






from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


__a      :       Any				  =   logging.get_logger(__name__)


class       __lowercase      (	lowercase_  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				["pixel_values"]




     def __init__(				self       :			List[Any] ,	UpperCamelCase_       :			bool = True ,	UpperCamelCase_       :			Union[int, float] = 1 / 255 ,	UpperCamelCase_       :			bool = True ,	UpperCamelCase_       :			int = 8 ,	**UpperCamelCase_       :			Optional[int] ,	):


         """simple docstring"""



         super().__init__(**UpperCamelCase_				)

         __A         =	do_rescale
         __A         =	rescale_factor
         __A         =	do_pad
         __A         =	pad_size




     def 		lowerCAmelCase_     (				self       :			Tuple ,	UpperCamelCase_       :			np.ndarray ,	UpperCamelCase_       :			float ,	UpperCamelCase_       :			Optional[Union[str, ChannelDimension]] = None ,	**UpperCamelCase_       :			Union[str, Any]				):


         """simple docstring"""



         return rescale(UpperCamelCase_ ,	scale=UpperCamelCase_ ,	data_format=UpperCamelCase_ ,	**UpperCamelCase_				)




     def 		lowerCAmelCase_     (				self       :			int ,	UpperCamelCase_       :			np.ndarray ,	UpperCamelCase_       :			int ,	UpperCamelCase_       :			Optional[Union[str, ChannelDimension]] = None				):


         """simple docstring"""



         __A    ,	__A         =	get_image_size(UpperCamelCase_				)
         __A         =	(old_height // size + 1) * size - old_height
         __A         =	(old_width // size + 1) * size - old_width

         return pad(UpperCamelCase_ ,	((0, pad_height), (0, pad_width)) ,	mode="""symmetric""" ,	data_format=UpperCamelCase_				)





     def 		lowerCAmelCase_     (				self       :			str ,	UpperCamelCase_       :			ImageInput ,	UpperCamelCase_       :			Optional[bool] = None ,	UpperCamelCase_       :			Optional[float] = None ,	UpperCamelCase_       :			Optional[bool] = None ,	UpperCamelCase_       :			Optional[int] = None ,	UpperCamelCase_       :			Optional[Union[str, TensorType]] = None ,	UpperCamelCase_       :			Union[str, ChannelDimension] = ChannelDimension.FIRST ,	**UpperCamelCase_       :			Optional[int] ,	):


         """simple docstring"""



         __A         =	do_rescale if do_rescale is not None else self.do_rescale
         __A         =	rescale_factor if rescale_factor is not None else self.rescale_factor
         __A         =	do_pad if do_pad is not None else self.do_pad
         __A         =	pad_size if pad_size is not None else self.pad_size

         __A         =	make_list_of_images(UpperCamelCase_				)

         if not valid_images(UpperCamelCase_				):
             raise ValueError(
                 """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                 """torch.Tensor, tf.Tensor or jax.ndarray."""				)

         if do_rescale and rescale_factor is None:
             raise ValueError("""Rescale factor must be specified if do_rescale is True."""				)

         # All transformations expect numpy arrays.
         __A         =	[to_numpy_array(UpperCamelCase_				) for image in images]

         if do_rescale:
             __A         =	[self.rescale(image=UpperCamelCase_ ,	scale=UpperCamelCase_				) for image in images]

         if do_pad:
             __A         =	[self.pad(UpperCamelCase_ ,	size=UpperCamelCase_				) for image in images]

         __A         =	[to_channel_dimension_format(UpperCamelCase_ ,	UpperCamelCase_				) for image in images]

         __A         =	{"""pixel_values""": images}
         return BatchFeature(data=UpperCamelCase_ ,	tensor_type=UpperCamelCase_				)












from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			float | Decimal			,				__lowercase   :			float = 1_0**-1_0    )						->      float:





    """simple docstring"""

    __A         =	a
    while True:
        __A         =	Decimal(__lowercase    ) - (
            Decimal(eval(__lowercase    )    ) / Decimal(eval(str(diff(__lowercase    )    )    )    )  # noqa: S307
        )
        # This number dictates the accuracy of the answer
        if abs(eval(__lowercase    )    ) < precision:  # noqa: S307
            return float(__lowercase    )


# 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
  print(f"""The root of x**2 - 5*x + 2 = 0 is {newton_raphson('x**2 - 5*x + 2', 0.4)}""")
  # Find Square Root of 5
  print(f"""The root of log(x) - 1 = 0 is {newton_raphson('log(x) - 1', 2)}""")
  # Exponential Roots
  print(f"""The root of exp(x) - 1 = 0 is {newton_raphson('exp(x) - 1', 0)}""")




