




'''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 ViltImageProcessor


class       _a  (     unittest.TestCase			):
       def __init__(     self						:     List[Any]       ,    lowercase						:     int       ,    lowercase						:     str=7       ,    lowercase						:     int=3       ,    lowercase						:     Dict=18       ,    lowercase						:     Union[str, Any]=30       ,    lowercase						:     Any=400       ,    lowercase						:     Tuple=True       ,    lowercase						:     Optional[Any]=None       ,    lowercase						:     List[str]=2       ,    lowercase						:     str=True       ,    lowercase						:     Optional[Any]=[0.5, 0.5, 0.5]       ,    lowercase						:     Optional[Any]=[0.5, 0.5, 0.5]       ,    ):



           '''simple docstring'''

           UpperCAmelCase      	=		size if size is not None else {'''shortest_edge''': 30}
           UpperCAmelCase      	=		parent
           UpperCAmelCase      	=		batch_size
           UpperCAmelCase      	=		num_channels
           UpperCAmelCase      	=		image_size
           UpperCAmelCase      	=		min_resolution
           UpperCAmelCase      	=		max_resolution
           UpperCAmelCase      	=		do_resize
           UpperCAmelCase      	=		size
           UpperCAmelCase      	=		size_divisor
           UpperCAmelCase      	=		do_normalize
           UpperCAmelCase      	=		image_mean
           UpperCAmelCase      	=		image_std
       def    A				(     self						:     int    ):



           '''simple docstring'''

           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    A				(     self						:     Optional[Any]       ,    lowercase						:     Optional[Any]       ,    lowercase						:     Dict=False    ):



           '''simple docstring'''

           if not batched:
               UpperCAmelCase      	=		self.size['''shortest_edge''']
               UpperCAmelCase      	=		image_inputs[0]
               if isinstance(lowercase       ,    Image.Image    ):
                   UpperCAmelCase	,					UpperCAmelCase      	=		image.size
               else:
                   UpperCAmelCase	,					UpperCAmelCase      	=		image.shape[1], image.shape[2]
               UpperCAmelCase      	=		size / min(lowercase       ,    lowercase    )
               if h < w:
                   UpperCAmelCase	,					UpperCAmelCase      	=		size, scale * w
               else:
                   UpperCAmelCase	,					UpperCAmelCase      	=		scale * h, size

               UpperCAmelCase      	=		int((1_333 / 800) * size    )
               if max(lowercase       ,    lowercase    ) > max_size:
                   UpperCAmelCase      	=		max_size / max(lowercase       ,    lowercase    )
                   UpperCAmelCase      	=		newh * scale
                   UpperCAmelCase      	=		neww * scale

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

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

           return expected_height, expected_width






@require_torch
@require_vision
class       _a  (     __a							,							unittest.TestCase			):
       __a    :					Union[str, Any]           = ViltImageProcessor if is_vision_available() else None
       def    A				(     self						:     Optional[int]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		ViltImageProcessingTester(self    )
       @property
       def    A				(     self						:     str    ):



           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()
       def    A				(     self						:     Union[str, Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.image_processing_class(**self.image_processor_dict    )
           self.assertTrue(hasattr(lowercase       ,    '''image_mean'''    )    )
           self.assertTrue(hasattr(lowercase       ,    '''image_std'''    )    )
           self.assertTrue(hasattr(lowercase       ,    '''do_normalize'''    )    )
           self.assertTrue(hasattr(lowercase       ,    '''do_resize'''    )    )
           self.assertTrue(hasattr(lowercase       ,    '''size'''    )    )
           self.assertTrue(hasattr(lowercase       ,    '''size_divisor'''    )    )
       def    A				(     self						:     Optional[int]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.image_processing_class.from_dict(self.image_processor_dict    )
           self.assertEqual(image_processor.size       ,    {'''shortest_edge''': 30}    )

           UpperCAmelCase      	=		self.image_processing_class.from_dict(self.image_processor_dict       ,    size=42    )
           self.assertEqual(image_processor.size       ,    {'''shortest_edge''': 42}    )
       def    A				(     self						:     Union[str, Any]    ):



           '''simple docstring'''

           pass
       def    A				(     self						:     Optional[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.image_processing_class(**self.image_processor_dict    )
           # create random PIL images
           UpperCAmelCase      	=		prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=lowercase    )
           for image in image_inputs:
               self.assertIsInstance(lowercase       ,    Image.Image    )

           # Test not batched input
           UpperCAmelCase      	=		image_processing(image_inputs[0]       ,    return_tensors='''pt'''    ).pixel_values

           UpperCAmelCase	,					UpperCAmelCase      	=		self.image_processor_tester.get_expected_values(lowercase    )
           self.assertEqual(
               encoded_images.shape       ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,    )

           # Test batched
           UpperCAmelCase      	=		image_processing(lowercase       ,    return_tensors='''pt'''    ).pixel_values

           UpperCAmelCase	,					UpperCAmelCase      	=		self.image_processor_tester.get_expected_values(lowercase       ,    batched=lowercase    )
           self.assertEqual(
               encoded_images.shape       ,    (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   expected_height,
                   expected_width,
               )       ,    )
       def    A				(     self						:     List[Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.image_processing_class(**self.image_processor_dict    )
           # create random numpy tensors
           UpperCAmelCase      	=		prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=lowercase       ,    numpify=lowercase    )
           for image in image_inputs:
               self.assertIsInstance(lowercase       ,    np.ndarray    )

           # Test not batched input
           UpperCAmelCase      	=		image_processing(image_inputs[0]       ,    return_tensors='''pt'''    ).pixel_values

           UpperCAmelCase	,					UpperCAmelCase      	=		self.image_processor_tester.get_expected_values(lowercase    )
           self.assertEqual(
               encoded_images.shape       ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,    )

           # Test batched
           UpperCAmelCase      	=		image_processing(lowercase       ,    return_tensors='''pt'''    ).pixel_values

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







       def    A				(     self						:     Tuple    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.image_processing_class(**self.image_processor_dict    )
           # create random PyTorch tensors
           UpperCAmelCase      	=		prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=lowercase       ,    torchify=lowercase    )
           for image in image_inputs:
               self.assertIsInstance(lowercase       ,    torch.Tensor    )

           # Test not batched input
           UpperCAmelCase      	=		image_processing(image_inputs[0]       ,    return_tensors='''pt'''    ).pixel_values

           UpperCAmelCase	,					UpperCAmelCase      	=		self.image_processor_tester.get_expected_values(lowercase    )
           self.assertEqual(
               encoded_images.shape       ,    (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,    )

           # Test batched
           UpperCAmelCase      	=		image_processing(lowercase       ,    return_tensors='''pt'''    ).pixel_values

           UpperCAmelCase	,					UpperCAmelCase      	=		self.image_processor_tester.get_expected_values(lowercase       ,    batched=lowercase    )
           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_							(_a     :							int = 9_9_8_0_0_1					):
    UpperCAmelCase      	=		0
    for i in range(9_9_9   ,       9_9   ,       -1					):  # 3 digit numbers range from 999 down to 100
        for j in range(9_9_9   ,       9_9   ,       -1					):
            UpperCAmelCase      	=		str(i * j					)
            if product_string == product_string[::-1] and i * j < n:
                UpperCAmelCase      	=		max(_a   ,       i * j					)
    return answer


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




