




'''simple docstring'''







lowerCAmelCase_    	=							{
    '''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 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   return [alphabet[char] for char in key.upper()]







def 	_A			(   UpperCAmelCase			,UpperCAmelCase	):






   '''simple docstring'''







   A__         =    ''
   A__         =    0
   A__         =    generate_table(UpperCAmelCase	)
   for char in words.upper():
      cipher += get_opponent(table[count]			,UpperCAmelCase	)
      A__         =    (count + 1) % len(UpperCAmelCase	)
   return cipher







def 	_A			(   UpperCAmelCase			,UpperCAmelCase	):






   '''simple docstring'''







   return encrypt(UpperCAmelCase			,UpperCAmelCase	)







def 	_A			(   UpperCAmelCase			,UpperCAmelCase	):






   '''simple docstring'''







   A__         =    0 if char in table[0] else 1
   A__         =    table[row].index(UpperCAmelCase	)
   return row, col







def 	_A			(   UpperCAmelCase			,UpperCAmelCase	):






   '''simple docstring'''







   A__     , A__         =    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...
    lowerCAmelCase_    	=							input('''Enter key: ''').strip()
    lowerCAmelCase_    	=							input('''Enter text to encrypt: ''').strip()
    lowerCAmelCase_    	=							encrypt(key, text)

    print(f'''Encrypted: {cipher_text}''')
    print(f'''Decrypted with key: {decrypt(key, cipher_text)}''')








'''simple docstring'''







import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


lowerCAmelCase_    	=							logging.get_logger(__name__)

if is_tf_available():
    import tensorflow as tf

@dataclass
class   _snake_case( UpperCAmelCase			):
 __snake_case:       Optional[Any]									=				'''tf'''
 __snake_case:       Optional[str]									=				field(
     default=UpperCAmelCase					,   metadata={'''help''': '''Name of TPU'''}					,   )

 __snake_case:       Optional[str]									=				field(
     default=UpperCAmelCase					,   metadata={'''help''': '''Zone of TPU'''}					,   )

 __snake_case:       Optional[str]									=				field(
     default=UpperCAmelCase					,   metadata={'''help''': '''Name of Cloud TPU-enabled project'''}					,   )

 __snake_case:       float									=				field(
     default=1.0					,   metadata={'''help''': '''Power for the Polynomial decay LR scheduler.'''}					,   )

 __snake_case:       bool									=				field(default=UpperCAmelCase					,   metadata={'''help''': '''Whether to activate the XLA compilation or not'''}			)






 @cached_property
 def 	_UpperCamelCase   (self					:     Tuple						)					->      Tuple["tf.distribute.Strategy", int]:






    """simple docstring"""


    requires_backends(self						,		['tf']						)
    logger.info('Tensorflow: setting up strategy'						)

    A__         =    tf.config.list_physical_devices('GPU'						)

    # Set to float16 at first
    if self.fpaa:
       tf.keras.mixed_precision.set_global_policy('mixed_float16'						)

    if self.no_cuda:
       A__         =    tf.distribute.OneDeviceStrategy(device='/cpu:0'						)
    else:
       try:
          if self.tpu_name:
             A__         =    tf.distribute.cluster_resolver.TPUClusterResolver(
                 self.tpu_name						,		zone=self.tpu_zone						,		project=self.gcp_project						)
          else:
             A__         =    tf.distribute.cluster_resolver.TPUClusterResolver()
       except ValueError:
          if self.tpu_name:
             raise RuntimeError(f"""Couldn't connect to TPU {self.tpu_name}!"""						)
          else:
             A__         =    None

       if tpu:
          # Set to bfloat16 in case of TPU
          if self.fpaa:
             tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'						)

          tf.config.experimental_connect_to_cluster(a						)
          tf.tpu.experimental.initialize_tpu_system(a						)

          A__         =    tf.distribute.TPUStrategy(a						)

       elif len(a						) == 0:
          A__         =    tf.distribute.OneDeviceStrategy(device='/cpu:0'						)
       elif len(a						) == 1:
          A__         =    tf.distribute.OneDeviceStrategy(device='/gpu:0'						)
       elif len(a						) > 1:
          # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
          A__         =    tf.distribute.MirroredStrategy()
       else:
          raise ValueError('Cannot find the proper strategy, please check your environment properties.'						)

    return strategy






 @property
 def 	_UpperCamelCase   (self					:     List[Any]						)					->      "tf.distribute.Strategy":






    """simple docstring"""


    requires_backends(self						,		['tf']						)
    return self._setup_strategy






 @property
 def 	_UpperCamelCase   (self					:     Tuple						)					->      int:






    """simple docstring"""


    requires_backends(self						,		['tf']						)
    return self._setup_strategy.num_replicas_in_sync






 @property
 def 	_UpperCamelCase   (self					:     Optional[Any]						)					->      int:






    """simple docstring"""


    return False  # TF Logging is handled by Keras not the Trainer






 @property
 def 	_UpperCamelCase   (self					:     Union[str, Any]						)					->      int:






    """simple docstring"""


    if self.per_gpu_train_batch_size:
       logger.warning(
           'Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future '
           'version. Using `--per_device_train_batch_size` is preferred.'						)
    A__         =    self.per_gpu_train_batch_size or self.per_device_train_batch_size
    return per_device_batch_size * self.n_replicas






 @property
 def 	_UpperCamelCase   (self					:     Optional[int]						)					->      int:






    """simple docstring"""


    if self.per_gpu_eval_batch_size:
       logger.warning(
           'Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future '
           'version. Using `--per_device_eval_batch_size` is preferred.'						)
    A__         =    self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
    return per_device_batch_size * self.n_replicas






 @property
 def 	_UpperCamelCase   (self					:     Optional[Any]						)					->      int:






    """simple docstring"""


    requires_backends(self						,		['tf']						)
    warnings.warn(
        'The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead.'						,		a						,		)
    return self._setup_strategy.num_replicas_in_sync


