





# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

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



class 					A__   (       __snake_case       ,   __snake_case			):
      _UpperCAmelCase	:Dict						    =   1





      @register_to_config
      def __init__( self       ,							A_=2000       ,							A_=0.1       ,							A_=20       ,							A_=1e-3   ):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   None
       UpperCamelCase	:					Any       	=   None
       UpperCamelCase	:					Dict       	=   None





      def        __UpperCamelCase( self       ,							A_       ,							A_ = None   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   torch.linspace(1       ,							self.config.sampling_eps       ,							A_       ,							device=A_   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_=None   ):
       '''simple docstring'''




       if self.timesteps is None:
        raise ValueError(
            "`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"   )

       # TODO(Patrick) better comments + non-PyTorch
       # postprocess model score
       UpperCamelCase	:					int       	=   (
           -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
       )
       UpperCamelCase	:					List[str]       	=   torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff   )   )
       UpperCamelCase	:					List[str]       	=   std.flatten()
       while len(std.shape   ) < len(score.shape   ):
        UpperCamelCase	:					Optional[Any]       	=   std.unsqueeze(-1   )
       UpperCamelCase	:					Union[str, Any]       	=   -score / std

       # compute
       UpperCamelCase	:					str       	=   -1.0 / len(self.timesteps   )

       UpperCamelCase	:					Optional[int]       	=   self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
       UpperCamelCase	:					Tuple       	=   beta_t.flatten()
       while len(beta_t.shape   ) < len(x.shape   ):
        UpperCamelCase	:					Optional[int]       	=   beta_t.unsqueeze(-1   )
       UpperCamelCase	:					List[Any]       	=   -0.5 * beta_t * x

       UpperCamelCase	:					List[Any]       	=   torch.sqrt(A_   )
       UpperCamelCase	:					Any       	=   drift - diffusion**2 * score
       UpperCamelCase	:					List[Any]       	=   x + drift * dt

       # add noise
       UpperCamelCase	:					str       	=   randn_tensor(x.shape       ,							layout=x.layout       ,							generator=A_       ,							device=x.device       ,							dtype=x.dtype   )
       UpperCamelCase	:					Union[str, Any]       	=   x_mean + diffusion * math.sqrt(-dt   ) * noise

       return x, x_mean





      def __len__( self   ):
       '''simple docstring'''




       return self.config.num_train_timesteps







def 		A_				(     _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					List[Any]       	=   ""
 for ch in key:
  if ch == " " or ch not in key_no_dups and ch.isalpha():
   key_no_dups += ch
 return key_no_dups



def 		A_				(     _lowerCAmelCase     )						-> dict[str, str]:
 UpperCamelCase	:					Optional[Any]       	=   [chr(i + 65     ) for i in range(26     )]
 # Remove duplicate characters from key
 UpperCamelCase	:					Tuple       	=   remove_duplicates(key.upper()     )
 UpperCamelCase	:					int       	=   len(_lowerCAmelCase     )
 # First fill cipher with key characters
 UpperCamelCase	:					int       	=   {alphabet[i]: char for i, char in enumerate(_lowerCAmelCase     )}
 # Then map remaining characters in alphabet to
 # the alphabet from the beginning
 for i in range(len(_lowerCAmelCase     )							,   26     ):
  UpperCamelCase	:					Optional[Any]       	=   alphabet[i - offset]
  # Ensure we are not mapping letters to letters previously mapped
  while char in key:
   offset -= 1
   UpperCamelCase	:					List[str]       	=   alphabet[i - offset]
  UpperCamelCase	:					List[Any]       	=   char
 return cipher_alphabet



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> str:
 return "".join(cipher_map.get(_lowerCAmelCase							,   _lowerCAmelCase     ) for ch in message.upper()     )



def 		A_				(     _lowerCAmelCase							,   _lowerCAmelCase     )						-> str:
 UpperCamelCase	:					Union[str, Any]       	=   {v: k for k, v in cipher_map.items()}
 return "".join(rev_cipher_map.get(_lowerCAmelCase							,   _lowerCAmelCase     ) for ch in message.upper()     )



def 		A_				(     )						-> None:
 UpperCamelCase	:					int       	=   input("Enter message to encode or decode: "     ).strip()
 UpperCamelCase	:					str       	=   input("Enter keyword: "     ).strip()
 UpperCamelCase	:					Union[str, Any]       	=   input("Encipher or decipher? E/D:"     ).strip()[0].lower()
 try:
  UpperCamelCase	:					List[str]       	=   {"e": encipher, "d": decipher}[option]
 except KeyError:
  raise KeyError("invalid input option"     )
 UpperCamelCase	:					str       	=   create_cipher_map(_lowerCAmelCase     )
 print(func(_lowerCAmelCase							,   _lowerCAmelCase     )     )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()
