code: 
  import unittest
  
  import numpy as np
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_image_processing_common import ImageProcessingSavingTestMixin
  
  
  if is_torch_available():
         import torch
  
  if is_vision_available():
         from PIL import Image
  
         from transformers import TvltImageProcessor
  
  
  def       a			(   a		,	a=10		,	a=10		,	a=False		,	a=False  )    ->List[str]:
  
  
  
  
     '''simple docstring'''
  
  
     SCREAMING_SNAKE_CASE    	=   []
     for i in range(image_processor_tester.num_frames  ):
        video.append(np.random.randint(255		,	size=(image_processor_tester.num_channels, width, height)		,	dtype=np.uinta  )  )
  
     if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        SCREAMING_SNAKE_CASE    	=   [Image.fromarray(np.moveaxis(lowercase_		,	0		,	-1  )  ) for frame in video]
  
     if torchify:
        SCREAMING_SNAKE_CASE    	=   [torch.from_numpy(lowercase_  ) for frame in video]
  
     return video
  
  
  def       a			(   a		,	a=False		,	a=False		,	a=False  )    ->List[str]:
  
  
  
  
     '''simple docstring'''
  
  
     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
  
     SCREAMING_SNAKE_CASE    	=   []
     for i in range(image_processor_tester.batch_size  ):
        if equal_resolution:
           SCREAMING_SNAKE_CASE    	=   image_processor_tester.max_resolution
        else:
           SCREAMING_SNAKE_CASE    	=   np.random.choice(
               np.arange(image_processor_tester.min_resolution		,	image_processor_tester.max_resolution  )		,	2  )
           SCREAMING_SNAKE_CASE    	=   prepare_video(
               image_processor_tester=lowercase_		,	width=lowercase_		,	height=lowercase_		,	numpify=lowercase_		,	torchify=lowercase_		,	)
        video_inputs.append(lowercase_  )
  
     return video_inputs
  
  
  
  
  
  class 						lowerCamelCase			(					unittest.TestCase				):
  
  
  
  
  
  
  
   def __init__(    self			:Optional[Any]      ,    lowercase			:Union[str, Any]      ,    lowercase			:Optional[int]=7      ,    lowercase			:List[Any]=3      ,    lowercase			:str=4      ,    lowercase			:Union[str, Any]=1_8      ,    lowercase			:str=3_0      ,    lowercase			:List[str]=4_0_0      ,    lowercase			:Tuple=True      ,    lowercase			:Union[str, Any]=None      ,    lowercase			:List[str]=True      ,    lowercase			:Optional[Any]=[0.5, 0.5, 0.5]      ,    lowercase			:Any=[0.5, 0.5, 0.5]      ,    lowercase			:str=True      ,    lowercase			:Optional[Any]=None      ,    )		->			Union[str, Any]:
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      SCREAMING_SNAKE_CASE    	=   size if size is not None else {'shortest_edge': 1_8}
      SCREAMING_SNAKE_CASE    	=   crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
  
      SCREAMING_SNAKE_CASE    	=   parent
      SCREAMING_SNAKE_CASE    	=   batch_size
      SCREAMING_SNAKE_CASE    	=   num_channels
      SCREAMING_SNAKE_CASE    	=   num_frames
      SCREAMING_SNAKE_CASE    	=   image_size
      SCREAMING_SNAKE_CASE    	=   min_resolution
      SCREAMING_SNAKE_CASE    	=   max_resolution
      SCREAMING_SNAKE_CASE    	=   do_resize
      SCREAMING_SNAKE_CASE    	=   size
      SCREAMING_SNAKE_CASE    	=   do_normalize
      SCREAMING_SNAKE_CASE    	=   image_mean
      SCREAMING_SNAKE_CASE    	=   image_std
      SCREAMING_SNAKE_CASE    	=   do_center_crop
      SCREAMING_SNAKE_CASE    	=   crop_size
  
  
  
  
  
  
  
   def    snake_case__    (    self			:Tuple )		->			Optional[Any]:
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }
  
  
  
  
  
