'''simple docstring'''






import math as m





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : Optional[Any]      ,		__snake_case       : int      ,		__snake_case       : Union[str, Any]=0.0_01							):
     return (f(a + h							) - f(a - h							)) / (2 * h)





def    _SCREAMING_SNAKE_CASE		(			__snake_case       : int      ,		__snake_case       : Dict=0      ,		__snake_case       : List[Any]=1_0_0      ,		__snake_case       : str=0.00_01      ,		__snake_case       : Optional[int]=1e-6      ,		__snake_case       : Any=False							):
     _A								=     xa  # set the initial guess
     _A								=     [a]
     _A								=     abs(f(__snake_case							)							)
     _A								=     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'							)
          _A								=     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

  _UpperCAmelCase						:    Optional[int]     					=					lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
  _UpperCAmelCase						:    Any     					=					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}}}''')




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase						:    Dict     					=					{
    '''configuration_decision_transformer''': [
        '''DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DecisionTransformerConfig''',
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase						:    Union[str, Any]     					=					[
       '''DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''DecisionTransformerGPT2Model''',
       '''DecisionTransformerGPT2PreTrainedModel''',
       '''DecisionTransformerModel''',
       '''DecisionTransformerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_decision_transformer import (
       DECISION_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       DecisionTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          DecisionTransformerGPTaModel,
          DecisionTransformerGPTaPreTrainedModel,
          DecisionTransformerModel,
          DecisionTransformerPreTrainedModel,
      )


else:
   import sys

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



