


'''simple docstring'''







import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 			__lowerCamelCase			(	a_	):
    """simple docstring"""
    a													=   42
    a													=   None






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      int=0.999  ,lowerCamelCase					:      Tuple="cosine"  ,):
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(lowerCamelCase					:      Optional[Any]       ):
                     return math.cos((t + 0.008) / 1.008 * math.pi / 2       ) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(lowerCamelCase					:      Optional[Any]       ):
                     return math.exp(t * -12.0       )

       else:
              raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'       )

       _A					:     List[Any]     = []
       for i in range(lowerCamelCase       ):
              _A					:     Tuple     = i / num_diffusion_timesteps
              _A					:     int     = (i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(lowerCamelCase       ) / alpha_bar_fn(lowerCamelCase       )  ,lowerCamelCase       )       )
       return torch.tensor(lowerCamelCase  ,dtype=torch.floataa       )


class 			__lowerCamelCase			(	a_    , a_	):
    """simple docstring"""


    @register_to_config
    def __init__(  self      :							str				,							SCREAMING_SNAKE_CASE      :							int = 1000				,							SCREAMING_SNAKE_CASE      :							str = "fixed_small_log"				,							SCREAMING_SNAKE_CASE      :							bool = True				,							SCREAMING_SNAKE_CASE      :							Optional[float] = 1.0				,							SCREAMING_SNAKE_CASE      :							str = "epsilon"				,							SCREAMING_SNAKE_CASE      :							str = "squaredcos_cap_v2"				,							):
           if beta_schedule != "squaredcos_cap_v2":
                  raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'')

           _A					:     int     = betas_for_alpha_bar(SCREAMING_SNAKE_CASE)

           _A					:     Tuple     = 1.0 - self.betas
           _A					:     Union[str, Any]     = torch.cumprod(self.alphas				,							dim=0)
           _A					:     int     = torch.tensor(1.0)

           # standard deviation of the initial noise distribution
           _A					:     List[str]     = 1.0

           # setable values
           _A					:     Union[str, Any]     = None
           _A					:     Optional[int]     = torch.from_numpy(np.arange(0				,							SCREAMING_SNAKE_CASE)[::-1].copy())

           _A					:     Dict     = variance_type


    def        A   (  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							torch.FloatTensor				,							SCREAMING_SNAKE_CASE      :							Optional[int] = None):
           return sample


    def        A   (  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							Union[str, torch.device] = None):
           _A					:     List[Any]     = num_inference_steps
           _A					:     Optional[Any]     = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
           _A					:     Tuple     = (np.arange(0				,							SCREAMING_SNAKE_CASE) * step_ratio).round()[::-1].copy().astype(np.intaa)
           _A					:     List[str]     = torch.from_numpy(SCREAMING_SNAKE_CASE).to(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							str=None				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=None				,							SCREAMING_SNAKE_CASE      :							List[Any]=None):
           if prev_timestep is None:
                  _A					:     List[str]     = t - 1

           _A					:     Dict     = self.alphas_cumprod[t]
           _A					:     int     = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
           _A					:     Union[str, Any]     = 1 - alpha_prod_t
           _A					:     Dict     = 1 - alpha_prod_t_prev

           if prev_timestep == t - 1:
                  _A					:     Optional[Any]     = self.betas[t]
           else:
                  _A					:     List[Any]     = 1 - alpha_prod_t / alpha_prod_t_prev

           # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
           # and sample from it to get previous sample
           # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
           _A					:     Union[str, Any]     = beta_prod_t_prev / beta_prod_t * beta

           if variance_type is None:
                  _A					:     List[str]     = self.config.variance_type

           # hacks - were probably added for training stability
           if variance_type == "fixed_small_log":
                  _A					:     Any     = torch.log(torch.clamp(SCREAMING_SNAKE_CASE				,							min=1e-20))
                  _A					:     List[str]     = torch.exp(0.5 * variance)
           elif variance_type == "learned_range":
                  # NOTE difference with DDPM scheduler
                  _A					:     Optional[int]     = variance.log()
                  _A					:     List[Any]     = beta.log()

