




'''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


lowerCamelCase_			=						logging.get_logger(__name__)

lowerCamelCase_			=						{
    '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 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''deberta-v2'''



   def __init__(   self :     Optional[int]  ,				__lowerCamelCase :     Tuple=1_2_8_1_0_0  ,				__lowerCamelCase :     List[str]=1_5_3_6  ,				__lowerCamelCase :     List[str]=2_4  ,				__lowerCamelCase :     Tuple=2_4  ,				__lowerCamelCase :     Tuple=6_1_4_4  ,				__lowerCamelCase :     Optional[Any]="gelu"  ,				__lowerCamelCase :     Optional[int]=0.1  ,				__lowerCamelCase :     Union[str, Any]=0.1  ,				__lowerCamelCase :     Tuple=5_1_2  ,				__lowerCamelCase :     Dict=0  ,				__lowerCamelCase :     Optional[Any]=0.0_2  ,				__lowerCamelCase :     int=1e-7  ,				__lowerCamelCase :     Dict=False  ,				__lowerCamelCase :     int=-1  ,				__lowerCamelCase :     Dict=0  ,				__lowerCamelCase :     int=True  ,				__lowerCamelCase :     List[Any]=None  ,				__lowerCamelCase :     Optional[Any]=0  ,				__lowerCamelCase :     List[str]="gelu"  ,				**__lowerCamelCase :     Union[str, Any]  ,				):

      """simple docstring"""
      super().__init__(**_lowerCAmelCase   )

      _SCREAMING_SNAKE_CASE	=				hidden_size
      _SCREAMING_SNAKE_CASE	=				num_hidden_layers
      _SCREAMING_SNAKE_CASE	=				num_attention_heads
      _SCREAMING_SNAKE_CASE	=				intermediate_size
      _SCREAMING_SNAKE_CASE	=				hidden_act
      _SCREAMING_SNAKE_CASE	=				hidden_dropout_prob
      _SCREAMING_SNAKE_CASE	=				attention_probs_dropout_prob
      _SCREAMING_SNAKE_CASE	=				max_position_embeddings
      _SCREAMING_SNAKE_CASE	=				type_vocab_size
      _SCREAMING_SNAKE_CASE	=				initializer_range
      _SCREAMING_SNAKE_CASE	=				relative_attention
      _SCREAMING_SNAKE_CASE	=				max_relative_positions
      _SCREAMING_SNAKE_CASE	=				pad_token_id
      _SCREAMING_SNAKE_CASE	=				position_biased_input

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

      _SCREAMING_SNAKE_CASE	=				pos_att_type
      _SCREAMING_SNAKE_CASE	=				vocab_size
      _SCREAMING_SNAKE_CASE	=				layer_norm_eps

      _SCREAMING_SNAKE_CASE	=				kwargs.get("pooler_hidden_size"  ,				_lowerCAmelCase   )
      _SCREAMING_SNAKE_CASE	=				pooler_dropout
      _SCREAMING_SNAKE_CASE	=				pooler_hidden_act







class 		lowercase_   (			A       ):


   """simple docstring"""

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

      """simple docstring"""

      if self.task == "multiple-choice":
         _SCREAMING_SNAKE_CASE	=				{0: "batch", 1: "choice", 2: "sequence"}
      else:
         _SCREAMING_SNAKE_CASE	=				{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 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      return 1_2



   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]  ,				__lowerCamelCase :     int = -1  ,				__lowerCamelCase :     int = -1  ,				__lowerCamelCase :     int = -1  ,				__lowerCamelCase :     bool = False  ,				__lowerCamelCase :     Optional["TensorType"] = None  ,				__lowerCamelCase :     int = 3  ,				__lowerCamelCase :     int = 4_0  ,				__lowerCamelCase :     int = 4_0  ,				__lowerCamelCase :     "PreTrainedTokenizerBase" = None  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				super().generate_dummy_inputs(preprocessor=_lowerCAmelCase  ,				framework=_lowerCAmelCase   )
      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 __future__ import annotations
def 			SCREAMING_SNAKE_CASE_			(							__A    :			float	,					__A    :			float	,					__A    :			float	,					)			->			tuple:
   if (electron_conc, hole_conc, intrinsic_conc).count(0  ) != 1:
      raise ValueError("You cannot supply more or less than 2 values"  )
   elif electron_conc < 0:
      raise ValueError("Electron concentration cannot be negative in a semiconductor"  )
   elif hole_conc < 0:
      raise ValueError("Hole concentration cannot be negative in a semiconductor"  )
   elif intrinsic_conc < 0:
      raise ValueError(
          "Intrinsic concentration cannot be negative in a semiconductor"  )
   elif electron_conc == 0:
      return (
          "electron_conc",
          intrinsic_conc**2 / hole_conc,
      )
   elif hole_conc == 0:
      return (
          "hole_conc",
          intrinsic_conc**2 / electron_conc,
      )
   elif intrinsic_conc == 0:
      return (
          "intrinsic_conc",
          (electron_conc * hole_conc) ** 0.5,
      )
   else:
      return (-1, -1)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

