


'''simple docstring'''



import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


A__		: Optional[int]												=  logging.get_logger(__name__)

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

A__		: Any												=  {
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

A__		: int												=  {
    """microsoft/deberta-base""": 512,
    """microsoft/deberta-large""": 512,
    """microsoft/deberta-xlarge""": 512,
    """microsoft/deberta-base-mnli""": 512,
    """microsoft/deberta-large-mnli""": 512,
    """microsoft/deberta-xlarge-mnli""": 512,
}

A__		: Tuple												=  {
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}





class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    lowerCamelCase  : int												=      VOCAB_FILES_NAMES
    lowerCamelCase  : Union[str, Any]												=      PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase  : int												=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase  : Union[str, Any]												=      ['input_ids', 'attention_mask', 'token_type_ids']
    lowerCamelCase  : Union[str, Any]												=      DebertaTokenizer







    def __init__(							self      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_="replace"      ,					SCREAMING_SNAKE_CASE_="[CLS]"      ,					SCREAMING_SNAKE_CASE_="[SEP]"      ,					SCREAMING_SNAKE_CASE_="[SEP]"      ,					SCREAMING_SNAKE_CASE_="[CLS]"      ,					SCREAMING_SNAKE_CASE_="[UNK]"      ,					SCREAMING_SNAKE_CASE_="[PAD]"      ,					SCREAMING_SNAKE_CASE_="[MASK]"      ,					SCREAMING_SNAKE_CASE_=False      ,					**SCREAMING_SNAKE_CASE_      ,					)    ->					Union[str, Any]:
         super().__init__(
             SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					tokenizer_file=SCREAMING_SNAKE_CASE_      ,					errors=SCREAMING_SNAKE_CASE_      ,					bos_token=SCREAMING_SNAKE_CASE_      ,					eos_token=SCREAMING_SNAKE_CASE_      ,					unk_token=SCREAMING_SNAKE_CASE_      ,					sep_token=SCREAMING_SNAKE_CASE_      ,					cls_token=SCREAMING_SNAKE_CASE_      ,					pad_token=SCREAMING_SNAKE_CASE_      ,					mask_token=SCREAMING_SNAKE_CASE_      ,					add_prefix_space=SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_      ,					)
         __lowerCamelCase       :     Optional[int]        =		kwargs.pop('add_bos_token'      ,					SCREAMING_SNAKE_CASE_     )

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

         __lowerCamelCase       :     str        =		add_prefix_space







    @property
    def 				lowercase_    (							self     )    ->					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 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_     )    ->					Optional[int]:
         __lowerCamelCase       :     List[str]        =		AddedToken(SCREAMING_SNAKE_CASE_      ,					lstrip=SCREAMING_SNAKE_CASE_      ,					rstrip=SCREAMING_SNAKE_CASE_     ) if isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ) else value
         __lowerCamelCase       :     int        =		value







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = None     )    ->					List[int]:
         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         __lowerCamelCase       :     str        =		[self.cls_token_id]
         __lowerCamelCase       :     Any        =		[self.sep_token_id]
         return cls + token_ids_a + sep + token_ids_a + sep







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = None     )    ->					List[int]:
         __lowerCamelCase       :     Any        =		[self.sep_token_id]
         __lowerCamelCase       :     Tuple        =		[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      ,					*SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_     )    ->					BatchEncoding:
         __lowerCamelCase       :     Any        =		kwargs.get('is_split_into_words'      ,					SCREAMING_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(*SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_     )







    def 				lowercase_    (							self      ,					*SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_     )    ->					BatchEncoding:
         __lowerCamelCase       :     List[str]        =		kwargs.get('is_split_into_words'      ,					SCREAMING_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(*SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_     )







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_ = None     )    ->					Tuple[str]:
         __lowerCamelCase       :     Optional[int]        =		self._tokenizer.model.save(SCREAMING_SNAKE_CASE_      ,					name=SCREAMING_SNAKE_CASE_     )
         return tuple(SCREAMING_SNAKE_CASE_     )







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_     )    ->					List[int]:
         __lowerCamelCase       :     str        =		[]
         for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_      ,					add_special_tokens=SCREAMING_SNAKE_CASE_     ) + [self.eos_token_id]     )

         if len(SCREAMING_SNAKE_CASE_     ) > self.model_max_length:
              __lowerCamelCase       :     Optional[int]        =		input_ids[-self.model_max_length :]
         return input_ids










'''simple docstring'''



from typing import TYPE_CHECKING

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


A__		: str												=  {"""configuration_hubert""": ["""HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """HubertConfig"""]}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		: List[str]												=  [
          """HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """HubertForCTC""",
          """HubertForSequenceClassification""",
          """HubertModel""",
          """HubertPreTrainedModel""",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__		: Optional[int]												=  [
          """TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFHubertForCTC""",
          """TFHubertModel""",
          """TFHubertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_hubert import (
                HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                HubertForCTC,
                HubertForSequenceClassification,
                HubertModel,
                HubertPreTrainedModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_hubert import (
                TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFHubertForCTC,
                TFHubertModel,
                TFHubertPreTrainedModel,
            )


else:
      import sys

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






