




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 ConvNextImageProcessor
class 				__snake_case     (unittest.TestCase						):



				def __init__(					self					:						Optional[int]						,		_UpperCAmelCase					:						Optional[Any]						,		_UpperCAmelCase					:						List[Any]=7						,		_UpperCAmelCase					:						Union[str, Any]=3						,		_UpperCAmelCase					:						Union[str, Any]=18						,		_UpperCAmelCase					:						Optional[Any]=30						,		_UpperCAmelCase					:						Tuple=400						,		_UpperCAmelCase					:						Any=True						,		_UpperCAmelCase					:						Dict=None						,		_UpperCAmelCase					:						Optional[Any]=0.875						,		_UpperCAmelCase					:						str=True						,		_UpperCAmelCase					:						Any=[0.5, 0.5, 0.5]						,		_UpperCAmelCase					:						Union[str, Any]=[0.5, 0.5, 0.5]						,		)     ->			Any:





								'''simple docstring'''




								_lowerCAmelCase			:			Tuple        =					size if size is not None else {"""shortest_edge""": 20}
								_lowerCAmelCase			:			Any        =					parent
								_lowerCAmelCase			:			Optional[int]        =					batch_size
								_lowerCAmelCase			:			int        =					num_channels
								_lowerCAmelCase			:			Dict        =					image_size
								_lowerCAmelCase			:			Dict        =					min_resolution
								_lowerCAmelCase			:			Any        =					max_resolution
								_lowerCAmelCase			:			List[Any]        =					do_resize
								_lowerCAmelCase			:			Dict        =					size
								_lowerCAmelCase			:			int        =					crop_pct
								_lowerCAmelCase			:			List[str]        =					do_normalize
								_lowerCAmelCase			:			Optional[int]        =					image_mean
								_lowerCAmelCase			:			str        =					image_std



				def 					SCREAMING_SNAKE_CASE				(					self					:						str					)     ->			List[Any]:





								'''simple docstring'''




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






@require_torch
@require_vision
class 				__snake_case     (_a  ,		unittest.TestCase						):
				lowerCAmelCase__			=   ConvNextImageProcessor if is_vision_available() else None



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase			:			Any        =					ConvNextImageProcessingTester(self					)



				@property
				def 					SCREAMING_SNAKE_CASE				(					self					:						str					)     ->			Union[str, Any]:





								'''simple docstring'''




								return self.image_processor_tester.prepare_image_processor_dict()



				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			List[Any]        =					self.image_processing_class(**self.image_processor_dict					)
								self.assertTrue(hasattr(_UpperCAmelCase						,		"""do_resize"""					)					)
								self.assertTrue(hasattr(_UpperCAmelCase						,		"""size"""					)					)
								self.assertTrue(hasattr(_UpperCAmelCase						,		"""crop_pct"""					)					)
								self.assertTrue(hasattr(_UpperCAmelCase						,		"""do_normalize"""					)					)
								self.assertTrue(hasattr(_UpperCAmelCase						,		"""image_mean"""					)					)
								self.assertTrue(hasattr(_UpperCAmelCase						,		"""image_std"""					)					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			Union[str, Any]        =					self.image_processing_class.from_dict(self.image_processor_dict					)
								self.assertEqual(image_processor.size						,		{"""shortest_edge""": 20}					)

								_lowerCAmelCase			:			Optional[int]        =					self.image_processing_class.from_dict(self.image_processor_dict						,		size=42					)
								self.assertEqual(image_processor.size						,		{"""shortest_edge""": 42}					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						int					)     ->			Tuple:





								'''simple docstring'''




								pass



				def 					SCREAMING_SNAKE_CASE				(					self					:						Tuple					)     ->			Dict:





								'''simple docstring'''




								_lowerCAmelCase			:			Union[str, Any]        =					self.image_processing_class(**self.image_processor_dict					)
								# create random PIL images
								_lowerCAmelCase			:			Tuple        =					prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_UpperCAmelCase					)
								for image in image_inputs:
												self.assertIsInstance(_UpperCAmelCase						,		Image.Image					)

								# Test not batched input
								_lowerCAmelCase			:			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.size["""shortest_edge"""],
								        self.image_processor_tester.size["""shortest_edge"""],
								    )						,		)

								# Test batched
								_lowerCAmelCase			:			List[str]        =					image_processing(_UpperCAmelCase						,		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["""shortest_edge"""],
								        self.image_processor_tester.size["""shortest_edge"""],
								    )						,		)



				def 					SCREAMING_SNAKE_CASE				(					self					:						int					)     ->			Optional[int]:





								'''simple docstring'''




								_lowerCAmelCase			:			Any        =					self.image_processing_class(**self.image_processor_dict					)
								# create random numpy tensors
								_lowerCAmelCase			:			Union[str, Any]        =					prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_UpperCAmelCase						,		numpify=_UpperCAmelCase					)
								for image in image_inputs:
												self.assertIsInstance(_UpperCAmelCase						,		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["""shortest_edge"""],
								        self.image_processor_tester.size["""shortest_edge"""],
								    )						,		)

								# Test batched
								_lowerCAmelCase			:			List[Any]        =					image_processing(_UpperCAmelCase						,		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["""shortest_edge"""],
								        self.image_processor_tester.size["""shortest_edge"""],
								    )						,		)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict					)     ->			Any:





								'''simple docstring'''




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

								# Test not batched input
								_lowerCAmelCase			:			Optional[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["""shortest_edge"""],
								        self.image_processor_tester.size["""shortest_edge"""],
								    )						,		)

								# Test batched
								_lowerCAmelCase			:			Optional[int]        =					image_processing(_UpperCAmelCase						,		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["""shortest_edge"""],
								        self.image_processor_tester.size["""shortest_edge"""],
								    )						,		)








"""simple docstring"""





def 		lowerCAmelCase_	(  lowercase_   :							Any		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Optional[int]												=       len(lowercase_		)
 for i in range(length - 1		):
  __SCREAMING_SNAKE_CASE :			str												=       False
  for j in range(length - 1 - i		):
   if collection[j] > collection[j + 1]:
    __SCREAMING_SNAKE_CASE :			Any												=       True
    __SCREAMING_SNAKE_CASE							,      __SCREAMING_SNAKE_CASE :			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()

    _lowerCamelCase       =	input('''Enter numbers separated by a comma:''').strip()
    _lowerCamelCase       =	[int(item) for item in user_input.split(''',''')]
    _lowerCamelCase       =	time.process_time()
    print(*bubble_sort(unsorted), sep=''',''')
    print(f'Processing time: {(time.process_time() - start)%1e9 + 7}')

