

'''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    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						,			)  -> str:





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







    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           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    A__ ( _snake_case  ,  unittest.TestCase     ):
    lowercase		   =						MobileNetVaImageProcessor if is_vision_available() else None



    def    snake_case_     (   self    )  -> List[str]:





           '''simple docstring'''
           A_		   = MobileNetVaImageProcessingTester(self    )



    @property
    def    snake_case_     (   self    )  -> Optional[int]:





           '''simple docstring'''
           return self.image_processor_tester.prepare_image_processor_dict()



    def    snake_case_     (   self    )  -> str:





           '''simple docstring'''
           A_		   = self.image_processing_class(**self.image_processor_dict    )
           self.assertTrue(hasattr(UpperCamelCase__						,			"""do_resize"""    )    )
           self.assertTrue(hasattr(UpperCamelCase__						,			"""size"""    )    )
           self.assertTrue(hasattr(UpperCamelCase__						,			"""do_center_crop"""    )    )
           self.assertTrue(hasattr(UpperCamelCase__						,			"""crop_size"""    )    )



    def    snake_case_     (   self    )  -> Tuple:





           '''simple docstring'''
           A_		   = 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}    )

           A_		   = 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    snake_case_     (   self    )  -> Tuple:





           '''simple docstring'''
           pass



    def    snake_case_     (   self    )  -> Dict:





           '''simple docstring'''
           # Initialize image_processing
           A_		   = self.image_processing_class(**self.image_processor_dict    )
           # create random PIL images
           A_		   = prepare_image_inputs(self.image_processor_tester						,			equal_resolution=UpperCamelCase__    )
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase__						,			Image.Image    )

           # Test not batched input
           A_		   = 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
           A_		   = image_processing(UpperCamelCase__						,			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    snake_case_     (   self    )  -> Union[str, Any]:





           '''simple docstring'''
           # Initialize image_processing
           A_		   = self.image_processing_class(**self.image_processor_dict    )
           # create random numpy tensors
           A_		   = prepare_image_inputs(self.image_processor_tester						,			equal_resolution=UpperCamelCase__						,			numpify=UpperCamelCase__    )
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase__						,			np.ndarray    )

           # Test not batched input
           A_		   = 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
           A_		   = image_processing(UpperCamelCase__						,			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    snake_case_     (   self    )  -> Optional[Any]:





           '''simple docstring'''
           # Initialize image_processing
           A_		   = self.image_processing_class(**self.image_processor_dict    )
           # create random PyTorch tensors
           A_		   = prepare_image_inputs(self.image_processor_tester						,			equal_resolution=UpperCamelCase__						,			torchify=UpperCamelCase__    )
           for image in image_inputs:
                  self.assertIsInstance(UpperCamelCase__						,			torch.Tensor    )

           # Test not batched input
           A_		   = 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
           A_		   = image_processing(UpperCamelCase__						,			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  UpperCAmelCase__      ( UpperCAmelCase__ = 1_00			)     ->     int:
       A_		   = n * (n + 1) * (2 * n + 1) / 6
       A_		   = (n * (n + 1) / 2) ** 2
       return int(square_of_sum - sum_of_squares			)


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