

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__	:   Any								=     {"configuration_visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__	:   Any								=     [
           "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "VisualBertForMultipleChoice",
           "VisualBertForPreTraining",
           "VisualBertForQuestionAnswering",
           "VisualBertForRegionToPhraseAlignment",
           "VisualBertForVisualReasoning",
           "VisualBertLayer",
           "VisualBertModel",
           "VisualBertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_visual_bert import (
                  VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VisualBertForMultipleChoice,
                  VisualBertForPreTraining,
                  VisualBertForQuestionAnswering,
                  VisualBertForRegionToPhraseAlignment,
                  VisualBertForVisualReasoning,
                  VisualBertLayer,
                  VisualBertModel,
                  VisualBertPreTrainedModel,
              )


else:
       import sys

       UpperCamelCase__	:   Union[str, Any]								=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)







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


UpperCamelCase__			=    logging.get_logger(__name__)

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

UpperCamelCase__			=    {
    "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"
        ),
    },
}

UpperCamelCase__			=    {
    "microsoft/deberta-base": 5_12,
    "microsoft/deberta-large": 5_12,
    "microsoft/deberta-xlarge": 5_12,
    "microsoft/deberta-base-mnli": 5_12,
    "microsoft/deberta-large-mnli": 5_12,
    "microsoft/deberta-xlarge-mnli": 5_12,
}

UpperCamelCase__			=    {
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}
class       _lowerCAmelCase (			_UpperCAmelCase      ):






			"""simple docstring"""





			lowercase__   :	Dict  			=      VOCAB_FILES_NAMES
			lowercase__   :	Dict  			=      PRETRAINED_VOCAB_FILES_MAP
			lowercase__   :	str  			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase__   :	Tuple  			=      ["""input_ids""", """attention_mask""", """token_type_ids"""]
			lowercase__   :	List[Any]  			=      DebertaTokenizer





			def __init__(					self						:							Dict      ,  lowercase						:							Union[str, Any]=None      ,  lowercase						:							Union[str, Any]=None      ,  lowercase						:							Any=None      ,  lowercase						:							Optional[Any]="replace"      ,  lowercase						:							Optional[Any]="[CLS]"      ,  lowercase						:							List[Any]="[SEP]"      ,  lowercase						:							int="[SEP]"      ,  lowercase						:							str="[CLS]"      ,  lowercase						:							Any="[UNK]"      ,  lowercase						:							List[Any]="[PAD]"      ,  lowercase						:							List[str]="[MASK]"      ,  lowercase						:							Tuple=False      ,  **lowercase						:							Tuple      ,  )    ->       List[Any]:
									"""simple docstring"""







									super().__init__(
									    lowercase      ,  lowercase      ,  tokenizer_file=lowercase      ,  errors=lowercase      ,  bos_token=lowercase      ,  eos_token=lowercase      ,  unk_token=lowercase      ,  sep_token=lowercase      ,  cls_token=lowercase      ,  pad_token=lowercase      ,  mask_token=lowercase      ,  add_prefix_space=lowercase      ,  **lowercase      ,  )
									__lowercase											=					kwargs.pop("""add_bos_token"""      ,  lowercase )

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

									__lowercase											=					add_prefix_space





			@property
			def 		snake_case__      (					self						:							Tuple )    ->       str:
									"""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						:							List[Any]      ,  lowercase						:							Tuple )    ->       Optional[int]:
									"""simple docstring"""







									__lowercase											=					AddedToken(lowercase      ,  lstrip=lowercase      ,  rstrip=lowercase ) if isinstance(lowercase      ,  lowercase ) else value
									__lowercase											=					value





			def 		snake_case__      (					self						:							List[str]      ,  lowercase						:							List[int]      ,  lowercase						:							Optional[List[int]] = None )    ->       List[int]:
									"""simple docstring"""







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





			def 		snake_case__      (					self						:							Dict      ,  lowercase						:							List[int]      ,  lowercase						:							Optional[List[int]] = None )    ->       List[int]:
									"""simple docstring"""







									__lowercase											=					[self.sep_token_id]
									__lowercase											=					[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						:							Optional[int]      ,  *lowercase						:							List[str]      ,  **lowercase						:							Tuple )    ->       BatchEncoding:
									"""simple docstring"""







									__lowercase											=					kwargs.get("""is_split_into_words"""      ,  lowercase )
									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(*lowercase      ,  **lowercase )





			def 		snake_case__      (					self						:							List[Any]      ,  *lowercase						:							str      ,  **lowercase						:							List[Any] )    ->       BatchEncoding:
									"""simple docstring"""







									__lowercase											=					kwargs.get("""is_split_into_words"""      ,  lowercase )

									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(*lowercase      ,  **lowercase )





			def 		snake_case__      (					self						:							Union[str, Any]      ,  lowercase						:							str      ,  lowercase						:							Optional[str] = None )    ->       Tuple[str]:
									"""simple docstring"""







									__lowercase											=					self._tokenizer.model.save(lowercase      ,  name=lowercase )
									return tuple(lowercase )







			def 		snake_case__      (					self						:							List[str]      ,  lowercase						:							"Conversation" )    ->       List[int]:
									"""simple docstring"""







									__lowercase											=					[]
									for is_user, text in conversation.iter_texts():
															input_ids.extend(self.encode(lowercase      ,  add_special_tokens=lowercase ) + [self.eos_token_id] )

									if len(lowercase ) > self.model_max_length:
															__lowercase											=					input_ids[-self.model_max_length :]
									return input_ids





