



"""simple docstring"""

from string import ascii_uppercase

lowercase__					:						str												=			{char: i for i, char in enumerate(ascii_uppercase)}
lowercase__					:						List[Any]												=			dict(enumerate(ascii_uppercase))




def __lowercase    (							_a	,    _a					):
    snake_case_				:  List[str]       =			len(_a					)
    snake_case_				:  Dict       =			0
    while True:
        if x == i:
            snake_case_				:  Any       =			0
        if len(_a					) == len(_a					):
            break
        key += key[i]
        i += 1
    return key




def __lowercase    (							_a	,    _a					):
    snake_case_				:  Optional[int]       =			''''''
    snake_case_				:  List[Any]       =			0
    for letter in message:
        if letter == " ":
            cipher_text += " "
        else:
            snake_case_				:  Optional[int]       =			(dicta[letter] - dicta[key_new[i]]) % 26
            i += 1
            cipher_text += dicta[x]
    return cipher_text




def __lowercase    (							_a	,    _a					):
    snake_case_				:  Optional[Any]       =			''''''
    snake_case_				:  str       =			0
    for letter in cipher_text:
        if letter == " ":
            or_txt += " "
        else:
            snake_case_				:  str       =			(dicta[letter] + dicta[key_new[i]] + 26) % 26
            i += 1
            or_txt += dicta[x]
    return or_txt




def __lowercase    (							):
    snake_case_				:  List[Any]       =			'''THE GERMAN ATTACK'''
    snake_case_				:  Optional[int]       =			'''SECRET'''
    snake_case_				:  Optional[Any]       =			generate_key(_a	,    _a					)
    snake_case_				:  List[str]       =			cipher_text(_a	,    _a					)
    print(f"Encrypted Text = {s}"					)
    print(f"Original Text = {original_text(_a	,    _a					)}"					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()










"""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  _UpperCAmelCase (    unittest.TestCase):

 @slow
 def 		_snake_case      (  self	: List[Any]       ):
     snake_case_				:  List[Any]       =			AutoImageProcessor.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''       )
     snake_case_				:  List[Any]       =			AutoModelForImageClassification.from_pretrained('''microsoft/dit-base-finetuned-rvlcdip'''       )
     model.to(lowercase_       )

     from datasets import load_dataset

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

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

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

     # forward pass
     with torch.no_grad():
         snake_case_				:  List[Any]       =			model(**lowercase_       )
         snake_case_				:  int       =			outputs.logits

     snake_case_				:  Any       =			torch.Size((1, 16)       )
     self.assertEqual(logits.shape						,  lowercase_       )

     snake_case_				:  Any       =			torch.tensor(
         [-0.41_58, -0.40_92, -0.43_47]						,  device=lowercase_						,  dtype=torch.float						,  )
     self.assertTrue(torch.allclose(logits[0, :3]						,  lowercase_						,  atol=1E-4       )       )





