import string

import numpy



def   __A		(			_A							,				_A					):



 """simple docstring"""


 return b if a == 0 else greatest_common_divisor(b % a							,				_lowerCAmelCase					)
class    A_ :
  _SCREAMING_SNAKE_CASE    				=			string.ascii_uppercase + string.digits
  # This cipher takes alphanumerics into account
  # i.e. a total of 36 characters

  # take x and return x % len(key_string)
  _SCREAMING_SNAKE_CASE    				=			numpy.vectorize(lambda a_ : x % 36 )

  _SCREAMING_SNAKE_CASE    				=			numpy.vectorize(__UpperCAmelCase )

  def __init__(		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      numpy.ndarray       ):
   __a       =  self.modulus(_lowerCamelCase       )  # mod36 calc's on the encrypt key
   self.check_determinant()  # validate the determinant of the encryption key
   __a       =  encrypt_key.shape[0]

  def 		_UpperCAmelCase   (		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):
   return self.key_string.index(_lowerCamelCase       )

  def 		_UpperCAmelCase   (		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      int       ):
   return self.key_string[round(_lowerCamelCase       )]

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       =  round(numpy.linalg.det(self.encrypt_key       )       )

   if det < 0:
    __a       =  det % len(self.key_string       )

   __a       =  len(self.key_string       )
   if greatest_common_divisor(_lowerCamelCase     ,							len(self.key_string       )       ) != 1:
    __a       =  (
        f"""determinant modular {req_l} of encryption key({det}) """
        f"""is not co prime w.r.t {req_l}.\nTry another key."""
    )
    raise ValueError(_lowerCamelCase       )

  def 		_UpperCAmelCase   (		self :      Dict     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  [char for char in text.upper() if char in self.key_string]

   __a       =  chars[-1]
   while len(_lowerCamelCase       ) % self.break_key != 0:
    chars.append(_lowerCamelCase       )

   return "".join(_lowerCamelCase       )

  def 		_UpperCAmelCase   (		self :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self.process_text(text.upper()       )
   __a       =  ""

   for i in range(0     ,							len(_lowerCamelCase       ) - self.break_key + 1     ,							self.break_key       ):
    __a       =  text[i : i + self.break_key]
    __a       =  [self.replace_letters(_lowerCamelCase       ) for char in batch]
    __a       =  numpy.array([vec]       ).T
    __a       =  self.modulus(self.encrypt_key.dot(_lowerCamelCase       )       ).T.tolist()[
        0
    ]
    __a       =  "".join(
        self.replace_digits(_lowerCamelCase       ) for num in batch_encrypted       )
    encrypted += encrypted_batch

   return encrypted

  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  round(numpy.linalg.det(self.encrypt_key       )       )

   if det < 0:
    __a       =  det % len(self.key_string       )
   __a       =  None
   for i in range(len(self.key_string       )       ):
    if (det * i) % len(self.key_string       ) == 1:
     __a       =  i
     break

   __a       =  (
       det_inv
       * numpy.linalg.det(self.encrypt_key       )
       * numpy.linalg.inv(self.encrypt_key       )
   )

   return self.to_int(self.modulus(_lowerCamelCase       )       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  self.make_decrypt_key()
   __a       =  self.process_text(text.upper()       )
   __a       =  ""

   for i in range(0     ,							len(_lowerCamelCase       ) - self.break_key + 1     ,							self.break_key       ):
    __a       =  text[i : i + self.break_key]
    __a       =  [self.replace_letters(_lowerCamelCase       ) for char in batch]
    __a       =  numpy.array([vec]       ).T
    __a       =  self.modulus(decrypt_key.dot(_lowerCamelCase       )       ).T.tolist()[0]
    __a       =  "".join(
        self.replace_digits(_lowerCamelCase       ) for num in batch_decrypted       )
    decrypted += decrypted_batch

   return decrypted



def   __A		(			):



 """simple docstring"""


 __a       =  int(input("Enter the order of the encryption key: "					)					)
 __a       =  []

 print("Enter each row of the encryption key with space separated integers"					)
 for _ in range(_lowerCAmelCase					):
  __a       =  [int(_lowerCAmelCase					) for x in input().split()]
  hill_matrix.append(_lowerCAmelCase					)

 __a       =  HillCipher(numpy.array(_lowerCAmelCase					)					)

 print("Would you like to encrypt or decrypt some text? (1 or 2)"					)
 __a       =  input("\n1. Encrypt\n2. Decrypt\n"					)
 if option == "1":
  __a       =  input("What text would you like to encrypt?: "					)
  print("Your encrypted text is:"					)
  print(hc.encrypt(_lowerCAmelCase					)					)
 elif option == "2":
  __a       =  input("What text would you like to decrypt?: "					)
  print("Your decrypted text is:"					)
  print(hc.decrypt(_lowerCAmelCase					)					)


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   main()



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE :	Dict							     =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :	List[str]							     =		{
    """facebook/xlm-roberta-xl""": """https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json""",
    """facebook/xlm-roberta-xxl""": """https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json""",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			"""xlm-roberta-xl"""

  def __init__(		self :      str     ,							__SCREAMING_SNAKE_CASE :      int=25_08_80     ,							__SCREAMING_SNAKE_CASE :      Dict=25_60     ,							__SCREAMING_SNAKE_CASE :      List[str]=36     ,							__SCREAMING_SNAKE_CASE :      int=32     ,							__SCREAMING_SNAKE_CASE :      int=1_02_40     ,							__SCREAMING_SNAKE_CASE :      Optional[int]="gelu"     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=0.1     ,							__SCREAMING_SNAKE_CASE :      int=5_14     ,							__SCREAMING_SNAKE_CASE :      List[Any]=1     ,							__SCREAMING_SNAKE_CASE :      Any=0.02     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=1E-05     ,							__SCREAMING_SNAKE_CASE :      str=1     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=0     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=2     ,							__SCREAMING_SNAKE_CASE :      Optional[int]="absolute"     ,							__SCREAMING_SNAKE_CASE :      str=True     ,							__SCREAMING_SNAKE_CASE :      str=None     ,							**__SCREAMING_SNAKE_CASE :      str     ,							):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,							bos_token_id=__SCREAMING_SNAKE_CASE     ,							eos_token_id=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )
   __a       =  vocab_size
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  hidden_act
   __a       =  intermediate_size
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  max_position_embeddings
   __a       =  type_vocab_size
   __a       =  initializer_range
   __a       =  layer_norm_eps
   __a       =  position_embedding_type
   __a       =  use_cache
   __a       =  classifier_dropout
class    A_ (       a_ ):

  @property
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   if self.task == "multiple-choice":
    __a       =  {0: "batch", 1: "choice", 2: "sequence"}
   else:
    __a       =  {0: "batch", 1: "sequence"}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
       ]       )


