




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 DeiTImageProcessor
class 		UpperCAmelCase       (    unittest.TestCase      ):







      '''simple docstring'''





      def __init__(						self							:					Any	,     __lowercase							:					Dict	,     __lowercase							:					Any=7	,     __lowercase							:					List[str]=3	,     __lowercase							:					Optional[Any]=18	,     __lowercase							:					List[str]=30	,     __lowercase							:					List[str]=4_00	,     __lowercase							:					int=True	,     __lowercase							:					str=None	,     __lowercase							:					Optional[Any]=True	,     __lowercase							:					str=None	,     __lowercase							:					Union[str, Any]=True	,     __lowercase							:					List[str]=[0.5, 0.5, 0.5]	,     __lowercase							:					List[Any]=[0.5, 0.5, 0.5]	,     ):






       """simple docstring"""






       snake_case_		   =					size if size is not None else {"height": 20, "width": 20}
       snake_case_		   =					crop_size if crop_size is not None else {"height": 18, "width": 18}

       snake_case_		   =					parent
       snake_case_		   =					batch_size
       snake_case_		   =					num_channels
       snake_case_		   =					image_size
       snake_case_		   =					min_resolution
       snake_case_		   =					max_resolution
       snake_case_		   =					do_resize
       snake_case_		   =					size
       snake_case_		   =					do_center_crop
       snake_case_		   =					crop_size
       snake_case_		   =					do_normalize
       snake_case_		   =					image_mean
       snake_case_		   =					image_std






      def 	snake_case__						(						self							:					List[str]						):






       """simple docstring"""






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







@require_torch
@require_vision
class 		UpperCAmelCase       (    UpperCAmelCase__	,       unittest.TestCase      ):







      '''simple docstring'''





      lowerCAmelCase_							=       DeiTImageProcessor if is_vision_available() else None
      lowerCAmelCase_							=       True






      def 	snake_case__						(						self							:					Dict						):






       """simple docstring"""






       snake_case_		   =					DeiTImageProcessingTester(self						)






      @property
      def 	snake_case__						(						self							:					Optional[int]						):






       """simple docstring"""






       return self.image_processor_tester.prepare_image_processor_dict()






      def 	snake_case__						(						self							:					str						):






       """simple docstring"""






       snake_case_		   =					self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(__lowercase	,     "do_resize"						)						)
       self.assertTrue(hasattr(__lowercase	,     "size"						)						)
       self.assertTrue(hasattr(__lowercase	,     "do_center_crop"						)						)
       self.assertTrue(hasattr(__lowercase	,     "center_crop"						)						)
       self.assertTrue(hasattr(__lowercase	,     "do_normalize"						)						)
       self.assertTrue(hasattr(__lowercase	,     "image_mean"						)						)
       self.assertTrue(hasattr(__lowercase	,     "image_std"						)						)






      def 	snake_case__						(						self							:					Optional[int]						):






       """simple docstring"""






       snake_case_		   =					self.image_processing_class.from_dict(self.image_processor_dict						)
       self.assertEqual(image_processor.size	,     {"height": 20, "width": 20}						)
       self.assertEqual(image_processor.crop_size	,     {"height": 18, "width": 18}						)

       snake_case_		   =					self.image_processing_class.from_dict(self.image_processor_dict	,     size=42	,     crop_size=84						)
       self.assertEqual(image_processor.size	,     {"height": 42, "width": 42}						)
       self.assertEqual(image_processor.crop_size	,     {"height": 84, "width": 84}						)






      def 	snake_case__						(						self							:					Dict						):






       """simple docstring"""






       pass






      def 	snake_case__						(						self							:					str						):






       """simple docstring"""






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

       # Test not batched input
       snake_case_		   =					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.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )	,     )

       # Test batched
       snake_case_		   =					image_processing(__lowercase	,     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.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )	,     )






      def 	snake_case__						(						self							:					Any						):






       """simple docstring"""






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

       # Test not batched input
       snake_case_		   =					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.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )	,     )

       # Test batched
       snake_case_		   =					image_processing(__lowercase	,     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.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )	,     )






      def 	snake_case__						(						self							:					str						):






       """simple docstring"""






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

       # Test not batched input
       snake_case_		   =					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.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )	,     )

       # Test batched
       snake_case_		   =					image_processing(__lowercase	,     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.crop_size["height"],
               self.image_processor_tester.crop_size["width"],
           )	,     )






def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	if right <= left:
		return
	snake_case_		   =					snake_case_		   =					left
	snake_case_		   =					right
	snake_case_		   =					sorting[left]
	while i <= b:
		if sorting[i] < pivot:
			snake_case_   ,				snake_case_		   =					sorting[i], sorting[a]
			a += 1
			i += 1
		elif sorting[i] > pivot:
			snake_case_   ,				snake_case_		   =					sorting[i], sorting[b]
			b -= 1
		else:
			i += 1
	quick_sort_apartition(_A					,     _A					,     a - 1							)
	quick_sort_apartition(_A					,     b + 1					,     _A							)


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	if left < right:
		snake_case_		   =					lomuto_partition(_A					,     _A					,     _A							)
		quick_sort_lomuto_partition(_A					,     _A					,     pivot_index - 1							)
		quick_sort_lomuto_partition(_A					,     pivot_index + 1					,     _A							)


def 						lowerCamelCase__   (		_A					,     _A					,     _A							):







	'''simple docstring'''





	snake_case_		   =					sorting[right]
	snake_case_		   =					left
	for i in range(_A					,     _A							):
		if sorting[i] < pivot:
			snake_case_   ,				snake_case_		   =					sorting[i], sorting[store_index]
			store_index += 1
	snake_case_   ,				snake_case_		   =					sorting[store_index], sorting[right]
	return store_index


def 						lowerCamelCase__   (		_A							):







	'''simple docstring'''





	if len(_A							) <= 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)

		lowercase__    :					Tuple					=						input("Enter numbers separated by a comma:\n").strip()
		lowercase__    :					int					=						[int(item) for item in user_input.split(",")]
		quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
		print(unsorted)
