





'''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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=0.9_9_9				,						__UpperCAmelCase="cosine"				,						)    ->       int:

   '''simple docstring'''







   if alpha_transform_type == "cosine":

      def alpha_bar_fn(__UpperCAmelCase   ):
         return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2   ) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(__UpperCAmelCase   ):
         return math.exp(t * -1_2.0   )

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

   __SCREAMING_SNAKE_CASE   				=							[]
   for i in range(__UpperCAmelCase   ):
      __SCREAMING_SNAKE_CASE   				=							i / num_diffusion_timesteps
      __SCREAMING_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 __a			( _snake_case, _snake_case      ):
   __UpperCamelCase :			Tuple						   =						[e.name for e in KarrasDiffusionSchedulers]
   __UpperCamelCase :			Union[str, Any]						   =						2





   @register_to_config
   def __init__(       self					:      Union[str, Any]					,lowerCamelCase					:      int = 1000					,lowerCamelCase					:      float = 0.00_085					,lowerCamelCase					:      float = 0.012					,lowerCamelCase					:      str = "linear"					,lowerCamelCase					:      Optional[Union[np.ndarray, List[float]]] = None					,lowerCamelCase					:      str = "epsilon"					,lowerCamelCase					:      str = "linspace"					,lowerCamelCase					:      int = 0					,):

      '''simple docstring'''

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

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

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





   def   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      str					,lowerCamelCase					:      int=None     ):

      '''simple docstring'''

      if schedule_timesteps is None:
         __SCREAMING_SNAKE_CASE   				=							self.timesteps

      __SCREAMING_SNAKE_CASE   				=							(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:
         __SCREAMING_SNAKE_CASE   				=							1 if len(lowerCamelCase     ) > 1 else 0
      else:
         __SCREAMING_SNAKE_CASE   				=							timestep.cpu().item() if torch.is_tensor(lowerCamelCase     ) else timestep
         __SCREAMING_SNAKE_CASE   				=							self._index_counter[timestep_int]

      return indices[pos].item()





   @property
   def   UpperCAmelCase__  (       self					:      List[Any]     ):

      '''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					:      Dict					,lowerCamelCase					:      torch.FloatTensor					,lowerCamelCase					:      Union[float, torch.FloatTensor]					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.index_for_timestep(lowerCamelCase     )

      if self.state_in_first_order:
         __SCREAMING_SNAKE_CASE   				=							self.sigmas[step_index]
      else:
         __SCREAMING_SNAKE_CASE   				=							self.sigmas_interpol[step_index]

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





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

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							num_inference_steps

      __SCREAMING_SNAKE_CASE   				=							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":
         __SCREAMING_SNAKE_CASE   				=							np.linspace(0					,num_train_timesteps - 1					,lowerCamelCase					,dtype=lowerCamelCase     )[::-1].copy()
      elif self.config.timestep_spacing == "leading":
         __SCREAMING_SNAKE_CASE   				=							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
         __SCREAMING_SNAKE_CASE   				=							(np.arange(0					,lowerCamelCase     ) * step_ratio).round()[::-1].copy().astype(lowerCamelCase     )
         timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
         __SCREAMING_SNAKE_CASE   				=							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
         __SCREAMING_SNAKE_CASE   				=							(np.arange(lowerCamelCase					,0					,-step_ratio     )).round().copy().astype(lowerCamelCase     )
         timesteps -= 1
      else:
         raise ValueError(
             f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""     )

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

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

      # interpolate sigmas
      __SCREAMING_SNAKE_CASE   				=							sigmas.log().lerp(sigmas.roll(1     ).log()					,0.5     ).exp()

      __SCREAMING_SNAKE_CASE   				=							torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2     ), sigmas[-1:]]     )
      __SCREAMING_SNAKE_CASE   				=							torch.cat(
          [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2     ), sigmas_interpol[-1:]]     )

      if str(lowerCamelCase     ).startswith("""mps"""     ):
         # mps does not support float64
         __SCREAMING_SNAKE_CASE   				=							torch.from_numpy(lowerCamelCase     ).to(lowerCamelCase					,dtype=torch.floataa     )
      else:
         __SCREAMING_SNAKE_CASE   				=							torch.from_numpy(lowerCamelCase     ).to(lowerCamelCase     )

