code: 
  
  
  
  """simple docstring"""
  
  
  
  
  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
  
  
  if is_torch_available():
   import torch
  
  if is_vision_available():
   from PIL import Image
  
   from transformers import BlipImageProcessor
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       unittest.TestCase		):
  
  
  
        def __init__(  self  :			Union[str, Any]  ,			A  :			Optional[int]  ,			A  :			int=7  ,			A  :			Union[str, Any]=3  ,			A  :			str=1_8  ,			A  :			Optional[int]=3_0  ,			A  :			str=4_0_0  ,			A  :			List[Any]=True  ,			A  :			Optional[int]=None  ,			A  :			Any=True  ,			A  :			str=False  ,			A  :			Any=[0.48_145_466, 0.4_578_275, 0.40_821_073]  ,			A  :			Dict=[0.26_862_954, 0.26_130_258, 0.27_577_711]  ,			A  :			List[Any]=True  ,			):
             _UpperCAmelCase    :			Any              =					size if size is not None else {"height": 2_0, "width": 2_0}
             _UpperCAmelCase    :			Tuple              =					parent
             _UpperCAmelCase    :			Tuple              =					batch_size
             _UpperCAmelCase    :			Optional[Any]              =					num_channels
             _UpperCAmelCase    :			Optional[Any]              =					image_size
             _UpperCAmelCase    :			Any              =					min_resolution
             _UpperCAmelCase    :			Dict              =					max_resolution
             _UpperCAmelCase    :			Optional[int]              =					do_resize
             _UpperCAmelCase    :			Any              =					size
             _UpperCAmelCase    :			int              =					do_normalize
             _UpperCAmelCase    :			Union[str, Any]              =					image_mean
             _UpperCAmelCase    :			str              =					image_std
             _UpperCAmelCase    :			Union[str, Any]              =					do_pad
             _UpperCAmelCase    :			List[str]              =					do_convert_rgb
  
  
  
        def 		snake_case_      (  self  :			Any		):
             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_normalize": self.do_normalize,
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_convert_rgb": self.do_convert_rgb,
                 "do_pad": self.do_pad,
             }
  
  
  
        def 		snake_case_      (  self  :			str  ,			A  :			Optional[int]=False  ,			A  :			Union[str, Any]=False  ,			A  :			Union[str, Any]=False		):
             assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
             if equal_resolution:
                  _UpperCAmelCase    :			List[str]              =					[]
                  for i in range(self.batch_size		):
                       image_inputs.append(
                           np.random.randint(
                               2_5_5  ,			size=(self.num_channels, self.max_resolution, self.max_resolution)  ,			dtype=np.uinta		)		)
             else:
                  _UpperCAmelCase    :			Optional[int]              =					[]
                  for i in range(self.batch_size		):
                       _UpperCAmelCase       , _UpperCAmelCase    :			Dict              =					np.random.choice(np.arange(self.min_resolution  ,			self.max_resolution		)  ,			2		)
                       image_inputs.append(np.random.randint(2_5_5  ,			size=(self.num_channels, width, height)  ,			dtype=np.uinta		)		)
  
             if not numpify and not torchify:
                  # PIL expects the channel dimension as last dimension
                  _UpperCAmelCase    :			int              =					[Image.fromarray(np.moveaxis(A  ,			0  ,			-1		)		) for x in image_inputs]
  
             if torchify:
                  _UpperCAmelCase    :			Dict              =					[torch.from_numpy(A		) for x in image_inputs]
  
             return image_inputs
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 			UpperCAmelCase_						(       _UpperCamelCase ,    unittest.TestCase		):
        __SCREAMING_SNAKE_CASE		:     List[Any]				  =				BlipImageProcessor if is_vision_available() else None
  
  
  
        def 		snake_case_      (  self  :			str		):
             _UpperCAmelCase    :			Union[str, Any]              =					BlipImageProcessingTester(self		)
  
  
  
