






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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


A        =			logging.get_logger(__name__)


class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				Optional[Any]				    =						["""pixel_values"""]


		def __init__(						self ,    UpperCamelCase__ = True ,    UpperCamelCase__ = None ,    UpperCamelCase__ = PILImageResampling.BILINEAR ,    UpperCamelCase__ = True ,    UpperCamelCase__ = None ,    UpperCamelCase__ = True ,    UpperCamelCase__ = 1 / 255 ,    UpperCamelCase__ = True ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						super().__init__(**UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   size if size is not None else {'shortest_edge': 256}
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   get_size_dict(UpperCamelCase__ ,    default_to_square=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	str													=   crop_size if crop_size is not None else {'height': 224, 'width': 224}
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   get_size_dict(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   do_resize
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   size
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   resample
						SCREAMING_SNAKE_CASE_   :	Any													=   do_center_crop
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   crop_size
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   do_rescale
						SCREAMING_SNAKE_CASE_   :	int													=   rescale_factor
						SCREAMING_SNAKE_CASE_   :	Any													=   do_normalize
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						SCREAMING_SNAKE_CASE_   :	Dict													=   image_std if image_std is not None else IMAGENET_STANDARD_STD


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = PILImageResampling.BICUBIC ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						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_   :	Optional[int]													=   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   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   get_size_dict(UpperCamelCase__       )
						return center_crop(UpperCamelCase__ ,    size=(size['height'], size['width']) ,    data_format=UpperCamelCase__ ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    **UpperCamelCase__       ):
						return rescale(UpperCamelCase__ ,    scale=UpperCamelCase__ ,    data_format=UpperCamelCase__ ,    **UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						return normalize(UpperCamelCase__ ,    mean=UpperCamelCase__ ,    std=UpperCamelCase__ ,    data_format=UpperCamelCase__ ,    **UpperCamelCase__       )






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = None ,    UpperCamelCase__ = ChannelDimension.FIRST ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	str													=   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__ ,    default_to_square=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	int													=   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_   :	List[str]													=   crop_size if crop_size is not None else self.crop_size
						SCREAMING_SNAKE_CASE_   :	Dict													=   get_size_dict(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Dict													=   do_rescale if do_rescale is not None else self.do_rescale
						SCREAMING_SNAKE_CASE_   :	int													=   rescale_factor if rescale_factor is not None else self.rescale_factor
						SCREAMING_SNAKE_CASE_   :	Dict													=   do_normalize if do_normalize is not None else self.do_normalize
						SCREAMING_SNAKE_CASE_   :	Tuple													=   image_mean if image_mean is not None else self.image_mean
						SCREAMING_SNAKE_CASE_   :	Any													=   image_std if image_std is not None else self.image_std

						SCREAMING_SNAKE_CASE_   :	int													=   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.'       )

						# All transformations expect numpy arrays.
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   [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_   :	Dict													=   [self.center_crop(image=UpperCamelCase__ ,    size=UpperCamelCase__       ) for image in images]

						if do_rescale:
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   [self.rescale(image=UpperCamelCase__ ,    scale=UpperCamelCase__       ) for image in images]

						if do_normalize:
										SCREAMING_SNAKE_CASE_   :	Optional[int]													=   [self.normalize(image=UpperCamelCase__ ,    mean=UpperCamelCase__ ,    std=UpperCamelCase__       ) for image in images]

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [to_channel_dimension_format(UpperCamelCase__ ,    UpperCamelCase__       ) for image in images]

						SCREAMING_SNAKE_CASE_   :	Dict													=   {'pixel_values': images}
						return BatchFeature(data=UpperCamelCase__ ,    tensor_type=UpperCamelCase__       )










import math



def 					_lowerCamelCase(			lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				return math.pow(lowerCAmelCase__    ,    2      ) - a



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				return 2 * x



def 					_lowerCamelCase(			lowerCAmelCase__				:		float      ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   2.0

				while start <= a:
								SCREAMING_SNAKE_CASE_   :	str													=   math.pow(lowerCAmelCase__    ,    2      )

				return start



def 					_lowerCamelCase(			lowerCAmelCase__				:		float    ,    lowerCAmelCase__				:		int = 9999    ,    lowerCAmelCase__				:		float = 0.00_000_000_000_001      ):





				'''simple docstring'''







				if a < 0:
								raise ValueError('math domain error'      )

				SCREAMING_SNAKE_CASE_   :	Optional[int]													=   get_initial_point(lowerCAmelCase__      )

				for _ in range(lowerCAmelCase__      ):
								SCREAMING_SNAKE_CASE_   :	Tuple													=   value
								SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   value - fx(lowerCAmelCase__    ,    lowerCAmelCase__      ) / fx_derivative(lowerCAmelCase__      )
								if abs(prev_value - value      ) < tolerance:
												return value

				return value


if __name__ == "__main__":
				from doctest import testmod

				testmod()