  @require_torch
  @require_vision
  class 						lowerCamelCase			(					a_   ,		unittest.TestCase				):
   UpperCamelCase_     :				Any								=  TvltImageProcessor if is_vision_available() else None
  
  
  
  
  
  
  
   def    snake_case__    (    self			:int )		->			Optional[int]:
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      SCREAMING_SNAKE_CASE    	=   TvltImageProcessorTester(self )
  
  
  
  
  
  
  
   @property
   def    snake_case__    (    self			:Optional[Any] )		->			Any:
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      return self.image_processor_tester.prepare_image_processor_dict()
  
  
  
  
  
  
  
   def    snake_case__    (    self			:Union[str, Any] )		->			str:
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      SCREAMING_SNAKE_CASE    	=   self.image_processing_class(**self.image_processor_dict )
      self.assertTrue(hasattr(lowercase      ,    '''image_mean''' ) )
      self.assertTrue(hasattr(lowercase      ,    '''image_std''' ) )
      self.assertTrue(hasattr(lowercase      ,    '''do_normalize''' ) )
      self.assertTrue(hasattr(lowercase      ,    '''do_resize''' ) )
      self.assertTrue(hasattr(lowercase      ,    '''do_center_crop''' ) )
      self.assertTrue(hasattr(lowercase      ,    '''size''' ) )
  
  
  
  
  
  
  
   def    snake_case__    (    self			:Dict )		->			Optional[int]:
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      SCREAMING_SNAKE_CASE    	=   self.image_processing_class(**self.image_processor_dict )
      # create random PIL videos
      SCREAMING_SNAKE_CASE    	=   prepare_video_inputs(self.image_processor_tester      ,    equal_resolution=lowercase )
      for video in video_inputs:
         self.assertIsInstance(lowercase      ,    lowercase )
         self.assertIsInstance(video[0]      ,    Image.Image )
  
      # Test not batched input
      SCREAMING_SNAKE_CASE    	=   image_processor(video_inputs[0]      ,    return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_videos.shape      ,    (
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )      ,    )
  
      # Test batched
      SCREAMING_SNAKE_CASE    	=   image_processor(lowercase      ,    return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_videos.shape      ,    (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )      ,    )
  
  
  
  
  
  
  
   def    snake_case__    (    self			:Union[str, Any] )		->			List[str]:
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      SCREAMING_SNAKE_CASE    	=   self.image_processing_class(**self.image_processor_dict )
      # create random numpy tensors
      SCREAMING_SNAKE_CASE    	=   prepare_video_inputs(self.image_processor_tester      ,    equal_resolution=lowercase      ,    numpify=lowercase )
      for video in video_inputs:
         self.assertIsInstance(lowercase      ,    lowercase )
         self.assertIsInstance(video[0]      ,    np.ndarray )
  
      # Test not batched input
      SCREAMING_SNAKE_CASE    	=   image_processor(video_inputs[0]      ,    return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_videos.shape      ,    (
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )      ,    )
  
      # Test batched
      SCREAMING_SNAKE_CASE    	=   image_processor(lowercase      ,    return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_videos.shape      ,    (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )      ,    )
  
  
  
  
  
  
  
   def    snake_case__    (    self			:Optional[Any] )		->			Any:
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      SCREAMING_SNAKE_CASE    	=   self.image_processing_class(**self.image_processor_dict )
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE    	=   prepare_video_inputs(self.image_processor_tester      ,    equal_resolution=lowercase      ,    torchify=lowercase )
      for video in video_inputs:
         self.assertIsInstance(lowercase      ,    lowercase )
         self.assertIsInstance(video[0]      ,    torch.Tensor )
  
      # Test not batched input
      SCREAMING_SNAKE_CASE    	=   image_processor(video_inputs[0]      ,    return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_videos.shape      ,    (
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )      ,    )
  
