
"""simple docstring"""





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

import numpy as np
import torch
from scipy import integrate

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


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		torch.FloatTensor
  SCREAMING_SNAKE_CASE__     :		Optional[torch.FloatTensor]    	=						None







def       __a	(						__lowerCamelCase,						__lowerCamelCase=0.999,						__lowerCamelCase="cosine",						):
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(__lowerCamelCase    ):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2    ) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(__lowerCamelCase    ):
         return math.exp(t * -12.0    )

   else:
      raise ValueError(f"""Unsupported alpha_tranform_type: {alpha_transform_type}"""    )

   UpperCAmelCase_   :   Dict     	=       []
   for i in range(__lowerCamelCase    ):
      UpperCAmelCase_   :   Optional[Any]     	=       i / num_diffusion_timesteps
      UpperCAmelCase_   :   List[str]     	=       (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 A_      (lowercase__							,lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		Optional[Any]    	=						[e.name for e in KarrasDiffusionSchedulers]
  SCREAMING_SNAKE_CASE__     :		int    	=						1







  @register_to_config
  def __init__(						self							,  lowercase_ = 1000							,  lowercase_ = 0.00_01							,  lowercase_ = 0.02							,  lowercase_ = "linear"							,  lowercase_ = None							,  lowercase_ = False							,  lowercase_ = "epsilon"							,  lowercase_ = "linspace"							,  lowercase_ = 0							,  ):

     """simple docstring"""
     if trained_betas is not None:
        UpperCAmelCase_   :   int     	=       torch.tensor(lowercase_							,  dtype=torch.floataa    )
     elif beta_schedule == "linear":
        UpperCAmelCase_   :   List[str]     	=       torch.linspace(lowercase_							,  lowercase_							,  lowercase_							,  dtype=torch.floataa    )
     elif beta_schedule == "scaled_linear":
        # this schedule is very specific to the latent diffusion model.
        UpperCAmelCase_   :   Optional[int]     	=       (
            torch.linspace(beta_start**0.5							,  beta_end**0.5							,  lowercase_							,  dtype=torch.floataa    ) ** 2
        )
     elif beta_schedule == "squaredcos_cap_v2":
        # Glide cosine schedule
        UpperCAmelCase_   :   Any     	=       betas_for_alpha_bar(lowercase_    )
     else:
        raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""    )

     UpperCAmelCase_   :   Dict     	=       1.0 - self.betas
     UpperCAmelCase_   :   Tuple     	=       torch.cumprod(self.alphas							,  dim=0    )

     UpperCAmelCase_   :   List[Any]     	=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
     UpperCAmelCase_   :   str     	=       np.concatenate([sigmas[::-1], [0.0]]    ).astype(np.floataa    )
     UpperCAmelCase_   :   str     	=       torch.from_numpy(lowercase_    )

     # setable values
     UpperCAmelCase_   :   List[str]     	=       None
     UpperCAmelCase_   :   int     	=       use_karras_sigmas
     self.set_timesteps(lowercase_							,  lowercase_    )
     UpperCAmelCase_   :   int     	=       []
     UpperCAmelCase_   :   Tuple     	=       False







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     # standard deviation of the initial noise distribution
     if self.config.timestep_spacing in ["linspace", "trailing"]:
        return self.sigmas.max()

     return (self.sigmas.max() ** 2 + 1) ** 0.5







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     if isinstance(lowercase_							,  torch.Tensor    ):
        UpperCAmelCase_   :   Optional[int]     	=       timestep.to(self.timesteps.device    )
     UpperCAmelCase_   :   Tuple     	=       (self.timesteps == timestep).nonzero().item()
     UpperCAmelCase_   :   int     	=       self.sigmas[step_index]
     UpperCAmelCase_   :   Union[str, Any]     	=       sample / ((sigma**2 + 1) ** 0.5)
     UpperCAmelCase_   :   int     	=       True
     return sample







