



"""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, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor

class 			__SCREAMING_SNAKE_CASE      ( unittest.TestCase  ):


					'''simple docstring'''





					def __init__(			self				:    Dict,    lowerCamelCase				:    List[Any],    lowerCamelCase				:    List[Any]=7,    lowerCamelCase				:    Optional[Any]=3,    lowerCamelCase				:    Any=18,    lowerCamelCase				:    Optional[int]=30,    lowerCamelCase				:    Dict=400,    lowerCamelCase				:    Optional[int]=True,    lowerCamelCase				:    Tuple=None,    lowerCamelCase				:    str=True,    lowerCamelCase				:    Union[str, Any]=[0.5, 0.5, 0.5],    lowerCamelCase				:    str=[0.5, 0.5, 0.5],    )->   List[Any]:
								lowerCamelCase__						:						Optional[Any]						      =size if size is not None else {'''height''': 18, '''width''': 18}
								lowerCamelCase__						:						str						      =parent
								lowerCamelCase__						:						List[str]						      =batch_size
								lowerCamelCase__						:						Dict						      =num_channels
								lowerCamelCase__						:						str						      =image_size
								lowerCamelCase__						:						Any						      =min_resolution
								lowerCamelCase__						:						Union[str, Any]						      =max_resolution
								lowerCamelCase__						:						List[Any]						      =do_resize
								lowerCamelCase__						:						Tuple						      =size
								lowerCamelCase__						:						Optional[Any]						      =do_normalize
								lowerCamelCase__						:						Optional[Any]						      =image_mean
								lowerCamelCase__						:						List[Any]						      =image_std




					def 						snake_case			(			self				:    Dict	)->   int:
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								}





@require_torch
@require_vision
class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_						,  unittest.TestCase  ):


					'''simple docstring'''





					_a       =				ViTImageProcessor if is_vision_available() else None



					def 						snake_case			(			self				:    List[Any]	)->   Dict:
								lowerCamelCase__						:						Union[str, Any]						      =ViTImageProcessingTester(self	)



					@property
					def 						snake_case			(			self				:    int	)->   Any:
								return self.image_processor_tester.prepare_image_processor_dict()



					def 						snake_case			(			self				:    int	)->   Dict:
								lowerCamelCase__						:						Optional[int]						      =self.image_processing_class(**self.image_processor_dict	)
								self.assertTrue(hasattr(lowerCamelCase,    '''image_mean'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''image_std'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''do_normalize'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''do_resize'''	)	)
								self.assertTrue(hasattr(lowerCamelCase,    '''size'''	)	)



					def 						snake_case			(			self				:    Tuple	)->   List[Any]:
								lowerCamelCase__						:						List[str]						      =self.image_processing_class.from_dict(self.image_processor_dict	)
								self.assertEqual(image_processor.size,    {'''height''': 18, '''width''': 18}	)

								lowerCamelCase__						:						Union[str, Any]						      =self.image_processing_class.from_dict(self.image_processor_dict,    size=42	)
								self.assertEqual(image_processor.size,    {'''height''': 42, '''width''': 42}	)



					def 						snake_case			(			self				:    Optional[int]	)->   List[str]:
								pass



					def 						snake_case			(			self				:    Any	)->   int:
								# Initialize image_processing
								lowerCamelCase__						:						Union[str, Any]						      =self.image_processing_class(**self.image_processor_dict	)
								# create random PIL images
								lowerCamelCase__						:						Optional[Any]						      =prepare_image_inputs(self.image_processor_tester,    equal_resolution=lowerCamelCase	)
								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,    Image.Image	)

								# Test not batched input
								lowerCamelCase__						:						Tuple						      =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['''height'''],
								        self.image_processor_tester.size['''width'''],
								    ),    )

								# Test batched
								lowerCamelCase__						:						Tuple						      =image_processing(lowerCamelCase,    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				:    Dict	)->   Optional[int]:
								# Initialize image_processing
								lowerCamelCase__						:						int						      =self.image_processing_class(**self.image_processor_dict	)
								# create random numpy tensors
								lowerCamelCase__						:						int						      =prepare_image_inputs(self.image_processor_tester,    equal_resolution=lowerCamelCase,    numpify=lowerCamelCase	)
								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,    np.ndarray	)

								# Test not batched input
								lowerCamelCase__						:						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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    ),    )

								# Test batched
								lowerCamelCase__						:						str						      =image_processing(lowerCamelCase,    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				:    Tuple	)->   Any:
								# Initialize image_processing
								lowerCamelCase__						:						Any						      =self.image_processing_class(**self.image_processor_dict	)
								# create random PyTorch tensors
								lowerCamelCase__						:						Optional[int]						      =prepare_image_inputs(self.image_processor_tester,    equal_resolution=lowerCamelCase,    torchify=lowerCamelCase	)
								for image in image_inputs:
											self.assertIsInstance(lowerCamelCase,    torch.Tensor	)

								# Test not batched input
								lowerCamelCase__						:						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.size['''height'''],
								        self.image_processor_tester.size['''width'''],
								    ),    )

								# Test batched
								lowerCamelCase__						:						Any						      =image_processing(lowerCamelCase,    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'''],
								    ),    )









"""simple docstring"""






def 	snake_case__  (						__lowerCamelCase							:       Union[str, Any]      ):
			"""simple docstring"""
			lowerCamelCase__						:						Dict						      =len(__lowerCamelCase      )
			for i in range(length - 1      ):
						lowerCamelCase__						:						Any						      =False
						for j in range(length - 1 - i      ):
									if collection[j] > collection[j + 1]:
												lowerCamelCase__						:						List[str]						      =True
												lowerCamelCase__      ,  lowerCamelCase__						:						List[str]						      =collection[j + 1], collection[j]
						if not swapped:
									break  # Stop iteration if the collection is sorted.
			return collection


if __name__ == "__main__":
		import doctest
		import time

		doctest.testmod()

		_lowercase					:					str 		=		input("Enter numbers separated by a comma:").strip()
		_lowercase					:					Dict 		=		[int(item) for item in user_input.split(",")]
		_lowercase					:					Optional[Any] 		=		time.process_time()
		print(*bubble_sort(unsorted), sep=",")
		print(f'Processing time: {(time.process_time() - start)%1E9 + 7}')




