code: 
  
  
  import random
  import sys
  
  __snake_case             =	'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
  
  
  
  
  
  
  def 		_A		(		)				->   None:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = input('Enter message: '   )
       __UpperCamelCase   = 'LFWOAYUISVKMNXPBDCRJTQEGHZ'
       __UpperCamelCase   = input('Encrypt/Decrypt [e/d]: '   )
  
       check_valid_key(_lowercase   )
  
       if resp.lower().startswith('e'   ):
            __UpperCamelCase   = 'encrypt'
            __UpperCamelCase   = encrypt_message(_lowercase				,     _lowercase   )
       elif resp.lower().startswith('d'   ):
            __UpperCamelCase   = 'decrypt'
            __UpperCamelCase   = decrypt_message(_lowercase				,     _lowercase   )
  
       print(f'''\n{mode.title()}ion: \n{translated}'''   )
  
  
  
  
  
  
  def 		_A		(		_lowercase   )				->   None:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = list(_lowercase   )
       __UpperCamelCase   = list(_lowercase   )
       key_list.sort()
       letters_list.sort()
  
       if key_list != letters_list:
            sys.exit('Error in the key or symbol set.'   )
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase   )				->   str:
  
  
       """simple docstring"""
  
  
       return translate_message(_lowercase				,     _lowercase				,     'encrypt'   )
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase   )				->   str:
  
  
       """simple docstring"""
  
  
       return translate_message(_lowercase				,     _lowercase				,     'decrypt'   )
  
  
  
  
  
  
  def 		_A		(		_lowercase				,     _lowercase				,     _lowercase   )				->   str:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = ''
       __UpperCamelCase   = LETTERS
       __UpperCamelCase   = key
  
       if mode == "decrypt":
            __UpperCamelCase,						__UpperCamelCase   = chars_b, chars_a
  
       for symbol in message:
            if symbol.upper() in chars_a:
                 __UpperCamelCase   = chars_a.find(symbol.upper()   )
                 if symbol.isupper():
                      translated += chars_b[sym_index].upper()
                 else:
                      translated += chars_b[sym_index].lower()
            else:
                 translated += symbol
  
       return translated
  
  
  
  
  
  
  def 		_A		(		)				->   str:
  
  
       """simple docstring"""
  
  
       __UpperCamelCase   = list(_lowercase   )
       random.shuffle(_lowercase   )
       return "".join(_lowercase   )
  
  
  if __name__ == "__main__":
      main()
  
  
  
  
  
style_context: 
  
  
  import unittest
  
  import requests
  
  from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
  from transformers.pipelines import pipeline
  from transformers.testing_utils import (
      is_pipeline_test,
      is_torch_available,
      require_tf,
      require_torch,
      require_vision,
      slow,
  )
  
  from .test_pipelines_common import ANY
  
  
  if is_torch_available():
      from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
  else:
      __snake_case             =	False
  
  
  if is_vision_available():
      from PIL import Image
  else:
  
  
  
      class   __lowerCamelCase :
  
  
  
  
  
  
  
             @staticmethod
             def 		snake_case_  ( *A_:   List[str],**A_:   Any				):
  
  
  
  
  
  
                  '''simple docstring'''
  
  
  
  
                  pass
  
  
  
  @is_pipeline_test
  @require_vision
  class   __lowerCamelCase (unittest.TestCase	):
         _lowercase       							=    MODEL_FOR_VISION_2_SEQ_MAPPING
         _lowercase       							=    TF_MODEL_FOR_VISION_2_SEQ_MAPPING
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Dict,A_:   int,A_:   List[str],A_:   List[str]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = pipeline('image-to-text',model=A_,tokenizer=A_,image_processor=A_				)
              __UpperCamelCase   = [
                  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				),
                  './tests/fixtures/tests_samples/COCO/000000039769.png',
              ]
              return pipe, examples
  
  
  
  
  
  
  
         def 		snake_case_  ( self:   Union[str, Any],A_:   Optional[Any],A_:   Optional[int]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = pipe(A_				)
              self.assertEqual(
                  A_,[
                      [{'generated_text': ANY(A_				)}],
                      [{'generated_text': ANY(A_				)}],
                  ],)
  
  
  
  
  
  
  
         @require_tf
         def 		snake_case_  ( self:   str				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = pipeline('image-to-text',model='hf-internal-testing/tiny-random-vit-gpt2',framework='tf'				)
              __UpperCamelCase   = './tests/fixtures/tests_samples/COCO/000000039769.png'
  
              __UpperCamelCase   = pipe(A_				)
              self.assertEqual(
                  A_,[
                      {
                          'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                      },
                  ],)
  
              __UpperCamelCase   = pipe([image, image]				)
              self.assertEqual(
                  A_,[
                      [
                          {
                              'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                          }
                      ],
                      [
                          {
                              'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                          }
                      ],
                  ],)
  
              __UpperCamelCase   = pipe(A_,max_new_tokens=1				)
              self.assertEqual(
                  A_,[{'generated_text': 'growth'}],)
  
  
  
  
  
  
  
         @require_torch
         def 		snake_case_  ( self:   Any				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = pipeline('image-to-text',model='hf-internal-testing/tiny-random-vit-gpt2'				)
              __UpperCamelCase   = './tests/fixtures/tests_samples/COCO/000000039769.png'
  
