






import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

__A  							=  [(1,)]







def 			lowercase__  (				)  ->							str:







       """simple docstring"""






       __UpperCAmelCase    							=bool(os.getenv("""RUN_SLOW"""     ,			0		)		)
       if run_slow:
              assert True
       else:
              assert False, "should have been skipped"







def 			lowercase__  (				)  ->							Optional[int]:







       """simple docstring"""






       __UpperCAmelCase    							=bool(os.getenv("""RUN_SLOW"""     ,			0		)		)
       if run_slow and torch_device == "cuda":
              assert True
       else:
              assert False, "should have been skipped"



@require_torch
class 							_A (    unittest.TestCase							):
    """simple docstring"""

    @slow
    @require_torch_gpu
    def _a       (							self			:    List[Any]			)			->       Optional[int]:
           check_slow_torch_cuda()



    @require_torch_gpu
    @slow
    def _a       (							self			:    Optional[int]			)			->       int:
           check_slow_torch_cuda()



    @parameterized.expand(__SCREAMING_SNAKE_CASE			)
    @slow
    def _a       (							self			:    Union[str, Any]   ,    __SCREAMING_SNAKE_CASE			:    Dict=None			)			->       int:
           check_slow()







@slow
@require_torch_gpu
def 			lowercase__  (				)  ->							str:







       """simple docstring"""






       check_slow_torch_cuda()







@require_torch_gpu
@slow
def 			lowercase__  (				)  ->							Optional[Any]:







       """simple docstring"""






       check_slow_torch_cuda()







@slow
@pytest.mark.parametrize("""param"""     ,			[1]		)
def 			lowercase__  (				A_:	Tuple		)  ->							Any:







       """simple docstring"""






       check_slow()







@pytest.mark.parametrize("""param"""     ,			[1]		)
@slow
def 			lowercase__  (				A_:	str		)  ->							Any:







       """simple docstring"""






       check_slow()












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}}}""")




