
'''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 ChineseCLIPImageProcessor






class  lowerCAmelCase__   (  unittest.TestCase     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=7				,							__SCREAMING_SNAKE_CASE=3				,							__SCREAMING_SNAKE_CASE=18				,							__SCREAMING_SNAKE_CASE=30				,							__SCREAMING_SNAKE_CASE=4_00				,							__SCREAMING_SNAKE_CASE=True				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=True				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=True				,							__SCREAMING_SNAKE_CASE=[0.48_145_466, 0.4_578_275, 0.40_821_073]				,							__SCREAMING_SNAKE_CASE=[0.26_862_954, 0.26_130_258, 0.27_577_711]				,							__SCREAMING_SNAKE_CASE=True				,							):


											"""simple docstring"""





											lowercase_	:     str													=       size if size is not None else {'height': 2_24, 'width': 2_24}
											lowercase_	:     Tuple													=       crop_size if crop_size is not None else {'height': 18, 'width': 18}
											lowercase_	:     Dict													=       parent
											lowercase_	:     Dict													=       batch_size
											lowercase_	:     Any													=       num_channels
											lowercase_	:     Any													=       image_size
											lowercase_	:     Optional[int]													=       min_resolution
											lowercase_	:     Tuple													=       max_resolution
											lowercase_	:     int													=       do_resize
											lowercase_	:     Union[str, Any]													=       size
											lowercase_	:     Optional[int]													=       do_center_crop
											lowercase_	:     Optional[int]													=       crop_size
											lowercase_	:     str													=       do_normalize
											lowercase_	:     List[str]													=       image_mean
											lowercase_	:     str													=       image_std
											lowercase_	:     Union[str, Any]													=       do_convert_rgb






					def 	_snake_case    (   self		):


											"""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,
											    "do_convert_rgb": self.do_convert_rgb,
											}






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE=False				,							__SCREAMING_SNAKE_CASE=False				,							__SCREAMING_SNAKE_CASE=False		):


											"""simple docstring"""





											assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

											if equal_resolution:
																	lowercase_	:     Optional[int]													=       []
																	for i in range(self.batch_size		):
																							image_inputs.append(
																							    np.random.randint(
																							        2_55				,							size=(self.num_channels, self.max_resolution, self.max_resolution)				,							dtype=np.uinta		)		)
											else:
																	lowercase_	:     Tuple													=       []
																	for i in range(self.batch_size		):
																							lowercase_	:     Tuple													=       np.random.choice(np.arange(self.min_resolution				,							self.max_resolution		)				,							2		)
																							image_inputs.append(np.random.randint(2_55				,							size=(self.num_channels, width, height)				,							dtype=np.uinta		)		)

											if not numpify and not torchify:
																	# PIL expects the channel dimension as last dimension
																	lowercase_	:     List[Any]													=       [Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE				,							0				,							-1		)		) for x in image_inputs]

											if torchify:
																	lowercase_	:     Any													=       [torch.from_numpy(__SCREAMING_SNAKE_CASE		) for x in image_inputs]

											return image_inputs







@require_torch
@require_vision
class  lowerCAmelCase__   (  __UpperCAmelCase    ,						unittest.TestCase     ):
					lowerCAmelCase_        =     ChineseCLIPImageProcessor if is_vision_available() else None






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Union[str, Any]													=       ChineseCLIPImageProcessingTester(self				,							do_center_crop=__SCREAMING_SNAKE_CASE		)






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self.image_processor_tester.prepare_image_processor_dict()






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       self.image_processing_class(**self.image_processor_dict		)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE				,							'''do_resize'''		)		)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE				,							'''size'''		)		)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE				,							'''do_center_crop'''		)		)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE				,							'''center_crop'''		)		)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE				,							'''do_normalize'''		)		)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE				,							'''image_mean'''		)		)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE				,							'''image_std'''		)		)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE				,							'''do_convert_rgb'''		)		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       self.image_processing_class.from_dict(self.image_processor_dict		)
											self.assertEqual(image_processor.size				,							{'''height''': 2_24, '''width''': 2_24}		)
											self.assertEqual(image_processor.crop_size				,							{'''height''': 18, '''width''': 18}		)

											lowercase_	:     List[str]													=       self.image_processing_class.from_dict(self.image_processor_dict				,							size=42				,							crop_size=84		)
											self.assertEqual(image_processor.size				,							{'''shortest_edge''': 42}		)
											self.assertEqual(image_processor.crop_size				,							{'''height''': 84, '''width''': 84}		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											pass






