



"""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


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

lowercase__		:    str				   =     {
    """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 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					"""deberta-v2"""



      def __init__(				self    :				Optional[Any]   ,      SCREAMING_SNAKE_CASE_    :				Any=1_2_8_1_0_0   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=1_5_3_6   ,      SCREAMING_SNAKE_CASE_    :				str=2_4   ,      SCREAMING_SNAKE_CASE_    :				int=2_4   ,      SCREAMING_SNAKE_CASE_    :				int=6_1_4_4   ,      SCREAMING_SNAKE_CASE_    :				int="gelu"   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=0.1   ,      SCREAMING_SNAKE_CASE_    :				Any=0.1   ,      SCREAMING_SNAKE_CASE_    :				str=5_1_2   ,      SCREAMING_SNAKE_CASE_    :				str=0   ,      SCREAMING_SNAKE_CASE_    :				Dict=0.02   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=1E-7   ,      SCREAMING_SNAKE_CASE_    :				str=False   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=-1   ,      SCREAMING_SNAKE_CASE_    :				int=0   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=True   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]=None   ,      SCREAMING_SNAKE_CASE_    :				Any=0   ,      SCREAMING_SNAKE_CASE_    :				List[str]="gelu"   ,      **SCREAMING_SNAKE_CASE_    :				Any   ,      ):
        super().__init__(**SCREAMING_SNAKE_CASE_	)

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

        # Backwards compatibility
        if type(SCREAMING_SNAKE_CASE_	) == str:
          lowerCAmelCase_       :    List[Any]								=			[x.strip() for x in pos_att_type.lower().split('|'	)]

        lowerCAmelCase_       :    Tuple								=			pos_att_type
        lowerCAmelCase_       :    List[Any]								=			vocab_size
        lowerCAmelCase_       :    Optional[int]								=			layer_norm_eps

        lowerCAmelCase_       :    List[Any]								=			kwargs.get('pooler_hidden_size'   ,      SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Dict								=			pooler_dropout
        lowerCAmelCase_       :    int								=			pooler_hidden_act







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple	):
        if self.task == "multiple-choice":
          lowerCAmelCase_       :    int								=			{0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
          lowerCAmelCase_       :    Dict								=			{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 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        return 1_2





      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]   ,      SCREAMING_SNAKE_CASE_    :				int = -1   ,      SCREAMING_SNAKE_CASE_    :				int = -1   ,      SCREAMING_SNAKE_CASE_    :				int = -1   ,      SCREAMING_SNAKE_CASE_    :				bool = False   ,      SCREAMING_SNAKE_CASE_    :				Optional["TensorType"] = None   ,      SCREAMING_SNAKE_CASE_    :				int = 3   ,      SCREAMING_SNAKE_CASE_    :				int = 4_0   ,      SCREAMING_SNAKE_CASE_    :				int = 4_0   ,      SCREAMING_SNAKE_CASE_    :				"PreTrainedTokenizerBase" = None   ,      ):
        lowerCAmelCase_       :    Optional[int]								=			super().generate_dummy_inputs(preprocessor=SCREAMING_SNAKE_CASE_   ,      framework=SCREAMING_SNAKE_CASE_	)
        if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
          del dummy_inputs["token_type_ids"]
        return dummy_inputs










"""simple docstring"""







from collections.abc import Iterable
from typing import Generic, TypeVar

lowercase__		:    Any				   =     TypeVar("""_T""")







class 				UpperCamelCase__       (     Generic[_T]  ):




      """simple docstring"""




      def __init__(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Iterable[_T] | None = None	):
        lowerCAmelCase_       :    list[_T]								=			list(iterable or []	)
        lowerCAmelCase_       :    list[_T]								=			[]



      def __len__(				self    :				List[str]	):
        return len(self._stacka	) + len(self._stacka	)



      def __repr__(				self    :				int	):
        return F"Queue({tuple(self._stacka[::-1] + self._stacka	)})"



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Tuple   ,      SCREAMING_SNAKE_CASE_    :				_T	):
        self._stacka.append(SCREAMING_SNAKE_CASE_	)





      def 					SCREAMING_SNAKE_CASE__			(				self    :				str	):
        lowerCAmelCase_       :    List[str]								=			self._stacka.pop
        lowerCAmelCase_       :    int								=			self._stacka.append

        if not self._stacka:
          while self._stacka:
            stacka_append(stacka_pop()	)

        if not self._stacka:
          raise IndexError('Queue is empty'	)
        return self._stacka.pop()


if __name__ == "__main__":
     from doctest import testmod

     testmod()





