





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


UpperCAmelCase_							=				logging.get_logger(__name__)


if is_vision_available():
							import PIL
class     UpperCamelCase__					(     _SCREAMING_SNAKE_CASE				):






		'''simple docstring'''






		__a       :       List[Any]       =	["""pixel_values"""]






		def __init__(							self,   snake_case__ = True,   snake_case__ = None,   snake_case__ = PILImageResampling.BICUBIC,   snake_case__ = True,   snake_case__ = None,   snake_case__ = True,   snake_case__ = 1 / 2_55,   snake_case__ = True,   snake_case__ = None,   snake_case__ = None,   snake_case__ = True,   **snake_case__,   )       -> str:






									"""simple docstring"""
									super().__init__(**lowerCAmelCase_  )
									lowercase_	:   List[Any]  				=     size if size is not None else {"""shortest_edge""": 2_24}
									lowercase_	:   Any  				=     get_size_dict(lowerCAmelCase_,   default_to_square=lowerCAmelCase_  )
									lowercase_	:   Tuple  				=     crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
									lowercase_	:   Dict  				=     get_size_dict(lowerCAmelCase_,   default_to_square=lowerCAmelCase_,   param_name="""crop_size"""  )

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






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__ = PILImageResampling.BICUBIC,   snake_case__ = None,   **snake_case__,   )       -> int:






									"""simple docstring"""
									lowercase_	:   List[Any]  				=     get_size_dict(lowerCAmelCase_,   default_to_square=lowerCAmelCase_  )
									if "shortest_edge" not in size:
																raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""  )
									lowercase_	:   Optional[int]  				=     get_resize_output_image_size(lowerCAmelCase_,   size=size["""shortest_edge"""],   default_to_square=lowerCAmelCase_  )
									return resize(lowerCAmelCase_,   size=lowerCAmelCase_,   resample=lowerCAmelCase_,   data_format=lowerCAmelCase_,   **lowerCAmelCase_  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__ = None,   **snake_case__,   )       -> Optional[int]:






									"""simple docstring"""
									lowercase_	:   Tuple  				=     get_size_dict(lowerCAmelCase_  )
									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(lowerCAmelCase_,   size=(size["""height"""], size["""width"""]),   data_format=lowerCAmelCase_,   **lowerCAmelCase_  )






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__ = None,   **snake_case__,   )       -> Tuple:






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






		def 						snake_case__				(							self,   snake_case__,   snake_case__,   snake_case__,   snake_case__ = None,   **snake_case__,   )       -> int:






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






		def 						snake_case__				(							self,   snake_case__,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = None,   snake_case__ = ChannelDimension.FIRST,   **snake_case__,   )       -> Tuple:






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

									lowercase_	:   Tuple  				=     make_list_of_images(lowerCAmelCase_  )

									if not valid_images(lowerCAmelCase_  ):
																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:
																lowercase_	:   Union[str, Any]  				=     [convert_to_rgb(lowerCAmelCase_  ) for image in images]

									# All transformations expect numpy arrays.
									lowercase_	:   str  				=     [to_numpy_array(lowerCAmelCase_  ) for image in images]

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

									if do_center_crop:
																lowercase_	:   str  				=     [self.center_crop(image=lowerCAmelCase_,   size=lowerCAmelCase_  ) for image in images]

									if do_rescale:
																lowercase_	:   Dict  				=     [self.rescale(image=lowerCAmelCase_,   scale=lowerCAmelCase_  ) for image in images]

									if do_normalize:
																lowercase_	:   Union[str, Any]  				=     [self.normalize(image=lowerCAmelCase_,   mean=lowerCAmelCase_,   std=lowerCAmelCase_  ) for image in images]

									lowercase_	:   Optional[Any]  				=     [to_channel_dimension_format(lowerCAmelCase_,   lowerCAmelCase_  ) for image in images]

									lowercase_	:   Dict  				=     {"""pixel_values""": images}
									return BatchFeature(data=lowerCAmelCase_,   tensor_type=lowerCAmelCase_  )







"""simple docstring"""
from __future__ import annotations

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

from sympy import diff






def       __UpperCamelCase						(		snake_case__ , snake_case__ , snake_case__ = 10**-10       ):
	A_		:    Tuple						     = a
	while True:
		A_		:    List[str]						     = Decimal(snake_case__       ) - (
		    Decimal(eval(snake_case__       )       ) / Decimal(eval(str(diff(snake_case__       )       )       )       )  # noqa: S307
		)
		# This number dictates the accuracy of the answer
		if abs(eval(snake_case__       )       ) < precision:  # noqa: S307
			return float(snake_case__       )


# 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)}')

