




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-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/config.json',
    'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/config.json',
    'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json',
    'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json',
    'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json',
    'microsoft/deberta-xlarge-mnli': 'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json',
}


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):
					_lowerCamelCase						    =					"""deberta"""

					def __init__(							self					,			__lowercase=50_265					,			__lowercase=768					,			__lowercase=12					,			__lowercase=12					,			__lowercase=3_072					,			__lowercase="gelu"					,			__lowercase=0.1					,			__lowercase=0.1					,			__lowercase=512					,			__lowercase=0					,			__lowercase=0.02					,			__lowercase=1E-7					,			__lowercase=False					,			__lowercase=-1					,			__lowercase=0					,			__lowercase=True					,			__lowercase=None					,			__lowercase=0					,			__lowercase="gelu"					,			**__lowercase					,			):







						'''simple docstring'''




						super().__init__(**__lowercase   )

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

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

						lowerCamelCase_				:						List[str]				  =				pos_att_type
						lowerCamelCase_				:						int				  =				vocab_size
						lowerCamelCase_				:						Optional[Any]				  =				layer_norm_eps

						lowerCamelCase_				:						str				  =				kwargs.get('pooler_hidden_size'					,			__lowercase   )
						lowerCamelCase_				:						Optional[Any]				  =				pooler_dropout
						lowerCamelCase_				:						Optional[int]				  =				pooler_hidden_act


class 							__SCREAMING_SNAKE_CASE				(						lowerCamelCase    ):

					@property
					def       lowercase_				(							self   ):







						'''simple docstring'''




						if self.task == "multiple-choice":
							lowerCamelCase_				:						Tuple				  =				{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       lowercase_				(							self   ):







						'''simple docstring'''




						return 12





					def       lowercase_				(							self					,			__lowercase					,			__lowercase = -1					,			__lowercase = -1					,			__lowercase = -1					,			__lowercase = False					,			__lowercase = None					,			__lowercase = 3					,			__lowercase = 40					,			__lowercase = 40					,			__lowercase = None					,			):







						'''simple docstring'''




						lowerCamelCase_				:						List[str]				  =				super().generate_dummy_inputs(preprocessor=__lowercase					,			framework=__lowercase   )
						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 random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

_UpperCamelCase						:    Optional[int]		=						TypeVar('T', bound=bool)

class 			a	(  Generic[T]      ):







    def __init__(     self				,						_lowerCamelCase			):
      lowercase				       =			value
      lowercase				       =			None
      lowercase				       =			None







    @property
    def      UpperCamelCase_ (     self			):
      return self._value







    @staticmethod
    def      UpperCamelCase_ (     _lowerCamelCase				,						_lowerCamelCase			):
      if not roota:
        return roota

      if not roota:
        return roota

      if roota.value > roota.value:
        lowercase       ,	lowercase				       =			roota, roota

      if random.choice([True, False]			):
        lowercase       ,	lowercase				       =			roota.right, roota.left

      lowercase				       =			RandomizedHeapNode.merge(roota.left				,						_lowerCamelCase			)

      return roota


class 			a	(  Generic[T]      ):







    def __init__(     self				,						_lowerCamelCase = ()			):
      lowercase				       =			None

      if data:
        for item in data:
          self.insert(_lowerCamelCase			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			RandomizedHeapNode.merge(self._root				,						RandomizedHeapNode(_lowerCamelCase			)			)







    def      UpperCamelCase_ (     self			):
      lowercase				       =			self.top()

      if self._root is None:
        return None

      lowercase				       =			RandomizedHeapNode.merge(self._root.left				,						self._root.right			)

      return result







    def      UpperCamelCase_ (     self			):
      if not self._root:
        raise IndexError('Can\'t get top element for the empty heap.'			)
      return self._root.value







    def      UpperCamelCase_ (     self			):
      lowercase				       =			None







    def      UpperCamelCase_ (     self			):
      lowercase				       =			[]
      while self:
        result.append(self.pop()			)

      return result







    def __bool__(     self			):
      return self._root is not None


if __name__ == "__main__":
    import doctest

    doctest.testmod()


