

from __future__ import annotations

from random import choice






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     List[str]:
	return choice(lowerCamelCase__			)






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     int:
	__lowerCamelCase				:				Any					       = random_pivot(lowerCamelCase__			)

	# partition based on pivot
	# linear time
	__lowerCamelCase				:				int					       = [e for e in lst if e < pivot]
	__lowerCamelCase				:				List[Any]					       = [e for e in lst if e > pivot]

	# if we get lucky, pivot might be the element we want.
	# we can easily see this:
	# small (elements smaller than k)
	# + pivot (kth element)
	# + big (elements larger than k)
	if len(lowerCamelCase__			) == k - 1:
		return pivot
	# pivot is in elements bigger than k
	elif len(lowerCamelCase__			) < k - 1:
		return kth_number(lowerCamelCase__	, k - len(lowerCamelCase__			) - 1			)
	# pivot is in elements smaller than k
	else:
		return kth_number(lowerCamelCase__	, lowerCamelCase__			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()





from typing import List, Optional, Union

import numpy as np
import PIL.Image

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


__lowercase							 =					logging.get_logger(__name__)


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






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







    def __init__(	self , __lowercase = True , __lowercase = 32 , __lowercase=PILImageResampling.BILINEAR , __lowercase = True , **__lowercase , )       ->	None:
        __UpperCamelCase     :Optional[int]			  =		do_resize
        __UpperCamelCase     :Any			  =		do_rescale
        __UpperCamelCase     :str			  =		size_divisor
        __UpperCamelCase     :Dict			  =		resample
        super().__init__(**__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase , __lowercase = None , **__lowercase)       ->	np.ndarray:
        __UpperCamelCase  ,       __UpperCamelCase     :int			  =		get_image_size(__lowercase)
        # Rounds the height and width down to the closest multiple of size_divisor
        __UpperCamelCase     :List[Any]			  =		height // size_divisor * size_divisor
        __UpperCamelCase     :List[str]			  =		width // size_divisor * size_divisor
        __UpperCamelCase     :str			  =		resize(__lowercase , (new_h, new_w) , resample=__lowercase , data_format=__lowercase , **__lowercase)
        return image







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase = None , **__lowercase)       ->	np.ndarray:
        return rescale(image=__lowercase , scale=__lowercase , data_format=__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None , __lowercase = None , __lowercase=None , __lowercase = None , __lowercase = None , __lowercase = ChannelDimension.FIRST , **__lowercase , )       ->	BatchFeature:
        __UpperCamelCase     :Union[str, Any]			  =		do_resize if do_resize is not None else self.do_resize
        __UpperCamelCase     :Tuple			  =		do_rescale if do_rescale is not None else self.do_rescale
        __UpperCamelCase     :List[str]			  =		size_divisor if size_divisor is not None else self.size_divisor
        __UpperCamelCase     :List[Any]			  =		resample if resample is not None else self.resample

        if do_resize and size_divisor is None:
            raise ValueError('''size_divisor is required for resizing''')

        __UpperCamelCase     :List[Any]			  =		make_list_of_images(__lowercase)

        if not valid_images(__lowercase):
            raise ValueError('''Invalid image(s)''')

        # All transformations expect numpy arrays.
        __UpperCamelCase     :Optional[Any]			  =		[to_numpy_array(__lowercase) for img in images]

        if do_resize:
            __UpperCamelCase     :List[str]			  =		[self.resize(__lowercase , size_divisor=__lowercase , resample=__lowercase) for image in images]

        if do_rescale:
            __UpperCamelCase     :Dict			  =		[self.rescale(__lowercase , scale=1 / 255) for image in images]

        __UpperCamelCase     :str			  =		[to_channel_dimension_format(__lowercase , __lowercase) for image in images]

        __UpperCamelCase     :int			  =		{'''pixel_values''': images}
        return BatchFeature(data=__lowercase , tensor_type=__lowercase)





