
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 		_UpperCAmelCase   (	UpperCamelCase:     Optional[int]			,							UpperCamelCase:     Any=0.999			,							UpperCamelCase:     Union[str, Any]="cosine"			,							):






 """simple docstring"""


 if alpha_transform_type == "cosine":

  def alpha_bar_fn(UpperCamelCase:     List[str]  ):
   return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

 elif alpha_transform_type == "exp":

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

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

 __lowerCAmelCase         =							[]
 for i in range(__UpperCAmelCase  ):
  __lowerCAmelCase         =							i / num_diffusion_timesteps
  __lowerCAmelCase         =							(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					):
       lowercase_		:						int   							=  [e.name for e in KarrasDiffusionSchedulers]
       lowercase_		:						str   							=  2







       @register_to_config
       def __init__(						self  :  int      ,				snake_case__  :  int = 1_000      ,				snake_case__  :  float = 0.0_0_0_8_5      ,				snake_case__  :  float = 0.0_1_2      ,				snake_case__  :  str = "linear"      ,				snake_case__  :  Optional[Union[np.ndarray, List[float]]] = None      ,				snake_case__  :  str = "epsilon"      ,				snake_case__  :  Optional[bool] = False      ,				snake_case__  :  Optional[bool] = False      ,				snake_case__  :  float = 1.0      ,				snake_case__  :  str = "linspace"      ,				snake_case__  :  int = 0      ,				):
        """simple docstring"""







        if trained_betas is not None:
         __lowerCAmelCase         =							torch.tensor(snake_case__      ,				dtype=torch.floataa )
        elif beta_schedule == "linear":
         __lowerCAmelCase         =							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.
         __lowerCAmelCase         =							(
             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
         __lowerCAmelCase         =							betas_for_alpha_bar(snake_case__      ,				alpha_transform_type="cosine" )
        elif beta_schedule == "exp":
         __lowerCAmelCase         =							betas_for_alpha_bar(snake_case__      ,				alpha_transform_type="exp" )
        else:
         raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}" )

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

        #  set all values
        self.set_timesteps(snake_case__      ,				snake_case__      ,				snake_case__ )
        __lowerCAmelCase         =							use_karras_sigmas







       def       UpperCAmelCase__					(						self  :  Optional[int]      ,				snake_case__  :  int      ,				snake_case__  :  Dict=None ):
        """simple docstring"""







        if schedule_timesteps is None:
         __lowerCAmelCase         =							self.timesteps

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

        return indices[pos].item()







       @property
       def       UpperCAmelCase__					(						self  :  Optional[int] ):
        """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  :  Optional[Any]      ,				snake_case__  :  torch.FloatTensor      ,				snake_case__  :  Union[float, torch.FloatTensor]      ,				):
        """simple docstring"""







        __lowerCAmelCase         =							self.index_for_timestep(snake_case__ )

        __lowerCAmelCase         =							self.sigmas[step_index]
        __lowerCAmelCase         =							sample / ((sigma**2 + 1) ** 0.5)
        return sample







       def       UpperCAmelCase__					(						self  :  int      ,				snake_case__  :  int      ,				snake_case__  :  Union[str, torch.device] = None      ,				snake_case__  :  Optional[int] = None      ,				):
        """simple docstring"""







        __lowerCAmelCase         =							num_inference_steps

        __lowerCAmelCase         =							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":
         __lowerCAmelCase         =							np.linspace(0      ,				num_train_timesteps - 1      ,				snake_case__      ,				dtype=snake_case__ )[::-1].copy()
        elif self.config.timestep_spacing == "leading":
         __lowerCAmelCase         =							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
         __lowerCAmelCase         =							(np.arange(0      ,				snake_case__ ) * step_ratio).round()[::-1].copy().astype(snake_case__ )
         timesteps += self.config.steps_offset
        elif self.config.timestep_spacing == "trailing":
         __lowerCAmelCase         =							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
         __lowerCAmelCase         =							(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'." )

