code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  a__				: Dict									=    {
      '''A''': ('''ABCDEFGHIJKLM''', '''NOPQRSTUVWXYZ'''),
      '''B''': ('''ABCDEFGHIJKLM''', '''NOPQRSTUVWXYZ'''),
      '''C''': ('''ABCDEFGHIJKLM''', '''ZNOPQRSTUVWXY'''),
      '''D''': ('''ABCDEFGHIJKLM''', '''ZNOPQRSTUVWXY'''),
      '''E''': ('''ABCDEFGHIJKLM''', '''YZNOPQRSTUVWX'''),
      '''F''': ('''ABCDEFGHIJKLM''', '''YZNOPQRSTUVWX'''),
      '''G''': ('''ABCDEFGHIJKLM''', '''XYZNOPQRSTUVW'''),
      '''H''': ('''ABCDEFGHIJKLM''', '''XYZNOPQRSTUVW'''),
      '''I''': ('''ABCDEFGHIJKLM''', '''WXYZNOPQRSTUV'''),
      '''J''': ('''ABCDEFGHIJKLM''', '''WXYZNOPQRSTUV'''),
      '''K''': ('''ABCDEFGHIJKLM''', '''VWXYZNOPQRSTU'''),
      '''L''': ('''ABCDEFGHIJKLM''', '''VWXYZNOPQRSTU'''),
      '''M''': ('''ABCDEFGHIJKLM''', '''UVWXYZNOPQRST'''),
      '''N''': ('''ABCDEFGHIJKLM''', '''UVWXYZNOPQRST'''),
      '''O''': ('''ABCDEFGHIJKLM''', '''TUVWXYZNOPQRS'''),
      '''P''': ('''ABCDEFGHIJKLM''', '''TUVWXYZNOPQRS'''),
      '''Q''': ('''ABCDEFGHIJKLM''', '''STUVWXYZNOPQR'''),
      '''R''': ('''ABCDEFGHIJKLM''', '''STUVWXYZNOPQR'''),
      '''S''': ('''ABCDEFGHIJKLM''', '''RSTUVWXYZNOPQ'''),
      '''T''': ('''ABCDEFGHIJKLM''', '''RSTUVWXYZNOPQ'''),
      '''U''': ('''ABCDEFGHIJKLM''', '''QRSTUVWXYZNOP'''),
      '''V''': ('''ABCDEFGHIJKLM''', '''QRSTUVWXYZNOP'''),
      '''W''': ('''ABCDEFGHIJKLM''', '''PQRSTUVWXYZNO'''),
      '''X''': ('''ABCDEFGHIJKLM''', '''PQRSTUVWXYZNO'''),
      '''Y''': ('''ABCDEFGHIJKLM''', '''OPQRSTUVWXYZN'''),
      '''Z''': ('''ABCDEFGHIJKLM''', '''OPQRSTUVWXYZN'''),
  }
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_       )      ->list[tuple[str, str]]:
  	return [alphabet[char] for char in key.upper()]
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->str:
  	snake_case__  					=      ''
  	snake_case__  					=      0
  	snake_case__  					=      generate_table(UpperCAmelCase_       )
  	for char in words.upper():
  		cipher += get_opponent(table[count]				,							UpperCAmelCase_       )
  		snake_case__  					=      (count + 1) % len(UpperCAmelCase_       )
  	return cipher
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->str:
  	return encrypt(UpperCAmelCase_				,							UpperCAmelCase_       )
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->tuple[int, int]:
  	snake_case__  					=      0 if char in table[0] else 1
  	snake_case__  					=      table[row].index(UpperCAmelCase_       )
  	return row, col
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->str:
  	snake_case__					,     snake_case__  					=      get_position(UpperCAmelCase_				,							char.upper()       )
  	if row == 1:
  		return table[0][col]
  	else:
  		return table[1][col] if row == 0 else char
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()  # Fist ensure that all our tests are passing...
  			a__				: Optional[Any]									=    input('''Enter key: ''').strip()
  			a__				: Optional[int]									=    input('''Enter text to encrypt: ''').strip()
  			a__				: Optional[int]									=    encrypt(key, text)
  
  			print(f"""Encrypted: {cipher_text}""")
  			print(f"""Decrypted with key: {decrypt(key, cipher_text)}""")
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import json
  import os
  import random
  import shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers import BertTokenizer, BertTokenizerFast
  from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
  from transformers.testing_utils import require_vision
  from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
  
  
  if is_vision_available():
  			from PIL import Image
  
  			from transformers import FlavaImageProcessor, FlavaProcessor
  			from transformers.models.flava.image_processing_flava import (
  			    FLAVA_CODEBOOK_MEAN,
  			    FLAVA_CODEBOOK_STD,
  			    FLAVA_IMAGE_MEAN,
  			    FLAVA_IMAGE_STD,
  			)
  