        @property
        def 		snake_case_      (  self  :			str		):
             return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
        def 		snake_case_      (  self  :			Dict		):
             _UpperCAmelCase    :			List[str]              =					self.image_processing_class(**self.image_processor_dict		)
             self.assertTrue(hasattr(A  ,			"do_resize"		)		)
             self.assertTrue(hasattr(A  ,			"size"		)		)
             self.assertTrue(hasattr(A  ,			"do_normalize"		)		)
             self.assertTrue(hasattr(A  ,			"image_mean"		)		)
             self.assertTrue(hasattr(A  ,			"image_std"		)		)
             self.assertTrue(hasattr(A  ,			"do_convert_rgb"		)		)
  
  
  
        def 		snake_case_      (  self  :			Any		):
             pass
  
  
  
        def 		snake_case_      (  self  :			Any		):
             # Initialize image_processor
             _UpperCAmelCase    :			Any              =					self.image_processing_class(**self.image_processor_dict		)
             # create random PIL images
             _UpperCAmelCase    :			Optional[Any]              =					self.image_processor_tester.prepare_inputs(equal_resolution=A		)
             for image in image_inputs:
                  self.assertIsInstance(A  ,			Image.Image		)
  
             # Test not batched input
             _UpperCAmelCase    :			Optional[Any]              =					image_processor(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["height"],
                     self.image_processor_tester.size["width"],
                 )  ,			)
  
             # Test batched
             _UpperCAmelCase    :			Optional[Any]              =					image_processor(A  ,			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["height"],
                     self.image_processor_tester.size["width"],
                 )  ,			)
  
  
  
        def 		snake_case_      (  self  :			int		):
             # Initialize image_processor
             _UpperCAmelCase    :			str              =					self.image_processing_class(**self.image_processor_dict		)
             # create random numpy tensors
             _UpperCAmelCase    :			int              =					self.image_processor_tester.prepare_inputs(equal_resolution=A  ,			numpify=A		)
             for image in image_inputs:
                  self.assertIsInstance(A  ,			np.ndarray		)
  
             # Test not batched input
             _UpperCAmelCase    :			List[Any]              =					image_processor(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["height"],
                     self.image_processor_tester.size["width"],
                 )  ,			)
  
             # Test batched
             _UpperCAmelCase    :			Any              =					image_processor(A  ,			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["height"],
                     self.image_processor_tester.size["width"],
                 )  ,			)
  
  
  
        def 		snake_case_      (  self  :			Optional[Any]		):
             # Initialize image_processor
             _UpperCAmelCase    :			Tuple              =					self.image_processing_class(**self.image_processor_dict		)
             # create random PyTorch tensors
             _UpperCAmelCase    :			Dict              =					self.image_processor_tester.prepare_inputs(equal_resolution=A  ,			torchify=A		)
             for image in image_inputs:
                  self.assertIsInstance(A  ,			torch.Tensor		)
  
             # Test not batched input
             _UpperCAmelCase    :			Optional[int]              =					image_processor(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["height"],
                     self.image_processor_tester.size["width"],
                 )  ,			)
  
             # Test batched
             _UpperCAmelCase    :			Dict              =					image_processor(A  ,			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["height"],
                     self.image_processor_tester.size["width"],
                 )  ,			)
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 			UpperCAmelCase_						(       _UpperCamelCase ,    unittest.TestCase		):
        __SCREAMING_SNAKE_CASE		:     Optional[int]				  =				BlipImageProcessor if is_vision_available() else None
  
  
  
        def 		snake_case_      (  self  :			List[str]		):
             _UpperCAmelCase    :			List[str]              =					BlipImageProcessingTester(self  ,			num_channels=4		)
             _UpperCAmelCase    :			List[str]              =					3
  
  
  
        @property
        def 		snake_case_      (  self  :			Any		):
             return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
        def 		snake_case_      (  self  :			str		):
             _UpperCAmelCase    :			str              =					self.image_processing_class(**self.image_processor_dict		)
             self.assertTrue(hasattr(A  ,			"do_resize"		)		)
             self.assertTrue(hasattr(A  ,			"size"		)		)
             self.assertTrue(hasattr(A  ,			"do_normalize"		)		)
             self.assertTrue(hasattr(A  ,			"image_mean"		)		)
             self.assertTrue(hasattr(A  ,			"image_std"		)		)
             self.assertTrue(hasattr(A  ,			"do_convert_rgb"		)		)
  
