



"""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"],
               )  ,			)





"""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)