        __lowerCAmelCase         =							np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
        __lowerCAmelCase         =							np.log(snake_case__ )
        __lowerCAmelCase         =							np.interp(snake_case__      ,				np.arange(0      ,				len(snake_case__ ) )      ,				snake_case__ )

        if self.config.use_karras_sigmas:
         __lowerCAmelCase         =							self._convert_to_karras(in_sigmas=snake_case__      ,				num_inference_steps=self.num_inference_steps )
         __lowerCAmelCase         =							np.array([self._sigma_to_t(snake_case__      ,				snake_case__ ) for sigma in sigmas] )

        __lowerCAmelCase         =							np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
        __lowerCAmelCase         =							torch.from_numpy(snake_case__ ).to(device=snake_case__ )
        __lowerCAmelCase         =							torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2 ), sigmas[-1:]] )

        __lowerCAmelCase         =							torch.from_numpy(snake_case__ )
        __lowerCAmelCase         =							torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2 )] )

        if str(snake_case__ ).startswith("mps" ):
         # mps does not support float64
         __lowerCAmelCase         =							timesteps.to(snake_case__      ,				dtype=torch.floataa )
        else:
         __lowerCAmelCase         =							timesteps.to(device=snake_case__ )

        # empty dt and derivative
        __lowerCAmelCase         =							None
        __lowerCAmelCase         =							None

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







       def       UpperCAmelCase__					(						self  :  List[str]      ,				snake_case__  :  Optional[Any]      ,				snake_case__  :  Dict ):
        """simple docstring"""







        __lowerCAmelCase         =							np.log(snake_case__ )

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

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

        __lowerCAmelCase         =							log_sigmas[low_idx]
        __lowerCAmelCase         =							log_sigmas[high_idx]

        # interpolate sigmas
        __lowerCAmelCase         =							(low - log_sigma) / (low - high)
        __lowerCAmelCase         =							np.clip(snake_case__      ,				0      ,				1 )

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







       def       UpperCAmelCase__					(						self  :  Optional[int]      ,				snake_case__  :  torch.FloatTensor      ,				snake_case__  :  int ):
        """simple docstring"""







        __lowerCAmelCase         =							in_sigmas[-1].item()
        __lowerCAmelCase         =							in_sigmas[0].item()

        __lowerCAmelCase         =							7.0  # 7.0 is the value used in the paper
        __lowerCAmelCase         =							np.linspace(0      ,				1      ,				snake_case__ )
        __lowerCAmelCase         =							sigma_min ** (1 / rho)
        __lowerCAmelCase         =							sigma_max ** (1 / rho)
        __lowerCAmelCase         =							(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
        return sigmas







       @property
       def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
        """simple docstring"""







        return self.dt is None







       def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  Union[torch.FloatTensor, np.ndarray]      ,				snake_case__  :  Union[float, torch.FloatTensor]      ,				snake_case__  :  Union[torch.FloatTensor, np.ndarray]      ,				snake_case__  :  bool = True      ,				):
        """simple docstring"""







        __lowerCAmelCase         =							self.index_for_timestep(snake_case__ )

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

        if self.state_in_first_order:
         __lowerCAmelCase         =							self.sigmas[step_index]
         __lowerCAmelCase         =							self.sigmas[step_index + 1]
        else:
         # 2nd order / Heun's method
         __lowerCAmelCase         =							self.sigmas[step_index - 1]
         __lowerCAmelCase         =							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
        __lowerCAmelCase         =							0
        __lowerCAmelCase         =							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":
         __lowerCAmelCase         =							sigma_hat if self.state_in_first_order else sigma_next
         __lowerCAmelCase         =							sample - sigma_input * model_output
        elif self.config.prediction_type == "v_prediction":
         __lowerCAmelCase         =							sigma_hat if self.state_in_first_order else sigma_next
         __lowerCAmelCase         =							model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
             sample / (sigma_input**2 + 1)
         )
        elif self.config.prediction_type == "sample":
         __lowerCAmelCase         =							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:
         __lowerCAmelCase         =							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
         __lowerCAmelCase         =							(sample - pred_original_sample) / sigma_hat
         # 3. delta timestep
         __lowerCAmelCase         =							sigma_next - sigma_hat