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""

     def lms_derivative(lowercase_    ):
        UpperCAmelCase_   :   Dict     	=       1.0
        for k in range(lowercase_    ):
           if current_order == k:
              continue
           prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
        return prod

     UpperCAmelCase_   :   List[str]     	=       integrate.quad(lowercase_							,  self.sigmas[t]							,  self.sigmas[t + 1]							,  epsrel=1E-4    )[0]

     return integrated_coeff







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_ = None    ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       num_inference_steps

     # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
     if self.config.timestep_spacing == "linspace":
        UpperCAmelCase_   :   Dict     	=       np.linspace(0							,  self.config.num_train_timesteps - 1							,  lowercase_							,  dtype=lowercase_    )[
            ::-1
        ].copy()
     elif self.config.timestep_spacing == "leading":
        UpperCAmelCase_   :   Optional[int]     	=       self.config.num_train_timesteps // self.num_inference_steps
        # creates integer timesteps by multiplying by ratio
        # casting to int to avoid issues when num_inference_step is power of 3
        UpperCAmelCase_   :   Tuple     	=       (np.arange(0							,  lowercase_    ) * step_ratio).round()[::-1].copy().astype(lowercase_    )
        timesteps += self.config.steps_offset
     elif self.config.timestep_spacing == "trailing":
        UpperCAmelCase_   :   Optional[Any]     	=       self.config.num_train_timesteps / self.num_inference_steps
        # creates integer timesteps by multiplying by ratio
        # casting to int to avoid issues when num_inference_step is power of 3
        UpperCAmelCase_   :   Optional[Any]     	=       (np.arange(self.config.num_train_timesteps							,  0							,  -step_ratio    )).round().copy().astype(lowercase_    )
        timesteps -= 1
     else:
        raise ValueError(
            F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""    )

     UpperCAmelCase_   :   int     	=       np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
     UpperCAmelCase_   :   Union[str, Any]     	=       np.log(lowercase_    )
     UpperCAmelCase_   :   str     	=       np.interp(lowercase_							,  np.arange(0							,  len(lowercase_    )    )							,  lowercase_    )

     if self.use_karras_sigmas:
        UpperCAmelCase_   :   List[Any]     	=       self._convert_to_karras(in_sigmas=lowercase_    )
        UpperCAmelCase_   :   Any     	=       np.array([self._sigma_to_t(lowercase_							,  lowercase_    ) for sigma in sigmas]    )

     UpperCAmelCase_   :   Union[str, Any]     	=       np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )

     UpperCAmelCase_   :   Dict     	=       torch.from_numpy(lowercase_    ).to(device=lowercase_    )
     if str(lowercase_    ).startswith("mps"    ):
        # mps does not support float64
        UpperCAmelCase_   :   List[str]     	=       torch.from_numpy(lowercase_    ).to(lowercase_							,  dtype=torch.floataa    )
     else:
        UpperCAmelCase_   :   str     	=       torch.from_numpy(lowercase_    ).to(device=lowercase_    )

     UpperCAmelCase_   :   int     	=       []







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     # get log sigma
     UpperCAmelCase_   :   List[str]     	=       np.log(lowercase_    )

     # get distribution
     UpperCAmelCase_   :   str     	=       log_sigma - log_sigmas[:, np.newaxis]

     # get sigmas range
     UpperCAmelCase_   :   Dict     	=       np.cumsum((dists >= 0)							,  axis=0    ).argmax(axis=0    ).clip(max=log_sigmas.shape[0] - 2    )
     UpperCAmelCase_   :   Tuple     	=       low_idx + 1

     UpperCAmelCase_   :   Optional[Any]     	=       log_sigmas[low_idx]
     UpperCAmelCase_   :   Dict     	=       log_sigmas[high_idx]

     # interpolate sigmas
     UpperCAmelCase_   :   List[Any]     	=       (low - log_sigma) / (low - high)
     UpperCAmelCase_   :   Optional[Any]     	=       np.clip(lowercase_							,  0							,  1    )

