



import math as m


def     UpperCamelCase(						__UpperCamelCase							:	Tuple  ,__UpperCamelCase							:	Optional[int]  ,__UpperCamelCase							:	Tuple=0.0_0_1				):
  return (f(a + h				) - f(a - h				)) / (2 * h)


def     UpperCamelCase(						__UpperCamelCase							:	Union[str, Any]  ,__UpperCamelCase							:	List[str]=0  ,__UpperCamelCase							:	Any=100  ,__UpperCamelCase							:	str=0.0_0_0_1  ,__UpperCamelCase							:	List[str]=1e-6  ,__UpperCamelCase							:	int=False				):
  lowerCAmelCase_    :  int             =				xa  # set the initial guess
  lowerCAmelCase_    :  str             =				[a]
  lowerCAmelCase_    :  List[str]             =				abs(f(UpperCamelCase__				)				)
  lowerCAmelCase_    :  int             =				lambda __UpperCamelCase				: calc_derivative(UpperCamelCase__  ,UpperCamelCase__  ,h=UpperCamelCase__				)  # noqa: E731  Derivative of f(x)
  for _ in range(UpperCamelCase__				):
    if fa(UpperCamelCase__				) == 0:
      raise ValueError('''No converging solution found'''				)
    lowerCAmelCase_    :  Tuple             =				a - f(UpperCamelCase__				) / fa(UpperCamelCase__				)  # Calculate the next estimate
    if logsteps:
      steps.append(UpperCamelCase__				)
    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__						:						Tuple          =      lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
      A__      ,  A__      ,  A__						:						Optional[Any]          =      newton_raphson(
          f, xa=10, maxiter=1000, 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 typing import TYPE_CHECKING

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


A__						:						List[str]          =      {
    '''configuration_segformer''': ['''SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SegformerConfig''', '''SegformerOnnxConfig''']
}

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

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

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A__						:						List[Any]          =      [
						    '''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__						:						Dict          =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





