



'''simple docstring'''

from __future__ import annotations






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						int							)      ->						bool:







 """simple docstring"""
 lowerCAmelCase   =     str(_SCREAMING_SNAKE_CASE							)
 return len(_SCREAMING_SNAKE_CASE							) == 9 and set(_SCREAMING_SNAKE_CASE							) == set("""123456789"""							)






def 		_snake_case		( )      ->						int | None:







 """simple docstring"""
 for base_num in range(9_999							,		4_999							,		-1							):
  lowerCAmelCase   =     100_002 * base_num
  if is_9_pandigital(_SCREAMING_SNAKE_CASE							):
   return candidate

 for base_num in range(333							,		99							,		-1							):
  lowerCAmelCase   =     1_002_003 * base_num
  if is_9_pandigital(_SCREAMING_SNAKE_CASE							):
   return candidate

 return None


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




'''simple docstring'''

import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
				import torch

				from transformers import AutoModelForImageClassification

if is_vision_available():
				from transformers import AutoImageProcessor

@require_torch
@require_vision
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						@slow
						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase   =     AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							lowerCAmelCase   =     AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""				)
							model.to(A_				)

							from datasets import load_dataset

							lowerCAmelCase   =     load_dataset("""nielsr/rvlcdip-demo"""				)

							lowerCAmelCase   =     dataset["""train"""][0]["""image"""].convert("""RGB"""				)

							lowerCAmelCase   =     image_processor(A_     ,	return_tensors="""pt"""				).to(A_				)

							# forward pass
							with torch.no_grad():
								lowerCAmelCase   =     model(**A_				)
								lowerCAmelCase   =     outputs.logits

							lowerCAmelCase   =     torch.Size((1, 16)				)
							self.assertEqual(logits.shape     ,	A_				)

							lowerCAmelCase   =     torch.tensor(
							    [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]     ,	device=A_     ,	dtype=torch.float     ,	)
							self.assertTrue(torch.allclose(logits[0, :3]     ,	A_     ,	atol=1e-4				)				)