     # transform interpolation to time range
     UpperCAmelCase_   :   Tuple     	=       (1 - w) * low_idx + w * high_idx
     UpperCAmelCase_   :   Tuple     	=       t.reshape(sigma.shape    )
     return t







  def 			UpperCamelCase__   (						self							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   float     	=       in_sigmas[-1].item()
     UpperCAmelCase_   :   float     	=       in_sigmas[0].item()

     UpperCAmelCase_   :   List[str]     	=       7.0  # 7.0 is the value used in the paper
     UpperCAmelCase_   :   List[str]     	=       np.linspace(0							,  1							,  self.num_inference_steps    )
     UpperCAmelCase_   :   int     	=       sigma_min ** (1 / rho)
     UpperCAmelCase_   :   List[str]     	=       sigma_max ** (1 / rho)
     UpperCAmelCase_   :   int     	=       (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
     return sigmas







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_ = 4							,  lowercase_ = True							,  ):

     """simple docstring"""
     if not self.is_scale_input_called:
        warnings.warn(
            "The `scale_model_input` function should be called before `step` to ensure correct denoising. "
            "See `StableDiffusionPipeline` for a usage example."    )

     if isinstance(lowercase_							,  torch.Tensor    ):
        UpperCAmelCase_   :   List[Any]     	=       timestep.to(self.timesteps.device    )
     UpperCAmelCase_   :   Union[str, Any]     	=       (self.timesteps == timestep).nonzero().item()
     UpperCAmelCase_   :   str     	=       self.sigmas[step_index]

     # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
     if self.config.prediction_type == "epsilon":
        UpperCAmelCase_   :   Union[str, Any]     	=       sample - sigma * model_output
     elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        UpperCAmelCase_   :   Optional[int]     	=       model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
     elif self.config.prediction_type == "sample":
        UpperCAmelCase_   :   int     	=       model_output
     else:
        raise ValueError(
            F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""    )

     # 2. Convert to an ODE derivative
     UpperCAmelCase_   :   Dict     	=       (sample - pred_original_sample) / sigma
     self.derivatives.append(lowercase_    )
     if len(self.derivatives    ) > order:
        self.derivatives.pop(0    )

     # 3. Compute linear multistep coefficients
     UpperCAmelCase_   :   Optional[int]     	=       min(step_index + 1							,  lowercase_    )
     UpperCAmelCase_   :   str     	=       [self.get_lms_coefficient(lowercase_							,  lowercase_							,  lowercase_    ) for curr_order in range(lowercase_    )]

     # 4. Compute previous sample based on the derivatives path
     UpperCAmelCase_   :   Optional[int]     	=       sample + sum(
         coeff * derivative for coeff, derivative in zip(lowercase_							,  reversed(self.derivatives    )    )    )

     if not return_dict:
        return (prev_sample,)

     return LMSDiscreteSchedulerOutput(prev_sample=lowercase_							,  pred_original_sample=lowercase_    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_							,  ):

     """simple docstring"""
     # Make sure sigmas and timesteps have the same device and dtype as original_samples
     UpperCAmelCase_   :   Any     	=       self.sigmas.to(device=original_samples.device							,  dtype=original_samples.dtype    )
     if original_samples.device.type == "mps" and torch.is_floating_point(lowercase_    ):
        # mps does not support float64
        UpperCAmelCase_   :   int     	=       self.timesteps.to(original_samples.device							,  dtype=torch.floataa    )
        UpperCAmelCase_   :   Tuple     	=       timesteps.to(original_samples.device							,  dtype=torch.floataa    )
     else:
        UpperCAmelCase_   :   List[str]     	=       self.timesteps.to(original_samples.device    )
        UpperCAmelCase_   :   Optional[int]     	=       timesteps.to(original_samples.device    )