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     str													=       self.image_processing_class(**self.image_processor_dict		)
											# create random PIL images
											lowercase_	:     Optional[Any]													=       self.image_processor_tester.prepare_inputs(equal_resolution=__SCREAMING_SNAKE_CASE		)
											for image in image_inputs:
																	self.assertIsInstance(__SCREAMING_SNAKE_CASE				,							Image.Image		)

											# Test not batched input
											lowercase_	:     Dict													=       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
											lowercase_	:     str													=       image_processing(__SCREAMING_SNAKE_CASE				,							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		):


											"""simple docstring"""





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

											# Test not batched input
											lowercase_	:     Any													=       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
											lowercase_	:     List[Any]													=       image_processing(__SCREAMING_SNAKE_CASE				,							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		):


											"""simple docstring"""





											lowercase_	:     int													=       self.image_processing_class(**self.image_processor_dict		)
											# create random PyTorch tensors
											lowercase_	:     List[str]													=       self.image_processor_tester.prepare_inputs(equal_resolution=__SCREAMING_SNAKE_CASE				,							torchify=__SCREAMING_SNAKE_CASE		)
											for image in image_inputs:
																	self.assertIsInstance(__SCREAMING_SNAKE_CASE				,							torch.Tensor		)

											# Test not batched input
											lowercase_	:     List[Any]													=       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
											lowercase_	:     str													=       image_processing(__SCREAMING_SNAKE_CASE				,							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'''],
											    )				,							)







@require_torch
@require_vision
class  lowerCAmelCase__   (  __UpperCAmelCase    ,						unittest.TestCase     ):
					lowerCAmelCase_        =     ChineseCLIPImageProcessor if is_vision_available() else None






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Union[str, Any]													=       ChineseCLIPImageProcessingTester(self				,							num_channels=4				,							do_center_crop=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[Any]													=       3






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self.image_processor_tester.prepare_image_processor_dict()






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Dict													=       self.image_processing_class(**self.image_processor_dict		)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE				,							'''do_resize'''		)		)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE				,							'''size'''		)		)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE				,							'''do_center_crop'''		)		)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE				,							'''center_crop'''		)		)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE				,							'''do_normalize'''		)		)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE				,							'''image_mean'''		)		)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE				,							'''image_std'''		)		)
											self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE				,							'''do_convert_rgb'''		)		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											pass






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       self.image_processing_class(**self.image_processor_dict		)
											# create random PIL images
											lowercase_	:     Optional[int]													=       self.image_processor_tester.prepare_inputs(equal_resolution=__SCREAMING_SNAKE_CASE		)
											for image in image_inputs:
																	self.assertIsInstance(__SCREAMING_SNAKE_CASE				,							Image.Image		)

											# Test not batched input
											lowercase_	:     List[str]													=       image_processing(image_inputs[0]				,							return_tensors='''pt'''		).pixel_values
											self.assertEqual(
											    encoded_images.shape				,							(
											        1,
											        self.expected_encoded_image_num_channels,
											        self.image_processor_tester.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )				,							)

											# Test batched
											lowercase_	:     Optional[Any]													=       image_processing(__SCREAMING_SNAKE_CASE				,							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.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )				,							)










def A_	(		A__				)  ->		int:
       stooge(A__		,      0		,      len(A__				) - 1				)
       return arr



def A_	(		A__		,      A__		,      A__				)  ->		List[Any]:
       if i >= h:
              return

       # If first element is smaller than the last then swap them
       if arr[i] > arr[h]:
              a__ ,							a__						:	List[str] =				arr[h], arr[i]

       # If there are more than 2 elements in the array
       if h - i + 1 > 2:
              a__						:	Dict =				(int)((h - i + 1) / 3				)

              # Recursively sort first 2/3 elements
              stooge(A__		,      A__		,      (h - t)				)

              # Recursively sort last 2/3 elements
              stooge(A__		,      i + t		,      (A__)				)

              # Recursively sort first 2/3 elements
              stooge(A__		,      A__		,      (h - t)				)


if __name__ == "__main__":
    lowercase      :       Dict									=  input("""Enter numbers separated by a comma:\n""").strip()
    lowercase      :       Dict									=  [int(item) for item in user_input.split(""",""")]
    print(stooge_sort(unsorted))


