





from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403






def lowerCamelCase__    (							snake_case_	:  str       ,      snake_case_	:  complex       ,      snake_case_	:  str = "x"       ,      snake_case_	:  float = 10**-10       ,      snake_case_	:  int = 1       ,      )      ->				complex:
   __snake_case							       =   symbols(snake_case_       )
   __snake_case							       =   lambdify(snake_case_       ,      snake_case_       )
   __snake_case							       =   lambdify(snake_case_       ,      diff(snake_case_       ,      snake_case_       )       )

   __snake_case							       =   starting_point

   while True:
      if diff_function(snake_case_       ) != 0:
         __snake_case							       =   prev_guess - multiplicity * func(snake_case_       ) / diff_function(
             snake_case_       )
      else:
         raise ZeroDivisionError('''Could not find root'''       ) from None

      # Precision is checked by comparing the difference of consecutive guesses
      if abs(next_guess - prev_guess       ) < precision:
         return next_guess

      __snake_case							       =   next_guess


# Let's Execute
if __name__ == "__main__":
    # Find root of trigonometric function
    # Find value of pi
    print(F'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')

    # Find root of polynomial
    # Find fourth Root of 5
    print(F'The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5j)}')

    # Find value of e
    print(
        'The root of log(y) - 1 = 0 is ',
        F'{newton_raphson("log(y) - 1", 2, variable="y")}',
    )

    # Exponential Roots
    print(
        'The root of exp(x) - 1 = 0 is',
        F'{newton_raphson("exp(x) - 1", 10, precision=0.0_05)}',
    )

    # Find root of cos(x)
    print(F'The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}')








from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

snake_case_				=			logging.get_logger(__name__)

@add_end_docstrings(_UpperCAmelCase  )
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):


      def __init__(self       :      Optional[int]				,			*a__       :      Any				,			**a__       :      Dict      ):





         """simple docstring"""



         super().__init__(*a__				,			**a__      )
         requires_backends(self				,			'''vision'''      )
         self.check_model_type(a__      )


      def __call__(self       :      Optional[int]				,			a__       :      Union[str, List[str], "Image.Image", List["Image.Image"]]				,			**a__       :      Tuple      ):





         """simple docstring"""



         return super().__call__(a__				,			**a__      )


      def    a (self       :      Dict				,			**a__       :      Any      ):





         """simple docstring"""



         return {}, {}, {}


      def    a (self       :      List[str]				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case							       =   load_image(a__      )
         __snake_case							       =   image.size
         __snake_case							       =   self.image_processor(images=a__				,			return_tensors=self.framework      )
         return model_inputs


      def    a (self       :      int				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model(**a__      )
         return model_outputs


      def    a (self       :      int				,			a__       :      str      ):





         """simple docstring"""



         __snake_case							       =   model_outputs.predicted_depth
         __snake_case							       =   torch.nn.functional.interpolate(
             predicted_depth.unsqueeze(1      )				,			size=self.image_size[::-1]				,			mode='''bicubic'''				,			align_corners=a__      )
         __snake_case							       =   prediction.squeeze().cpu().numpy()
         __snake_case							       =   (output * 255 / np.max(a__      )).astype('''uint8'''      )
         __snake_case							       =   Image.fromarray(a__      )
         __snake_case							       =   {}
         __snake_case							       =   predicted_depth
         __snake_case							       =   depth
         return output_dict

