





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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

def     UpperCAmelCase_						(						_UpperCAmelCase       :List[str]     ,	_UpperCAmelCase       :Any=0.999     ,	_UpperCAmelCase       :Any="cosine"     ,	)  ->							Optional[Any]:
     '''simple docstring'''


     if alpha_transform_type == "cosine":

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

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(_UpperCAmelCase       :List[str]							):
               return math.exp(t * -12.0							)

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

     A_					      =					[]
     for i in range(_UpperCAmelCase							):
          A_					      =					i / num_diffusion_timesteps
          A_					      =					(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 UpperCAmelCase_							(   _SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE   ):
  __lowerCAmelCase      :	str  	=      [e.name for e in KarrasDiffusionSchedulers]
  __lowerCAmelCase      :	List[str]  	=      2


  @register_to_config
  def __init__(       self   ,__snake_case = 1_0_0_0   ,__snake_case = 0.0_0085   ,__snake_case = 0.012   ,__snake_case = "linear"   ,__snake_case = None   ,__snake_case = "epsilon"   ,__snake_case = "linspace"   ,__snake_case = 0   ,):



       """simple docstring"""





       if trained_betas is not None:
            A_					      =					torch.tensor(__snake_case   ,dtype=torch.floataa   )
       elif beta_schedule == "linear":
            A_					      =					torch.linspace(__snake_case   ,__snake_case   ,__snake_case   ,dtype=torch.floataa   )
       elif beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            A_					      =					(
                torch.linspace(beta_start**0.5   ,beta_end**0.5   ,__snake_case   ,dtype=torch.floataa   ) ** 2
            )
       elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            A_					      =					betas_for_alpha_bar(__snake_case   )
       else:
            raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'   )

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

       #  set all values
       self.set_timesteps(__snake_case   ,__snake_case   ,__snake_case   )


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



       """simple docstring"""





       if schedule_timesteps is None:
            A_					      =					self.timesteps

       A_					      =					(schedule_timesteps == timestep).nonzero()

       # The sigma index that is taken for the **very** first `step`
       # is always the second index (or the last index if there is only 1)
       # This way we can ensure we don't accidentally skip a sigma in
       # case we start in the middle of the denoising schedule (e.g. for image-to-image)
       if len(self._index_counter   ) == 0:
            A_					      =					1 if len(__snake_case   ) > 1 else 0
       else:
            A_					      =					timestep.cpu().item() if torch.is_tensor(__snake_case   ) else timestep
            A_					      =					self._index_counter[timestep_int]

       return indices[pos].item()


  @property
  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       if self.config.timestep_spacing in ["linspace", "trailing"]:
            return self.sigmas.max()

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


  def 	__UpperCAmelCase    (       self   ,__snake_case   ,__snake_case   ,):



       """simple docstring"""





       A_					      =					self.index_for_timestep(__snake_case   )

       if self.state_in_first_order:
            A_					      =					self.sigmas[step_index]
       else:
            A_					      =					self.sigmas_interpol[step_index - 1]

       A_					      =					sample / ((sigma**2 + 1) ** 0.5)
       return sample


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



       """simple docstring"""





       A_					      =					num_inference_steps

       A_					      =					num_train_timesteps or self.config.num_train_timesteps

       # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
       if self.config.timestep_spacing == "linspace":
            A_					      =					np.linspace(0   ,num_train_timesteps - 1   ,__snake_case   ,dtype=__snake_case   )[::-1].copy()
       elif self.config.timestep_spacing == "leading":
            A_					      =					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
            A_					      =					(np.arange(0   ,__snake_case   ) * step_ratio).round()[::-1].copy().astype(__snake_case   )
            timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
            A_					      =					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
            A_					      =					(np.arange(__snake_case   ,0   ,-step_ratio   )).round().copy().astype(__snake_case   )
            timesteps -= 1
       else:
            raise ValueError(
                f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'   )

       A_					      =					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
       A_					      =					torch.from_numpy(np.log(__snake_case   )   ).to(__snake_case   )

       A_					      =					np.interp(__snake_case   ,np.arange(0   ,len(__snake_case   )   )   ,__snake_case   )
       A_					      =					np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
       A_					      =					torch.from_numpy(__snake_case   ).to(device=__snake_case   )

       # compute up and down sigmas
       A_					      =					sigmas.roll(-1   )
       A_					      =					0.0
       A_					      =					(sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
       A_					      =					(sigmas_next**2 - sigmas_up**2) ** 0.5
       A_					      =					0.0

