
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


SCREAMING_SNAKE_CASE							:Union[str, Any]										=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:List[Any]										=    {
    '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				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"deberta-v2"






	def __init__(	self :			str							,A :			List[Any]=12_81_00							,A :			Optional[int]=15_36							,A :			str=24							,A :			Union[str, Any]=24							,A :			int=61_44							,A :			Optional[Any]="gelu"							,A :			List[str]=0.1							,A :			Union[str, Any]=0.1							,A :			List[str]=5_12							,A :			int=0							,A :			Optional[Any]=0.02							,A :			str=1E-7							,A :			str=False							,A :			Any=-1							,A :			Tuple=0							,A :			int=True							,A :			Any=None							,A :			List[Any]=0							,A :			Dict="gelu"							,**A :			Tuple							,):
					super().__init__(**A			)

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

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

					__A             =			pos_att_type
					__A             =			vocab_size
					__A             =			layer_norm_eps

					__A             =			kwargs.get("pooler_hidden_size"							,A			)
					__A             =			pooler_dropout
					__A             =			pooler_hidden_act





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	@property
	def     UpperCamelCase_  (	self :			Tuple			):
					if self.task == "multiple-choice":
									__A             =			{0: "batch", 1: "choice", 2: "sequence"}
					else:
									__A             =			{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			):
					return 12







	def     UpperCamelCase_  (	self :			Any							,A :			Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]							,A :			int = -1							,A :			int = -1							,A :			int = -1							,A :			bool = False							,A :			Optional["TensorType"] = None							,A :			int = 3							,A :			int = 40							,A :			int = 40							,A :			"PreTrainedTokenizerBase" = None							,):
					__A             =			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 math import log

from scipy.constants import Boltzmann, physical_constants

SCREAMING_SNAKE_CASE							:Dict										=    300  # TEMPERATURE (unit = K)


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       )		->     float:







				"""simple docstring"""



				if donor_conc <= 0:
								raise ValueError("Donor concentration should be positive"						)
				elif acceptor_conc <= 0:
								raise ValueError("Acceptor concentration should be positive"						)
				elif intrinsic_conc <= 0:
								raise ValueError("Intrinsic concentration should be positive"						)
				elif donor_conc <= intrinsic_conc:
								raise ValueError(
								    "Donor concentration should be greater than intrinsic concentration"						)
				elif acceptor_conc <= intrinsic_conc:
								raise ValueError(
								    "Acceptor concentration should be greater than intrinsic concentration"						)
				else:
								return (
								    Boltzmann
								    * T
								    * log((donor_conc * acceptor_conc) / intrinsic_conc**2						)
								    / physical_constants["electron volt"][0]
								)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