                  _A					:     Any     = (predicted_variance + 1) / 2
                  _A					:     int     = frac * max_log + (1 - frac) * min_log

           return variance


    def        A   (  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							torch.FloatTensor				,							SCREAMING_SNAKE_CASE      :							int				,							SCREAMING_SNAKE_CASE      :							torch.FloatTensor				,							SCREAMING_SNAKE_CASE      :							Optional[int] = None				,							SCREAMING_SNAKE_CASE      :							int=None				,							SCREAMING_SNAKE_CASE      :							bool = True				,							):
           _A					:     Optional[Any]     = timestep

           if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
                  _A  ,			_A					:     List[str]     = torch.split(SCREAMING_SNAKE_CASE				,							sample.shape[1]				,							dim=1)
           else:
                  _A					:     Dict     = None

           # 1. compute alphas, betas
           if prev_timestep is None:
                  _A					:     Any     = t - 1

           _A					:     Dict     = self.alphas_cumprod[t]
           _A					:     Union[str, Any]     = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
           _A					:     int     = 1 - alpha_prod_t
           _A					:     List[str]     = 1 - alpha_prod_t_prev

           if prev_timestep == t - 1:
                  _A					:     Optional[int]     = self.betas[t]
                  _A					:     str     = self.alphas[t]
           else:
                  _A					:     int     = 1 - alpha_prod_t / alpha_prod_t_prev
                  _A					:     Tuple     = 1 - beta

           # 2. compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
           if self.config.prediction_type == "epsilon":
                  _A					:     Optional[int]     = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
           elif self.config.prediction_type == "sample":
                  _A					:     List[Any]     = model_output
           else:
                  raise ValueError(
                      F'prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'
                      ' for the UnCLIPScheduler.')

           # 3. Clip "predicted x_0"
           if self.config.clip_sample:
                  _A					:     List[Any]     = torch.clamp(
                      SCREAMING_SNAKE_CASE				,							-self.config.clip_sample_range				,							self.config.clip_sample_range)

           # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
           # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
           _A					:     List[Any]     = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
           _A					:     Tuple     = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

           # 5. Compute predicted previous sample µ_t
           # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
           _A					:     Union[str, Any]     = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

           # 6. Add noise
           _A					:     List[Any]     = 0
           if t > 0:
                  _A					:     Tuple     = randn_tensor(
                      model_output.shape				,							dtype=model_output.dtype				,							generator=SCREAMING_SNAKE_CASE				,							device=model_output.device)

                  _A					:     int     = self._get_variance(
                      SCREAMING_SNAKE_CASE				,							predicted_variance=SCREAMING_SNAKE_CASE				,							prev_timestep=SCREAMING_SNAKE_CASE				,							)

                  if self.variance_type == "fixed_small_log":
                         _A					:     List[str]     = variance
                  elif self.variance_type == "learned_range":
                         _A					:     Tuple     = (0.5 * variance).exp()
                  else:
                         raise ValueError(
                             F'variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'
                             ' for the UnCLIPScheduler.')

                  _A					:     str     = variance * variance_noise

           _A					:     str     = pred_prev_sample + variance

           if not return_dict:
                  return (pred_prev_sample,)

           return UnCLIPSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE				,							pred_original_sample=SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Tuple				,							SCREAMING_SNAKE_CASE      :							torch.FloatTensor				,							SCREAMING_SNAKE_CASE      :							torch.FloatTensor				,							SCREAMING_SNAKE_CASE      :							torch.IntTensor				,							):
           # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
           _A					:     List[str]     = self.alphas_cumprod.to(device=original_samples.device				,							dtype=original_samples.dtype)
           _A					:     List[str]     = timesteps.to(original_samples.device)

           _A					:     Any     = alphas_cumprod[timesteps] ** 0.5
           _A					:     Any     = sqrt_alpha_prod.flatten()
           while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
                  _A					:     str     = sqrt_alpha_prod.unsqueeze(-1)

           _A					:     Union[str, Any]     = (1 - alphas_cumprod[timesteps]) ** 0.5
           _A					:     int     = sqrt_one_minus_alpha_prod.flatten()
           while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
                  _A					:     Tuple     = sqrt_one_minus_alpha_prod.unsqueeze(-1)

           _A					:     Union[str, Any]     = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
           return noisy_samples










