





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}}}""")










'''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__)



