





'''simple docstring'''

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


__lowerCamelCase : Tuple											=			logging.get_logger(__name__)

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	Optional[Any] 			=		['''pixel_values''']




		def __init__(self      :	Optional[Any]							,			A__      :	bool = True							,			A__      :	int = 3_2							,			A__      :	Union[str, Any]=PILImageResampling.BILINEAR							,			A__      :	bool = True							,			**A__      :	List[Any]							,			)					-> None:
						lowercase         = do_resize
						lowercase         = do_rescale
						lowercase         = size_divisor
						lowercase         = resample
						super().__init__(**A__				)




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




		def 				UpperCAmelCase__   (self      :	Tuple							,			A__      :	np.ndarray							,			A__      :	float							,			A__      :	Optional[ChannelDimension] = None							,			**A__      :	Optional[Any]				)					-> np.ndarray:
						return rescale(image=A__							,			scale=A__							,			data_format=A__							,			**A__				)





		def 				UpperCAmelCase__   (self      :	Any							,			A__      :	Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]							,			A__      :	Optional[bool] = None							,			A__      :	Optional[int] = None							,			A__      :	Union[str, Any]=None							,			A__      :	Optional[bool] = None							,			A__      :	Optional[Union[TensorType, str]] = None							,			A__      :	ChannelDimension = ChannelDimension.FIRST							,			**A__      :	List[Any]							,			)					-> BatchFeature:
						lowercase         = do_resize if do_resize is not None else self.do_resize
						lowercase         = do_rescale if do_rescale is not None else self.do_rescale
						lowercase         = size_divisor if size_divisor is not None else self.size_divisor
						lowercase         = 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"				)

						lowercase         = make_list_of_images(A__				)

						if not valid_images(A__				):
										raise ValueError("Invalid image(s)"				)

						# All transformations expect numpy arrays.
						lowercase         = [to_numpy_array(A__				) for img in images]

						if do_resize:
										lowercase         = [self.resize(A__							,			size_divisor=A__							,			resample=A__				) for image in images]

						if do_rescale:
										lowercase         = [self.rescale(A__							,			scale=1 / 2_5_5				) for image in images]

						lowercase         = [to_channel_dimension_format(A__							,			A__				) for image in images]

						lowercase         = {"pixel_values": images}
						return BatchFeature(data=A__							,			tensor_type=A__				)











'''simple docstring'''

# Algorithm for the pigeonhole sorting









def 							UpperCAmelCase_    (	lowerCAmelCase_							):

				"""simple docstring"""






				lowercase         = min(lowerCAmelCase_							)  # min() finds the minimum value
				lowercase         = max(lowerCAmelCase_							)  # max() finds the maximum value

				lowercase         = max_val - min_val + 1  # size is difference of max and min values plus one

				# list of pigeonholes of size equal to the variable size
				lowercase         = [0] * size

				# Populate the pigeonholes.
				for x in a:
								assert isinstance(lowerCAmelCase_		,							lowerCAmelCase_							), "integers only please"
								holes[x - min_val] += 1

				# Putting the elements back into the array in an order.
				lowercase         = 0
				for count in range(lowerCAmelCase_							):
								while holes[count] > 0:
												holes[count] -= 1
												lowercase         = count + min_val
												i += 1







def 							UpperCAmelCase_    (	):

				"""simple docstring"""






				lowercase         = [8, 3, 2, 7, 4, 6, 8]
				pigeonhole_sort(lowerCAmelCase_							)
				print("Sorted order is:"		,							" ".join(lowerCAmelCase_							)							)


if __name__ == "__main__":
			main()




