

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    A_  (							a					,   a=0.9_99					,   a="cosine"					,   ):

      """simple docstring"""



      if alpha_transform_type == "cosine":

            def alpha_bar_fn(a  ):
                  return math.cos((t + 0.0_08) / 1.0_08 * 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}"  )

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



class  _A		(  __magic_name__   ,     __magic_name__):
  SCREAMING_SNAKE_CASE    :      Dict       =  [e.name for e in KarrasDiffusionSchedulers]
  SCREAMING_SNAKE_CASE    :      str       =  2

  @register_to_config
  def __init__(				self ,					_SCREAMING_SNAKE_CASE = 1000 ,					_SCREAMING_SNAKE_CASE = 0.00085 ,					_SCREAMING_SNAKE_CASE = 0.012 ,					_SCREAMING_SNAKE_CASE = "linear" ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = "epsilon" ,					_SCREAMING_SNAKE_CASE = False ,					_SCREAMING_SNAKE_CASE = False ,					_SCREAMING_SNAKE_CASE = 1.0 ,					_SCREAMING_SNAKE_CASE = "linspace" ,					_SCREAMING_SNAKE_CASE = 0 ,					):





        """simple docstring"""







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

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

        #  set all values
        self.set_timesteps(__a ,					__a ,					__a					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     use_karras_sigmas

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=None					):





        """simple docstring"""







        if schedule_timesteps is None:
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.timesteps

        SCREAMING_SNAKE_CASE_				:  Tuple           =     (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_				:  Tuple           =     1 if len(__a					) > 1 else 0
        else:
              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     timestep.cpu().item() if torch.is_tensor(__a					) else timestep
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     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 ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.index_for_timestep(__a					)

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.sigmas[step_index]
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     sample / ((sigma**2 + 1) ** 0.5)
        return sample

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = None ,					_SCREAMING_SNAKE_CASE = None ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     num_inference_steps

        SCREAMING_SNAKE_CASE_				:  str           =     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_				:  Optional[int]           =     np.linspace(0 ,					num_train_timesteps - 1 ,					__a ,					dtype=__a					)[::-1].copy()
        elif self.config.timestep_spacing == "leading":
              SCREAMING_SNAKE_CASE_				:  Tuple           =     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_				:  Optional[int]           =     (np.arange(0 ,					__a					) * step_ratio).round()[::-1].copy().astype(__a					)
              timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
              SCREAMING_SNAKE_CASE_				:  Any           =     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_				:  Union[str, Any]           =     (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\'."					)

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     np.log(__a					)
        SCREAMING_SNAKE_CASE_				:  Any           =     np.interp(__a ,					np.arange(0 ,					len(__a					)					) ,					__a					)

        if self.config.use_karras_sigmas:
              SCREAMING_SNAKE_CASE_				:  Dict           =     self._convert_to_karras(in_sigmas=__a ,					num_inference_steps=self.num_inference_steps					)
              SCREAMING_SNAKE_CASE_				:  List[str]           =     np.array([self._sigma_to_t(__a ,					__a					) for sigma in sigmas]					)

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     np.concatenate([sigmas, [0.0]]					).astype(np.floataa					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     torch.from_numpy(__a					).to(device=__a					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2					), sigmas[-1:]]					)

        SCREAMING_SNAKE_CASE_				:  int           =     torch.from_numpy(__a					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2					)]					)

        if str(__a					).startswith('mps'					):
              # mps does not support float64
              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     timesteps.to(__a ,					dtype=torch.floataa					)
        else:
              SCREAMING_SNAKE_CASE_				:  List[str]           =     timesteps.to(device=__a					)

        # empty dt and derivative
        SCREAMING_SNAKE_CASE_				:  str           =     None
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     None

        # for exp beta schedules, such as the one for `pipeline_shap_e.py`
        # we need an index counter
        SCREAMING_SNAKE_CASE_				:  str           =     defaultdict(__a					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     np.log(__a					)

        # get distribution
        SCREAMING_SNAKE_CASE_				:  int           =     log_sigma - log_sigmas[:, np.newaxis]

        # get sigmas range
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     np.cumsum((dists >= 0) ,					axis=0					).argmax(axis=0					).clip(max=log_sigmas.shape[0] - 2					)
        SCREAMING_SNAKE_CASE_				:  str           =     low_idx + 1

        SCREAMING_SNAKE_CASE_				:  List[str]           =     log_sigmas[low_idx]
        SCREAMING_SNAKE_CASE_				:  Dict           =     log_sigmas[high_idx]

        # interpolate sigmas
        SCREAMING_SNAKE_CASE_				:  List[str]           =     (low - log_sigma) / (low - high)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     np.clip(__a ,					0 ,					1					)

        # transform interpolation to time range
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     (1 - w) * low_idx + w * high_idx
        SCREAMING_SNAKE_CASE_				:  Tuple           =     t.reshape(sigma.shape					)
        return t

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     in_sigmas[-1].item()
        SCREAMING_SNAKE_CASE_				:  int           =     in_sigmas[0].item()

