


'''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 _SCREAMING_SNAKE_CASE      (A  , A=0.999  , A="cosine"  , )       ->       Optional[Any]:






       """simple docstring"""







       if alpha_transform_type == "cosine":

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

       elif alpha_transform_type == "exp":

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

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

       lowercase__      			= []
       for i in range(A       ):
              lowercase__      			= i / num_diffusion_timesteps
              lowercase__      			= (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   __lowerCAmelCase						(lowercase_  ,     lowercase_     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Dict  					=							[e.name for e in KarrasDiffusionSchedulers]
      lowerCAmelCase__					:     Optional[int]  					=							2



      @register_to_config
      def __init__(self    :    int   ,						UpperCamelCase    :    int = 1000   ,						UpperCamelCase    :    float = 0.0_00_85   ,						UpperCamelCase    :    float = 0.0_12   ,						UpperCamelCase    :    str = "linear"   ,						UpperCamelCase    :    Optional[Union[np.ndarray, List[float]]] = None   ,						UpperCamelCase    :    str = "epsilon"   ,						UpperCamelCase    :    str = "linspace"   ,						UpperCamelCase    :    int = 0   ,						):

             '''simple docstring'''







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

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

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



      def       UpperCamelCase__		(self    :    Any   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    int=None ):

             '''simple docstring'''







             if schedule_timesteps is None:
                    lowercase__      			= self.timesteps

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

             return indices[pos].item()



      @property
      def       UpperCamelCase__		(self    :    List[str] ):

             '''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    :    str   ,						UpperCamelCase    :    torch.FloatTensor   ,						UpperCamelCase    :    Union[float, torch.FloatTensor]   ,						):

             '''simple docstring'''







             lowercase__      			= self.index_for_timestep(UpperCamelCase )

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

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



      def       UpperCamelCase__		(self    :    Optional[Any]   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    Union[str, torch.device] = None   ,						UpperCamelCase    :    Optional[int] = None   ,						):

             '''simple docstring'''







             lowercase__      			= num_inference_steps

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

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

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

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

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

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

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

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

             lowercase__      			= None

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



      def       UpperCamelCase__		(self    :    List[str]   ,						UpperCamelCase    :    List[Any] ):

             '''simple docstring'''







             lowercase__      			= sigma.log()

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

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

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

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

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



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

             '''simple docstring'''







             return self.sample is None



      def       UpperCamelCase__		(self    :    str   ,						UpperCamelCase    :    Union[torch.FloatTensor, np.ndarray]   ,						UpperCamelCase    :    Union[float, torch.FloatTensor]   ,						UpperCamelCase    :    Union[torch.FloatTensor, np.ndarray]   ,						UpperCamelCase    :    bool = True   ,						):

             '''simple docstring'''







             lowercase__      			= self.index_for_timestep(UpperCamelCase )

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

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

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

                    # 3. delta timestep
                    lowercase__      			= sigma_next - sigma_hat

                    lowercase__      			= self.sample
                    lowercase__      			= None

             lowercase__      			= sample + derivative * dt

             if not return_dict:
                    return (prev_sample,)

             return SchedulerOutput(prev_sample=UpperCamelCase )



      def       UpperCamelCase__		(self    :    Optional[Any]   ,						UpperCamelCase    :    torch.FloatTensor   ,						UpperCamelCase    :    torch.FloatTensor   ,						UpperCamelCase    :    torch.FloatTensor   ,						):

             '''simple docstring'''







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

             lowercase__      			= [self.index_for_timestep(UpperCamelCase   ,						UpperCamelCase ) for t in timesteps]

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

             lowercase__      			= original_samples + noise * sigma
             return noisy_samples






      def __len__(self    :    List[Any] ):

             '''simple docstring'''







             return self.config.num_train_timesteps








'''simple docstring'''

from __future__ import annotations

def _SCREAMING_SNAKE_CASE      (A       )       ->       bool:






       """simple docstring"""







       return len(set(A       )       ) == len(A       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




