


"""simple docstring"""






import math as m
def       UpperCamelCase					(			_lowerCAmelCase     :						Optional[Any],  _lowerCAmelCase     :						int,  _lowerCAmelCase     :						Union[str, Any]=0.001 )				->			Optional[int]:
	return (f(a + h ) - f(a - h )) / (2 * h)
def       UpperCamelCase					(			_lowerCAmelCase     :						int,  _lowerCAmelCase     :						Dict=0,  _lowerCAmelCase     :						List[Any]=100,  _lowerCAmelCase     :						str=0.0001,  _lowerCAmelCase     :						Optional[int]=1E-6,  _lowerCAmelCase     :						Any=False )				->			Dict:
	_UpperCAmelCase    :							int									=					xa  # set the initial guess
	_UpperCAmelCase    :							List[str]									=					[a]
	_UpperCAmelCase    :							Any									=					abs(f(_lowerCAmelCase ) )
	_UpperCAmelCase    :							List[str]									=					lambda _lowerCAmelCase : calc_derivative(_lowerCAmelCase,  _lowerCAmelCase,  h=_lowerCAmelCase )  # noqa: E731  Derivative of f(x)
	for _ in range(_lowerCAmelCase ):
		if fa(_lowerCAmelCase ) == 0:
			raise ValueError("""No converging solution found""" )
		_UpperCAmelCase    :							Any									=					a - f(_lowerCAmelCase ) / fa(_lowerCAmelCase )  # Calculate the next estimate
		if logsteps:
			steps.append(_lowerCAmelCase )
		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

						lowerCamelCase__				:  Optional[int]													=  lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
						lowerCamelCase__				:  Any													=  newton_raphson(
						    f, xa=10, maxiter=10_00, 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}}}''')








"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__				:  Union[str, Any]													=  {
    '''configuration_dpr''': ['''DPR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPRConfig'''],
    '''tokenization_dpr''': [
        '''DPRContextEncoderTokenizer''',
        '''DPRQuestionEncoderTokenizer''',
        '''DPRReaderOutput''',
        '''DPRReaderTokenizer''',
    ],
}


try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  List[Any]													=  [
						    '''DPRContextEncoderTokenizerFast''',
						    '''DPRQuestionEncoderTokenizerFast''',
						    '''DPRReaderTokenizerFast''',
						]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  Dict													=  [
						    '''DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''DPRContextEncoder''',
						    '''DPRPretrainedContextEncoder''',
						    '''DPRPreTrainedModel''',
						    '''DPRPretrainedQuestionEncoder''',
						    '''DPRPretrainedReader''',
						    '''DPRQuestionEncoder''',
						    '''DPRReader''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase__				:  Any													=  [
						    '''TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFDPRContextEncoder''',
						    '''TFDPRPretrainedContextEncoder''',
						    '''TFDPRPretrainedQuestionEncoder''',
						    '''TFDPRPretrainedReader''',
						    '''TFDPRQuestionEncoder''',
						    '''TFDPRReader''',
						]


if TYPE_CHECKING:
						from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
						from .tokenization_dpr import (
						    DPRContextEncoderTokenizer,
						    DPRQuestionEncoderTokenizer,
						    DPRReaderOutput,
						    DPRReaderTokenizer,
						)

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_dpr_fast import (
												    DPRContextEncoderTokenizerFast,
												    DPRQuestionEncoderTokenizerFast,
												    DPRReaderTokenizerFast,
												)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_dpr import (
												    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DPRContextEncoder,
												    DPRPretrainedContextEncoder,
												    DPRPreTrainedModel,
												    DPRPretrainedQuestionEncoder,
												    DPRPretrainedReader,
												    DPRQuestionEncoder,
												    DPRReader,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_dpr import (
												    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFDPRContextEncoder,
												    TFDPRPretrainedContextEncoder,
												    TFDPRPretrainedQuestionEncoder,
												    TFDPRPretrainedReader,
												    TFDPRQuestionEncoder,
												    TFDPRReader,
												)

else:
						import sys

						lowerCamelCase__				:  Optional[Any]													=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





