






'''simple docstring'''




import string







def       __UpperCAmelCase						(  lowerCamelCase_)							->     str:
		UpperCamelCase__				:					Optional[int]					=					''
		for i in sequence:
				UpperCamelCase__				:					str					=					ord(lowerCamelCase_)
				if 65 <= extract <= 90:
						output += chr(155 - extract)
				elif 97 <= extract <= 122:
						output += chr(219 - extract)
				else:
						output += i
		return output







def       __UpperCAmelCase						(  lowerCamelCase_)							->     str:
		UpperCamelCase__				:					int					=					string.ascii_letters
		UpperCamelCase__				:					Dict					=					string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
		return "".join(
		    letters_reversed[letters.index(lowerCamelCase_)] if c in letters else c for c in sequence)







def       __UpperCAmelCase						(  )							->     None:
		from timeit import timeit

		print('Running performance benchmarks...')
		UpperCamelCase__				:					List[str]					=					'from string import printable ; from __main__ import atbash, atbash_slow'
		print(f'> atbash_slow(): {timeit("atbash_slow(printable)"     ,      setup=lowerCamelCase_)} seconds')
		print(f'>      atbash(): {timeit("atbash(printable)"     ,      setup=lowerCamelCase_)} seconds')


if __name__ == "__main__":
		for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
				print(f'''{example} encrypted in atbash: {atbash(example)}''')
		benchmark()












'''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       __lowercase					(unittest.TestCase    ):

	@slow
	def      __UpperCamelCase							(      self   :						int):
			UpperCamelCase__				:					Union[str, Any]					=					AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip')
			UpperCamelCase__				:					List[str]					=					AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip')
			model.to(UpperCAmelCase_)

			from datasets import load_dataset

			UpperCamelCase__				:					Optional[Any]					=					load_dataset('nielsr/rvlcdip-demo')

			UpperCamelCase__				:					int					=					dataset['train'][0]['image'].convert('RGB')

			UpperCamelCase__				:					Union[str, Any]					=					image_processor(UpperCAmelCase_  ,					return_tensors='pt').to(UpperCAmelCase_)

			# forward pass
			with torch.no_grad():
					UpperCamelCase__				:					Optional[Any]					=					model(**UpperCAmelCase_)
					UpperCamelCase__				:					Tuple					=					outputs.logits

			UpperCamelCase__				:					str					=					torch.Size((1, 16))
			self.assertEqual(logits.shape  ,					UpperCAmelCase_)

			UpperCamelCase__				:					Tuple					=					torch.tensor(
			    [-0.41_58, -0.40_92, -0.43_47]  ,					device=UpperCAmelCase_  ,					dtype=torch.float  ,					)
			self.assertTrue(torch.allclose(logits[0, :3]  ,					UpperCAmelCase_  ,					atol=1e-4))




