

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()








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"""}),
			    ]       )





