

"""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 MobileNetVaImageProcessor







class 			lowercase    (	unittest.TestCase      ):




				def __init__( self				,       snake_case				,       snake_case=7				,       snake_case=3				,       snake_case=18				,       snake_case=30				,       snake_case=400				,       snake_case=True				,       snake_case=None				,       snake_case=True				,       snake_case=None				,       ):
											snake_case_          =   size if size is not None else {'shortest_edge': 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




				def    a							( self	):
											return {
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_center_crop": self.do_center_crop,
											    "crop_size": self.crop_size,
											}







@require_torch
@require_vision
class 			lowercase    (	lowercase_     ,  unittest.TestCase      ):
				__SCREAMING_SNAKE_CASE :			int	      =      MobileNetVaImageProcessor if is_vision_available() else None




				def    a							( self	):
											snake_case_          =   MobileNetVaImageProcessingTester(self	)




				@property
				def    a							( self	):
											return self.image_processor_tester.prepare_image_processor_dict()




				def    a							( self	):
											snake_case_          =   self.image_processing_class(**self.image_processor_dict	)
											self.assertTrue(hasattr(snake_case				,       'do_resize'	)	)
											self.assertTrue(hasattr(snake_case				,       'size'	)	)
											self.assertTrue(hasattr(snake_case				,       'do_center_crop'	)	)
											self.assertTrue(hasattr(snake_case				,       'crop_size'	)	)




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




				def    a							( self	):
											pass




				def    a							( self	):
											# Initialize image_processing
											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=snake_case	)
											for image in image_inputs:
																		self.assertIsInstance(snake_case				,       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(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    a							( self	):
											# Initialize image_processing
											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=snake_case				,       numpify=snake_case	)
											for image in image_inputs:
																		self.assertIsInstance(snake_case				,       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(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    a							( self	):
											# Initialize image_processing
											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=snake_case				,       torchify=snake_case	)
											for image in image_inputs:
																		self.assertIsInstance(snake_case				,       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(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  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   len(UpperCamelCase__				)
							for _ in range(UpperCamelCase__				):
														for i in range(_ % 2       ,							arr_size - 1       ,							2				):
																					if arr[i + 1] < arr[i]:
																												snake_case_    ,   snake_case_          =   arr[i + 1], arr[i]

							return arr


if __name__ == "__main__":
					_UpperCAmelCase		:       Dict						    =	list(range(10, 0, -1))
					print(F'''Original: {arr}. Sorted: {odd_even_transposition(arr)}''')




