

"""simple docstring"""




import string

import numpy
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 return b if a == 0 else greatest_common_divisor(b % a				,     UpperCamelCase__   )







class   __lowercase      :
  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)
  snake_case_												=							numpy.vectorize(lambda __lowerCamelCase       : x % 3_6       )

  snake_case_												=							numpy.vectorize(__lowerCamelCase       )

  def __init__(	self		:      Any       ,A		:      numpy.ndarray     ):






   '''simple docstring'''
   UpperCAmelCase__       :	int        =       self.modulus(__a     )  # mod36 calc's on the encrypt key
   self.check_determinant()  # validate the determinant of the encryption key
   UpperCAmelCase__       :	Union[str, Any]        =       encrypt_key.shape[0]

  def  __lowercase							(	self		:      Union[str, Any]       ,A		:      str     ):






   '''simple docstring'''
   return self.key_string.index(__a     )

  def  __lowercase							(	self		:      Any       ,A		:      int     ):






   '''simple docstring'''
   return self.key_string[round(__a     )]

  def  __lowercase							(	self		:      Tuple     ):






   '''simple docstring'''
   UpperCAmelCase__       :	int        =       round(numpy.linalg.det(self.encrypt_key     )     )

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

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

  def  __lowercase							(	self		:      Optional[Any]       ,A		:      str     ):






   '''simple docstring'''
   UpperCAmelCase__       :	List[str]        =       [char for char in text.upper() if char in self.key_string]

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

   return "".join(__a     )

  def  __lowercase							(	self		:      Optional[Any]       ,A		:      str     ):






   '''simple docstring'''
   UpperCAmelCase__       :	List[Any]        =       self.process_text(text.upper()     )
   UpperCAmelCase__       :	str        =       """"""

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

   return encrypted

  def  __lowercase							(	self		:      Optional[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Union[str, Any]        =       round(numpy.linalg.det(self.encrypt_key     )     )

   if det < 0:
    UpperCAmelCase__       :	Optional[int]        =       det % len(self.key_string     )
   UpperCAmelCase__       :	Optional[int]        =       None
   for i in range(len(self.key_string     )     ):
    if (det * i) % len(self.key_string     ) == 1:
     UpperCAmelCase__       :	List[Any]        =       i
     break

   UpperCAmelCase__       :	Optional[Any]        =       (
       det_inv
       * numpy.linalg.det(self.encrypt_key     )
       * numpy.linalg.inv(self.encrypt_key     )
   )

   return self.to_int(self.modulus(__a     )     )




  def  __lowercase							(	self		:      Optional[Any]       ,A		:      str     ):






   '''simple docstring'''
   UpperCAmelCase__       :	int        =       self.make_decrypt_key()
   UpperCAmelCase__       :	Union[str, Any]        =       self.process_text(text.upper()     )
   UpperCAmelCase__       :	int        =       """"""

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

   return decrypted







def 	lowerCAmelCase    (       ):
 '''simple docstring'''





 UpperCAmelCase__       :	Optional[Any]        =       int(input("""Enter the order of the encryption key: """   )   )
 UpperCAmelCase__       :	Union[str, Any]        =       []

 print("""Enter each row of the encryption key with space separated integers"""   )
 for _ in range(UpperCamelCase__   ):
  UpperCAmelCase__       :	Union[str, Any]        =       [int(UpperCamelCase__   ) for x in input().split()]
  hill_matrix.append(UpperCamelCase__   )

 UpperCAmelCase__       :	int        =       HillCipher(numpy.array(UpperCamelCase__   )   )

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


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      main()






'''simple docstring'''


import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput






class     A ( UpperCAmelCase							,						UpperCAmelCase ):
							a_										=   1





							@register_to_config
							def __init__(   self					:				Tuple  ,		__a					:				int = 1_0_0_0  ,		__a					:				Optional[Union[np.ndarray, List[float]]] = None						)				->						int:
								# set `betas`, `alphas`, `timesteps`
								self.set_timesteps(__a						)

