



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


__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)

class 	lowerCamelCase_    ( __lowercase						):






      '''simple docstring'''
      a__   =  ['pixel_values']







      def __init__(	self      :			Union[str, Any]	,	__lowerCamelCase      :			List[str] = True	,	__lowerCamelCase      :			Optional[int] = 1 / 2_55	,	__lowerCamelCase      :			Dict = True	,	__lowerCamelCase      :			Any = 8	,	**__lowerCamelCase      :			Dict	,	)      ->				None:
         super().__init__(**__A )

         A							:					Dict              =						do_rescale
         A							:					List[Any]              =						rescale_factor
         A							:					int              =						do_pad
         A							:					List[Any]              =						pad_size







      def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int]	,	__lowerCamelCase      :			Dict	,	__lowerCamelCase      :			Any	,	__lowerCamelCase      :			str = None	,	**__lowerCamelCase      :			Optional[int] )      ->				np.ndarray:
         return rescale(__A	,	scale=__A	,	data_format=__A	,	**__A )







      def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict	,	__lowerCamelCase      :			List[Any]	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			Tuple = None )      ->				int:
         A       , A							:					Tuple              =						get_image_size(__A )
         A							:					Any              =						(old_height // size + 1) * size - old_height
         A							:					Any              =						(old_width // size + 1) * size - old_width

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







      def 			SCREAMING_SNAKE_CASE__		(	self      :			int	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			List[Any] = None	,	__lowerCamelCase      :			Optional[int] = None	,	__lowerCamelCase      :			Any = None	,	__lowerCamelCase      :			List[Any] = None	,	__lowerCamelCase      :			Tuple = None	,	__lowerCamelCase      :			Optional[int] = ChannelDimension.FIRST	,	**__lowerCamelCase      :			Union[str, Any]	,	)      ->				Dict:
         A							:					str              =						do_rescale if do_rescale is not None else self.do_rescale
         A							:					Tuple              =						rescale_factor if rescale_factor is not None else self.rescale_factor
         A							:					Tuple              =						do_pad if do_pad is not None else self.do_pad
         A							:					int              =						pad_size if pad_size is not None else self.pad_size

         A							:					int              =						make_list_of_images(__A )

         if not valid_images(__A ):
            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							:					List[Any]              =						[to_numpy_array(__A ) for image in images]

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

         if do_pad:
            A							:					Union[str, Any]              =						[self.pad(__A	,	size=__A ) for image in images]

         A							:					Tuple              =						[to_channel_dimension_format(__A	,	__A ) for image in images]

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




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




def 	UpperCAmelCase   (						_lowerCamelCase ,    _lowerCamelCase ,    _lowerCamelCase = "x" ,    _lowerCamelCase = 10**-10 ,    _lowerCamelCase = 1 ,    ):
			A							:					str              =						symbols(_lowerCamelCase    )
			A							:					int              =						lambdify(_lowerCamelCase ,    _lowerCamelCase    )
			A							:					List[str]              =						lambdify(_lowerCamelCase ,    diff(_lowerCamelCase ,    _lowerCamelCase    )    )

			A							:					Optional[int]              =						starting_point

			while True:
						if diff_function(_lowerCamelCase    ) != 0:
									A							:					Optional[Any]              =						prev_guess - multiplicity * func(_lowerCamelCase    ) / diff_function(
									    _lowerCamelCase    )
						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

						A							:					int              =						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=0.005)}""",
							)

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