




'''simple docstring'''





from __future__ import annotations

from string import ascii_letters
def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_ = None		):
    a__      =       alphabet or ascii_letters

    # The final result string
    a__      =       """"""

    for character in input_string:
        if character not in alpha:
            # Append without encryption if character is not in the alphabet
            result += character
        else:
            # Get the index of the new key and make sure it isn't too large
            a__      =       (alpha.index(UpperCAmelCase_		) + key) % len(UpperCAmelCase_		)

            # Append the encoded character to the alphabet
            result += alpha[new_key]

    return result
def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_ = None		):
    key *= -1

    return encrypt(UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_		)
def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_ = None		):
    a__      =       alphabet or ascii_letters

    # To store data on all the combinations
    a__      =       {}

    # Cycle through each combination
    for key in range(1   ,    len(UpperCAmelCase_		) + 1		):
        # Decrypt the message and store the result in the data
        a__      =       decrypt(UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_		)

    return brute_force_data


if __name__ == "__main__":
 while True:
  print(F"\n{'-' * 10}\n Menu\n{'-' * 10}")
  print(*['''1.Encrypt''', '''2.Decrypt''', '''3.BruteForce''', '''4.Quit'''], sep='''\n''')

  # get user input
  SCREAMING_SNAKE_CASE_						:					Union[str, Any]			   =       input('''\nWhat would you like to do?: ''').strip() or '''4'''

  # run functions based on what the user chose
  if choice not in ("1", "2", "3", "4"):
   print('''Invalid choice, please enter a valid choice''')
  elif choice == "1":
   SCREAMING_SNAKE_CASE_						:					Tuple			   =       input('''Please enter the string to be encrypted: ''')
   SCREAMING_SNAKE_CASE_						:					Union[str, Any]			   =       int(input('''Please enter off-set: ''').strip())

   print(encrypt(input_string, key))
  elif choice == "2":
   SCREAMING_SNAKE_CASE_						:					Optional[Any]			   =       input('''Please enter the string to be decrypted: ''')
   SCREAMING_SNAKE_CASE_						:					Union[str, Any]			   =       int(input('''Please enter off-set: ''').strip())

   print(decrypt(input_string, key))
  elif choice == "3":
   SCREAMING_SNAKE_CASE_						:					int			   =       input('''Please enter the string to be decrypted: ''')
   SCREAMING_SNAKE_CASE_						:					Optional[int]			   =       brute_force(input_string)

   for key, value in brute_force_data.items():
    print(F"Key: {key} | Message: {value}")

  elif choice == "4":
   print('''Goodbye.''')
   break












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


SCREAMING_SNAKE_CASE_						:					Optional[Any]			   =       logging.get_logger(__name__)

if is_tf_available():
 import tensorflow as tf







@dataclass
class 		__UpperCAmelCase(		lowercase__ ):





     """simple docstring"""



     __UpperCAmelCase	: int						=    """tf"""
     __UpperCAmelCase	: Optional[str]						=    field(
         default=lowercase__       ,	metadata={"""help""": """Name of TPU"""}       ,	)

     __UpperCAmelCase	: Optional[str]						=    field(
         default=lowercase__       ,	metadata={"""help""": """Zone of TPU"""}       ,	)

     __UpperCAmelCase	: Optional[str]						=    field(
         default=lowercase__       ,	metadata={"""help""": """Name of Cloud TPU-enabled project"""}       ,	)

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

     __UpperCAmelCase	: bool						=    field(default=lowercase__       ,	metadata={"""help""": """Whether to activate the XLA compilation or not"""} )

     @cached_property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			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 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			"tf.distribute.Strategy":






         """simple docstring"""
         requires_backends(self						,				["""tf"""]    )
         return self._setup_strategy

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			int:






         """simple docstring"""
         requires_backends(self						,				["""tf"""]    )
         return self._setup_strategy.num_replicas_in_sync

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			List[Any]:






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

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[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 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			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 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			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






