



'''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


UpperCAmelCase     		=   logging.get_logger(__name__)

class __snake_case(				lowerCamelCase_ ):


						'''simple docstring'''



						UpperCAmelCase							:   int									=		["""pixel_values"""]


						def __init__( self     ,	A_ = True     ,	A_ = 32     ,	A_=PILImageResampling.BILINEAR     ,	A_ = True     ,	**A_     ,	)     ->     None:
							lowerCAmelCase   =     do_resize
							lowerCAmelCase   =     do_rescale
							lowerCAmelCase   =     size_divisor
							lowerCAmelCase   =     resample
							super().__init__(**lowerCAmelCase__				)


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_     ,	A_ = None     ,	**A_				)     ->     np.ndarray:
							lowerCAmelCase,       lowerCAmelCase   =     get_image_size(lowerCAmelCase__				)
							# Rounds the height and width down to the closest multiple of size_divisor
							lowerCAmelCase   =     height // size_divisor * size_divisor
							lowerCAmelCase   =     width // size_divisor * size_divisor
							lowerCAmelCase   =     resize(lowerCAmelCase__     ,	(new_h, new_w)     ,	resample=lowerCAmelCase__     ,	data_format=lowerCAmelCase__     ,	**lowerCAmelCase__				)
							return image


						def     __snake_case   ( self     ,	A_     ,	A_     ,	A_ = None     ,	**A_				)     ->     np.ndarray:
							return rescale(image=lowerCAmelCase__     ,	scale=lowerCAmelCase__     ,	data_format=lowerCAmelCase__     ,	**lowerCAmelCase__				)




						def     __snake_case   ( self     ,	A_     ,	A_ = None     ,	A_ = None     ,	A_=None     ,	A_ = None     ,	A_ = None     ,	A_ = ChannelDimension.FIRST     ,	**A_     ,	)     ->     BatchFeature:
							lowerCAmelCase   =     do_resize if do_resize is not None else self.do_resize
							lowerCAmelCase   =     do_rescale if do_rescale is not None else self.do_rescale
							lowerCAmelCase   =     size_divisor if size_divisor is not None else self.size_divisor
							lowerCAmelCase   =     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"""				)

							lowerCAmelCase   =     make_list_of_images(lowerCAmelCase__				)

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

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

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

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

							lowerCAmelCase   =     [to_channel_dimension_format(lowerCAmelCase__     ,	lowerCAmelCase__				) for image in images]

							lowerCAmelCase   =     {"""pixel_values""": images}
							return BatchFeature(data=lowerCAmelCase__     ,	tensor_type=lowerCAmelCase__				)




'''simple docstring'''

def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						list							)      ->						list:







	"""simple docstring"""

	if len(_SCREAMING_SNAKE_CASE							) < 2:
		return collection

	def circle_sort_util(_SCREAMING_SNAKE_CASE					:						list							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						int							) -> bool:
		lowerCAmelCase   =     False

		if low == high:
			return swapped

		lowerCAmelCase   =     low
		lowerCAmelCase   =     high

		while left < right:
			if collection[left] > collection[right]:
				lowerCAmelCase,       lowerCAmelCase   =     (
				    collection[right],
				    collection[left],
				)
				lowerCAmelCase   =     True

			left += 1
			right -= 1

		if left == right and collection[left] > collection[right + 1]:
			lowerCAmelCase,       lowerCAmelCase   =     (
			    collection[right + 1],
			    collection[left],
			)

			lowerCAmelCase   =     True

		lowerCAmelCase   =     low + int((high - low) / 2							)
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							,		_SCREAMING_SNAKE_CASE							)
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		mid + 1							,		_SCREAMING_SNAKE_CASE							)

		return swapped or left_swap or right_swap

	lowerCAmelCase   =     True

	while is_not_sorted is True:
		lowerCAmelCase   =     circle_sort_util(_SCREAMING_SNAKE_CASE							,		0							,		len(_SCREAMING_SNAKE_CASE							) - 1							)

	return collection


if __name__ == "__main__":
				UpperCAmelCase     		=   input('Enter numbers separated by a comma:\n').strip()
				UpperCAmelCase     		=   [int(item) for item in user_input.split(',')]
				print(circle_sort(unsorted))