





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

import numpy as np
import torch

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


a__			:    Dict			      =		logging.get_logger(__name__)  # pylint: disable=invalid-name





@dataclass
class UpperCAmelCase_							(   _SCREAMING_SNAKE_CASE   ):
  __lowerCAmelCase      :	torch.FloatTensor





class UpperCAmelCase_							(   _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   ):
  __lowerCAmelCase      :	Union[str, Any]  	=      1


  @register_to_config
  def __init__(       self   ,__snake_case = 4_0   ,__snake_case = 0.002   ,__snake_case = 80.0   ,__snake_case = 0.5   ,__snake_case = 1.0   ,__snake_case = 7.0   ,__snake_case = True   ,):



       """simple docstring"""





       A_					      =					sigma_max

       A_					      =					np.linspace(0   ,1   ,__snake_case   )
       A_					      =					self._convert_to_karras(__snake_case   )
       A_					      =					self.sigma_to_t(__snake_case   )

       # setable values
       A_					      =					None
       A_					      =					torch.from_numpy(__snake_case   )
       A_					      =					torch.from_numpy(__snake_case   )
       A_					      =					False
       A_					      =					False


  def 	__UpperCAmelCase    (       self   ,__snake_case   ,__snake_case=None   ):



       """simple docstring"""





       if schedule_timesteps is None:
            A_					      =					self.timesteps

       A_					      =					(schedule_timesteps == timestep).nonzero()
       return indices.item()


  def 	__UpperCAmelCase    (       self   ,__snake_case   ,__snake_case   ):



       """simple docstring"""





       if isinstance(__snake_case   ,torch.Tensor   ):
            A_					      =					timestep.to(self.timesteps.device   )
       A_					      =					self.index_for_timestep(__snake_case   )
       A_					      =					self.sigmas[step_idx]

       A_					      =					sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

       A_					      =					True
       return sample


  def 	__UpperCAmelCase    (       self   ,__snake_case   ):



       """simple docstring"""





       if not isinstance(__snake_case   ,np.ndarray   ):
            A_					      =					np.array(__snake_case   ,dtype=np.floataa   )

       A_					      =					1_0_0_0 * 0.25 * np.log(sigmas + 1E-44   )

       return timesteps


  def 	__UpperCAmelCase    (       self   ,__snake_case = None   ,__snake_case = None   ,__snake_case = None   ,):



       """simple docstring"""





       if num_inference_steps is None and timesteps is None:
            raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''   )

       if num_inference_steps is not None and timesteps is not None:
            raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''   )

       # Follow DDPMScheduler custom timesteps logic
       if timesteps is not None:
            for i in range(1   ,len(__snake_case   )   ):
                 if timesteps[i] >= timesteps[i - 1]:
                      raise ValueError('''`timesteps` must be in descending order.'''   )

            if timesteps[0] >= self.config.num_train_timesteps:
                 raise ValueError(
                     f'`timesteps` must start before `self.config.train_timesteps`:'
                     f' {self.config.num_train_timesteps}.'   )

            A_					      =					np.array(__snake_case   ,dtype=np.intaa   )
            A_					      =					True
       else:
            if num_inference_steps > self.config.num_train_timesteps:
                 raise ValueError(
                     f'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
                     f' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
                     f' maximal {self.config.num_train_timesteps} timesteps.'   )

            A_					      =					num_inference_steps

            A_					      =					self.config.num_train_timesteps // self.num_inference_steps
            A_					      =					(np.arange(0   ,__snake_case   ) * step_ratio).round()[::-1].copy().astype(np.intaa   )
            A_					      =					False

       # Map timesteps to Karras sigmas directly for multistep sampling
       # See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
       A_					      =					self.config.num_train_timesteps
       A_					      =					timesteps[::-1].copy()
       A_					      =					ramp / (num_train_timesteps - 1)
       A_					      =					self._convert_to_karras(__snake_case   )
       A_					      =					self.sigma_to_t(__snake_case   )

       A_					      =					np.concatenate([sigmas, [self.sigma_min]]   ).astype(np.floataa   )
       A_					      =					torch.from_numpy(__snake_case   ).to(device=__snake_case   )

       if str(__snake_case   ).startswith('''mps'''   ):
            # mps does not support float64
            A_					      =					torch.from_numpy(__snake_case   ).to(__snake_case   ,dtype=torch.floataa   )
       else:
            A_					      =					torch.from_numpy(__snake_case   ).to(device=__snake_case   )


  def 	__UpperCAmelCase    (       self   ,__snake_case   ):



       """simple docstring"""





       A_					      =					self.config.sigma_min
       A_					      =					self.config.sigma_max

       A_					      =					self.config.rho
       A_					      =					sigma_min ** (1 / rho)
       A_					      =					sigma_max ** (1 / rho)
       A_					      =					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas


  def 	__UpperCAmelCase    (       self   ,__snake_case   ):



       """simple docstring"""