  
  
  
  
  
  @require_vision
  class 							__snake_case    (				unittest.TestCase       ):
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[Any]:
  		snake_case__  					=      tempfile.mkdtemp()
  
  		# fmt: off
  		snake_case__  					=      ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
  		# fmt: on
  		snake_case__  					=      os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES['vocab_file']		)
  
  		with open(self.vocab_file			,					'w'			,					encoding='utf-8'		) as fp:
  			fp.write(''.join([x + '\n' for x in vocab_tokens]		)		)
  
  		snake_case__  					=      {
  		    'image_mean': FLAVA_IMAGE_MEAN,
  		    'image_std': FLAVA_IMAGE_STD,
  		    'do_normalize': True,
  		    'do_resize': True,
  		    'size': 224,
  		    'do_center_crop': True,
  		    'crop_size': 224,
  		    'input_size_patches': 14,
  		    'total_mask_patches': 75,
  		    'mask_group_max_patches': None,
  		    'mask_group_min_patches': 16,
  		    'mask_group_min_aspect_ratio': 0.3,
  		    'mask_group_max_aspect_ratio': None,
  		    'codebook_do_resize': True,
  		    'codebook_size': 112,
  		    'codebook_do_center_crop': True,
  		    'codebook_crop_size': 112,
  		    'codebook_do_map_pixels': True,
  		    'codebook_do_normalize': True,
  		    'codebook_image_mean': FLAVA_CODEBOOK_MEAN,
  		    'codebook_image_std': FLAVA_CODEBOOK_STD,
  		}
  
  		snake_case__  					=      os.path.join(self.tmpdirname			,					UpperCamelCase_		)
  		with open(self.image_processor_file			,					'w'			,					encoding='utf-8'		) as fp:
  			json.dump(UpperCamelCase_			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		List[str]:
  		return BertTokenizer.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		Optional[Any]:
  		return BertTokenizerFast.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self			,					**UpperCamelCase_		)			->		Any:
  		return FlavaImageProcessor.from_pretrained(self.tmpdirname			,					**UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Union[str, Any]:
  		shutil.rmtree(self.tmpdirname		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		snake_case__  					=      [np.random.randint(255			,					size=(3, 30, 400)			,					dtype=np.uinta		)]
  
  		snake_case__  					=      [Image.fromarray(np.moveaxis(UpperCamelCase_			,					0			,					-1		)		) for x in image_inputs]
  
  		return image_inputs
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Optional[int]:
  		snake_case__  					=      self.get_tokenizer()
  		snake_case__  					=      self.get_rust_tokenizer()
  		snake_case__  					=      self.get_image_processor()
  
  		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
  		processor_slow.save_pretrained(self.tmpdirname		)
  		snake_case__  					=      FlavaProcessor.from_pretrained(self.tmpdirname			,					use_fast=UpperCamelCase_		)
  
  		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
  		processor_fast.save_pretrained(self.tmpdirname		)
  		snake_case__  					=      FlavaProcessor.from_pretrained(self.tmpdirname		)
  
  		self.assertEqual(processor_slow.tokenizer.get_vocab()			,					tokenizer_slow.get_vocab()		)
  		self.assertEqual(processor_fast.tokenizer.get_vocab()			,					tokenizer_fast.get_vocab()		)
  		self.assertEqual(tokenizer_slow.get_vocab()			,					tokenizer_fast.get_vocab()		)
  		self.assertIsInstance(processor_slow.tokenizer			,					UpperCamelCase_		)
  		self.assertIsInstance(processor_fast.tokenizer			,					UpperCamelCase_		)
  
  		self.assertEqual(processor_slow.image_processor.to_json_string()			,					image_processor.to_json_string()		)
  		self.assertEqual(processor_fast.image_processor.to_json_string()			,					image_processor.to_json_string()		)
  		self.assertIsInstance(processor_slow.image_processor			,					UpperCamelCase_		)
  		self.assertIsInstance(processor_fast.image_processor			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[str]:
  		snake_case__  					=      FlavaProcessor(tokenizer=self.get_tokenizer()			,					image_processor=self.get_image_processor()		)
  		processor.save_pretrained(self.tmpdirname		)
  
