




'''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 ConvNextImageProcessor






class     lowercase  ( unittest.TestCase):






      def __init__(self			,	a__			,	a__=7			,	a__=3			,	a__=1_8			,	a__=3_0			,	a__=4_0_0			,	a__=True			,	a__=None			,	a__=0.8_75			,	a__=True			,	a__=[0.5, 0.5, 0.5]			,	a__=[0.5, 0.5, 0.5]			,	)							-> Tuple:
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			size if size is not None else {'''shortest_edge''': 2_0}
         SCREAMING_SNAKE_CASE__      :					str							    =			parent
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			batch_size
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			num_channels
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			image_size
         SCREAMING_SNAKE_CASE__      :					str							    =			min_resolution
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			max_resolution
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			do_resize
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			size
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			crop_pct
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			do_normalize
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			image_mean
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			image_std






      def  A__(self       )							-> Dict:
         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     lowercase  ( __UpperCAmelCase					,      unittest.TestCase):
      __lowercase					=	ConvNextImageProcessor if is_vision_available() else None






      def  A__(self       )							-> Dict:
         SCREAMING_SNAKE_CASE__      :					str							    =			ConvNextImageProcessingTester(self       )






      @property
      def  A__(self       )							-> List[Any]:
         return self.image_processor_tester.prepare_image_processor_dict()






      def  A__(self       )							-> List[Any]:
         SCREAMING_SNAKE_CASE__      :					Dict							    =			self.image_processing_class(**self.image_processor_dict       )
         self.assertTrue(hasattr(a__			,	'''do_resize'''       )       )
         self.assertTrue(hasattr(a__			,	'''size'''       )       )
         self.assertTrue(hasattr(a__			,	'''crop_pct'''       )       )
         self.assertTrue(hasattr(a__			,	'''do_normalize'''       )       )
         self.assertTrue(hasattr(a__			,	'''image_mean'''       )       )
         self.assertTrue(hasattr(a__			,	'''image_std'''       )       )






      def  A__(self       )							-> List[str]:
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			self.image_processing_class.from_dict(self.image_processor_dict       )
         self.assertEqual(image_processor.size			,	{'''shortest_edge''': 2_0}       )

         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			self.image_processing_class.from_dict(self.image_processor_dict			,	size=4_2       )
         self.assertEqual(image_processor.size			,	{'''shortest_edge''': 4_2}       )






      def  A__(self       )							-> Any:
         pass






      def  A__(self       )							-> List[Any]:
         # Initialize image_processing
         SCREAMING_SNAKE_CASE__      :					Any							    =			self.image_processing_class(**self.image_processor_dict       )
         # create random PIL images
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			prepare_image_inputs(self.image_processor_tester			,	equal_resolution=a__       )
         for image in image_inputs:
            self.assertIsInstance(a__			,	Image.Image       )

         # Test not batched input
         SCREAMING_SNAKE_CASE__      :					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
         SCREAMING_SNAKE_CASE__      :					int							    =			image_processing(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['''shortest_edge'''],
                 self.image_processor_tester.size['''shortest_edge'''],
             )			,	)






      def  A__(self       )							-> str:
         # Initialize image_processing
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			self.image_processing_class(**self.image_processor_dict       )
         # create random numpy tensors
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			prepare_image_inputs(self.image_processor_tester			,	equal_resolution=a__			,	numpify=a__       )
         for image in image_inputs:
            self.assertIsInstance(a__			,	np.ndarray       )

         # Test not batched input
         SCREAMING_SNAKE_CASE__      :					str							    =			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
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			image_processing(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['''shortest_edge'''],
                 self.image_processor_tester.size['''shortest_edge'''],
             )			,	)






      def  A__(self       )							-> List[Any]:
         # Initialize image_processing
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.image_processing_class(**self.image_processor_dict       )
         # create random PyTorch tensors
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			prepare_image_inputs(self.image_processor_tester			,	equal_resolution=a__			,	torchify=a__       )
         for image in image_inputs:
            self.assertIsInstance(a__			,	torch.Tensor       )

         # Test not batched input
         SCREAMING_SNAKE_CASE__      :					str							    =			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
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			image_processing(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['''shortest_edge'''],
                 self.image_processor_tester.size['''shortest_edge'''],
             )			,	)



'''simple docstring'''







def       _a		(						_SCREAMING_SNAKE_CASE		: str	):
						_SCREAMING_SNAKE_CASE					=			len(_SCREAMING_SNAKE_CASE	)
						for i in range(length - 1	):
												_SCREAMING_SNAKE_CASE					=			False
												for j in range(length - 1 - i	):
																		if collection[j] > collection[j + 1]:
																								_SCREAMING_SNAKE_CASE					=			True
																								_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			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()

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