






import math as m



def 			__UpperCAmelCase				(lowercase__	,lowercase__	,lowercase__=0.001				)		->				int:

						'''simple docstring'''


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



def 			__UpperCAmelCase				(lowercase__	,lowercase__=0	,lowercase__=100	,lowercase__=0.0001	,lowercase__=1e-6	,lowercase__=False				)		->				Any:

						'''simple docstring'''


						a_				     =   xa  # set the initial guess
						a_				     =   [a]
						a_				     =   abs(f(lowercase__				)				)
						a_				     =   lambda lowercase__				: calc_derivative(lowercase__	,lowercase__	,h=lowercase__				)  # noqa: E731  Derivative of f(x)
						for _ in range(lowercase__				):
												if fa(lowercase__				) == 0:
																		raise ValueError("No converging solution found"				)
												a_				     =   a - f(lowercase__				) / fa(lowercase__				)  # Calculate the next estimate
												if logsteps:
																		steps.append(lowercase__				)
												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

							a_      = lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
							a_ ,    a_ ,    a_      = newton_raphson(
							    f, xa=10, maxiter=1_000, 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_torch_available,
    is_vision_available,
)


a_      = {
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a_      = ['SegformerFeatureExtractor']
							a_      = ['SegformerImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a_      = [
							    'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SegformerDecodeHead',
							    'SegformerForImageClassification',
							    'SegformerForSemanticSegmentation',
							    'SegformerLayer',
							    'SegformerModel',
							    'SegformerPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a_      = [
							    'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFSegformerDecodeHead',
							    'TFSegformerForImageClassification',
							    'TFSegformerForSemanticSegmentation',
							    'TFSegformerModel',
							    'TFSegformerPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_segformer import SegformerFeatureExtractor
														from .image_processing_segformer import SegformerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_segformer import (
														    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SegformerDecodeHead,
														    SegformerForImageClassification,
														    SegformerForSemanticSegmentation,
														    SegformerLayer,
														    SegformerModel,
														    SegformerPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_segformer import (
														    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFSegformerDecodeHead,
														    TFSegformerForImageClassification,
														    TFSegformerForSemanticSegmentation,
														    TFSegformerModel,
														    TFSegformerPreTrainedModel,
														)

else:
							import sys

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