'''simple docstring'''







import string

import numpy






def 				lowerCAmelCase__	(    lowerCamelCase					:      int  ,lowerCamelCase					:      int       ):
       return b if a == 0 else greatest_common_divisor(b % a  ,lowerCamelCase       )


class 			__lowerCamelCase			:
    """simple docstring"""
    a													=   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)
    a													=   numpy.vectorize(lambda a_	: x % 36	)

    a													=   numpy.vectorize(a_	)


    def __init__(  self      :							List[Any]				,							SCREAMING_SNAKE_CASE      :							numpy.ndarray):
           _A					:     Union[str, Any]     = self.modulus(SCREAMING_SNAKE_CASE)  # mod36 calc's on the encrypt key
           self.check_determinant()  # validate the determinant of the encryption key
           _A					:     Optional[int]     = encrypt_key.shape[0]


    def        A   (  self      :							List[str]				,							SCREAMING_SNAKE_CASE      :							str):
           return self.key_string.index(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							int):
           return self.key_string[round(SCREAMING_SNAKE_CASE)]


    def        A   (  self      :							List[Any]):
           _A					:     Optional[Any]     = round(numpy.linalg.det(self.encrypt_key))

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

           _A					:     str     = len(self.key_string)
           if greatest_common_divisor(SCREAMING_SNAKE_CASE				,							len(self.key_string)) != 1:
                  _A					:     Optional[int]     = (
                      F'determinant modular {req_l} of encryption key({det}) '
                      F'is not co prime w.r.t {req_l}.\nTry another key.'
                  )
                  raise ValueError(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							int				,							SCREAMING_SNAKE_CASE      :							str):
           _A					:     List[Any]     = [char for char in text.upper() if char in self.key_string]

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

           return "".join(SCREAMING_SNAKE_CASE)


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							str):
           _A					:     Optional[int]     = self.process_text(text.upper())
           _A					:     List[str]     = ''

           for i in range(0				,							len(SCREAMING_SNAKE_CASE) - self.break_key + 1				,							self.break_key):
                  _A					:     Optional[Any]     = text[i : i + self.break_key]
                  _A					:     Optional[int]     = [self.replace_letters(SCREAMING_SNAKE_CASE) for char in batch]
                  _A					:     Tuple     = numpy.array([vec]).T
                  _A					:     List[str]     = self.modulus(self.encrypt_key.dot(SCREAMING_SNAKE_CASE)).T.tolist()[
                      0
                  ]
                  _A					:     str     = ''.join(
                      self.replace_digits(SCREAMING_SNAKE_CASE) for num in batch_encrypted)
                  encrypted += encrypted_batch

           return encrypted


    def        A   (  self      :							Union[str, Any]):
           _A					:     List[Any]     = round(numpy.linalg.det(self.encrypt_key))

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

           _A					:     Dict     = (
               det_inv
               * numpy.linalg.det(self.encrypt_key)
               * numpy.linalg.inv(self.encrypt_key)
           )

           return self.to_int(self.modulus(SCREAMING_SNAKE_CASE))


    def        A   (  self      :							Any				,							SCREAMING_SNAKE_CASE      :							str):
           _A					:     List[str]     = self.make_decrypt_key()
           _A					:     Dict     = self.process_text(text.upper())
           _A					:     str     = ''

           for i in range(0				,							len(SCREAMING_SNAKE_CASE) - self.break_key + 1				,							self.break_key):
                  _A					:     Optional[int]     = text[i : i + self.break_key]
                  _A					:     Union[str, Any]     = [self.replace_letters(SCREAMING_SNAKE_CASE) for char in batch]
                  _A					:     Tuple     = numpy.array([vec]).T
                  _A					:     Optional[int]     = self.modulus(decrypt_key.dot(SCREAMING_SNAKE_CASE)).T.tolist()[0]
                  _A					:     Tuple     = ''.join(
                      self.replace_digits(SCREAMING_SNAKE_CASE) for num in batch_decrypted)
                  decrypted += decrypted_batch

           return decrypted






def 				lowerCAmelCase__	(    ):
       _A					:     List[Any]     = int(input('Enter the order of the encryption key: '       )       )
       _A					:     List[str]     = []

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

       _A					:     Dict     = HillCipher(numpy.array(lowerCamelCase       )       )

       print('Would you like to encrypt or decrypt some text? (1 or 2)'       )
       _A					:     List[str]     = input('\n1. Encrypt\n2. Decrypt\n'       )
       if option == "1":
              _A					:     int     = input('What text would you like to encrypt?: '       )
              print('Your encrypted text is:'       )
              print(hc.encrypt(lowerCamelCase       )       )
       elif option == "2":
              _A					:     int     = 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()






