
'''simple docstring'''






import math as m







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE=0.0_01  ):
		return (f(a + h  ) - f(a - h  )) / (2 * h)







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE=0  ,					_SCREAMING_SNAKE_CASE=100  ,					_SCREAMING_SNAKE_CASE=0.00_01  ,					_SCREAMING_SNAKE_CASE=1e-6  ,					_SCREAMING_SNAKE_CASE=False  ):
		lowerCAmelCase_   :       Any    							=xa  # set the initial guess
		lowerCAmelCase_   :       List[str]    							=[a]
		lowerCAmelCase_   :       Any    							=abs(f(_SCREAMING_SNAKE_CASE  )  )
		lowerCAmelCase_   :       List[Any]    							=lambda _SCREAMING_SNAKE_CASE  : calc_derivative(_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					h=_SCREAMING_SNAKE_CASE  )  # noqa: E731  Derivative of f(x)
		for _ in range(_SCREAMING_SNAKE_CASE  ):
				if fa(_SCREAMING_SNAKE_CASE  ) == 0:
						raise ValueError('''No converging solution found'''  )
				lowerCAmelCase_   :       Any    							=a - f(_SCREAMING_SNAKE_CASE  ) / fa(_SCREAMING_SNAKE_CASE  )  # Calculate the next estimate
				if logsteps:
						steps.append(_SCREAMING_SNAKE_CASE  )
				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

							__lowercase	=    lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
							__lowercase	,				__lowercase	,				__lowercase	=    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_sentencepiece_available, is_torch_available


__lowercase	=    {
    '''configuration_xlm_prophetnet''': ['''XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMProphetNetConfig'''],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    ['''XLMProphetNetTokenizer''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XLMProphetNetDecoder''',
							    '''XLMProphetNetEncoder''',
							    '''XLMProphetNetForCausalLM''',
							    '''XLMProphetNetForConditionalGeneration''',
							    '''XLMProphetNetModel''',
							    '''XLMProphetNetPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xlm_prophetnet import XLMProphetNetTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xlm_prophetnet import (
														    XLM_PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XLMProphetNetDecoder,
														    XLMProphetNetEncoder,
														    XLMProphetNetForCausalLM,
														    XLMProphetNetForConditionalGeneration,
														    XLMProphetNetModel,
														    XLMProphetNetPreTrainedModel,
														)

else:
							import sys

							__lowercase	=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






