






def 						__UpperCAmelCase			(     a_ = 10							,     a_ = 22):
							snake_case_				=							range(1							,     a_)
							snake_case_				=							range(1							,     a_)
							return sum(
							    1 for power in powers for base in bases if len(str(base**power)) == power)


if __name__ == "__main__":
	print(f'{solution(10, 22) = }')













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  UpperCamelCase_      (			unittest.TestCase				):



	'''simple docstring'''




	@slow
	def 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_				=							AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'					)
								snake_case_				=							AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'					)
								model.to(a					)

								from datasets import load_dataset

								snake_case_				=							load_dataset('nielsr/rvlcdip-demo'					)

								snake_case_				=							dataset['train'][0]['image'].convert('RGB'					)

								snake_case_				=							image_processor(a       ,			return_tensors='pt'					).to(a					)

								# forward pass
								with torch.no_grad():
															snake_case_				=							model(**a					)
															snake_case_				=							outputs.logits

								snake_case_				=							torch.Size((1, 16)					)
								self.assertEqual(logits.shape       ,			a					)

								snake_case_				=							torch.tensor(
								    [-0.4_158, -0.4_092, -0.4_347]       ,			device=a       ,			dtype=torch.float       ,			)
								self.assertTrue(torch.allclose(logits[0, :3]       ,			a       ,			atol=1E-4					)					)