								# standard deviation of the initial noise distribution
								__UpperCAmelCase      					=      1.0

								# For now we only support F-PNDM, i.e. the runge-kutta method
								# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
								# mainly at formula (9), (12), (13) and the Algorithm 2.
								__UpperCAmelCase      					=      4

								# running values
								__UpperCAmelCase      					=      []





							def    snake_case__    (   self					:				str  ,		__a					:				int  ,		__a					:				Union[str, torch.device] = None						)				->						str:
								__UpperCAmelCase      					=      num_inference_steps
								__UpperCAmelCase      					=      torch.linspace(1  ,		0  ,		num_inference_steps + 1						)[:-1]
								__UpperCAmelCase      					=      torch.cat([steps, torch.tensor([0.0]						)]						)

								if self.config.trained_betas is not None:
									__UpperCAmelCase      					=      torch.tensor(self.config.trained_betas  ,		dtype=torch.floataa						)
								else:
									__UpperCAmelCase      					=      torch.sin(steps * math.pi / 2						) ** 2

								__UpperCAmelCase      					=      (1.0 - self.betas**2) ** 0.5

								__UpperCAmelCase      					=      (torch.atana(self.betas  ,		self.alphas						) / math.pi * 2)[:-1]
								__UpperCAmelCase      					=      timesteps.to(__a						)

								__UpperCAmelCase      					=      []





							def    snake_case__    (   self					:				List[Any]  ,		__a					:				torch.FloatTensor  ,		__a					:				int  ,		__a					:				torch.FloatTensor  ,		__a					:				bool = True  ,		)				->						Union[SchedulerOutput, Tuple]:
								if self.num_inference_steps is None:
									raise ValueError(
									    '''Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'''						)

								__UpperCAmelCase      					=      (self.timesteps == timestep).nonzero().item()
								__UpperCAmelCase      					=      timestep_index + 1

								__UpperCAmelCase      					=      sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
								self.ets.append(__a						)

								if len(self.ets						) == 1:
									__UpperCAmelCase      					=      self.ets[-1]
								elif len(self.ets						) == 2:
									__UpperCAmelCase      					=      (3 * self.ets[-1] - self.ets[-2]) / 2
								elif len(self.ets						) == 3:
									__UpperCAmelCase      					=      (2_3 * self.ets[-1] - 1_6 * self.ets[-2] + 5 * self.ets[-3]) / 1_2
								else:
									__UpperCAmelCase      					=      (1 / 2_4) * (5_5 * self.ets[-1] - 5_9 * self.ets[-2] + 3_7 * self.ets[-3] - 9 * self.ets[-4])

								__UpperCAmelCase      					=      self._get_prev_sample(__a  ,		__a  ,		__a  ,		__a						)

								if not return_dict:
									return (prev_sample,)

								return SchedulerOutput(prev_sample=__a						)





							def    snake_case__    (   self					:				List[Any]  ,		__a					:				torch.FloatTensor  ,		*__a					:				Any  ,		**__a					:				Optional[int]						)				->						torch.FloatTensor:
								return sample





							def    snake_case__    (   self					:				int  ,		__a					:				List[Any]  ,		__a					:				Any  ,		__a					:				int  ,		__a					:				Union[str, Any]						)				->						Optional[Any]:
								__UpperCAmelCase      					=      self.alphas[timestep_index]
								__UpperCAmelCase      					=      self.betas[timestep_index]

								__UpperCAmelCase      					=      self.alphas[prev_timestep_index]
								__UpperCAmelCase      					=      self.betas[prev_timestep_index]

								__UpperCAmelCase      					=      (sample - sigma * ets) / max(__a  ,		1e-8						)
								__UpperCAmelCase      					=      next_alpha * pred + ets * next_sigma

								return prev_sample






							def __len__(   self					:				Dict						)				->						str:
								return self.config.num_train_timesteps
