
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
							import PIL


lowerCamelCase_						: str         =  logging.get_logger(__name__)







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	int:
				UpperCamelCase_:							str	=						b.T
				UpperCamelCase_:							int	=						np.sum(np.square(lowerCamelCase						)     ,    axis=1						)
				UpperCamelCase_:							Optional[Any]	=						np.sum(np.square(lowerCamelCase						)     ,    axis=0						)
				UpperCamelCase_:							str	=						np.matmul(lowerCamelCase     ,    lowerCamelCase						)
				UpperCamelCase_:							int	=						aa[:, None] - 2 * ab + ba[None, :]
				return d







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	Any:
				UpperCamelCase_:							Tuple	=						x.reshape(-1     ,    3						)
				UpperCamelCase_:							int	=						squared_euclidean_distance(lowerCamelCase     ,    lowerCamelCase						)
				return np.argmin(lowerCamelCase     ,    axis=1						)





class        _UpperCamelCase				(					_A  ):






			'''simple docstring'''

			__UpperCamelCase      :						Optional[int]					      = ["""pixel_values"""]

			def __init__(       self :       Optional[Any]						,	snake_case_ :       Optional[Union[List[List[int]], np.ndarray]] = None						,	snake_case_ :       bool = True						,	snake_case_ :       Dict[str, int] = None						,	snake_case_ :       PILImageResampling = PILImageResampling.BILINEAR						,	snake_case_ :       bool = True						,	snake_case_ :       bool = True						,	**snake_case_ :       int						,	):
							super().__init__(**snake_case_      )
							UpperCamelCase_:							List[Any]	=						size if size is not None else {"""height""": 256, """width""": 256}
							UpperCamelCase_:							str	=						get_size_dict(snake_case_      )
							UpperCamelCase_:							Optional[Any]	=						np.array(snake_case_      ) if clusters is not None else None
							UpperCamelCase_:							Dict	=						do_resize
							UpperCamelCase_:							Optional[Any]	=						size
							UpperCamelCase_:							Dict	=						resample
							UpperCamelCase_:							str	=						do_normalize
							UpperCamelCase_:							Optional[Any]	=						do_color_quantize

			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       np.ndarray						,	snake_case_ :       Dict[str, int]						,	snake_case_ :       PILImageResampling = PILImageResampling.BILINEAR						,	snake_case_ :       Optional[Union[str, ChannelDimension]] = None						,	**snake_case_ :       Optional[Any]						,	):
							UpperCamelCase_:							Optional[Any]	=						get_size_dict(snake_case_      )
							if "height" not in size or "width" not in size:
											raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}'''      )
							return resize(
							    snake_case_						,	size=(size["""height"""], size["""width"""])						,	resample=snake_case_						,	data_format=snake_case_						,	**snake_case_      )

			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       np.ndarray						,	snake_case_ :       Optional[Union[str, ChannelDimension]] = None						,	):
							UpperCamelCase_:							List[Any]	=						rescale(image=snake_case_						,	scale=1 / 127.5						,	data_format=snake_case_      )
							UpperCamelCase_:							Tuple	=						image - 1
							return image




			def       lowerCAmelCase__	(       self :       Any						,	snake_case_ :       ImageInput						,	snake_case_ :       bool = None						,	snake_case_ :       Dict[str, int] = None						,	snake_case_ :       PILImageResampling = None						,	snake_case_ :       bool = None						,	snake_case_ :       Optional[bool] = None						,	snake_case_ :       Optional[Union[List[List[int]], np.ndarray]] = None						,	snake_case_ :       Optional[Union[str, TensorType]] = None						,	snake_case_ :       Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST						,	**snake_case_ :       Tuple						,	):
							UpperCamelCase_:							str	=						do_resize if do_resize is not None else self.do_resize
							UpperCamelCase_:							Optional[int]	=						size if size is not None else self.size
							UpperCamelCase_:							str	=						get_size_dict(snake_case_      )
							UpperCamelCase_:							str	=						resample if resample is not None else self.resample
							UpperCamelCase_:							int	=						do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase_:							Dict	=						do_color_quantize if do_color_quantize is not None else self.do_color_quantize
							UpperCamelCase_:							Optional[Any]	=						clusters if clusters is not None else self.clusters
							UpperCamelCase_:							Dict	=						np.array(snake_case_      )

							UpperCamelCase_:							str	=						make_list_of_images(snake_case_      )

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

							if do_resize and size is None or resample is None:
											raise ValueError("""Size and resample must be specified if do_resize is True."""      )

							if do_color_quantize and clusters is None:
											raise ValueError("""Clusters must be specified if do_color_quantize is True."""      )

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

							if do_resize:
											UpperCamelCase_:							int	=						[self.resize(image=snake_case_						,	size=snake_case_						,	resample=snake_case_      ) for image in images]

							if do_normalize:
											UpperCamelCase_:							Dict	=						[self.normalize(image=snake_case_      ) for image in images]

							if do_color_quantize:
											UpperCamelCase_:							Any	=						[to_channel_dimension_format(snake_case_						,	ChannelDimension.LAST      ) for image in images]
											# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
											UpperCamelCase_:							Any	=						np.array(snake_case_      )
											UpperCamelCase_:							Tuple	=						color_quantize(snake_case_						,	snake_case_      ).reshape(images.shape[:-1]      )

											# flatten to (batch_size, height*width)
											UpperCamelCase_:							Tuple	=						images.shape[0]
											UpperCamelCase_:							Optional[int]	=						images.reshape(snake_case_						,	-1      )

											# We need to convert back to a list of images to keep consistent behaviour across processors.
											UpperCamelCase_:							List[Any]	=						list(snake_case_      )
							else:
											UpperCamelCase_:							Any	=						[to_channel_dimension_format(snake_case_						,	snake_case_      ) for image in images]

							UpperCamelCase_:							Dict	=						{"""input_ids""": images}
							return BatchFeature(data=snake_case_						,	tensor_type=snake_case_      )








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







def        A__					(						lowerCamelCase     ,    lowerCamelCase     ,    lowerCamelCase = "x"     ,    lowerCamelCase = 10**-10     ,    lowerCamelCase = 1     ,    )  ->	complex:
				UpperCamelCase_:							Optional[Any]	=						symbols(lowerCamelCase						)
				UpperCamelCase_:							int	=						lambdify(lowerCamelCase     ,    lowerCamelCase						)
				UpperCamelCase_:							Optional[Any]	=						lambdify(lowerCamelCase     ,    diff(lowerCamelCase     ,    lowerCamelCase						)						)

				UpperCamelCase_:							Tuple	=						starting_point

				while True:
								if diff_function(lowerCamelCase						) != 0:
												UpperCamelCase_:							List[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

								UpperCamelCase_:							Any	=						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)}""")






