





import math as m



def 							lowerCAmelCase_				(    A_  ,A_  ,A_=0.001):
    return (f(a + h) - f(a - h)) / (2 * h)



def 							lowerCAmelCase_				(    A_  ,A_=0  ,A_=1_00  ,A_=0.0001  ,A_=1e-6  ,A_=False):
    UpperCamelCase__:					int												=      xa  # set the initial guess
    UpperCamelCase__:					Dict												=      [a]
    UpperCamelCase__:					Dict												=      abs(f(A_))
    UpperCamelCase__:					List[str]												=      lambda A_: calc_derivative(A_  ,A_  ,h=A_)  # noqa: E731  Derivative of f(x)
    for _ in range(A_):
        if fa(A_) == 0:
            raise ValueError("No converging solution found")
        UpperCamelCase__:					str												=      a - f(A_) / fa(A_)  # Calculate the next estimate
        if logsteps:
            steps.append(A_)
        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__:  str       					=					lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
   A__:  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_torch_available


A__:  List[str]       					=					{
    '''configuration_wav2vec2_conformer''': [
        '''WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Wav2Vec2ConformerConfig''',
    ],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:  int       					=					[
       '''WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Wav2Vec2ConformerForAudioFrameClassification''',
       '''Wav2Vec2ConformerForCTC''',
       '''Wav2Vec2ConformerForPreTraining''',
       '''Wav2Vec2ConformerForSequenceClassification''',
       '''Wav2Vec2ConformerForXVector''',
       '''Wav2Vec2ConformerModel''',
       '''Wav2Vec2ConformerPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_wavaveca_conformer import (
       WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       WavaVecaConformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_wavaveca_conformer import (
          WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          WavaVecaConformerForAudioFrameClassification,
          WavaVecaConformerForCTC,
          WavaVecaConformerForPreTraining,
          WavaVecaConformerForSequenceClassification,
          WavaVecaConformerForXVector,
          WavaVecaConformerModel,
          WavaVecaConformerPreTrainedModel,
      )

else:
   import sys

   A__:  Dict       					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

