




'''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      )				->    List[str]:
     __a			=					AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'      )
     __a			=					AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'      )
     model.to(_UpperCAmelCase      )

     from datasets import load_dataset

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

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

     __a			=					image_processor(_UpperCAmelCase      ,							return_tensors='pt'      ).to(_UpperCAmelCase      )

     # forward pass
     with torch.no_grad():
       __a			=					model(**_UpperCAmelCase      )
       __a			=					outputs.logits

     __a			=					torch.Size((1, 16)      )
     self.assertEqual(logits.shape      ,							_UpperCAmelCase      )

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









'''simple docstring'''


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  __a			=					''
  for ch in key:
    if ch == " " or ch not in key_no_dups and ch.isalpha():
      key_no_dups += ch
  return key_no_dups


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  __a			=					[chr(i + 65			) for i in range(26			)]
  # Remove duplicate characters from key
  __a			=					remove_duplicates(key.upper()			)
  __a			=					len(a_			)
  # First fill cipher with key characters
  __a			=					{alphabet[i]: char for i, char in enumerate(a_			)}
  # Then map remaining characters in alphabet to
  # the alphabet from the beginning
  for i in range(len(a_			)				,					26			):
    __a			=					alphabet[i - offset]
    # Ensure we are not mapping letters to letters previously mapped
    while char in key:
      offset -= 1
      __a			=					alphabet[i - offset]
    __a			=					char
  return cipher_alphabet


def 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							dict[str, str]			):
  return "".join(cipher_map.get(a_				,					a_			) for ch in message.upper()			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							dict[str, str]			):
  __a			=					{v: k for k, v in cipher_map.items()}
  return "".join(rev_cipher_map.get(a_				,					a_			) for ch in message.upper()			)


def 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					input('Enter message to encode or decode: '			).strip()
  __a			=					input('Enter keyword: '			).strip()
  __a			=					input('Encipher or decipher? E/D:'			).strip()[0].lower()
  try:
    __a			=					{'e': encipher, 'd': decipher}[option]
  except KeyError:
    raise KeyError('invalid input option'			)
  __a			=					create_cipher_map(a_			)
  print(func(a_				,					a_			)			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()



