





"""simple docstring"""


import os
import sys
import time

from . import transposition_cipher as trans_cipher





def 							a__	(     )			->     None:

 '''simple docstring'''






 snake_case__										= "Prehistoric Men.txt"
 snake_case__										= "Output.txt"
 snake_case__										= int(input("Enter key: "		)		)
 snake_case__										= input("Encrypt/Decrypt [e/d]: "		)

 if not os.path.exists(_UpperCAmelCase		):
  print(f"File {input_file} does not exist. Quitting..."		)
  sys.exit()
 if os.path.exists(_UpperCAmelCase		):
  print(f"Overwrite {output_file}? [y/n]"		)
  snake_case__										= input("> "		)
  if not response.lower().startswith("y"		):
   sys.exit()

 snake_case__										= time.time()
 if mode.lower().startswith("e"		):
  with open(_UpperCAmelCase		) as f:
   snake_case__										= f.read()
  snake_case__										= trans_cipher.encrypt_message(_UpperCAmelCase		,   _UpperCAmelCase		)
 elif mode.lower().startswith("d"		):
  with open(_UpperCAmelCase		) as f:
   snake_case__										= f.read()
  snake_case__										= trans_cipher.decrypt_message(_UpperCAmelCase		,   _UpperCAmelCase		)

 with open(_UpperCAmelCase		,   "w"		) as output_obj:
  output_obj.write(_UpperCAmelCase		)

 snake_case__										= round(time.time() - start_time		,   2		)
 print(("Done (", total_time, "seconds )")		)


if __name__ == "__main__":
   main()








"""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
class    lowercase_ (   __lowerCAmelCase							):
       snake_case__ :							torch.FloatTensor
       snake_case__ :							torch.FloatTensor





def 							a__	(     _UpperCAmelCase		,   _UpperCAmelCase=0.999		,   _UpperCAmelCase="cosine"		,   )			->     str:

 '''simple docstring'''






 if alpha_transform_type == "cosine":

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

 elif alpha_transform_type == "exp":

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

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

 snake_case__										= []
 for i in range(_UpperCAmelCase		):
  snake_case__										= i / num_diffusion_timesteps
  snake_case__										= (i + 1) / num_diffusion_timesteps
  betas.append(min(1 - alpha_bar_fn(_UpperCAmelCase		) / alpha_bar_fn(_UpperCAmelCase		)		,   _UpperCAmelCase		)		)
 return torch.tensor(_UpperCAmelCase		,   dtype=torch.floataa		)



class    lowercase_ (   __lowerCAmelCase ,__lowerCAmelCase							):
       snake_case__ :							List[Any] 							=   1

       @register_to_config
       def __init__(	self      ,    __SCREAMING_SNAKE_CASE = 1000      ,    __SCREAMING_SNAKE_CASE = 0.0001      ,    __SCREAMING_SNAKE_CASE = 0.02      ,    __SCREAMING_SNAKE_CASE = "linear"      ,    __SCREAMING_SNAKE_CASE = 0.0      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = True      ,    ):






        '''simple docstring'''

        if trained_betas is not None:
         snake_case__										= torch.from_numpy(__SCREAMING_SNAKE_CASE   )
        elif beta_schedule == "linear":
         snake_case__										= torch.linspace(__SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    dtype=torch.floataa   )
        elif beta_schedule == "scaled_linear":
         # this schedule is very specific to the latent diffusion model.
         snake_case__										= (
             torch.linspace(beta_start**0.5      ,    beta_end**0.5      ,    __SCREAMING_SNAKE_CASE      ,    dtype=torch.floataa   ) ** 2
         )
        elif beta_schedule == "squaredcos_cap_v2":
         # Glide cosine schedule
         snake_case__										= betas_for_alpha_bar(__SCREAMING_SNAKE_CASE   )
        elif beta_schedule == "sigmoid":
         # GeoDiff sigmoid schedule
         snake_case__										= torch.linspace(-6      ,    6      ,    __SCREAMING_SNAKE_CASE   )
         snake_case__										= torch.sigmoid(__SCREAMING_SNAKE_CASE   ) * (beta_end - beta_start) + beta_start
        else:
         raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}"   )

        snake_case__										= 1.0 - self.betas
        snake_case__										= torch.cumprod(self.alphas      ,    dim=0   )
        snake_case__										= torch.tensor(1.0   )

        snake_case__										= torch.tensor(1.0   )

        # standard deviation of the initial noise distribution
        snake_case__										= 1.0

        # setable values
        snake_case__										= None
        snake_case__										= torch.from_numpy(np.arange(0      ,    __SCREAMING_SNAKE_CASE   )[::-1].copy()   )

        snake_case__										= eta

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None   ):






