






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A  							=  {
    "configuration_codegen": ["CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP", "CodeGenConfig", "CodeGenOnnxConfig"],
    "tokenization_codegen": ["CodeGenTokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __A  							=  ["CodeGenTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __A  							=  [
       "CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST",
       "CodeGenForCausalLM",
       "CodeGenModel",
       "CodeGenPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
   from .tokenization_codegen import CodeGenTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_codegen_fast import CodeGenTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_codegen import (
          CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
          CodeGenForCausalLM,
          CodeGenModel,
          CodeGenPreTrainedModel,
      )

else:
   import sys

   __A  							=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









import math as m







def 			lowercase__  (				A_:	Tuple     ,			A_:	str     ,			A_:	Union[str, Any]=0.0_0_1		)  ->							List[str]:







       """simple docstring"""






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







def 			lowercase__  (				A_:	str     ,			A_:	Union[str, Any]=0     ,			A_:	List[Any]=100     ,			A_:	Optional[Any]=0.0_0_0_1     ,			A_:	Tuple=1e-6     ,			A_:	Union[str, Any]=False		)  ->							Union[str, Any]:







       """simple docstring"""






       __UpperCAmelCase    							=xa  # set the initial guess
       __UpperCAmelCase    							=[a]
       __UpperCAmelCase    							=abs(f(A_		)		)
       __UpperCAmelCase    							=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    							=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  							=  lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
   __A							,    __A							,    __A  							=  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}}}""")




