






"""simple docstring"""


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 .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput




def     snake_case						(A_   :Optional[Any]			,    A_   :Dict=0.999			,    A_   :List[Any]="cosine"			,    ):







  '''simple docstring'''

  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

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

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

  a  :    int       =     []
  for i in range(A_   ):
    a  :    List[Any]       =     i / num_diffusion_timesteps
    a  :    str       =     (i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(A_   ) / alpha_bar_fn(A_   )			,    A_   )   )
  return torch.tensor(A_			,    dtype=torch.floataa   )






class 		snake_case						(						UpperCAmelCase     ,				UpperCAmelCase ):
    __magic_name__					  =  [e.name for e in KarrasDiffusionSchedulers]
    __magic_name__					  =  2





    @register_to_config
    def __init__(							self			:      int       ,	A			:      int = 1_0_0_0       ,	A			:      float = 0.0_00_85       ,	A			:      float = 0.0_12       ,	A			:      str = "linear"       ,	A			:      Optional[Union[np.ndarray, List[float]]] = None       ,	A			:      str = "epsilon"       ,	A			:      str = "linspace"       ,	A			:      int = 0       ,	):


      '''simple docstring'''




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

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

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





    def        lowerCamelCase__   (							self			:      Union[str, Any]       ,	A			:      Union[str, Any]       ,	A			:      Any=None    ):


      '''simple docstring'''




      if schedule_timesteps is None:
        a  :    str       =     self.timesteps

      a  :    Optional[int]       =     (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  :    Dict       =     1 if len(A    ) > 1 else 0
      else:
        a  :    str       =     timestep.cpu().item() if torch.is_tensor(A    ) else timestep
        a  :    int       =     self._index_counter[timestep_int]

      return indices[pos].item()





    @property
    def        lowerCamelCase__   (							self			:      Union[str, Any]    ):


      '''simple docstring'''




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

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





    def        lowerCamelCase__   (							self			:      List[Any]       ,	A			:      torch.FloatTensor       ,	A			:      Union[float, torch.FloatTensor]       ,	):


      '''simple docstring'''




      a  :    Any       =     self.index_for_timestep(A    )

      if self.state_in_first_order:
        a  :    str       =     self.sigmas[step_index]
      else:
        a  :    Union[str, Any]       =     self.sigmas_interpol[step_index]

      a  :    Optional[int]       =     sample / ((sigma**2 + 1) ** 0.5)
      return sample





    def        lowerCamelCase__   (							self			:      str       ,	A			:      int       ,	A			:      Union[str, torch.device] = None       ,	A			:      Optional[int] = None       ,	):


      '''simple docstring'''




      a  :    Union[str, Any]       =     num_inference_steps

      a  :    Tuple       =     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  :    int       =     np.linspace(0       ,	num_train_timesteps - 1       ,	A       ,	dtype=A    )[::-1].copy()
      elif self.config.timestep_spacing == "leading":
        a  :    str       =     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  :    str       =     (np.arange(0       ,	A    ) * step_ratio).round()[::-1].copy().astype(A    )
        timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
        a  :    List[str]       =     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  :    Tuple       =     (np.arange(A       ,	0       ,	-step_ratio    )).round().copy().astype(A    )
        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  :    Optional[Any]       =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5    )
      a  :    Any       =     torch.from_numpy(np.log(A    )    ).to(A    )

      a  :    int       =     np.interp(A       ,	np.arange(0       ,	len(A    )    )       ,	A    )
      a  :    Union[str, Any]       =     np.concatenate([sigmas, [0.0]]    ).astype(np.floataa    )
      a  :    Optional[int]       =     torch.from_numpy(A    ).to(device=A    )

      # interpolate sigmas
      a  :    Optional[Any]       =     sigmas.log().lerp(sigmas.roll(1    ).log()       ,	0.5    ).exp()

      a  :    List[str]       =     torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2    ), sigmas[-1:]]    )
      a  :    int       =     torch.cat(
          [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2    ), sigmas_interpol[-1:]]    )

      if str(A    ).startswith('mps'    ):
        # mps does not support float64
        a  :    List[str]       =     torch.from_numpy(A    ).to(A       ,	dtype=torch.floataa    )
      else:
        a  :    Dict       =     torch.from_numpy(A    ).to(A    )

      # interpolate timesteps
      a  :    Optional[Any]       =     self.sigma_to_t(A    ).to(A       ,	dtype=timesteps.dtype    )
      a  :    Any       =     torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])       ,	dim=-1    ).flatten()

      a  :    List[Any]       =     torch.cat([timesteps[:1], interleaved_timesteps]    )

      a  :    List[Any]       =     None

      # for exp beta schedules, such as the one for `pipeline_shap_e.py`
      # we need an index counter
      a  :    Optional[int]       =     defaultdict(A    )





    def        lowerCamelCase__   (							self			:      Any       ,	A			:      Optional[int]    ):


