






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 				lowerCAmelCase_	(		unittest.TestCase			):


				def __init__(					self ,						_lowerCAmelCase ,						_lowerCAmelCase=7 ,						_lowerCAmelCase=3 ,						_lowerCAmelCase=1_8 ,						_lowerCAmelCase=3_0 ,						_lowerCAmelCase=4_0_0 ,						_lowerCAmelCase=True ,						_lowerCAmelCase=None ,						_lowerCAmelCase=True ,						_lowerCAmelCase=None ,						_lowerCAmelCase=True ,						):
											_lowercase							:   Optional[Any] 							=   size if size is not None else {'shortest_edge': 2_0}
											_lowercase							:   List[str] 							=   crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
											_lowercase							:   Optional[Any] 							=   parent
											_lowercase							:   List[Any] 							=   batch_size
											_lowercase							:   Tuple 							=   num_channels
											_lowercase							:   Optional[Any] 							=   image_size
											_lowercase							:   str 							=   min_resolution
											_lowercase							:   Optional[Any] 							=   max_resolution
											_lowercase							:   Union[str, Any] 							=   do_resize
											_lowercase							:   List[Any] 							=   size
											_lowercase							:   List[str] 							=   do_center_crop
											_lowercase							:   Tuple 							=   crop_size
											_lowercase							:   List[Any] 							=   do_flip_channel_order


				def 						__a					(					self      ):
											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 				lowerCAmelCase_	(		__snake_case   ,		unittest.TestCase			):
				_UpperCamelCase					:       Optional[int]							       = MobileViTImageProcessor if is_vision_available() else None


				def 						__a					(					self      ):
											_lowercase							:   List[str] 							=   MobileViTImageProcessingTester(self      )


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


				def 						__a					(					self      ):
											_lowercase							:   int 							=   self.image_processing_class(**self.image_processor_dict      )
											self.assertTrue(hasattr(_lowerCAmelCase ,						'do_resize'      )      )
											self.assertTrue(hasattr(_lowerCAmelCase ,						'size'      )      )
											self.assertTrue(hasattr(_lowerCAmelCase ,						'do_center_crop'      )      )
											self.assertTrue(hasattr(_lowerCAmelCase ,						'center_crop'      )      )
											self.assertTrue(hasattr(_lowerCAmelCase ,						'do_flip_channel_order'      )      )


				def 						__a					(					self      ):
											_lowercase							:   List[Any] 							=   self.image_processing_class.from_dict(self.image_processor_dict      )
											self.assertEqual(image_processor.size ,						{'shortest_edge': 2_0}      )
											self.assertEqual(image_processor.crop_size ,						{'height': 1_8, 'width': 1_8}      )

											_lowercase							:   Union[str, Any] 							=   self.image_processing_class.from_dict(self.image_processor_dict ,						size=4_2 ,						crop_size=8_4      )
											self.assertEqual(image_processor.size ,						{'shortest_edge': 4_2}      )
											self.assertEqual(image_processor.crop_size ,						{'height': 8_4, 'width': 8_4}      )


				def 						__a					(					self      ):
											pass


				def 						__a					(					self      ):
											# Initialize image_processing
											_lowercase							:   List[Any] 							=   self.image_processing_class(**self.image_processor_dict      )
											# create random PIL images
											_lowercase							:   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
											_lowercase							:   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.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    ) ,						)

											# Test batched
											_lowercase							:   List[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.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    ) ,						)


				def 						__a					(					self      ):
											# Initialize image_processing
											_lowercase							:   Union[str, Any] 							=   self.image_processing_class(**self.image_processor_dict      )
											# create random numpy tensors
											_lowercase							:   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
											_lowercase							:   int 							=   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
											_lowercase							:   Union[str, 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.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    ) ,						)


				def 						__a					(					self      ):
											# Initialize image_processing
											_lowercase							:   Dict 							=   self.image_processing_class(**self.image_processor_dict      )
											# create random PyTorch tensors
											_lowercase							:   Optional[Any] 							=   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
											_lowercase							:   Union[str, 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.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    ) ,						)

											# Test batched
											_lowercase							:   List[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.crop_size['height'],
											        self.image_processor_tester.crop_size['width'],
											    ) ,						)








def 						_lowerCAmelCase							(     _lowerCAmelCase = 100			)    ->						int:


						'''simple docstring'''






						__snake_case				=		n * (n + 1) * (2 * n + 1) / 6
						__snake_case				=		(n * (n + 1) / 2) ** 2
						return int(square_of_sum - sum_of_squares			)


if __name__ == "__main__":
	print(f'''{solution() = }''')





