





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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_lowerCamelCase :    Union[str, Any]  							= logging.get_logger(__name__)


if is_vision_available():
		import PIL


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   ["pixel_values"]






							def __init__( self     :					Optional[Any],			_UpperCAmelCase     :					bool = True,			_UpperCAmelCase     :					Dict[str, int] = None,			_UpperCAmelCase     :					PILImageResampling = PILImageResampling.BICUBIC,			_UpperCAmelCase     :					bool = True,			_UpperCAmelCase     :					Dict[str, int] = None,			_UpperCAmelCase     :					bool = True,			_UpperCAmelCase     :					Union[int, float] = 1 / 2_5_5,			_UpperCAmelCase     :					bool = True,			_UpperCAmelCase     :					Optional[Union[float, List[float]]] = None,			_UpperCAmelCase     :					Optional[Union[float, List[float]]] = None,			_UpperCAmelCase     :					bool = True,			**_UpperCAmelCase     :					Tuple,			)				->			None:





														"""simple docstring"""
														super().__init__(**_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						size if size is not None else {"shortest_edge": 2_2_4}
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						get_size_dict(_UpperCAmelCase,			default_to_square=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						get_size_dict(_UpperCAmelCase,			default_to_square=_UpperCAmelCase,			param_name="crop_size"						)

														SCREAMING_SNAKE_CASE__       :      List[str]     =						do_resize
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						resample
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						do_center_crop
														SCREAMING_SNAKE_CASE__       :      int     =						crop_size
														SCREAMING_SNAKE_CASE__       :      str     =						do_rescale
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						rescale_factor
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						do_normalize
														SCREAMING_SNAKE_CASE__       :      int     =						image_mean if image_mean is not None else OPENAI_CLIP_MEAN
														SCREAMING_SNAKE_CASE__       :      Any     =						image_std if image_std is not None else OPENAI_CLIP_STD
														SCREAMING_SNAKE_CASE__       :      str     =						do_convert_rgb






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					np.ndarray,			_UpperCAmelCase     :					Dict[str, int],			_UpperCAmelCase     :					PILImageResampling = PILImageResampling.BICUBIC,			_UpperCAmelCase     :					Optional[Union[str, ChannelDimension]] = None,			**_UpperCAmelCase     :					List[Any],			)				->			np.ndarray:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						get_size_dict(_UpperCAmelCase,			default_to_square=_UpperCAmelCase						)
														if "shortest_edge" not in size:
																					raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						get_resize_output_image_size(_UpperCAmelCase,			size=size["shortest_edge"],			default_to_square=_UpperCAmelCase						)
														return resize(_UpperCAmelCase,			size=_UpperCAmelCase,			resample=_UpperCAmelCase,			data_format=_UpperCAmelCase,			**_UpperCAmelCase						)






							def  A_     ( self     :					Optional[int],			_UpperCAmelCase     :					np.ndarray,			_UpperCAmelCase     :					Dict[str, int],			_UpperCAmelCase     :					Optional[Union[str, ChannelDimension]] = None,			**_UpperCAmelCase     :					Dict,			)				->			np.ndarray:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						get_size_dict(_UpperCAmelCase						)
														if "height" not in size or "width" not in size:
																					raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''						)
														return center_crop(_UpperCAmelCase,			size=(size["height"], size["width"]),			data_format=_UpperCAmelCase,			**_UpperCAmelCase						)






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					np.ndarray,			_UpperCAmelCase     :					Union[int, float],			_UpperCAmelCase     :					Optional[Union[str, ChannelDimension]] = None,			**_UpperCAmelCase     :					Optional[Any],			)				->			Any:





														"""simple docstring"""
														return rescale(_UpperCAmelCase,			scale=_UpperCAmelCase,			data_format=_UpperCAmelCase,			**_UpperCAmelCase						)






							def  A_     ( self     :					str,			_UpperCAmelCase     :					np.ndarray,			_UpperCAmelCase     :					Union[float, List[float]],			_UpperCAmelCase     :					Union[float, List[float]],			_UpperCAmelCase     :					Optional[Union[str, ChannelDimension]] = None,			**_UpperCAmelCase     :					str,			)				->			np.ndarray:





														"""simple docstring"""
														return normalize(_UpperCAmelCase,			mean=_UpperCAmelCase,			std=_UpperCAmelCase,			data_format=_UpperCAmelCase,			**_UpperCAmelCase						)