     UpperCAmelCase_   :   str     	=       [(schedule_timesteps == t).nonzero().item() for t in timesteps]

     UpperCAmelCase_   :   Tuple     	=       sigmas[step_indices].flatten()
     while len(sigma.shape    ) < len(original_samples.shape    ):
        UpperCAmelCase_   :   int     	=       sigma.unsqueeze(-1    )

     UpperCAmelCase_   :   str     	=       original_samples + noise * sigma
     return noisy_samples







  def __len__(						self    ):

     """simple docstring"""
     return self.config.num_train_timesteps




"""simple docstring"""





def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   if not isinstance(__lowerCamelCase,						__lowerCamelCase    ):
      raise TypeError("plaintext must be a string"    )
   if not isinstance(__lowerCamelCase,						__lowerCamelCase    ):
      raise TypeError("key must be a string"    )

   if not plaintext:
      raise ValueError("plaintext is empty"    )
   if not key:
      raise ValueError("key is empty"    )

   key += plaintext
   UpperCAmelCase_   :   Optional[int]     	=       plaintext.lower()
   UpperCAmelCase_   :   Any     	=       key.lower()
   UpperCAmelCase_   :   Optional[int]     	=       0
   UpperCAmelCase_   :   List[str]     	=       0
   UpperCAmelCase_   :   Optional[int]     	=       ""
   while plaintext_iterator < len(__lowerCamelCase    ):
      if (
          ord(plaintext[plaintext_iterator]    ) < 97
          or ord(plaintext[plaintext_iterator]    ) > 122
      ):
         ciphertext += plaintext[plaintext_iterator]
         plaintext_iterator += 1
      elif ord(key[key_iterator]    ) < 97 or ord(key[key_iterator]    ) > 122:
         key_iterator += 1
      else:
         ciphertext += chr(
             (
                 (ord(plaintext[plaintext_iterator]    ) - 97 + ord(key[key_iterator]    ))
                 - 97
             )
             % 26
             + 97    )
         key_iterator += 1
         plaintext_iterator += 1
   return ciphertext







def       __a	(						__lowerCamelCase,						__lowerCamelCase    ):
   if not isinstance(__lowerCamelCase,						__lowerCamelCase    ):
      raise TypeError("ciphertext must be a string"    )
   if not isinstance(__lowerCamelCase,						__lowerCamelCase    ):
      raise TypeError("key must be a string"    )

   if not ciphertext:
      raise ValueError("ciphertext is empty"    )
   if not key:
      raise ValueError("key is empty"    )

   UpperCAmelCase_   :   List[str]     	=       key.lower()
   UpperCAmelCase_   :   Tuple     	=       0
   UpperCAmelCase_   :   Optional[int]     	=       0
   UpperCAmelCase_   :   Dict     	=       ""
   while ciphertext_iterator < len(__lowerCamelCase    ):
      if (
          ord(ciphertext[ciphertext_iterator]    ) < 97
          or ord(ciphertext[ciphertext_iterator]    ) > 122
      ):
         plaintext += ciphertext[ciphertext_iterator]
      else:
         plaintext += chr(
             (ord(ciphertext[ciphertext_iterator]    ) - ord(key[key_iterator]    )) % 26
             + 97    )
         key += chr(
             (ord(ciphertext[ciphertext_iterator]    ) - ord(key[key_iterator]    )) % 26
             + 97    )
         key_iterator += 1
      ciphertext_iterator += 1
   return plaintext


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    _a									=				int(input('Type 1 to encrypt or 2 to decrypt:'))
    if operation == 1:
        _a									=				input('Typeplaintext to be encrypted:\n')
        _a									=				input('Type the key:\n')
        print(encrypt(plaintext, key))
    elif operation == 2:
        _a									=				input('Type the ciphertext to be decrypted:\n')
        _a									=				input('Type the key:\n')
        print(decrypt(ciphertext, key))
    decrypt('jsqqs avvwo', 'coffee')


