code: 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 				_lowercase						(    unittest.TestCase ):
  
  
  
  
  
  
      '''simple docstring'''
  
  
  
  
      def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__=7					,	lowerCamelCase__=3					,	lowerCamelCase__=18					,	lowerCamelCase__=30					,	lowerCamelCase__=400					,	lowerCamelCase__=True					,	lowerCamelCase__=None					,	lowerCamelCase__=True					,	lowerCamelCase__=None					,	):
         lowerCAmelCase_:       List[Any]         =  size if size is not None else {"shortest_edge": 20}
         lowerCAmelCase_:       Tuple         =  crop_size if crop_size is not None else {"height": 18, "width": 18}
         lowerCAmelCase_:       Optional[Any]         =  parent
         lowerCAmelCase_:       int         =  batch_size
         lowerCAmelCase_:       List[str]         =  num_channels
         lowerCAmelCase_:       Any         =  image_size
         lowerCAmelCase_:       List[str]         =  min_resolution
         lowerCAmelCase_:       List[Any]         =  max_resolution
         lowerCAmelCase_:       Any         =  do_resize
         lowerCAmelCase_:       List[str]         =  size
         lowerCAmelCase_:       Tuple         =  do_center_crop
         lowerCAmelCase_:       List[str]         =  crop_size
  
  
  
  
      def     _a						(						self   ):
         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 				_lowercase						(    UpperCAmelCase__   ,     unittest.TestCase ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							str        =						MobileNetVaImageProcessor if is_vision_available() else None
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Optional[Any]         =  MobileNetVaImageProcessingTester(self   )
  
  
  
  
      @property
      def     _a						(						self   ):
         return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Union[str, Any]         =  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"   )   )
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Dict         =  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_:       List[Any]         =  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   ):
         pass
  
  
  
  
      def     _a						(						self   ):
         # Initialize image_processing
         lowerCAmelCase_:       Dict         =  self.image_processing_class(**self.image_processor_dict   )
         # create random PIL images
         lowerCAmelCase_:       Optional[int]         =  prepare_image_inputs(self.image_processor_tester					,	equal_resolution=lowerCamelCase__   )
         for image in image_inputs:
            self.assertIsInstance(lowerCamelCase__					,	Image.Image   )
  
         # Test not batched input
         lowerCAmelCase_:       Tuple         =  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_:       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
         lowerCAmelCase_:       Optional[int]         =  self.image_processing_class(**self.image_processor_dict   )
         # create random numpy tensors
         lowerCAmelCase_:       List[str]         =  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
         lowerCAmelCase_:       List[str]         =  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_:       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
         lowerCAmelCase_:       List[str]         =  self.image_processing_class(**self.image_processor_dict   )
         # create random PyTorch tensors
         lowerCAmelCase_:       Union[str, 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
         lowerCAmelCase_:       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
         lowerCAmelCase_:       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"],
             )					,	)
style_context: import itertools
  def 		snake_case__  (		lowercase				):
     return (
         int("".join(combination[0:2]				)				) * int("".join(combination[2:5]				)				)
         == int("".join(combination[5:9]				)				)
     ) or (
         int("".join(combination[0]				)				) * int("".join(combination[1:5]				)				)
         == int("".join(combination[5:9]				)				)
     )
  def 		snake_case__  (		):
     return sum(
         {
             int("".join(pandigital[5:9]				)				)
             for pandigital in itertools.permutations("123456789"				)
             if is_combination_valid(lowercase				)
         }				)
  
  
  if __name__ == "__main__":
   print(solution())