






"""simple docstring"""



import math as m


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   List[str] ,				snake_case					:   int ,				snake_case					:   Any=0.001							)->		List[str]:






     '''simple docstring'''



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


def   SCREAMING_SNAKE_CASE__					(     snake_case					:   List[Any] ,				snake_case					:   List[str]=0 ,				snake_case					:   int=100 ,				snake_case					:   Any=0.0001 ,				snake_case					:   str=1E-6 ,				snake_case					:   str=False							)->		str:






     '''simple docstring'''



     UpperCAmelCase__	:						List[str]       =       xa  # set the initial guess
     UpperCAmelCase__	:						Optional[Any]       =       [a]
     UpperCAmelCase__	:						List[str]       =       abs(f(snake_case							)							)
     UpperCAmelCase__	:						List[str]       =       lambda snake_case							: calc_derivative(snake_case ,				snake_case ,				h=snake_case							)  # noqa: E731  Derivative of f(x)
     for _ in range(snake_case							):
          if fa(snake_case							) == 0:
               raise ValueError("No converging solution found"							)
          UpperCAmelCase__	:						List[str]       =       a - f(snake_case							) / fa(snake_case							)  # Calculate the next estimate
          if logsteps:
               steps.append(snake_case							)
          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  :  List[Any]   =			lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
 _lowerCAmelCase ,     _lowerCAmelCase ,     _lowerCAmelCase  :  List[Any]   =			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_tokenizers_available, is_torch_available


_lowerCAmelCase  :  List[Any]   =			{
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :  str   =			["""MarkupLMTokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase  :  List[str]   =			[
     """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """MarkupLMForQuestionAnswering""",
     """MarkupLMForSequenceClassification""",
     """MarkupLMForTokenClassification""",
     """MarkupLMModel""",
     """MarkupLMPreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
 from .feature_extraction_markuplm import MarkupLMFeatureExtractor
 from .processing_markuplm import MarkupLMProcessor
 from .tokenization_markuplm import MarkupLMTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_markuplm_fast import MarkupLMTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_markuplm import (
      MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      MarkupLMForQuestionAnswering,
      MarkupLMForSequenceClassification,
      MarkupLMForTokenClassification,
      MarkupLMModel,
      MarkupLMPreTrainedModel,
  )


else:
 import sys

 _lowerCAmelCase  :  Dict   =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)


