




import math as m






def       snake_case      (							A		,     A		,     A=0.001    )							->							Any:




	'''simple docstring'''


	return (f(a + h    ) - f(a - h    )) / (2 * h)






def       snake_case      (							A		,     A=0		,     A=100		,     A=0.0001		,     A=1e-6		,     A=False    )							->							int:




	'''simple docstring'''


	lowerCamelCase_							:	str							     =							xa  # set the initial guess
	lowerCamelCase_							:	Optional[Any]							     =							[a]
	lowerCamelCase_							:	int							     =							abs(f(A    )    )
	lowerCamelCase_							:	Any							     =							lambda A    : calc_derivative(A		,     A		,     h=A    )  # noqa: E731  Derivative of f(x)
	for _ in range(A    ):
		if fa(A    ) == 0:
			raise ValueError('No converging solution found'    )
		lowerCamelCase_							:	int							     =							a - f(A    ) / fa(A    )  # Calculate the next estimate
		if logsteps:
			steps.append(A    )
		if error < maxerror:
			break
	else:
		raise ValueError('Iteration limit reached, no converging solution found'    )
	if logsteps:
		# If logstep is true, then log intermediate steps
		return a, error, steps
	return a, error


if __name__ == "__main__":
							from matplotlib import pyplot as plt

							UpperCamelCase__  =		lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
							UpperCamelCase__ ,				UpperCamelCase__ ,				UpperCamelCase__  =		newton_raphson(
							    f, xa=1_0, maxiter=1_0_0_0, step=1e-6, logsteps=True
							)
							plt.plot([abs(f(x)) for x in steps])
							plt.xlabel('step')
							plt.ylabel('error')
							plt.show()
							print(F'solution = {{{solution:f}}}, error = {{{error:f}}}')






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

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

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

						lowerCamelCase_							:	str							     =							kwargs.get('pooler_hidden_size'				, __lowercase      )
						lowerCamelCase_							:	List[Any]							     =							pooler_dropout
						lowerCamelCase_							:	str							     =							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_							:	List[str]							     =							{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_							:	Optional[Any]							     =							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