  
  
        def 		snake_case_      (  self  :			int		):
             pass
  
  
  
        def 		snake_case_      (  self  :			Dict		):
             # Initialize image_processor
             _UpperCAmelCase    :			Dict              =					self.image_processing_class(**self.image_processor_dict		)
             # create random PIL images
             _UpperCAmelCase    :			List[Any]              =					self.image_processor_tester.prepare_inputs(equal_resolution=A		)
             for image in image_inputs:
                  self.assertIsInstance(A  ,			Image.Image		)
  
             # Test not batched input
             _UpperCAmelCase    :			Union[str, Any]              =					image_processor(image_inputs[0]  ,			return_tensors="pt"		).pixel_values
             self.assertEqual(
                 encoded_images.shape  ,			(
                     1,
                     self.expected_encoded_image_num_channels,
                     self.image_processor_tester.size["height"],
                     self.image_processor_tester.size["width"],
                 )  ,			)
  
             # Test batched
             _UpperCAmelCase    :			Optional[int]              =					image_processor(A  ,			return_tensors="pt"		).pixel_values
             self.assertEqual(
                 encoded_images.shape  ,			(
                     self.image_processor_tester.batch_size,
                     self.expected_encoded_image_num_channels,
                     self.image_processor_tester.size["height"],
                     self.image_processor_tester.size["width"],
                 )  ,			)
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		int       )						->     None:
  
  
  
  
  
       '''simple docstring'''
  
       if right <= left:
            return
       _UpperCAmelCase    :			Any              =					left
       _UpperCAmelCase    :			Tuple              =					right
       _UpperCAmelCase    :			List[Any]              =					sorting[left]
       while i <= b:
            if sorting[i] < pivot:
                 _UpperCAmelCase       , _UpperCAmelCase    :			List[Any]              =					sorting[i], sorting[a]
                 a += 1
                 i += 1
            elif sorting[i] > pivot:
                 _UpperCAmelCase       , _UpperCAmelCase    :			Any              =					sorting[i], sorting[b]
                 b -= 1
            else:
                 i += 1
       quick_sort_apartition(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						a - 1       )
       quick_sort_apartition(SCREAMING_SNAKE_CASE__		,						b + 1		,						SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		int       )						->     None:
  
  
  
  
  
       '''simple docstring'''
  
       if left < right:
            _UpperCAmelCase    :			Optional[int]              =					lomuto_partition(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
            quick_sort_lomuto_partition(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						pivot_index - 1       )
            quick_sort_lomuto_partition(SCREAMING_SNAKE_CASE__		,						pivot_index + 1		,						SCREAMING_SNAKE_CASE__       )
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list		,						SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		int       )						->     int:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Dict              =					sorting[right]
       _UpperCAmelCase    :			Any              =					left
       for i in range(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       ):
            if sorting[i] < pivot:
                 _UpperCAmelCase       , _UpperCAmelCase    :			Dict              =					sorting[i], sorting[store_index]
                 store_index += 1
       _UpperCAmelCase       , _UpperCAmelCase    :			List[str]              =					sorting[store_index], sorting[right]
       return store_index
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		list       )						->     list:
  
  
  
  
  
       '''simple docstring'''
  
       if len(SCREAMING_SNAKE_CASE__       ) <= 1:
            return sorting
       return (
           three_way_radix_quicksort([i for i in sorting if i < sorting[0]]       )
           + [i for i in sorting if i == sorting[0]]
           + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]       )
       )
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod(verbose=True)
  
   _lowerCAmelCase       :      Union[str, Any]							=							input("Enter numbers separated by a comma:\n").strip()
   _lowerCAmelCase       :      List[str]							=							[int(item) for item in user_input.split(",")]
   quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
   print(unsorted)
  