      # Test batched
      SCREAMING_SNAKE_CASE    	=   image_processor(lowercase      ,    return_tensors='''pt''' ).pixel_values
      self.assertEqual(
          encoded_videos.shape      ,    (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )      ,    )
style_context: 
  from __future__ import annotations
  
  
  def       a			(   a		,	a  )    ->str:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   '''''', '''''', ''''''
  			SCREAMING_SNAKE_CASE    	=   []
  
  			for character in message_part:
  						tmp.append(character_to_number[character]  )
  
  			for each in tmp:
  						one += each[0]
  						two += each[1]
  						three += each[2]
  
  			return one + two + three
  
  
  def       a			(   a		,	a  )    ->tuple[str, str, str]:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   '''''', ''''''
  			SCREAMING_SNAKE_CASE    	=   []
  
  			for character in message_part:
  						this_part += character_to_number[character]
  
  			for digit in this_part:
  						tmp += digit
  						if len(a  ) == len(a  ):
  									result.append(a  )
  									SCREAMING_SNAKE_CASE    	=   ''''''
  
  			return result[0], result[1], result[2]
  
  
  def       a			(   a		,	a  )    ->tuple[str, str, dict[str, str], dict[str, str]]:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   alphabet.replace(''' '''		,	''''''  ).upper()
  			SCREAMING_SNAKE_CASE    	=   message.replace(''' '''		,	''''''  ).upper()
  
  			# Check length and characters
  			if len(a  ) != 27:
  						raise KeyError('''Length of alphabet has to be 27.'''  )
  			for each in message:
  						if each not in alphabet:
  									raise ValueError('''Each message character has to be included in alphabet!'''  )
  
      # Generate dictionares
  			SCREAMING_SNAKE_CASE    	=   (
  			    '''111''',
  			    '''112''',
  			    '''113''',
  			    '''121''',
  			    '''122''',
  			    '''123''',
  			    '''131''',
  			    '''132''',
  			    '''133''',
  			    '''211''',
  			    '''212''',
  			    '''213''',
  			    '''221''',
  			    '''222''',
  			    '''223''',
  			    '''231''',
  			    '''232''',
  			    '''233''',
  			    '''311''',
  			    '''312''',
  			    '''313''',
  			    '''321''',
  			    '''322''',
  			    '''323''',
  			    '''331''',
  			    '''332''',
  			    '''333''',
  			)
  			SCREAMING_SNAKE_CASE    	=   {}
  			SCREAMING_SNAKE_CASE    	=   {}
  			for letter, number in zip(a		,	a  ):
  						SCREAMING_SNAKE_CASE    	=   number
  						SCREAMING_SNAKE_CASE    	=   letter
  
  			return message, alphabet, character_to_number, number_to_character
  
  
  def       a			(   a		,	a = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."		,	a = 5  )    ->str:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   __prepare(
  			    a		,	a  )
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   '''''', ''''''
  
  			for i in range(0		,	len(a  ) + 1		,	a  ):
  						encrypted_numeric += __encrypt_part(
  						    message[i : i + period]		,	a  )
  
  			for i in range(0		,	len(a  )		,	3  ):
  						encrypted += number_to_character[encrypted_numeric[i : i + 3]]
  
  			return encrypted
  
  
  def       a			(   a		,	a = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."		,	a = 5  )    ->str:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   __prepare(
  			    a		,	a  )
  			SCREAMING_SNAKE_CASE    	=   []
  			SCREAMING_SNAKE_CASE    	=   ''''''
  
  			for i in range(0		,	len(a  ) + 1		,	a  ):
  						SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   __decrypt_part(message[i : i + period]		,	a  )
  
  						for j in range(0		,	len(a  )  ):
  									decrypted_numeric.append(a[j] + b[j] + c[j]  )
  
  			for each in decrypted_numeric:
  						decrypted += number_to_character[each]
  
  			return decrypted
  
  
  if __name__ == "__main__":
  							__lowerCAmelCase				      =					'DEFEND THE EAST WALL OF THE CASTLE.'
  							__lowerCAmelCase				      =					encrypt_message(msg, 'EPSDUCVWYM.ZLKXNBTFGORIJHAQ')
  							__lowerCAmelCase				      =					decrypt_message(encrypted, 'EPSDUCVWYM.ZLKXNBTFGORIJHAQ')
  							print(F'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')