





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 LevitImageProcessor

class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


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





         """simple docstring"""



         __snake_case							       =   size if size is not None else {'''shortest_edge''': 18}
         __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
         __snake_case							       =   do_normalize
         __snake_case							       =   image_mean
         __snake_case							       =   image_std


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



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






@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__				(       __a						,      unittest.TestCase  ):
      A_						:				Union[str, Any]							=    LevitImageProcessor if is_vision_available() else None


      def    a (self       :      Tuple      ):





         """simple docstring"""



         __snake_case							       =   LevitImageProcessingTester(self      )


      @property
      def    a (self       :      int      ):





         """simple docstring"""



         return self.image_processor_tester.prepare_image_processor_dict()


      def    a (self       :      Any      ):





         """simple docstring"""



         __snake_case							       =   self.image_processing_class(**self.image_processor_dict      )
         self.assertTrue(hasattr(lowerCAmelCase__				,			'''image_mean'''      )      )
         self.assertTrue(hasattr(lowerCAmelCase__				,			'''image_std'''      )      )
         self.assertTrue(hasattr(lowerCAmelCase__				,			'''do_normalize'''      )      )
         self.assertTrue(hasattr(lowerCAmelCase__				,			'''do_resize'''      )      )
         self.assertTrue(hasattr(lowerCAmelCase__				,			'''do_center_crop'''      )      )
         self.assertTrue(hasattr(lowerCAmelCase__				,			'''size'''      )      )


      def    a (self       :      Any      ):





         """simple docstring"""



         __snake_case							       =   self.image_processing_class.from_dict(self.image_processor_dict      )
         self.assertEqual(image_processor.size				,			{'''shortest_edge''': 18}      )
         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       :      List[str]      ):





         """simple docstring"""



         pass


      def    a (self       :      str      ):





         """simple docstring"""



         __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=lowerCAmelCase__      )
         for image in image_inputs:
            self.assertIsInstance(lowerCAmelCase__				,			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(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       :      List[Any]      ):





         """simple docstring"""



         __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=lowerCAmelCase__				,			numpify=lowerCAmelCase__      )
         for image in image_inputs:
            self.assertIsInstance(lowerCAmelCase__				,			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(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       :      Tuple      ):





         """simple docstring"""



         __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=lowerCAmelCase__				,			torchify=lowerCAmelCase__      )
         for image in image_inputs:
            self.assertIsInstance(lowerCAmelCase__				,			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(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'''],
             )				,			)



"""simple docstring"""





def 						_lowerCAmelCase				(				UpperCamelCase_ = 100				):
		__SCREAMING_SNAKE_CASE            = 0
		__SCREAMING_SNAKE_CASE            = 0
		for i in range(1  ,				n + 1				):
				sum_of_squares += i**2
				sum_of_ints += i
		return sum_of_ints**2 - sum_of_squares


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




