code: 
  
  
  import math as m
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						int       ,							UpperCamelCase__  :						Union[str, Any]=0.0_01		):
         return (f(a + h		) - f(a - h		)) / (2 * h)
  def 							_UpperCAmelCase       (UpperCamelCase__  :						int       ,							UpperCamelCase__  :						Dict=0       ,							UpperCamelCase__  :						List[Any]=100       ,							UpperCamelCase__  :						str=0.00_01       ,							UpperCamelCase__  :						Optional[int]=1E-6       ,							UpperCamelCase__  :						Any=False		):
         _A   : int       					=				xa  # set the initial guess
         _A   : List[str]       					=				[a]
         _A   : Any       					=				abs(f(UpperCamelCase__		)		)
         _A   : List[str]       					=				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"		)
                _A   : Any       					=				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
  
      lowerCAmelCase__ 	=				lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
      lowerCAmelCase__ 	=				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}}}")
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_torch_available,
      is_vision_available,
  )
  
  
  lowerCAmelCase__   	=				{
      'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
  }
  
  try:
      if not is_vision_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				['SegformerFeatureExtractor']
      lowerCAmelCase__   	=				['SegformerImageProcessor']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'SegformerDecodeHead',
          'SegformerForImageClassification',
          'SegformerForSemanticSegmentation',
          'SegformerLayer',
          'SegformerModel',
          'SegformerPreTrainedModel',
      ]
  
  try:
      if not is_tf_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          '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
  
      lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  