        '''simple docstring'''

        return sample

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = 10      ,    __SCREAMING_SNAKE_CASE = 10      ,    __SCREAMING_SNAKE_CASE = None      ,    ):






        '''simple docstring'''

        snake_case__										= min(self.config.num_train_timesteps      ,    __SCREAMING_SNAKE_CASE   )
        snake_case__										= num_inference_steps

        snake_case__										= []

        snake_case__										= {}
        for j in range(0      ,    num_inference_steps - jump_length      ,    __SCREAMING_SNAKE_CASE   ):
         snake_case__										= jump_n_sample - 1

        snake_case__										= num_inference_steps
        while t >= 1:
         snake_case__										= t - 1
         timesteps.append(__SCREAMING_SNAKE_CASE   )

         if jumps.get(__SCREAMING_SNAKE_CASE      ,    0   ) > 0:
          snake_case__										= jumps[t] - 1
          for _ in range(__SCREAMING_SNAKE_CASE   ):
           snake_case__										= t + 1
           timesteps.append(__SCREAMING_SNAKE_CASE   )

        snake_case__										= np.array(__SCREAMING_SNAKE_CASE   ) * (self.config.num_train_timesteps // self.num_inference_steps)
        snake_case__										= torch.from_numpy(__SCREAMING_SNAKE_CASE   ).to(__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE   ):






        '''simple docstring'''

        snake_case__										= t - self.config.num_train_timesteps // self.num_inference_steps

        snake_case__										= self.alphas_cumprod[t]
        snake_case__										= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        snake_case__										= 1 - alpha_prod_t
        snake_case__										= 1 - 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
        # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
        # without eta.
        # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
        snake_case__										= (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

        return variance

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE = None      ,    __SCREAMING_SNAKE_CASE = True      ,    ):






        '''simple docstring'''

        snake_case__										= timestep
        snake_case__										= timestep - self.config.num_train_timesteps // self.num_inference_steps

        # 1. compute alphas, betas
        snake_case__										= self.alphas_cumprod[t]
        snake_case__										= self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        snake_case__										= 1 - alpha_prod_t

        # 2. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
        snake_case__										= (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
         snake_case__										= torch.clamp(__SCREAMING_SNAKE_CASE      ,    -1      ,    1   )

        # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
        # substitute formula (7) in the algorithm coming from DDPM paper
        # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
        # DDIM schedule gives the same results as DDPM with eta = 1.0
        # Noise is being reused in 7. and 8., but no impact on quality has
        # been observed.

        # 5. Add noise
        snake_case__										= model_output.device
        snake_case__										= randn_tensor(model_output.shape      ,    generator=__SCREAMING_SNAKE_CASE      ,    device=__SCREAMING_SNAKE_CASE      ,    dtype=model_output.dtype   )
        snake_case__										= self.eta * self._get_variance(__SCREAMING_SNAKE_CASE   ) ** 0.5

        snake_case__										= 0
        if t > 0 and self.eta > 0:
         snake_case__										= std_dev_t * noise

        # 6. compute "direction pointing to x_t" of formula (12)
        # from https://arxiv.org/pdf/2010.02502.pdf
        snake_case__										= (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

        # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        snake_case__										= alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

        # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
        snake_case__										= (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

        # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
        snake_case__										= mask * prev_known_part + (1.0 - mask) * prev_unknown_part

        if not return_dict:
         return (
             pred_prev_sample,
             pred_original_sample,
         )

        return RePaintSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE      ,    pred_original_sample=__SCREAMING_SNAKE_CASE   )

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE=None   ):






        '''simple docstring'''

        snake_case__										= self.config.num_train_timesteps // self.num_inference_steps

        for i in range(__SCREAMING_SNAKE_CASE   ):
         snake_case__										= self.betas[timestep + i]
         if sample.device.type == "mps":
          # randn does not work reproducibly on mps
          snake_case__										= randn_tensor(sample.shape      ,    dtype=sample.dtype      ,    generator=__SCREAMING_SNAKE_CASE   )
          snake_case__										= noise.to(sample.device   )
         else:
          snake_case__										= randn_tensor(sample.shape      ,    generator=__SCREAMING_SNAKE_CASE      ,    device=sample.device      ,    dtype=sample.dtype   )

         # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
         snake_case__										= (1 - beta) ** 0.5 * sample + beta**0.5 * noise

        return sample

       def 						_SCREAMING_SNAKE_CASE(	self      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    __SCREAMING_SNAKE_CASE      ,    ):






        '''simple docstring'''

        raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."   )

       def __len__(	self   ):






        '''simple docstring'''

        return self.config.num_train_timesteps