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					ImageInput,			_UpperCAmelCase     :					bool = None,			_UpperCAmelCase     :					Dict[str, int] = None,			_UpperCAmelCase     :					PILImageResampling = None,			_UpperCAmelCase     :					bool = None,			_UpperCAmelCase     :					int = None,			_UpperCAmelCase     :					bool = None,			_UpperCAmelCase     :					float = None,			_UpperCAmelCase     :					bool = None,			_UpperCAmelCase     :					Optional[Union[float, List[float]]] = None,			_UpperCAmelCase     :					Optional[Union[float, List[float]]] = None,			_UpperCAmelCase     :					bool = None,			_UpperCAmelCase     :					Optional[Union[str, TensorType]] = None,			_UpperCAmelCase     :					Optional[ChannelDimension] = ChannelDimension.FIRST,			**_UpperCAmelCase     :					Dict,			)				->			PIL.Image.Image:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						do_resize if do_resize is not None else self.do_resize
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						size if size is not None else self.size
														SCREAMING_SNAKE_CASE__       :      int     =						get_size_dict(_UpperCAmelCase,			param_name="size",			default_to_square=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						resample if resample is not None else self.resample
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						do_center_crop if do_center_crop is not None else self.do_center_crop
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						crop_size if crop_size is not None else self.crop_size
														SCREAMING_SNAKE_CASE__       :      str     =						get_size_dict(_UpperCAmelCase,			param_name="crop_size",			default_to_square=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						do_rescale if do_rescale is not None else self.do_rescale
														SCREAMING_SNAKE_CASE__       :      Tuple     =						rescale_factor if rescale_factor is not None else self.rescale_factor
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						do_normalize if do_normalize is not None else self.do_normalize
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						image_mean if image_mean is not None else self.image_mean
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						image_std if image_std is not None else self.image_std
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

														SCREAMING_SNAKE_CASE__       :      Dict     =						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_resize and size is None:
																					raise ValueError("Size must be specified if do_resize is True."						)

														if do_center_crop and crop_size is None:
																					raise ValueError("Crop size must be specified if do_center_crop is True."						)

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

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError("Image mean and std must be specified if do_normalize is True."						)

														# PIL RGBA images are converted to RGB
														if do_convert_rgb:
																					SCREAMING_SNAKE_CASE__       :      Dict     =						[convert_to_rgb(_UpperCAmelCase						) for image in images]

														# All transformations expect numpy arrays.
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[to_numpy_array(_UpperCAmelCase						) for image in images]

														if do_resize:
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[self.resize(image=_UpperCAmelCase,			size=_UpperCAmelCase,			resample=_UpperCAmelCase						) for image in images]

														if do_center_crop:
																					SCREAMING_SNAKE_CASE__       :      Any     =						[self.center_crop(image=_UpperCAmelCase,			size=_UpperCAmelCase						) for image in images]

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

														if do_normalize:
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						[self.normalize(image=_UpperCAmelCase,			mean=_UpperCAmelCase,			std=_UpperCAmelCase						) for image in images]

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[to_channel_dimension_format(_UpperCAmelCase,			_UpperCAmelCase						) for image in images]

														SCREAMING_SNAKE_CASE__       :      str     =						{"pixel_values": images}
														return BatchFeature(data=_UpperCAmelCase,			tensor_type=_UpperCAmelCase						)













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




def _a (  SCREAMING_SNAKE_CASE__      :  str			,				SCREAMING_SNAKE_CASE__      :  complex			,				SCREAMING_SNAKE_CASE__      :  str = "x"			,				SCREAMING_SNAKE_CASE__      :  float = 10**-10			,				SCREAMING_SNAKE_CASE__      :  int = 1			,				)	->    complex:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						symbols(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      str     =						lambdify(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      int     =						lambdify(SCREAMING_SNAKE_CASE__			,				diff(SCREAMING_SNAKE_CASE__			,				SCREAMING_SNAKE_CASE__       )       )

							SCREAMING_SNAKE_CASE__       :      Any     =						starting_point

							while True:
														if diff_function(SCREAMING_SNAKE_CASE__       ) != 0:
																					SCREAMING_SNAKE_CASE__       :      Any     =						prev_guess - multiplicity * func(SCREAMING_SNAKE_CASE__       ) / diff_function(
																					    SCREAMING_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

														SCREAMING_SNAKE_CASE__       :      Optional[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', 1_0, precision=0.005)}",
		)

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