       # compute interpolated sigmas
       A_					      =					sigmas.log().lerp(sigmas_down.log()   ,0.5   ).exp()
       A_					      =					0.0

       # set sigmas
       A_					      =					torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2   ), sigmas[-1:]]   )
       A_					      =					torch.cat(
           [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2   ), sigmas_interpol[-1:]]   )
       A_					      =					torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2   ), sigmas_up[-1:]]   )
       A_					      =					torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2   ), sigmas_down[-1:]]   )

       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(__snake_case   )

       A_					      =					self.sigma_to_t(__snake_case   ).to(__snake_case   ,dtype=timesteps.dtype   )
       A_					      =					torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])   ,dim=-1   ).flatten()

       A_					      =					torch.cat([timesteps[:1], interleaved_timesteps]   )

       A_					      =					None

       # for exp beta schedules, such as the one for `pipeline_shap_e.py`
       # we need an index counter
       A_					      =					defaultdict(__snake_case   )


  def 	__UpperCAmelCase    (       self   ,__snake_case   ):



       """simple docstring"""





       A_					      =					sigma.log()

       # get distribution
       A_					      =					log_sigma - self.log_sigmas[:, None]

       # get sigmas range
       A_					      =					dists.ge(0   ).cumsum(dim=0   ).argmax(dim=0   ).clamp(max=self.log_sigmas.shape[0] - 2   )
       A_					      =					low_idx + 1

       A_					      =					self.log_sigmas[low_idx]
       A_					      =					self.log_sigmas[high_idx]

       # interpolate sigmas
       A_					      =					(low - log_sigma) / (low - high)
       A_					      =					w.clamp(0   ,1   )

       # transform interpolation to time range
       A_					      =					(1 - w) * low_idx + w * high_idx
       A_					      =					t.view(sigma.shape   )
       return t


  @property
  def 	__UpperCAmelCase    (       self   ):



       """simple docstring"""





       return self.sample is None


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



       """simple docstring"""





       A_					      =					self.index_for_timestep(__snake_case   )

       # advance index counter by 1
       A_					      =					timestep.cpu().item() if torch.is_tensor(__snake_case   ) else timestep
       self._index_counter[timestep_int] += 1

       if self.state_in_first_order:
            A_					      =					self.sigmas[step_index]
            A_					      =					self.sigmas_interpol[step_index]
            A_					      =					self.sigmas_up[step_index]
            A_					      =					self.sigmas_down[step_index - 1]
       else:
            # 2nd order / KPDM2's method
            A_					      =					self.sigmas[step_index - 1]
            A_					      =					self.sigmas_interpol[step_index - 1]
            A_					      =					self.sigmas_up[step_index - 1]
            A_					      =					self.sigmas_down[step_index - 1]

       # currently only gamma=0 is supported. This usually works best anyways.
       # We can support gamma in the future but then need to scale the timestep before
       # passing it to the model which requires a change in API
       A_					      =					0
       A_					      =					sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

       A_					      =					model_output.device
       A_					      =					randn_tensor(model_output.shape   ,dtype=model_output.dtype   ,device=__snake_case   ,generator=__snake_case   )

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
            A_					      =					sigma_hat if self.state_in_first_order else sigma_interpol
            A_					      =					sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
            A_					      =					sigma_hat if self.state_in_first_order else sigma_interpol
            A_					      =					model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                sample / (sigma_input**2 + 1)
            )
       elif self.config.prediction_type == "sample":
            raise NotImplementedError('''prediction_type not implemented yet: sample'''   )
       else:
            raise ValueError(
                f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'   )

       if self.state_in_first_order:
            # 2. Convert to an ODE derivative for 1st order
            A_					      =					(sample - pred_original_sample) / sigma_hat
            # 3. delta timestep
            A_					      =					sigma_interpol - sigma_hat

            # store for 2nd order step
            A_					      =					sample
            A_					      =					dt
            A_					      =					sample + derivative * dt
       else:
            # DPM-Solver-2
            # 2. Convert to an ODE derivative for 2nd order
            A_					      =					(sample - pred_original_sample) / sigma_interpol
            # 3. delta timestep
            A_					      =					sigma_down - sigma_hat

            A_					      =					self.sample
            A_					      =					None

            A_					      =					sample + derivative * dt
            A_					      =					prev_sample + noise * sigma_up

       if not return_dict:
            return (prev_sample,)

       return SchedulerOutput(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_					      =					[self.index_for_timestep(__snake_case   ,__snake_case   ) 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() = }''')



