ( A__  ):



  """simple docstring"""



  _lowercase									=		VOCAB_FILES_NAMES
  _lowercase									=		PRETRAINED_VOCAB_FILES_MAP
  _lowercase									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase									=		['input_ids', 'attention_mask']
  _lowercase									=		RobertaTokenizer

  def __init__( self			:  List[str]				,				lowerCamelCase__			:  Any=None				,				lowerCamelCase__			:  List[Any]=None				,				lowerCamelCase__			:  Dict=None				,				lowerCamelCase__			:  List[str]="replace"				,				lowerCamelCase__			:  List[str]="<s>"				,				lowerCamelCase__			:  Union[str, Any]="</s>"				,				lowerCamelCase__			:  Any="</s>"				,				lowerCamelCase__			:  Any="<s>"				,				lowerCamelCase__			:  int="<unk>"				,				lowerCamelCase__			:  Any="<pad>"				,				lowerCamelCase__			:  Tuple="<mask>"				,				lowerCamelCase__			:  Any=False				,				lowerCamelCase__			:  Dict=True				,				**lowerCamelCase__			:  Optional[Any]				,				):
        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__				,				)

        a__	:  List[Any]     =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
        if pre_tok_state.get("add_prefix_space"				,				lowerCamelCase__       ) != add_prefix_space:
              a__	:  Any     =   getattr(lowerCamelCase__				,				pre_tok_state.pop("type"       )       )
              a__	:  int     =   add_prefix_space
              a__	:  Tuple     =   pre_tok_class(**lowerCamelCase__       )

        a__	:  str     =   add_prefix_space

        a__	:  Tuple     =   "post_processor"
        a__	:  Dict     =   getattr(self.backend_tokenizer				,				lowerCamelCase__				,				lowerCamelCase__       )
        if tokenizer_component_instance:
              a__	:  Tuple     =   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     =   tuple(state["sep"]       )
              if "cls" in state:
                    a__	:  str     =   tuple(state["cls"]       )

              a__	:  str     =   False

              if state.get("add_prefix_space"				,				lowerCamelCase__       ) != add_prefix_space:
                    a__	:  str     =   add_prefix_space
                    a__	:  Any     =   True

              if state.get("trim_offsets"				,				lowerCamelCase__       ) != trim_offsets:
                    a__	:  int     =   trim_offsets
                    a__	:  Dict     =   True

              if changes_to_apply:
                    a__	:  Union[str, Any]     =   getattr(lowerCamelCase__				,				state.pop("type"       )       )
                    a__	:  str     =   component_class(**lowerCamelCase__       )
                    setattr(self.backend_tokenizer				,				lowerCamelCase__				,				lowerCamelCase__       )

  @property
  def    _UpperCamelCase( self			:  Union[str, Any]       ):
        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    _UpperCamelCase( self			:  List[Any]				,				lowerCamelCase__			:  Tuple       ):
        a__	:  List[Any]     =   AddedToken(lowerCamelCase__				,				lstrip=lowerCamelCase__				,				rstrip=lowerCamelCase__       ) if isinstance(lowerCamelCase__				,				lowerCamelCase__       ) else value
        a__	:  List[str]     =   value

  def    _UpperCamelCase( self			:  Union[str, Any]				,				*lowerCamelCase__			:  int				,				**lowerCamelCase__			:  int       ):
        a__	:  Optional[int]     =   kwargs.get("is_split_into_words"				,				lowerCamelCase__       )
        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(*lowerCamelCase__				,				**lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Tuple				,				*lowerCamelCase__			:  Dict				,				**lowerCamelCase__			:  List[str]       ):
        a__	:  Dict     =   kwargs.get("is_split_into_words"				,				lowerCamelCase__       )

        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(*lowerCamelCase__				,				**lowerCamelCase__       )

  def    _UpperCamelCase( self			:  str				,				lowerCamelCase__			:  str				,				lowerCamelCase__			:  Optional[str] = None       ):
        a__	:  int     =   self._tokenizer.model.save(lowerCamelCase__				,				name=lowerCamelCase__       )
        return tuple(lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Optional[int]				,				lowerCamelCase__			:  Dict				,				lowerCamelCase__			:  Optional[int]=None       ):
        a__	:  Union[str, Any]     =   [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    _UpperCamelCase( self			:  Dict				,				lowerCamelCase__			:  List[int]				,				lowerCamelCase__			:  Optional[List[int]] = None       ):
        a__	:  Tuple     =   [self.sep_token_id]
        a__	:  int     =   [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_torch_available


UpperCAmelCase_			:  Optional[Any]										=   {
    'configuration_transfo_xl': ['TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TransfoXLConfig'],
    'tokenization_transfo_xl': ['TransfoXLCorpus', 'TransfoXLTokenizer'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Any										=   [
     'TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'AdaptiveEmbedding',
     'TransfoXLForSequenceClassification',
     'TransfoXLLMHeadModel',
     'TransfoXLModel',
     'TransfoXLPreTrainedModel',
     'load_tf_weights_in_transfo_xl',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase_			:  Optional[Any]										=   [
     'TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFAdaptiveEmbedding',
     'TFTransfoXLForSequenceClassification',
     'TFTransfoXLLMHeadModel',
     'TFTransfoXLMainLayer',
     'TFTransfoXLModel',
     'TFTransfoXLPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
 from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_transfo_xl import (
      TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      AdaptiveEmbedding,
      TransfoXLForSequenceClassification,
      TransfoXLLMHeadModel,
      TransfoXLModel,
      TransfoXLPreTrainedModel,
      load_tf_weights_in_transfo_xl,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_transfo_xl import (
      TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFAdaptiveEmbedding,
      TFTransfoXLForSequenceClassification,
      TFTransfoXLLMHeadModel,
      TFTransfoXLMainLayer,
      TFTransfoXLModel,
      TFTransfoXLPreTrainedModel,
  )

else:
 import sys

 UpperCAmelCase_			:  Optional[int]										=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






