






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


__snake_case												=  logging.get_logger(__name__)

__snake_case												=  {
    """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"""
      UpperCamelCase_				:					str           ='deberta-v2'
      def __init__(				self    ,				SCREAMING_SNAKE_CASE_=12_8100    ,				SCREAMING_SNAKE_CASE_=1536    ,				SCREAMING_SNAKE_CASE_=24    ,				SCREAMING_SNAKE_CASE_=24    ,				SCREAMING_SNAKE_CASE_=6144    ,				SCREAMING_SNAKE_CASE_="gelu"    ,				SCREAMING_SNAKE_CASE_=0.1    ,				SCREAMING_SNAKE_CASE_=0.1    ,				SCREAMING_SNAKE_CASE_=512    ,				SCREAMING_SNAKE_CASE_=0    ,				SCREAMING_SNAKE_CASE_=0.02    ,				SCREAMING_SNAKE_CASE_=1e-7    ,				SCREAMING_SNAKE_CASE_=False    ,				SCREAMING_SNAKE_CASE_=-1    ,				SCREAMING_SNAKE_CASE_=0    ,				SCREAMING_SNAKE_CASE_=True    ,				SCREAMING_SNAKE_CASE_=None    ,				SCREAMING_SNAKE_CASE_=0    ,				SCREAMING_SNAKE_CASE_="gelu"    ,				**SCREAMING_SNAKE_CASE_    ,				)				->      Optional[int]:
          super().__init__(**SCREAMING_SNAKE_CASE_ )

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

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

          UpperCamelCase       :int								=   pos_att_type
          UpperCamelCase       :Optional[int]								=   vocab_size
          UpperCamelCase       :str								=   layer_norm_eps

          UpperCamelCase       :str								=   kwargs.get('''pooler_hidden_size'''    ,				SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Union[str, Any]								=   pooler_dropout
          UpperCamelCase       :Any								=   pooler_hidden_act







class 			UpperCAmelCase_						(					lowercase_ ):



      """simple docstring"""
      @property
      def     UpperCAmelCase						(				self )				->      Any:
          if self.task == "multiple-choice":
              UpperCamelCase       :Optional[int]								=   {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
              UpperCamelCase       :Tuple								=   {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     UpperCAmelCase						(				self )				->      List[str]:
          return 12




      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = -1    ,				SCREAMING_SNAKE_CASE_ = -1    ,				SCREAMING_SNAKE_CASE_ = -1    ,				SCREAMING_SNAKE_CASE_ = False    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = 3    ,				SCREAMING_SNAKE_CASE_ = 40    ,				SCREAMING_SNAKE_CASE_ = 40    ,				SCREAMING_SNAKE_CASE_ = None    ,				)				->      Optional[Any]:
          UpperCamelCase       :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 dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate







@dataclass(frozen=lowercase_  )
class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     field(default="""summarization""",					metadata={"""include_in_asdict_even_if_is_default""": True}  )
      _SCREAMING_SNAKE_CASE  				=     Features({"""text""": Value("""string"""  )}  )
      _SCREAMING_SNAKE_CASE  				=     Features({"""summary""": Value("""string"""  )}  )
      _SCREAMING_SNAKE_CASE  				=     "text"
      _SCREAMING_SNAKE_CASE  				=     "summary"



      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict       ):
        return {self.text_column: "text", self.summary_column: "summary"}