      '''simple docstring'''




      a  :    Optional[Any]       =     sigma.log()

      # get distribution
      a  :    Optional[Any]       =     log_sigma - self.log_sigmas[:, None]

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

      a  :    Dict       =     self.log_sigmas[low_idx]
      a  :    int       =     self.log_sigmas[high_idx]

      # interpolate sigmas
      a  :    Optional[Any]       =     (low - log_sigma) / (low - high)
      a  :    str       =     w.clamp(0       ,	1    )

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





    @property
    def        lowerCamelCase__   (							self			:      Optional[Any]    ):


      '''simple docstring'''




      return self.sample is None





    def        lowerCamelCase__   (							self			:      Union[str, Any]       ,	A			:      Union[torch.FloatTensor, np.ndarray]       ,	A			:      Union[float, torch.FloatTensor]       ,	A			:      Union[torch.FloatTensor, np.ndarray]       ,	A			:      bool = True       ,	):


      '''simple docstring'''




      a  :    str       =     self.index_for_timestep(A    )

      # advance index counter by 1
      a  :    Union[str, Any]       =     timestep.cpu().item() if torch.is_tensor(A    ) else timestep
      self._index_counter[timestep_int] += 1

      if self.state_in_first_order:
        a  :    List[str]       =     self.sigmas[step_index]
        a  :    int       =     self.sigmas_interpol[step_index + 1]
        a  :    List[Any]       =     self.sigmas[step_index + 1]
      else:
        # 2nd order / KDPM2's method
        a  :    str       =     self.sigmas[step_index - 1]
        a  :    str       =     self.sigmas_interpol[step_index]
        a  :    Optional[int]       =     self.sigmas[step_index]

      # 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  :    Optional[Any]       =     0
      a  :    List[Any]       =     sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
        a  :    str       =     sigma_hat if self.state_in_first_order else sigma_interpol
        a  :    List[str]       =     sample - sigma_input * model_output
      elif self.config.prediction_type == "v_prediction":
        a  :    Union[str, Any]       =     sigma_hat if self.state_in_first_order else sigma_interpol
        a  :    Optional[Any]       =     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  :    Optional[int]       =     (sample - pred_original_sample) / sigma_hat
        # 3. delta timestep
        a  :    List[str]       =     sigma_interpol - sigma_hat

        # store for 2nd order step
        a  :    Dict       =     sample
      else:
        # DPM-Solver-2
        # 2. Convert to an ODE derivative for 2nd order
        a  :    Optional[int]       =     (sample - pred_original_sample) / sigma_interpol

        # 3. delta timestep
        a  :    Union[str, Any]       =     sigma_next - sigma_hat

        a  :    List[Any]       =     self.sample
        a  :    str       =     None

      a  :    Tuple       =     sample + derivative * dt

      if not return_dict:
        return (prev_sample,)

      return SchedulerOutput(prev_sample=A    )





    def        lowerCamelCase__   (							self			:      Tuple       ,	A			:      torch.FloatTensor       ,	A			:      torch.FloatTensor       ,	A			:      torch.FloatTensor       ,	):


      '''simple docstring'''




      a  :    Optional[int]       =     self.sigmas.to(device=original_samples.device       ,	dtype=original_samples.dtype    )
      if original_samples.device.type == "mps" and torch.is_floating_point(A    ):
        # mps does not support float64
        a  :    Dict       =     self.timesteps.to(original_samples.device       ,	dtype=torch.floataa    )
        a  :    List[str]       =     timesteps.to(original_samples.device       ,	dtype=torch.floataa    )
      else:
        a  :    List[Any]       =     self.timesteps.to(original_samples.device    )
        a  :    List[str]       =     timesteps.to(original_samples.device    )

      a  :    Tuple       =     [self.index_for_timestep(A       ,	A    ) for t in timesteps]

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

      a  :    str       =     original_samples + noise * sigma
      return noisy_samples





    def __len__(							self			:      List[str]    ):


      '''simple docstring'''




      return self.config.num_train_timesteps












"""simple docstring"""


import os




def     snake_case						():







  '''simple docstring'''

  a  :    str       =     os.path.join(os.path.dirname(A_   )			,    'num.txt'   )
  with open(A_   ) as file_hand:
    return str(sum(int(A_   ) for line in file_hand   )   )[:1_0]


if __name__ == "__main__":
   print(solution())