              __UpperCamelCase   = pipe(A_				)
              self.assertEqual(
                  A_,[
                      {
                          'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                      },
                  ],)
  
              __UpperCamelCase   = pipe([image, image]				)
              self.assertEqual(
                  A_,[
                      [
                          {
                              'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                          }
                      ],
                      [
                          {
                              'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
                          }
                      ],
                  ],)
  
  
  
  
  
  
  
         @require_torch
         def 		snake_case_  ( self:   str				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = pipeline('image-to-text',model='hf-internal-testing/tiny-random-BlipForConditionalGeneration'				)
              __UpperCamelCase   = './tests/fixtures/tests_samples/COCO/000000039769.png'
              __UpperCamelCase   = 'a photo of'
  
              __UpperCamelCase   = pipe(A_,prompt=A_				)
              self.assertTrue(outputs[0]['generated_text'].startswith(A_				)				)
  
  
  
  
  
  
  
         @slow
         @require_torch
         def 		snake_case_  ( self:   Dict				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = pipeline('image-to-text',model='ydshieh/vit-gpt2-coco-en'				)
              __UpperCamelCase   = './tests/fixtures/tests_samples/COCO/000000039769.png'
  
              __UpperCamelCase   = pipe(A_				)
              self.assertEqual(A_,[{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]				)
  
              __UpperCamelCase   = pipe([image, image]				)
              self.assertEqual(
                  A_,[
                      [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
                      [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
                  ],)
  
  
  
  
  
  
  
         @slow
         @require_torch
         def 		snake_case_  ( self:   Dict				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = pipeline('image-to-text',model='Salesforce/blip-image-captioning-base'				)
              __UpperCamelCase   = 'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
              __UpperCamelCase   = Image.open(requests.get(A_,stream=A_				).raw				)
  
              __UpperCamelCase   = pipe(A_				)
              self.assertEqual(A_,[{'generated_text': 'a pink pokemon pokemon with a blue shirt and a blue shirt'}]				)
  
  
  
  
  
  
  
         @slow
         @require_torch
         def 		snake_case_  ( self:   Dict				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = pipeline('image-to-text',model='microsoft/git-base-coco'				)
              __UpperCamelCase   = 'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
              __UpperCamelCase   = Image.open(requests.get(A_,stream=A_				).raw				)
  
              __UpperCamelCase   = pipe(A_				)
              self.assertEqual(A_,[{'generated_text': 'a cartoon of a purple character.'}]				)
  
  
  
  
  
  
  
         @slow
         @require_torch
         def 		snake_case_  ( self:   Dict				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = pipeline('image-to-text',model='Salesforce/blip-image-captioning-base'				)
              __UpperCamelCase   = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
              __UpperCamelCase   = Image.open(requests.get(A_,stream=A_				).raw				)
  
              __UpperCamelCase   = 'a photography of'
  
              __UpperCamelCase   = pipe(A_,prompt=A_				)
              self.assertEqual(A_,[{'generated_text': 'a photography of a volcano'}]				)
  
              with self.assertRaises(A_				):
                   __UpperCamelCase   = pipe([image, image],prompt=[prompt, prompt]				)
  
  
  
  
  
  
  
         @slow
         @require_torch
         def 		snake_case_  ( self:   List[Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = pipeline('image-to-text',model='microsoft/git-base-coco'				)
              __UpperCamelCase   = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
              __UpperCamelCase   = Image.open(requests.get(A_,stream=A_				).raw				)
  
              __UpperCamelCase   = 'a photo of a'
  
              __UpperCamelCase   = pipe(A_,prompt=A_				)
              self.assertEqual(A_,[{'generated_text': 'a photo of a tent with a tent and a tent in the background.'}]				)
  
              with self.assertRaises(A_				):
                   __UpperCamelCase   = pipe([image, image],prompt=[prompt, prompt]				)
  
  
  
  
  
  
  
         @unittest.skipIf(
             not is_torch_greater_or_equal_than_1_11,reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'				)
         @slow
         @require_torch
         def 		snake_case_  ( self:   List[Any]				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = pipeline('image-to-text',model='google/pix2struct-ai2d-base'				)
              __UpperCamelCase   = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
              __UpperCamelCase   = Image.open(requests.get(A_,stream=A_				).raw				)
  
              __UpperCamelCase   = 'What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'
  
              __UpperCamelCase   = pipe(A_,prompt=A_				)
              self.assertEqual(A_,[{'generated_text': 'ash cloud'}]				)
  
              with self.assertRaises(A_				):
                   __UpperCamelCase   = pipe([image, image],prompt=[prompt, prompt]				)
  
  
  
  
  
  
  
         @slow
         @require_tf
         def 		snake_case_  ( self:   Any				):
  
  
  
  
  
  
              '''simple docstring'''
  
  
  
  
              __UpperCamelCase   = pipeline('image-to-text',model='ydshieh/vit-gpt2-coco-en',framework='tf'				)
              __UpperCamelCase   = './tests/fixtures/tests_samples/COCO/000000039769.png'
  
              __UpperCamelCase   = pipe(A_				)
              self.assertEqual(A_,[{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]				)
  
              __UpperCamelCase   = pipe([image, image]				)
              self.assertEqual(
                  A_,[
                      [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
                      [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
                  ],)
  
  
  