


import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import ConvNextImageProcessor







class     __lowerCamelCase     (  unittest.TestCase     ):


							"""simple docstring"""

							def __init__(  self						,       UpperCAmelCase						,       UpperCAmelCase=7						,       UpperCAmelCase=3						,       UpperCAmelCase=18						,       UpperCAmelCase=30						,       UpperCAmelCase=400						,       UpperCAmelCase=True						,       UpperCAmelCase=None						,       UpperCAmelCase=0.875						,       UpperCAmelCase=True						,       UpperCAmelCase=[0.5, 0.5, 0.5]						,       UpperCAmelCase=[0.5, 0.5, 0.5]						,       )     ->       Any:






								'''simple docstring'''






								lowercase_			 = size if size is not None else {"shortest_edge": 20}
								lowercase_			 = parent
								lowercase_			 = batch_size
								lowercase_			 = num_channels
								lowercase_			 = image_size
								lowercase_			 = min_resolution
								lowercase_			 = max_resolution
								lowercase_			 = do_resize
								lowercase_			 = size
								lowercase_			 = crop_pct
								lowercase_			 = do_normalize
								lowercase_			 = image_mean
								lowercase_			 = image_std




							def 				A__      (  self  )     ->       Union[str, Any]:






								'''simple docstring'''






								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "crop_pct": self.crop_pct,
								}







@require_torch
@require_vision
class     __lowerCamelCase     (  snake_case_							,   unittest.TestCase     ):


							"""simple docstring"""

							lowerCAmelCase__          =				ConvNextImageProcessor if is_vision_available() else None




							def 				A__      (  self  )     ->       Union[str, Any]:






								'''simple docstring'''






								lowercase_			 = ConvNextImageProcessingTester(self  )




							@property
							def 				A__      (  self  )     ->       List[str]:






								'''simple docstring'''






								return self.image_processor_tester.prepare_image_processor_dict()




							def 				A__      (  self  )     ->       Tuple:






								'''simple docstring'''






								lowercase_			 = self.image_processing_class(**self.image_processor_dict  )
								self.assertTrue(hasattr(UpperCAmelCase						,       "do_resize"  )  )
								self.assertTrue(hasattr(UpperCAmelCase						,       "size"  )  )
								self.assertTrue(hasattr(UpperCAmelCase						,       "crop_pct"  )  )
								self.assertTrue(hasattr(UpperCAmelCase						,       "do_normalize"  )  )
								self.assertTrue(hasattr(UpperCAmelCase						,       "image_mean"  )  )
								self.assertTrue(hasattr(UpperCAmelCase						,       "image_std"  )  )




							def 				A__      (  self  )     ->       int:






								'''simple docstring'''






								lowercase_			 = self.image_processing_class.from_dict(self.image_processor_dict  )
								self.assertEqual(image_processor.size						,       {"shortest_edge": 20}  )

								lowercase_			 = self.image_processing_class.from_dict(self.image_processor_dict						,       size=42  )
								self.assertEqual(image_processor.size						,       {"shortest_edge": 42}  )




							def 				A__      (  self  )     ->       str:






								'''simple docstring'''






								pass




							def 				A__      (  self  )     ->       List[str]:






								'''simple docstring'''






								lowercase_			 = self.image_processing_class(**self.image_processor_dict  )
								# create random PIL images
								lowercase_			 = prepare_image_inputs(self.image_processor_tester						,       equal_resolution=UpperCAmelCase  )
								for image in image_inputs:
									self.assertIsInstance(UpperCAmelCase						,       Image.Image  )

								# Test not batched input
								lowercase_			 = image_processing(image_inputs[0]						,       return_tensors="pt"  ).pixel_values
								self.assertEqual(
								    encoded_images.shape						,       (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )						,       )

								# Test batched
								lowercase_			 = image_processing(UpperCAmelCase						,       return_tensors="pt"  ).pixel_values
								self.assertEqual(
								    encoded_images.shape						,       (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )						,       )




							def 				A__      (  self  )     ->       Tuple:






								'''simple docstring'''






								lowercase_			 = self.image_processing_class(**self.image_processor_dict  )
								# create random numpy tensors
								lowercase_			 = prepare_image_inputs(self.image_processor_tester						,       equal_resolution=UpperCAmelCase						,       numpify=UpperCAmelCase  )
								for image in image_inputs:
									self.assertIsInstance(UpperCAmelCase						,       np.ndarray  )

								# Test not batched input
								lowercase_			 = image_processing(image_inputs[0]						,       return_tensors="pt"  ).pixel_values
								self.assertEqual(
								    encoded_images.shape						,       (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )						,       )

								# Test batched
								lowercase_			 = image_processing(UpperCAmelCase						,       return_tensors="pt"  ).pixel_values
								self.assertEqual(
								    encoded_images.shape						,       (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )						,       )




							def 				A__      (  self  )     ->       Tuple:






								'''simple docstring'''






								lowercase_			 = self.image_processing_class(**self.image_processor_dict  )
								# create random PyTorch tensors
								lowercase_			 = prepare_image_inputs(self.image_processor_tester						,       equal_resolution=UpperCAmelCase						,       torchify=UpperCAmelCase  )
								for image in image_inputs:
									self.assertIsInstance(UpperCAmelCase						,       torch.Tensor  )

								# Test not batched input
								lowercase_			 = image_processing(image_inputs[0]						,       return_tensors="pt"  ).pixel_values
								self.assertEqual(
								    encoded_images.shape						,       (
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )						,       )

								# Test batched
								lowercase_			 = image_processing(UpperCAmelCase						,       return_tensors="pt"  ).pixel_values
								self.assertEqual(
								    encoded_images.shape						,       (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    )						,       )





from __future__ import annotations

from random import randrange




def SCREAMING_SNAKE_CASE_  (				__lowerCamelCase:		list  ):







	'''simple docstring'''



	if len(__lowerCamelCase  ) < 2:
		return collection
	lowercase_			 = randrange(len(__lowerCamelCase  )  )  # Use random element as pivot
	lowercase_			 = collection[pivot_index]
	lowercase_			 = []  # All elements greater than pivot
	lowercase_			 = []  # 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__":
							SCREAMING_SNAKE_CASE__       			=				input("""Enter numbers separated by a comma:\n""").strip()
							SCREAMING_SNAKE_CASE__       			=				[int(item) for item in user_input.split(""",""")]
							print(quick_sort(unsorted))

