



import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
      from PIL import Image

class    _SCREAMING_SNAKE_CASE						(unittest.TestCase      ,     UpperCamelCase			):





     def 						__snake_case     (							self			:				Optional[Any]					)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						load_tool("image-segmentation"					)
      self.tool.setup()
      __SCREAMING_SNAKE_CASE							:     Any							=						load_tool("image-segmentation"  ,	remote=UpperCamelCase					)





     def 						__snake_case     (							self			:				int					)->str:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
      __SCREAMING_SNAKE_CASE							:     Tuple							=						self.tool(UpperCamelCase  ,	"cat"					)
      self.assertTrue(isinstance(UpperCamelCase  ,	Image.Image					)					)





     def 						__snake_case     (							self			:				List[str]					)->List[Any]:
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
      __SCREAMING_SNAKE_CASE							:     str							=						self.remote_tool(UpperCamelCase  ,	"cat"					)
      self.assertTrue(isinstance(UpperCamelCase  ,	Image.Image					)					)





     def 						__snake_case     (							self			:				str					)->List[Any]:
      __SCREAMING_SNAKE_CASE							:     int							=						Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
      __SCREAMING_SNAKE_CASE							:     Dict							=						self.tool(image=UpperCamelCase  ,	label="cat"					)
      self.assertTrue(isinstance(UpperCamelCase  ,	Image.Image					)					)






     def 						__snake_case     (							self			:				List[str]					)->Tuple:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
      __SCREAMING_SNAKE_CASE							:     Dict							=						self.remote_tool(image=UpperCamelCase  ,	label="cat"					)
      self.assertTrue(isinstance(UpperCamelCase  ,	Image.Image					)					)








import math as m






def     lowerCamelCase__				(   a						,						a						,						a=0.001 ) ->  Optional[int]:
   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 ) ->  List[Any]:
   _A:    Optional[Any]						     =			xa  # set the initial guess
   _A:    Dict						     =			[a]
   _A:    Tuple						     =			abs(f(a ) )
   _A:    Tuple						     =			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''' )
      _A:    Any						     =			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

   UpperCAmelCase__						:	Optional[Any]	     = lambda x: m.tanh(x) ** 2 - m.exp(3 * x)  # noqa: E731
   UpperCAmelCase__     ,UpperCAmelCase__     ,UpperCAmelCase__						:	str	     = 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}}}""")