       A_					      =					self.config.sigma_data

       A_					      =					sigma_data**2 / (sigma**2 + sigma_data**2)
       A_					      =					sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out


  def 	__UpperCAmelCase    (       self   ,__snake_case   ):



       """simple docstring"""





       A_					      =					self.config.sigma_min
       A_					      =					self.config.sigma_data

       A_					      =					sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
       A_					      =					(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
       return c_skip, c_out


  def 	__UpperCAmelCase    (       self   ,__snake_case   ,__snake_case   ,__snake_case   ,__snake_case = None   ,__snake_case = True   ,):



       """simple docstring"""





       if (
           isinstance(__snake_case   ,__snake_case   )
           or isinstance(__snake_case   ,torch.IntTensor   )
           or isinstance(__snake_case   ,torch.LongTensor   )
       ):
            raise ValueError(
                (
                    '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
                    f' `{self.__class__}.step()` is not supported. Make sure to pass'
                    ''' one of the `scheduler.timesteps` as a timestep.'''
                )   ,)

       if not self.is_scale_input_called:
            logger.warning(
                '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
                '''See `StableDiffusionPipeline` for a usage example.'''   )

       if isinstance(__snake_case   ,torch.Tensor   ):
            A_					      =					timestep.to(self.timesteps.device   )

       A_					      =					self.config.sigma_min
       A_					      =					self.config.sigma_max

       A_					      =					self.index_for_timestep(__snake_case   )

       # sigma_next corresponds to next_t in original implementation
       A_					      =					self.sigmas[step_index]
       if step_index + 1 < self.config.num_train_timesteps:
            A_					      =					self.sigmas[step_index + 1]
       else:
            # Set sigma_next to sigma_min
            A_					      =					self.sigmas[-1]

       # Get scalings for boundary conditions
       A_      ,						A_					      =					self.get_scalings_for_boundary_condition(__snake_case   )

       # 1. Denoise model output using boundary conditions
       A_					      =					c_out * model_output + c_skip * sample
       if self.config.clip_denoised:
            A_					      =					denoised.clamp(-1   ,1   )

       # 2. Sample z ~ N(0, s_noise^2 * I)
       # Noise is not used for onestep sampling.
       if len(self.timesteps   ) > 1:
            A_					      =					randn_tensor(
                model_output.shape   ,dtype=model_output.dtype   ,device=model_output.device   ,generator=__snake_case   )
       else:
            A_					      =					torch.zeros_like(__snake_case   )
       A_					      =					noise * self.config.s_noise

       A_					      =					sigma_next.clamp(min=__snake_case   ,max=__snake_case   )

       # 3. Return noisy sample
       # tau = sigma_hat, eps = sigma_min
       A_					      =					denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

       if not return_dict:
            return (prev_sample,)

       return CMStochasticIterativeSchedulerOutput(prev_sample=__snake_case   )


  def 	__UpperCAmelCase    (       self   ,__snake_case   ,__snake_case   ,__snake_case   ,):



       """simple docstring"""





       A_					      =					self.sigmas.to(device=original_samples.device   ,dtype=original_samples.dtype   )
       if original_samples.device.type == "mps" and torch.is_floating_point(__snake_case   ):
            # mps does not support float64
            A_					      =					self.timesteps.to(original_samples.device   ,dtype=torch.floataa   )
            A_					      =					timesteps.to(original_samples.device   ,dtype=torch.floataa   )
       else:
            A_					      =					self.timesteps.to(original_samples.device   )
            A_					      =					timesteps.to(original_samples.device   )

       A_					      =					[(schedule_timesteps == t).nonzero().item() for t in timesteps]

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

       A_					      =					original_samples + noise * sigma
       return noisy_samples




  def __len__(       self   ):



       """simple docstring"""





       return self.config.num_train_timesteps










import itertools
from pathlib import Path

def     UpperCAmelCase_						(						_UpperCAmelCase       :list[str]							)  ->							int:
     '''simple docstring'''


     A_					      =					[tuple(_UpperCAmelCase							) for login in logins]

     A_					      =					{char for login in split_logins for char in login}

     for permutation in itertools.permutations(_UpperCAmelCase							):
          A_					      =					True
          for login in logins:
               if not (
                   permutation.index(login[0]							)
                   < permutation.index(login[1]							)
                   < permutation.index(login[2]							)
               ):
                    A_					      =					False
                    break

          if satisfied:
               return int(''''''.join(_UpperCAmelCase							)							)

     raise Exception('''Unable to find the secret passcode'''							)

def     UpperCAmelCase_						(						_UpperCAmelCase       :str = "keylog.txt"							)  ->							int:
     '''simple docstring'''


     A_					      =					Path(_UpperCAmelCase							).parent.joinpath(_UpperCAmelCase							).read_text().splitlines()

     return find_secret_passcode(_UpperCAmelCase							)


if __name__ == "__main__":
      print(f'''{solution() = }''')



