



'''simple docstring'''



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


lowerCAmelCase						:		Union[str, Any]						      = logging.get_logger(__name__)


if is_vision_available():
	import PIL






class        UpperCAmelCase__						(		UpperCamelCase__			):
					a   :  List[Any]   =				["""pixel_values"""]



					def __init__(     self      ,					UpperCamelCase = True      ,					UpperCamelCase = None      ,					UpperCamelCase = PILImageResampling.BICUBIC      ,					UpperCamelCase = True      ,					UpperCamelCase = None      ,					UpperCamelCase = True      ,					UpperCamelCase = 1 / 255      ,					UpperCamelCase = True      ,					UpperCamelCase = None      ,					UpperCamelCase = None      ,					UpperCamelCase = True      ,					**UpperCamelCase      ,					)  ->      None:
						super().__init__(**UpperCamelCase  )
						__lowerCAmelCase       				=	size if size is not None else {"shortest_edge": 224}
						__lowerCAmelCase       				=	get_size_dict(UpperCamelCase      ,					default_to_square=UpperCamelCase  )
						__lowerCAmelCase       				=	crop_size if crop_size is not None else {"height": 224, "width": 224}
						__lowerCAmelCase       				=	get_size_dict(UpperCamelCase  )

						__lowerCAmelCase       				=	do_resize
						__lowerCAmelCase       				=	size
						__lowerCAmelCase       				=	resample
						__lowerCAmelCase       				=	do_center_crop
						__lowerCAmelCase       				=	crop_size
						__lowerCAmelCase       				=	do_rescale
						__lowerCAmelCase       				=	rescale_factor
						__lowerCAmelCase       				=	do_normalize
						__lowerCAmelCase       				=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
						__lowerCAmelCase       				=	image_std if image_std is not None else OPENAI_CLIP_STD
						__lowerCAmelCase       				=	do_convert_rgb



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase = PILImageResampling.BICUBIC      ,					UpperCamelCase = None      ,					**UpperCamelCase      ,					)  ->      np.ndarray:
						__lowerCAmelCase       				=	get_size_dict(UpperCamelCase      ,					default_to_square=UpperCamelCase  )
						__lowerCAmelCase       				=	get_resize_output_image_size(
						    UpperCamelCase      ,					size=(size["height"], size["width"])      ,					default_to_square=UpperCamelCase  )
						return resize(UpperCamelCase      ,					size=UpperCamelCase      ,					resample=UpperCamelCase      ,					data_format=UpperCamelCase      ,					**UpperCamelCase  )



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase = None      ,					**UpperCamelCase      ,					)  ->      np.ndarray:
						__lowerCAmelCase       				=	get_size_dict(UpperCamelCase  )
						return center_crop(UpperCamelCase      ,					size=(size["height"], size["width"])      ,					data_format=UpperCamelCase      ,					**UpperCamelCase  )



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase = None      ,					**UpperCamelCase      ,					)  ->      Any:
						return rescale(UpperCamelCase      ,					scale=UpperCamelCase      ,					data_format=UpperCamelCase      ,					**UpperCamelCase  )



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase = None      ,					**UpperCamelCase      ,					)  ->      np.ndarray:
						return normalize(UpperCamelCase      ,					mean=UpperCamelCase      ,					std=UpperCamelCase      ,					data_format=UpperCamelCase      ,					**UpperCamelCase  )



					def 							UpperCAmelCase_			(     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 = None      ,					UpperCamelCase = ChannelDimension.FIRST      ,					**UpperCamelCase      ,					)  ->      PIL.Image.Image:
						__lowerCAmelCase       				=	do_resize if do_resize is not None else self.do_resize
						__lowerCAmelCase       				=	size if size is not None else self.size
						__lowerCAmelCase       				=	get_size_dict(UpperCamelCase      ,					default_to_square=UpperCamelCase  )
						__lowerCAmelCase       				=	resample if resample is not None else self.resample
						__lowerCAmelCase       				=	do_center_crop if do_center_crop is not None else self.do_center_crop
						__lowerCAmelCase       				=	crop_size if crop_size is not None else self.crop_size
						__lowerCAmelCase       				=	get_size_dict(UpperCamelCase  )
						__lowerCAmelCase       				=	do_rescale if do_rescale is not None else self.do_rescale
						__lowerCAmelCase       				=	rescale_factor if rescale_factor is not None else self.rescale_factor
						__lowerCAmelCase       				=	do_normalize if do_normalize is not None else self.do_normalize
						__lowerCAmelCase       				=	image_mean if image_mean is not None else self.image_mean
						__lowerCAmelCase       				=	image_std if image_std is not None else self.image_std
						__lowerCAmelCase       				=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

						__lowerCAmelCase       				=	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."  )

						# PIL RGBA images are converted to RGB
						if do_convert_rgb:
							__lowerCAmelCase       				=	[convert_to_rgb(UpperCamelCase  ) for image in images]

						# All transformations expect numpy arrays.
						__lowerCAmelCase       				=	[to_numpy_array(UpperCamelCase  ) for image in images]

						if do_resize:
							__lowerCAmelCase       				=	[self.resize(image=UpperCamelCase      ,					size=UpperCamelCase      ,					resample=UpperCamelCase  ) for image in images]

						if do_center_crop:
							__lowerCAmelCase       				=	[self.center_crop(image=UpperCamelCase      ,					size=UpperCamelCase  ) for image in images]

						if do_rescale:
							__lowerCAmelCase       				=	[self.rescale(image=UpperCamelCase      ,					scale=UpperCamelCase  ) for image in images]

						if do_normalize:
							__lowerCAmelCase       				=	[self.normalize(image=UpperCamelCase      ,					mean=UpperCamelCase      ,					std=UpperCamelCase  ) for image in images]

						__lowerCAmelCase       				=	[to_channel_dimension_format(UpperCamelCase      ,					UpperCamelCase  ) for image in images]

						__lowerCAmelCase       				=	{"pixel_values": images}
						return BatchFeature(data=UpperCamelCase      ,					tensor_type=UpperCamelCase  )




'''simple docstring'''



from __future__ import annotations

from random import randrange



def 	__lowerCAmelCase       (				lowerCamelCase :    list       ):






	'''simple docstring'''




	if len(lowerCamelCase       ) < 2:
		return collection
	__lowerCAmelCase       				=	randrange(len(lowerCamelCase       )       )  # Use random element as pivot
	__lowerCAmelCase       				=	collection[pivot_index]
	__lowerCAmelCase       				=	[]  # All elements greater than pivot
	__lowerCAmelCase       				=	[]  # All elements less than or equal to pivot

	for element in collection[:pivot_index]:
		(greater if element > pivot else lesser).append(lowerCamelCase       )

	for element in collection[pivot_index + 1 :]:
		(greater if element > pivot else lesser).append(lowerCamelCase       )

	return [*quick_sort(lowerCamelCase       ), pivot, *quick_sort(lowerCamelCase       )]


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