





import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor




class 							UpperCamelCase_							(       unittest.TestCase		):







 """simple docstring"""

 def __init__(   self  ,				UpperCAmelCase  ,				UpperCAmelCase = True  ,				UpperCAmelCase = None  ,				UpperCAmelCase = 3_2  ,				UpperCAmelCase = True  ,				UpperCAmelCase = 1 / 2_5_5  ,				UpperCAmelCase = True  ,				UpperCAmelCase = True  ,				UpperCAmelCase = [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]  ,				UpperCAmelCase = [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]  ,				UpperCAmelCase = True  ,				UpperCAmelCase=7  ,				UpperCAmelCase=3_0  ,				UpperCAmelCase=4_0_0  ,				UpperCAmelCase=3  ,				):
       __lowerCamelCase				   =       parent
       __lowerCamelCase				   =       do_resize
       __lowerCamelCase				   =       size if size is not None else {"""shortest_edge""": 2_8_8}
       __lowerCamelCase				   =       size_divisor
       __lowerCamelCase				   =       do_rescale
       __lowerCamelCase				   =       rescale_factor
       __lowerCamelCase				   =       do_normalize
       __lowerCamelCase				   =       do_center_crop
       __lowerCamelCase				   =       image_mean
       __lowerCamelCase				   =       image_std
       __lowerCamelCase				   =       do_pad
       __lowerCamelCase				   =       batch_size
       __lowerCamelCase				   =       num_channels
       __lowerCamelCase				   =       min_resolution
       __lowerCamelCase				   =       max_resolution


 def 		lowerCamelCase_ (   self							):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "size_divisor": self.size_divisor,
       }



 def 		lowerCamelCase_ (   self  ,				UpperCAmelCase  ,				UpperCAmelCase=False							):
       if not batched:
             __lowerCamelCase				   =       self.size["""shortest_edge"""]
             __lowerCamelCase				   =       image_inputs[0]
             if isinstance(snake_case_  ,				Image.Image							):
                   __lowerCamelCase				   =       image.size
             else:
                   __lowerCamelCase				   =       image.shape[1], image.shape[2]
             __lowerCamelCase				   =       size / min(snake_case_  ,				snake_case_							)
             if h < w:
                   __lowerCamelCase				   =       size, scale * w
             else:
                   __lowerCamelCase				   =       scale * h, size

             __lowerCamelCase				   =       int((1_3_3_3 / 8_0_0) * size							)
             if max(snake_case_  ,				snake_case_							) > max_size:
                   __lowerCamelCase				   =       max_size / max(snake_case_  ,				snake_case_							)
                   __lowerCamelCase				   =       newh * scale
                   __lowerCamelCase				   =       neww * scale

             __lowerCamelCase				   =       int(newh + 0.5							), int(neww + 0.5							)
             __lowerCamelCase				   =       (
                 newh // self.size_divisor * self.size_divisor,
                 neww // self.size_divisor * self.size_divisor,
             )

       else:
             __lowerCamelCase				   =       []
             for image in image_inputs:
                   __lowerCamelCase				   =       self.get_expected_values([image]							)
                   expected_values.append((expected_height, expected_width)							)
             __lowerCamelCase				   =       max(snake_case_  ,				key=lambda UpperCAmelCase							: item[0]							)[0]
             __lowerCamelCase				   =       max(snake_case_  ,				key=lambda UpperCAmelCase							: item[1]							)[1]

       return expected_height, expected_width




@require_torch
@require_vision
class 							UpperCamelCase_							(       _a							,unittest.TestCase		):







 """simple docstring"""

 A			 =       BridgeTowerImageProcessor if is_vision_available() else None


 def 		lowerCamelCase_ (   self							):
       __lowerCamelCase				   =       BridgeTowerImageProcessingTester(self							)


