






"""simple docstring"""


import math as m






def  _snake_case			(						snake_case__				:       Any		, snake_case__				:       Tuple		, snake_case__				:       List[Any]=0.001       ):
	return (f(a + h       ) - f(a - h       )) / (2 * h)






def  _snake_case			(						snake_case__				:       Tuple		, snake_case__				:       Tuple=0		, snake_case__				:       Union[str, Any]=100		, snake_case__				:       List[str]=0.0001		, snake_case__				:       str=1e-6		, snake_case__				:       Optional[Any]=False       ):
	A  				=				xa  # set the initial guess
	A  				=				[a]
	A  				=				abs(f(snake_case__       )       )
	A  				=				lambda snake_case__       : calc_derivative(snake_case__		, snake_case__		, h=snake_case__       )  # noqa: E731  Derivative of f(x)
	for _ in range(snake_case__       ):
		if fa(snake_case__       ) == 0:
			raise ValueError('No converging solution found'       )
		A  				=				a - f(snake_case__       ) / fa(snake_case__       )  # Calculate the next estimate
		if logsteps:
			steps.append(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											=			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_torch_available, is_vision_available


_lowercase											=			{'''configuration_vilt''': ['''VILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViltConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase											=			['''ViltFeatureExtractor''']
		_lowercase											=			['''ViltImageProcessor''']
		_lowercase											=			['''ViltProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase											=			[
		    '''VILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ViltForImageAndTextRetrieval''',
		    '''ViltForImagesAndTextClassification''',
		    '''ViltForTokenClassification''',
		    '''ViltForMaskedLM''',
		    '''ViltForQuestionAnswering''',
		    '''ViltLayer''',
		    '''ViltModel''',
		    '''ViltPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_vilt import ViltFeatureExtractor
				from .image_processing_vilt import ViltImageProcessor
				from .processing_vilt import ViltProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vilt import (
				    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViltForImageAndTextRetrieval,
				    ViltForImagesAndTextClassification,
				    ViltForMaskedLM,
				    ViltForQuestionAnswering,
				    ViltForTokenClassification,
				    ViltLayer,
				    ViltModel,
				    ViltPreTrainedModel,
				)


else:
		import sys

		_lowercase											=			_LazyModule(__name__, globals()['''__file__'''], _import_structure)