


'''simple docstring'''


import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							["""image_processor""", """tokenizer"""]
  a_  					=							"""ViltImageProcessor"""
  a_  					=							("""BertTokenizer""", """BertTokenizerFast""")






  def __init__(							self :       List[Any]	,_a :       Optional[Any]=None	,_a :       List[str]=None	,**_a :       Any ):

        '''simple docstring'''




        A_						:  Any          =				None
        if "feature_extractor" in kwargs:
              warnings.warn(
                  """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                  """ instead."""	,_a	,)
              A_						:  List[str]          =				kwargs.pop("""feature_extractor""" )

        A_						:  List[Any]          =				image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
              raise ValueError("""You need to specify an `image_processor`.""" )
        if tokenizer is None:
              raise ValueError("""You need to specify a `tokenizer`.""" )

        super().__init__(_a	,_a )
        A_						:  Optional[Any]          =				self.image_processor






  def __call__(							self :       Any	,_a :       Tuple	,_a :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None	,_a :       bool = True	,_a :       Union[bool, str, PaddingStrategy] = False	,_a :       Union[bool, str, TruncationStrategy] = None	,_a :       Optional[int] = None	,_a :       int = 0	,_a :       Optional[int] = None	,_a :       Optional[bool] = None	,_a :       Optional[bool] = None	,_a :       bool = False	,_a :       bool = False	,_a :       bool = False	,_a :       bool = False	,_a :       bool = True	,_a :       Optional[Union[str, TensorType]] = None	,**_a :       Tuple	,):

        '''simple docstring'''




        A_						:  int          =				self.tokenizer(
            text=_a	,add_special_tokens=_a	,padding=_a	,truncation=_a	,max_length=_a	,stride=_a	,pad_to_multiple_of=_a	,return_token_type_ids=_a	,return_attention_mask=_a	,return_overflowing_tokens=_a	,return_special_tokens_mask=_a	,return_offsets_mapping=_a	,return_length=_a	,verbose=_a	,return_tensors=_a	,**_a	,)
        # add pixel_values + pixel_mask
        A_						:  Optional[int]          =				self.image_processor(_a	,return_tensors=_a )
        encoding.update(_a )

        return encoding






  def        _a							(							self :       List[Any]	,*_a :       Any	,**_a :       Any ):

        '''simple docstring'''




        return self.tokenizer.batch_decode(*_a	,**_a )






  def        _a							(							self :       int	,*_a :       int	,**_a :       Optional[int] ):

        '''simple docstring'''




        return self.tokenizer.decode(*_a	,**_a )






  @property
  def        _a							(							self :       List[Any] ):

        '''simple docstring'''




        A_						:  Optional[int]          =				self.tokenizer.model_input_names
        A_						:  str          =				self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )






  @property
  def        _a							(							self :       str ):

        '''simple docstring'''




        warnings.warn(
            """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""	,_a	,)
        return self.image_processor_class






  @property
  def        _a							(							self :       int ):

        '''simple docstring'''




        warnings.warn(
            """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""	,_a	,)
        return self.image_processor




'''simple docstring'''


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
     from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__magic_name__			=      logging.get_logger(__name__)

__magic_name__			=      {
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""deberta-v2"""






  def __init__(							self :       Optional[Any]	,_a :       Union[str, Any]=128100	,_a :       Optional[int]=1536	,_a :       Dict=24	,_a :       int=24	,_a :       Tuple=6144	,_a :       Union[str, Any]="gelu"	,_a :       List[Any]=0.1	,_a :       Dict=0.1	,_a :       int=512	,_a :       int=0	,_a :       int=0.02	,_a :       int=1e-7	,_a :       List[str]=False	,_a :       Union[str, Any]=-1	,_a :       List[Any]=0	,_a :       Optional[Any]=True	,_a :       Tuple=None	,_a :       Any=0	,_a :       int="gelu"	,**_a :       Any	,):

        '''simple docstring'''




        super().__init__(**_a )

        A_						:  Union[str, Any]          =				hidden_size
        A_						:  Dict          =				num_hidden_layers
        A_						:  Union[str, Any]          =				num_attention_heads
        A_						:  List[Any]          =				intermediate_size
        A_						:  List[Any]          =				hidden_act
        A_						:  Optional[int]          =				hidden_dropout_prob
        A_						:  Dict          =				attention_probs_dropout_prob
        A_						:  int          =				max_position_embeddings
        A_						:  Any          =				type_vocab_size
        A_						:  List[Any]          =				initializer_range
        A_						:  int          =				relative_attention
        A_						:  Tuple          =				max_relative_positions
        A_						:  int          =				pad_token_id
        A_						:  Tuple          =				position_biased_input

        # Backwards compatibility
        if type(_a ) == str:
              A_						:  str          =				[x.strip() for x in pos_att_type.lower().split("""|""" )]

        A_						:  Any          =				pos_att_type
        A_						:  Optional[int]          =				vocab_size
        A_						:  Tuple          =				layer_norm_eps

        A_						:  Any          =				kwargs.get("""pooler_hidden_size"""	,_a )
        A_						:  Union[str, Any]          =				pooler_dropout
        A_						:  List[Any]          =				pooler_hidden_act







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  @property
  def        _a							(							self :       Any ):

        '''simple docstring'''




        if self.task == "multiple-choice":
              A_						:  Any          =				{0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
              A_						:  Any          =				{0: """batch""", 1: """sequence"""}
        if self._config.type_vocab_size > 0:
              return OrderedDict(
                  [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)] )
        else:
              return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)] )






  @property
  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        return 12






  def        _a							(							self :       int	,_a :       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]	,_a :       int = -1	,_a :       int = -1	,_a :       int = -1	,_a :       bool = False	,_a :       Optional["TensorType"] = None	,_a :       int = 3	,_a :       int = 40	,_a :       int = 40	,_a :       "PreTrainedTokenizerBase" = None	,):

        '''simple docstring'''




        A_						:  Any          =				super().generate_dummy_inputs(preprocessor=_a	,framework=_a )
        if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
              del dummy_inputs["token_type_ids"]
        return dummy_inputs
