



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


UpperCAmelCase__   =       logging.get_logger(__name__)

UpperCAmelCase__   =       {
    """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    (			A						):
			UpperCamelCase   					=     "deberta-v2"



			def __init__(						self   :  Optional[int] ,		A   :  Dict=12_81_00 ,		A   :  List[Any]=15_36 ,		A   :  Dict=24 ,		A   :  Any=24 ,		A   :  Union[str, Any]=61_44 ,		A   :  str="gelu" ,		A   :  Dict=0.1 ,		A   :  List[str]=0.1 ,		A   :  List[Any]=5_12 ,		A   :  Optional[Any]=0 ,		A   :  Dict=0.0_2 ,		A   :  Union[str, Any]=1E-7 ,		A   :  List[Any]=False ,		A   :  List[Any]=-1 ,		A   :  Dict=0 ,		A   :  Dict=True ,		A   :  Tuple=None ,		A   :  str=0 ,		A   :  List[Any]="gelu" ,		**A   :  Union[str, Any] ,		)   ->    int:



						"""simple docstring"""






						super().__init__(**A)

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

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

						_UpperCAmelCase		       =   pos_att_type
						_UpperCAmelCase		       =   vocab_size
						_UpperCAmelCase		       =   layer_norm_eps

						_UpperCAmelCase		       =   kwargs.get('pooler_hidden_size' ,		A)
						_UpperCAmelCase		       =   pooler_dropout
						_UpperCAmelCase		       =   pooler_hidden_act







class 			__lowerCAmelCase    (			A						):



			@property
			def     _lowerCamelCase       (						self   :  Union[str, Any])   ->    Optional[int]:



						"""simple docstring"""






						if self.task == "multiple-choice":
									_UpperCAmelCase		       =   {0: """batch""", 1: """choice""", 2: """sequence"""}
						else:
									_UpperCAmelCase		       =   {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   :  List[str])   ->    str:



						"""simple docstring"""






						return 12



			def     _lowerCamelCase       (						self   :  str ,		A   :  Optional[int] ,		A   :  int = -1 ,		A   :  Optional[int] = -1 ,		A   :  List[str] = -1 ,		A   :  str = False ,		A   :  str = None ,		A   :  str = 3 ,		A   :  int = 40 ,		A   :  Optional[Any] = 40 ,		A   :  str = None ,		)   ->    List[Any]:



						"""simple docstring"""






						_UpperCAmelCase		       =   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


from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class __snake_case		:
    _a		:						int
    _a		:						TreeNode | None=				None
    _a		:						TreeNode | None=				None






lowercase  :					Dict						    =							namedtuple("""CoinsDistribResult""", """moves excess""")







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    int:

       if root is None:
              return 0

       # Validation
       def count_nodes(SCREAMING_SNAKE_CASE__				) -> int:
              if node is None:
                     return 0

              return count_nodes(node.left				) + count_nodes(node.right				) + 1

       def count_coins(SCREAMING_SNAKE_CASE__				) -> int:
              if node is None:
                     return 0

              return count_coins(node.left				) + count_coins(node.right				) + node.data

       if count_nodes(SCREAMING_SNAKE_CASE__				) != count_coins(SCREAMING_SNAKE_CASE__				):
              raise ValueError("""The nodes number should be same as the number of coins"""				)

       # Main calculation
       def get_distrib(SCREAMING_SNAKE_CASE__				) -> CoinsDistribResult:

              if node is None:
                     return CoinsDistribResult(0					,	1				)

              lowercase	,			lowercase							:     int       	=      get_distrib(node.left				)
              lowercase	,			lowercase							:     List[Any]       	=      get_distrib(node.right				)

              lowercase							:     Optional[Any]       	=      1 - left_distrib_excess
              lowercase							:     Union[str, Any]       	=      1 - right_distrib_excess

              lowercase							:     List[Any]       	=      (
                  left_distrib_moves
                  + right_distrib_moves
                  + abs(SCREAMING_SNAKE_CASE__				)
                  + abs(SCREAMING_SNAKE_CASE__				)
              )
              lowercase							:     Any       	=      node.data - coins_to_left - coins_to_right

              return CoinsDistribResult(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

       return get_distrib(SCREAMING_SNAKE_CASE__				)[0]


if __name__ == "__main__":
  import doctest

  doctest.testmod()