        SCREAMING_SNAKE_CASE_				:  Any           =     7.0  # 7.0 is the value used in the paper
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     np.linspace(0 ,					1 ,					__a					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     sigma_min ** (1 / rho)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     sigma_max ** (1 / rho)
        SCREAMING_SNAKE_CASE_				:  str           =     (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.dt is None

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = True ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     self.index_for_timestep(__a					)

        # advance index counter by 1
        SCREAMING_SNAKE_CASE_				:  int           =     timestep.cpu().item() if torch.is_tensor(__a					) else timestep
        self._index_counter[timestep_int] += 1

        if self.state_in_first_order:
              SCREAMING_SNAKE_CASE_				:  str           =     self.sigmas[step_index]
              SCREAMING_SNAKE_CASE_				:  str           =     self.sigmas[step_index + 1]
        else:
              # 2nd order / Heun's method
              SCREAMING_SNAKE_CASE_				:  List[str]           =     self.sigmas[step_index - 1]
              SCREAMING_SNAKE_CASE_				:  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
        SCREAMING_SNAKE_CASE_				:  int           =     0
        SCREAMING_SNAKE_CASE_				:  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":
              SCREAMING_SNAKE_CASE_				:  Any           =     sigma_hat if self.state_in_first_order else sigma_next
              SCREAMING_SNAKE_CASE_				:  str           =     sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
              SCREAMING_SNAKE_CASE_				:  str           =     sigma_hat if self.state_in_first_order else sigma_next
              SCREAMING_SNAKE_CASE_				:  Any           =     model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                  sample / (sigma_input**2 + 1)
              )
        elif self.config.prediction_type == "sample":
              SCREAMING_SNAKE_CASE_				:  int           =     model_output
        else:
              raise ValueError(
                  f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"					)

        if self.config.clip_sample:
              SCREAMING_SNAKE_CASE_				:  Any           =     pred_original_sample.clamp(
                  -self.config.clip_sample_range ,					self.config.clip_sample_range					)

        if self.state_in_first_order:
              # 2. Convert to an ODE derivative for 1st order
              SCREAMING_SNAKE_CASE_				:  str           =     (sample - pred_original_sample) / sigma_hat
              # 3. delta timestep
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     sigma_next - sigma_hat

              # store for 2nd order step
              SCREAMING_SNAKE_CASE_				:  Tuple           =     derivative
              SCREAMING_SNAKE_CASE_				:  List[str]           =     dt
              SCREAMING_SNAKE_CASE_				:  int           =     sample
        else:
              # 2. 2nd order / Heun's method
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     (sample - pred_original_sample) / sigma_next
              SCREAMING_SNAKE_CASE_				:  List[str]           =     (self.prev_derivative + derivative) / 2

              # 3. take prev timestep & sample
              SCREAMING_SNAKE_CASE_				:  Dict           =     self.dt
              SCREAMING_SNAKE_CASE_				:  int           =     self.sample

              # free dt and derivative
              # Note, this puts the scheduler in "first order mode"
              SCREAMING_SNAKE_CASE_				:  int           =     None
              SCREAMING_SNAKE_CASE_				:  Tuple           =     None
              SCREAMING_SNAKE_CASE_				:  List[str]           =     None

        SCREAMING_SNAKE_CASE_				:  int           =     sample + derivative * dt

        if not return_dict:
              return (prev_sample,)

        return SchedulerOutput(prev_sample=__a					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     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
              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.timesteps.to(original_samples.device ,					dtype=torch.floataa					)
              SCREAMING_SNAKE_CASE_				:  str           =     timesteps.to(original_samples.device ,					dtype=torch.floataa					)
        else:
              SCREAMING_SNAKE_CASE_				:  int           =     self.timesteps.to(original_samples.device					)
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     timesteps.to(original_samples.device					)

        SCREAMING_SNAKE_CASE_				:  str           =     [self.index_for_timestep(__a ,					__a					) for t in timesteps]

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

        SCREAMING_SNAKE_CASE_				:  Tuple           =     original_samples + noise * sigma
        return noisy_samples



  def __len__(				self					):





        """simple docstring"""







        return self.config.num_train_timesteps







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class  _A		(  __magic_name__):
  SCREAMING_SNAKE_CASE    :      List[str]       =  (PNDMScheduler,)
  SCREAMING_SNAKE_CASE    :      Dict       =  (('''num_inference_steps''', 50),)

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     {
            'num_train_timesteps': 1000,
            'beta_start': 0.0001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
        }

        config.update(**_SCREAMING_SNAKE_CASE					)
        return config

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE=0 ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     dict(self.forward_default_kwargs					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     kwargs.pop('num_inference_steps' ,					_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     self.dummy_sample
        SCREAMING_SNAKE_CASE_				:  Tuple           =     0.1 * sample
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  Any           =     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Any           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
              # copy over dummy past residuals
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     dummy_past_residuals[:]