      # interpolate timesteps
      __SCREAMING_SNAKE_CASE   				=							self.sigma_to_t(lowerCamelCase     ).to(lowerCamelCase					,dtype=timesteps.dtype     )
      __SCREAMING_SNAKE_CASE   				=							torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])					,dim=-1     ).flatten()

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

      __SCREAMING_SNAKE_CASE   				=							None

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





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      Dict     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							sigma.log()

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

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

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

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

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





   @property
   def   UpperCAmelCase__  (       self					:      Any     ):

      '''simple docstring'''

      return self.sample is None





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      Union[torch.FloatTensor, np.ndarray]					,lowerCamelCase					:      Union[float, torch.FloatTensor]					,lowerCamelCase					:      Union[torch.FloatTensor, np.ndarray]					,lowerCamelCase					:      bool = True					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							self.index_for_timestep(lowerCamelCase     )

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

      if self.state_in_first_order:
         __SCREAMING_SNAKE_CASE   				=							self.sigmas[step_index]
         __SCREAMING_SNAKE_CASE   				=							self.sigmas_interpol[step_index + 1]
         __SCREAMING_SNAKE_CASE   				=							self.sigmas[step_index + 1]
      else:
         # 2nd order / KDPM2's method
         __SCREAMING_SNAKE_CASE   				=							self.sigmas[step_index - 1]
         __SCREAMING_SNAKE_CASE   				=							self.sigmas_interpol[step_index]
         __SCREAMING_SNAKE_CASE   				=							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
      __SCREAMING_SNAKE_CASE   				=							0
      __SCREAMING_SNAKE_CASE   				=							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":
         __SCREAMING_SNAKE_CASE   				=							sigma_hat if self.state_in_first_order else sigma_interpol
         __SCREAMING_SNAKE_CASE   				=							sample - sigma_input * model_output
      elif self.config.prediction_type == "v_prediction":
         __SCREAMING_SNAKE_CASE   				=							sigma_hat if self.state_in_first_order else sigma_interpol
         __SCREAMING_SNAKE_CASE   				=							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
         __SCREAMING_SNAKE_CASE   				=							(sample - pred_original_sample) / sigma_hat
         # 3. delta timestep
         __SCREAMING_SNAKE_CASE   				=							sigma_interpol - sigma_hat

         # store for 2nd order step
         __SCREAMING_SNAKE_CASE   				=							sample
      else:
         # DPM-Solver-2
         # 2. Convert to an ODE derivative for 2nd order
         __SCREAMING_SNAKE_CASE   				=							(sample - pred_original_sample) / sigma_interpol

         # 3. delta timestep
         __SCREAMING_SNAKE_CASE   				=							sigma_next - sigma_hat

         __SCREAMING_SNAKE_CASE   				=							self.sample
         __SCREAMING_SNAKE_CASE   				=							None

      __SCREAMING_SNAKE_CASE   				=							sample + derivative * dt

      if not return_dict:
         return (prev_sample,)

      return SchedulerOutput(prev_sample=lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      torch.FloatTensor					,lowerCamelCase					:      torch.FloatTensor					,lowerCamelCase					:      torch.FloatTensor					,):

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE   				=							[self.index_for_timestep(lowerCamelCase					,lowerCamelCase     ) for t in timesteps]

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

      __SCREAMING_SNAKE_CASE   				=							original_samples + noise * sigma
      return noisy_samples





   def __len__(       self					:      List[Any]     ):

      '''simple docstring'''

      return self.config.num_train_timesteps










def        __lowerCAmelCase		(	__lowerCamelCase						:     int						)	->    bool:
					if number < 0:
										raise ValueError("""number must not be negative"""						)
					return number & (number - 1) == 0


if __name__ == "__main__":
				import doctest

				doctest.testmod()




