code: 
  
  import random
  import sys
  
  from . import cryptomath_module as cryptomath
  
  UpperCAmelCase_       :				Tuple		   =      (
      R" !\"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`"
      R"abcdefghijklmnopqrstuvwxyz{|}~"
  )
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Any ,a__						:      Optional[int] ,a__						:      Tuple					)				->							None:
   if mode == "encrypt":
    if key_a == 1:
     sys.exit(
         """The affine cipher becomes weak when key """
         """A is set to 1. Choose different key"""					)
    if key_b == 0:
     sys.exit(
         """The affine cipher becomes weak when key """
         """B is set to 0. Choose different key"""					)
   if key_a < 0 or key_b < 0 or key_b > len(snake_case_					) - 1:
    sys.exit(
        """Key A must be greater than 0 and key B must """
        f"""be between 0 and {len(snake_case_					) - 1}."""					)
   if cryptomath.gcd(snake_case_ ,len(snake_case_					)					) != 1:
    sys.exit(
        f"""Key A {key_a} and the symbol set size {len(snake_case_					)} """
        """are not relatively prime. Choose a different key."""					)
  def        __SCREAMING_SNAKE_CASE    (      a__						:      str ,a__						:      Tuple					)				->							str:
   __A   : Dict								= divmod(snake_case_ ,len(snake_case_					)					)
   check_keys(snake_case_ ,snake_case_ ,"""encrypt"""					)
   __A   : Any								= ''''''
   for symbol in message:
    if symbol in SYMBOLS:
     __A   : Tuple								= SYMBOLS.find(snake_case_					)
     cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(snake_case_					)]
    else:
     cipher_text += symbol
   return cipher_text
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Tuple ,a__						:      List[Any]					)				->							str:
   __A   : Optional[int]								= divmod(snake_case_ ,len(snake_case_					)					)
   check_keys(snake_case_ ,snake_case_ ,"""decrypt"""					)
   __A   : Tuple								= ''''''
   __A   : Tuple								= cryptomath.find_mod_inverse(snake_case_ ,len(snake_case_					)					)
   for symbol in message:
    if symbol in SYMBOLS:
     __A   : Dict								= SYMBOLS.find(snake_case_					)
     plain_text += SYMBOLS[
         (sym_index - key_b) * mod_inverse_of_key_a % len(snake_case_					)
     ]
    else:
     plain_text += symbol
   return plain_text
  def        __SCREAMING_SNAKE_CASE    (      )				->							int:
   while True:
    __A   : Optional[int]								= random.randint(2 ,len(snake_case_					)					)
    __A   : Union[str, Any]								= random.randint(2 ,len(snake_case_					)					)
    if cryptomath.gcd(snake_case_ ,len(snake_case_					)					) == 1 and key_b % len(snake_case_					) != 0:
     return key_b * len(snake_case_					) + key_b
  def        __SCREAMING_SNAKE_CASE    (      )				->							None:
   __A   : int								= input("""Enter message: """					).strip()
   __A   : Tuple								= int(input("""Enter key [2000 - 9000]: """					).strip()					)
   __A   : Optional[int]								= input("""Encrypt/Decrypt [E/D]: """					).strip().lower()
  
   if mode.startswith("""e"""					):
    __A   : Union[str, Any]								= '''encrypt'''
    __A   : List[Any]								= encrypt_message(snake_case_ ,snake_case_					)
   elif mode.startswith("""d"""					):
    __A   : Dict								= '''decrypt'''
    __A   : Optional[int]								= decrypt_message(snake_case_ ,snake_case_					)
   print(f"""\n{mode.title()}ed text: \n{translated}"""					)
  
  
  if __name__ == "__main__":
     import doctest
  
     doctest.testmod()
     # main()
  
  
  
  
  
  
style_context: 
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfigWithPast
  from ...utils import logging
  
  
  UpperCAmelCase_       :				int		   =      logging.get_logger(__name__)
  
  UpperCAmelCase_       :				Any		   =      {
      '''uclanlp/plbart-base''': '''https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json''',
      # See all PLBART models at https://huggingface.co/models?filter=plbart
  }
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase  ):
  	_lowercase		:      str    				=							'''plbart'''
  	_lowercase		:      str    				=							['''past_key_values''']
  	_lowercase		:      Any    				=							{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
  
  
  
  
  	def __init__(    self	:    List[Any]				,			__A	:    str=5_0005				,			__A	:    Union[str, Any]=1024				,			__A	:    int=6				,			__A	:    Dict=3072				,			__A	:    Union[str, Any]=12				,			__A	:    Tuple=6				,			__A	:    Tuple=3072				,			__A	:    Optional[int]=12				,			__A	:    int=0.0				,			__A	:    List[Any]=0.0				,			__A	:    Union[str, Any]=True				,			__A	:    Any=True				,			__A	:    Optional[int]="gelu"				,			__A	:    Optional[int]=768				,			__A	:    Any=0.1				,			__A	:    Union[str, Any]=0.1				,			__A	:    Union[str, Any]=0.0				,			__A	:    int=0.0_2				,			__A	:    Union[str, Any]=0.0				,			__A	:    Tuple=True				,			__A	:    List[str]=1				,			__A	:    str=0				,			__A	:    List[Any]=2				,			__A	:    Any=2				,			**__A	:    Union[str, Any]				,			):
  		__A   : List[Any]								= vocab_size
  		__A   : str								= max_position_embeddings
  		__A   : Union[str, Any]								= d_model
  		__A   : List[str]								= encoder_ffn_dim
  		__A   : Optional[Any]								= encoder_layers
  		__A   : Tuple								= encoder_attention_heads
  		__A   : List[Any]								= decoder_ffn_dim
  		__A   : Optional[int]								= decoder_layers
  		__A   : Any								= decoder_attention_heads
  		__A   : Optional[int]								= dropout
  		__A   : Any								= attention_dropout
  		__A   : Tuple								= activation_dropout
  		__A   : List[Any]								= activation_function
  		__A   : Tuple								= init_std
  		__A   : List[Any]								= encoder_layerdrop
  		__A   : str								= decoder_layerdrop
  		__A   : Union[str, Any]								= classifier_dropout
  		__A   : Optional[Any]								= use_cache
  		__A   : List[str]								= encoder_layers
  		__A   : Optional[int]								= scale_embedding  # scale factor will be sqrt(d_model) if True
  		super().__init__(
  		    pad_token_id=__A				,			bos_token_id=__A				,			eos_token_id=__A				,			is_encoder_decoder=__A				,			forced_eos_token_id=__A				,			**__A				,			)
  
  
  
  
  
  
  class     lowerCamelCase_			(  _lowercase  ):
  
  
  
  
  	@property
  	def 					lowerCAmelCase_					(    self	:    Any       ):
  		return OrderedDict(
  		    [
  		        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
  		        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
  		    ]       )
  
  
  
  
  	@property
  	def 					lowerCAmelCase_					(    self	:    Tuple       ):
  		if self.use_past:
  			return OrderedDict(
  			    [
  			        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  			        ("""past_keys""", {0: """batch""", 2: """sequence"""}),
  			        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  			    ]       )
  		else:
  			return OrderedDict(
  			    [
  			        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  			        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
  			    ]       )
  
  
  
  
  
  