  		snake_case__  					=      self.get_tokenizer(bos_token='(BOS)'			,					eos_token='(EOS)'		)
  		snake_case__  					=      self.get_image_processor(do_normalize=UpperCamelCase_			,					padding_value=1.0		)
  
  		snake_case__  					=      FlavaProcessor.from_pretrained(
  		    self.tmpdirname			,					bos_token='(BOS)'			,					eos_token='(EOS)'			,					do_normalize=UpperCamelCase_			,					padding_value=1.0		)
  
  		self.assertEqual(processor.tokenizer.get_vocab()			,					tokenizer_add_kwargs.get_vocab()		)
  		self.assertIsInstance(processor.tokenizer			,					UpperCamelCase_		)
  
  		self.assertEqual(processor.image_processor.to_json_string()			,					image_processor_add_kwargs.to_json_string()		)
  		self.assertIsInstance(processor.image_processor			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Union[str, Any]:
  		snake_case__  					=      self.get_image_processor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
  
  		snake_case__  					=      self.prepare_image_inputs()
  
  		snake_case__  					=      image_processor(UpperCamelCase_			,					return_tensors='np'		)
  		snake_case__  					=      processor(images=UpperCamelCase_			,					return_tensors='np'		)
  
  		for key in input_feat_extract.keys():
  			self.assertAlmostEqual(input_feat_extract[key].sum()			,					input_processor[key].sum()			,					delta=1E-2		)
  
  		# With rest of the args
  		random.seed(1234		)
  		snake_case__  					=      image_processor(
  		    UpperCamelCase_			,					return_image_mask=UpperCamelCase_			,					return_codebook_pixels=UpperCamelCase_			,					return_tensors='np'		)
  		random.seed(1234		)
  		snake_case__  					=      processor(
  		    images=UpperCamelCase_			,					return_image_mask=UpperCamelCase_			,					return_codebook_pixels=UpperCamelCase_			,					return_tensors='np'		)
  
  		for key in input_feat_extract.keys():
  			self.assertAlmostEqual(input_feat_extract[key].sum()			,					input_processor[key].sum()			,					delta=1E-2		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Dict:
  		snake_case__  					=      self.get_image_processor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
  
  		snake_case__  					=      'lower newer'
  
  		snake_case__  					=      processor(text=UpperCamelCase_		)
  
  		snake_case__  					=      tokenizer(UpperCamelCase_		)
  
  		for key in encoded_tok.keys():
  			self.assertListEqual(encoded_tok[key]			,					encoded_processor[key]		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Union[str, Any]:
  		snake_case__  					=      self.get_image_processor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
  
  		snake_case__  					=      'lower newer'
  		snake_case__  					=      self.prepare_image_inputs()
  
  		snake_case__  					=      processor(text=UpperCamelCase_			,					images=UpperCamelCase_		)
  
  		self.assertListEqual(list(inputs.keys()		)			,					['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']		)
  
  		# add extra args
  		snake_case__  					=      processor(text=UpperCamelCase_			,					images=UpperCamelCase_			,					return_codebook_pixels=UpperCamelCase_			,					return_image_mask=UpperCamelCase_		)
  
  		self.assertListEqual(
  		    list(inputs.keys()		)			,					[
  		        'input_ids',
  		        'token_type_ids',
  		        'attention_mask',
  		        'pixel_values',
  		        'codebook_pixel_values',
  		        'bool_masked_pos',
  		    ]			,					)
  
  		# test if it raises when no input is passed
  		with pytest.raises(UpperCamelCase_		):
  			processor()
  
  
  
  
  
  	def      _snake_case      (		self		)			->		List[Any]:
  		snake_case__  					=      self.get_image_processor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
  
  		snake_case__  					=      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  		snake_case__  					=      processor.batch_decode(UpperCamelCase_		)
  		snake_case__  					=      tokenizer.batch_decode(UpperCamelCase_		)
  
  		self.assertListEqual(UpperCamelCase_			,					UpperCamelCase_		)
  
  
  
  
  
  	def      _snake_case      (		self		)			->		Dict:
  		snake_case__  					=      self.get_image_processor()
  		snake_case__  					=      self.get_tokenizer()
  
  		snake_case__  					=      FlavaProcessor(tokenizer=UpperCamelCase_			,					image_processor=UpperCamelCase_		)
  
  		snake_case__  					=      'lower newer'
  		snake_case__  					=      self.prepare_image_inputs()
  
  		snake_case__  					=      processor(text=UpperCamelCase_			,					images=UpperCamelCase_		)
  
  		self.assertListEqual(list(inputs.keys()		)			,					processor.model_input_names		)
  
  
  
  