





"""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  _SCREAMING_SNAKE_CASE(       A  ):
       SCREAMING_SNAKE_CASE_       :				Tuple     						=  '''deberta-v2'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=12_81_00		,SCREAMING_SNAKE_CASE__=15_36		,SCREAMING_SNAKE_CASE__=24		,SCREAMING_SNAKE_CASE__=24		,SCREAMING_SNAKE_CASE__=61_44		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=5_12		,SCREAMING_SNAKE_CASE__=0		,SCREAMING_SNAKE_CASE__=0.0_2		,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__		,)      ->				Union[str, Any]:





             """simple docstring"""






             super().__init__(**SCREAMING_SNAKE_CASE__							)

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

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

             __SCREAMING_SNAKE_CASE       :List[Any]		    =  pos_att_type
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  vocab_size
             __SCREAMING_SNAKE_CASE       :Union[str, Any]		    =  layer_norm_eps

             __SCREAMING_SNAKE_CASE       :List[str]		    =  kwargs.get('''pooler_hidden_size'''		,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE       :str		    =  pooler_dropout
             __SCREAMING_SNAKE_CASE       :Optional[int]		    =  pooler_hidden_act






class  _SCREAMING_SNAKE_CASE(       A  ):




       @property
       def    _UpperCamelCase					(						self							)      ->				Mapping[str, Mapping[int, str]]:





             """simple docstring"""






             if self.task == "multiple-choice":
                   __SCREAMING_SNAKE_CASE       :Dict		    =  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
             else:
                   __SCREAMING_SNAKE_CASE       :Union[str, 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    _UpperCamelCase					(						self							)      ->				int:





             """simple docstring"""






             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		,)      ->				Mapping[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       :List[Any]		    =  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 __future__ import annotations

import math



def 					__lowerCamelCase       (							a_					:		int			,       a_					:		int			,       a_					:		bool			,       a_					:		list[int]			,       a_					:		float							)      ->				int:

      if depth < 0:
            raise ValueError('''Depth cannot be less than 0'''							)

      if len(a_							) == 0:
            raise ValueError('''Scores cannot be empty'''							)

      if depth == height:
            return scores[node_index]

      if is_max:
            return max(
                minimax(depth + 1			,       node_index * 2			,       a_			,       a_			,       a_							)			,       minimax(depth + 1			,       node_index * 2 + 1			,       a_			,       a_			,       a_							)			,       )

      return min(
          minimax(depth + 1			,       node_index * 2			,       a_			,       a_			,       a_							)			,       minimax(depth + 1			,       node_index * 2 + 1			,       a_			,       a_			,       a_							)			,       )



def 					__lowerCamelCase       (							)      ->				None:
      __SCREAMING_SNAKE_CASE       :Dict		    =  [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
      __SCREAMING_SNAKE_CASE       :Optional[int]		    =  math.log(len(a_							)			,       2							)
      print('''Optimal value : '''			,       end=''''''							)
      print(minimax(0			,       0			,       a_			,       a_			,       a_							)							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()