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


 def 		lowerCamelCase_ (   self							):
       __lowerCamelCase				   =       self.image_processing_class(**self.image_processor_dict							)
       self.assertTrue(hasattr(snake_case_  ,				"""image_mean"""							)							)
       self.assertTrue(hasattr(snake_case_  ,				"""image_std"""							)							)
       self.assertTrue(hasattr(snake_case_  ,				"""do_normalize"""							)							)
       self.assertTrue(hasattr(snake_case_  ,				"""do_resize"""							)							)
       self.assertTrue(hasattr(snake_case_  ,				"""size"""							)							)
       self.assertTrue(hasattr(snake_case_  ,				"""size_divisor"""							)							)


 def 		lowerCamelCase_ (   self							):
       pass


 def 		lowerCamelCase_ (   self							):
       # Initialize image processor
       __lowerCamelCase				   =       self.image_processing_class(**self.image_processor_dict							)
       # create random PIL images
       __lowerCamelCase				   =       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
       __lowerCamelCase				   =       image_processing(image_inputs[0]  ,				return_tensors="""pt"""							).pixel_values

       __lowerCamelCase				   =       self.image_processor_tester.get_expected_values(snake_case_							)
       self.assertEqual(
           encoded_images.shape  ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,				)

       # Test batched
       __lowerCamelCase				   =       image_processing(snake_case_  ,				return_tensors="""pt"""							).pixel_values

       __lowerCamelCase				   =       self.image_processor_tester.get_expected_values(snake_case_  ,				batched=snake_case_							)
       self.assertEqual(
           encoded_images.shape  ,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )  ,				)


 def 		lowerCamelCase_ (   self							):
       # Initialize image processor
       __lowerCamelCase				   =       self.image_processing_class(**self.image_processor_dict							)
       # create random numpy tensors
       __lowerCamelCase				   =       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
       __lowerCamelCase				   =       image_processing(image_inputs[0]  ,				return_tensors="""pt"""							).pixel_values

       __lowerCamelCase				   =       self.image_processor_tester.get_expected_values(snake_case_							)
       self.assertEqual(
           encoded_images.shape  ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,				)

       # Test batched
       __lowerCamelCase				   =       image_processing(snake_case_  ,				return_tensors="""pt"""							).pixel_values

       __lowerCamelCase				   =       self.image_processor_tester.get_expected_values(snake_case_  ,				batched=snake_case_							)
       self.assertEqual(
           encoded_images.shape  ,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )  ,				)



 def 		lowerCamelCase_ (   self							):
       # Initialize image processor
       __lowerCamelCase				   =       self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       __lowerCamelCase				   =       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
       __lowerCamelCase				   =       image_processing(image_inputs[0]  ,				return_tensors="""pt"""							).pixel_values

       __lowerCamelCase				   =       self.image_processor_tester.get_expected_values(snake_case_							)
       self.assertEqual(
           encoded_images.shape  ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,				)

       # Test batched
       __lowerCamelCase				   =       image_processing(snake_case_  ,				return_tensors="""pt"""							).pixel_values

       __lowerCamelCase				   =       self.image_processor_tester.get_expected_values(snake_case_  ,				batched=snake_case_							)
       self.assertEqual(
           encoded_images.shape  ,				(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )  ,				)











'''simple docstring'''





def 		__snake_case(							)						->					Optional[Any]:
      for n in range(1	,  1_000_000						):
            yield n * (n + 1) // 2





def 		__snake_case(							_lowerCAmelCase						)						->					str:
      snake_case__    :       Optional[int]       		= 1
      snake_case__    :       List[Any]       		= 2
      while i * i <= n:
            snake_case__    :       Dict       		= 0
            while n % i == 0:
                  n //= i
                  multiplicity += 1
            divisors_count *= multiplicity + 1
            i += 1
      if n > 1:
            divisors_count *= 2
      return divisors_count





def 		__snake_case(							)						->					List[str]:
      return next(i for i in triangle_number_generator() if count_divisors(_lowerCAmelCase						) > 500						)


if __name__ == "__main__":
       print(solution())
