code: 
  
  
  
  
  
  import math as m
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      List[Any]		, _lowerCamelCase      :      Optional[Any]		, _lowerCamelCase      :      Optional[Any]=0.0_0_1 ):
      return (f(a + h ) - f(a - h )) / (2 * h)
  
  
  
  
  
  def 		UpperCamelCase       (							_lowerCamelCase      :      Union[str, Any]		, _lowerCamelCase      :      Dict=0		, _lowerCamelCase      :      List[str]=1_00		, _lowerCamelCase      :      Any=0.0_0_0_1		, _lowerCamelCase      :      Tuple=1e-6		, _lowerCamelCase      :      int=False ):
      A__       =    xa  # set the initial guess
      A__       =    [a]
      A__       =    abs(f(UpperCamelCase__ ) )
      A__       =    lambda _lowerCamelCase : 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__       =    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							:			Dict				=lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
    __lowerCAmelCase			,  __lowerCAmelCase			,  __lowerCAmelCase							:			str				=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}}}""")
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  __lowerCAmelCase							:			int				={
      "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
      "tokenization_layoutlm": ["LayoutLMTokenizer"],
  }
  
  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Any				=["LayoutLMTokenizerFast"]
  
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			Dict				=[
        "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "LayoutLMForMaskedLM",
        "LayoutLMForSequenceClassification",
        "LayoutLMForTokenClassification",
        "LayoutLMForQuestionAnswering",
        "LayoutLMModel",
        "LayoutLMPreTrainedModel",
    ]
  
  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    __lowerCAmelCase							:			str				=[
        "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFLayoutLMForMaskedLM",
        "TFLayoutLMForSequenceClassification",
        "TFLayoutLMForTokenClassification",
        "TFLayoutLMForQuestionAnswering",
        "TFLayoutLMMainLayer",
        "TFLayoutLMModel",
        "TFLayoutLMPreTrainedModel",
    ]
  
  
  if TYPE_CHECKING:
    from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
    from .tokenization_layoutlm import LayoutLMTokenizer
  
    try:
      if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .tokenization_layoutlm_fast import LayoutLMTokenizerFast
  
    try:
      if not is_torch_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_layoutlm import (
          LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          LayoutLMForMaskedLM,
          LayoutLMForQuestionAnswering,
          LayoutLMForSequenceClassification,
          LayoutLMForTokenClassification,
          LayoutLMModel,
          LayoutLMPreTrainedModel,
      )
    try:
      if not is_tf_available():
        raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
      pass
    else:
      from .modeling_tf_layoutlm import (
          TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLayoutLMForMaskedLM,
          TFLayoutLMForQuestionAnswering,
          TFLayoutLMForSequenceClassification,
          TFLayoutLMForTokenClassification,
          TFLayoutLMMainLayer,
          TFLayoutLMModel,
          TFLayoutLMPreTrainedModel,
      )
  
  else:
    import sys
  
    __lowerCAmelCase							:			int				=_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  