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)
                    new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
                    # copy over dummy past residuals
                    SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_				:  str           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  Any           =     new_scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

              assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

              SCREAMING_SNAKE_CASE_				:  Tuple           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  int           =     new_scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

              assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        pass

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE=0 ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     dict(self.forward_default_kwargs					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     kwargs.pop('num_inference_steps' ,					_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.dummy_sample
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     0.1 * sample
        SCREAMING_SNAKE_CASE_				:  Tuple           =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  int           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)
              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

              # copy over dummy past residuals (must be after setting timesteps)
              SCREAMING_SNAKE_CASE_				:  Tuple           =     dummy_past_residuals[:]

              with tempfile.TemporaryDirectory() as tmpdirname:
                    scheduler.save_config(_SCREAMING_SNAKE_CASE					)
                    SCREAMING_SNAKE_CASE_				:  Dict           =     scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE					)
                    # copy over dummy past residuals
                    new_scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

                    # copy over dummy past residual (must be after setting timesteps)
                    SCREAMING_SNAKE_CASE_				:  str           =     dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  Tuple           =     new_scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

              assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

              SCREAMING_SNAKE_CASE_				:  Any           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  List[str]           =     new_scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

              assert torch.sum(torch.abs(output - new_output					)					) < 1e-5, "Scheduler outputs are not identical"

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.get_scheduler_config(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Any           =     10
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.dummy_model()
        SCREAMING_SNAKE_CASE_				:  Dict           =     self.dummy_sample_deter
        scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

        for i, t in enumerate(scheduler.prk_timesteps					):
              SCREAMING_SNAKE_CASE_				:  Any           =     model(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).prev_sample

        for i, t in enumerate(scheduler.plms_timesteps					):
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     model(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).prev_sample

        return sample

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     dict(self.forward_default_kwargs					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     kwargs.pop('num_inference_steps' ,					_SCREAMING_SNAKE_CASE					)

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

              SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.dummy_sample
              SCREAMING_SNAKE_CASE_				:  int           =     0.1 * sample

              if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE ,					'set_timesteps'					):
                    scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)
              elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE ,					'set_timesteps'					):
                    SCREAMING_SNAKE_CASE_				:  Optional[int]           =     num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              SCREAMING_SNAKE_CASE_				:  Dict           =     [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
              SCREAMING_SNAKE_CASE_				:  str           =     dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_				:  str           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					0 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  str           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					1 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

              self.assertEqual(output_a.shape ,					sample.shape					)
              self.assertEqual(output_a.shape ,					output_a.shape					)

              SCREAMING_SNAKE_CASE_				:  List[str]           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					0 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     scheduler.step_plms(_SCREAMING_SNAKE_CASE ,					1 ,					_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					).prev_sample

              self.assertEqual(output_a.shape ,					sample.shape					)
              self.assertEqual(output_a.shape ,					output_a.shape					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for timesteps in [100, 1000]:
              self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.scheduler_classes[0]
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.get_scheduler_config(steps_offset=1					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)
        scheduler.set_timesteps(10					)
        assert torch.equal(
            scheduler.timesteps ,					torch.LongTensor(
                [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]					) ,					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for beta_start, beta_end in zip([0.0001, 0.001] ,					[0.002, 0.02]					):
              self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE ,					beta_end=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for schedule in ["linear", "squaredcos_cap_v2"]:
              self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for t in [1, 5, 10]:
              self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        for t, num_inference_steps in zip([1, 5, 10] ,					[10, 50, 100]					):
              self.check_over_forward(num_inference_steps=_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     27

        for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_				:  int           =     self.dummy_sample
              SCREAMING_SNAKE_CASE_				:  Tuple           =     0.1 * sample

              SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  str           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

              scheduler.set_timesteps(_SCREAMING_SNAKE_CASE					)

              # before power of 3 fix, would error on first step, so we only need to do two
              for i, t in enumerate(scheduler.prk_timesteps[:2]					):
                    SCREAMING_SNAKE_CASE_				:  int           =     scheduler.step_prk(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					).prev_sample

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        with self.assertRaises(_SCREAMING_SNAKE_CASE					):
              SCREAMING_SNAKE_CASE_				:  str           =     self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_				:  Tuple           =     scheduler_class(**_SCREAMING_SNAKE_CASE					)

              scheduler.step_plms(self.dummy_sample ,					1 ,					self.dummy_sample					).prev_sample

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     self.full_loop()
        SCREAMING_SNAKE_CASE_				:  Tuple           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  str           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 198.1318					) < 1e-2
        assert abs(result_mean.item() - 0.2580					) < 1e-3

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.full_loop(prediction_type='v_prediction'					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 67.3986					) < 1e-2
        assert abs(result_mean.item() - 0.0878					) < 1e-3

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE ,					beta_start=0.01					)
        SCREAMING_SNAKE_CASE_				:  str           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 230.0399					) < 1e-2
        assert abs(result_mean.item() - 0.2995					) < 1e-3



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  str           =     self.full_loop(set_alpha_to_one=_SCREAMING_SNAKE_CASE ,					beta_start=0.01					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE					)					)

        assert abs(result_sum.item() - 186.9482					) < 1e-2
        assert abs(result_mean.item() - 0.2434					) < 1e-3






