




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 MobileViTImageProcessor


class  A		(  unittest.TestCase     ):



		def __init__(     self,	UpperCamelCase__,	UpperCamelCase__=7,	UpperCamelCase__=3,	UpperCamelCase__=18,	UpperCamelCase__=30,	UpperCamelCase__=400,	UpperCamelCase__=True,	UpperCamelCase__=None,	UpperCamelCase__=True,	UpperCamelCase__=None,	UpperCamelCase__=True,	):







								"""simple docstring"""



								lowerCAmelCase_      			=  size if size is not None else {'''shortest_edge''': 20}
								lowerCAmelCase_      			=  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
								lowerCAmelCase_      			=  parent
								lowerCAmelCase_      			=  batch_size
								lowerCAmelCase_      			=  num_channels
								lowerCAmelCase_      			=  image_size
								lowerCAmelCase_      			=  min_resolution
								lowerCAmelCase_      			=  max_resolution
								lowerCAmelCase_      			=  do_resize
								lowerCAmelCase_      			=  size
								lowerCAmelCase_      			=  do_center_crop
								lowerCAmelCase_      			=  crop_size
								lowerCAmelCase_      			=  do_flip_channel_order







		def        SCREAMING_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_flip_channel_order": self.do_flip_channel_order,
								}







@require_torch
@require_vision
class  A		(  __lowercase      ,				unittest.TestCase     ):
		__snake_case									=   MobileViTImageProcessor if is_vision_available() else None



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  MobileViTImageProcessingTester(self   )



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return self.image_processor_tester.prepare_image_processor_dict()



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.image_processing_class(**self.image_processor_dict   )
								self.assertTrue(hasattr(_a,	'''do_resize'''   )   )
								self.assertTrue(hasattr(_a,	'''size'''   )   )
								self.assertTrue(hasattr(_a,	'''do_center_crop'''   )   )
								self.assertTrue(hasattr(_a,	'''center_crop'''   )   )
								self.assertTrue(hasattr(_a,	'''do_flip_channel_order'''   )   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  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}   )

								lowerCAmelCase_      			=  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        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								pass



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.image_processing_class(**self.image_processor_dict   )
								# create random PIL images
								lowerCAmelCase_      			=  prepare_image_inputs(self.image_processor_tester,	equal_resolution=_a   )
								for image in image_inputs:
														self.assertIsInstance(_a,	Image.Image   )

								# Test not batched input
								lowerCAmelCase_      			=  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
								lowerCAmelCase_      			=  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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),	)



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.image_processing_class(**self.image_processor_dict   )
								# create random numpy tensors
								lowerCAmelCase_      			=  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
								lowerCAmelCase_      			=  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
								lowerCAmelCase_      			=  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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),	)







		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.image_processing_class(**self.image_processor_dict   )
								# create random PyTorch tensors
								lowerCAmelCase_      			=  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
								lowerCAmelCase_      			=  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
								lowerCAmelCase_      			=  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.crop_size['''height'''],
								        self.image_processor_tester.crop_size['''width'''],
								    ),	)







def    __UpperCAmelCase (	__a      :			str	)			->     int:

					"""simple docstring"""

					assert column_title.isupper()
					_a	:    Optional[Any]         =		0
					_a	:    List[Any]         =		len(__a	) - 1
					_a	:    List[str]         =		0

					while index >= 0:
										_a	:    Dict         =		(ord(column_title[index]	) - 64) * pow(26		,__a	)
										answer += value
										power += 1
										index -= 1

					return answer


if __name__ == "__main__":
			from doctest import testmod

			testmod()