         # store for 2nd order step
         __lowerCAmelCase         =							derivative
         __lowerCAmelCase         =							dt
         __lowerCAmelCase         =							sample
        else:
         # 2. 2nd order / Heun's method
         __lowerCAmelCase         =							(sample - pred_original_sample) / sigma_next
         __lowerCAmelCase         =							(self.prev_derivative + derivative) / 2

         # 3. take prev timestep & sample
         __lowerCAmelCase         =							self.dt
         __lowerCAmelCase         =							self.sample

         # free dt and derivative
         # Note, this puts the scheduler in "first order mode"
         __lowerCAmelCase         =							None
         __lowerCAmelCase         =							None
         __lowerCAmelCase         =							None

        __lowerCAmelCase         =							sample + derivative * dt

        if not return_dict:
         return (prev_sample,)

        return SchedulerOutput(prev_sample=snake_case__ )







       def       UpperCAmelCase__					(						self  :  Dict      ,				snake_case__  :  torch.FloatTensor      ,				snake_case__  :  torch.FloatTensor      ,				snake_case__  :  torch.FloatTensor      ,				):
        """simple docstring"""







        __lowerCAmelCase         =							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
         __lowerCAmelCase         =							self.timesteps.to(original_samples.device      ,				dtype=torch.floataa )
         __lowerCAmelCase         =							timesteps.to(original_samples.device      ,				dtype=torch.floataa )
        else:
         __lowerCAmelCase         =							self.timesteps.to(original_samples.device )
         __lowerCAmelCase         =							timesteps.to(original_samples.device )

        __lowerCAmelCase         =							[self.index_for_timestep(snake_case__      ,				snake_case__ ) for t in timesteps]

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

        __lowerCAmelCase         =							original_samples + noise * sigma
        return noisy_samples







       def __len__(						self  :  Optional[int] ):
        """simple docstring"""







        return self.config.num_train_timesteps








import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Dict   							=  (DPMSolverSinglestepScheduler,)
							lowercase_		:						Optional[Any]   							=  (('num_inference_steps', 25),)







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				**snake_case__  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							{
								    "num_train_timesteps": 1_000,
								    "beta_start": 0.0_0_0_1,
								    "beta_end": 0.0_2,
								    "beta_schedule": "linear",
								    "solver_order": 2,
								    "prediction_type": "epsilon",
								    "thresholding": False,
								    "sample_max_value": 1.0,
								    "algorithm_type": "dpmsolver++",
								    "solver_type": "midpoint",
								    "lambda_min_clipped": -float("inf" ),
								    "variance_type": None,
								}

								config.update(**snake_case__ )
								return config







							def       UpperCAmelCase__					(						self  :  Any      ,				snake_case__  :  Tuple=0      ,				**snake_case__  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							dict(self.forward_default_kwargs )
								__lowerCAmelCase         =							kwargs.pop("num_inference_steps"      ,				snake_case__ )
								__lowerCAmelCase         =							self.dummy_sample
								__lowerCAmelCase         =							0.1 * sample
								__lowerCAmelCase         =							[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
									__lowerCAmelCase         =							self.get_scheduler_config(**snake_case__ )
									__lowerCAmelCase         =							scheduler_class(**snake_case__ )
									scheduler.set_timesteps(snake_case__ )
									# copy over dummy past residuals
									__lowerCAmelCase         =							dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
										scheduler.save_config(snake_case__ )
										__lowerCAmelCase         =							scheduler_class.from_pretrained(snake_case__ )
										new_scheduler.set_timesteps(snake_case__ )
										# copy over dummy past residuals
										__lowerCAmelCase         =							dummy_past_residuals[: new_scheduler.config.solver_order]

									__lowerCAmelCase   ,						__lowerCAmelCase         =							sample, sample
									for t in range(snake_case__      ,				time_step + scheduler.config.solver_order + 1 ):
										__lowerCAmelCase         =							scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__      ,				**snake_case__ ).prev_sample
										__lowerCAmelCase         =							new_scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__      ,				**snake_case__ ).prev_sample

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







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								pass







							def       UpperCAmelCase__					(						self  :  str      ,				snake_case__  :  Tuple=0      ,				**snake_case__  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							dict(self.forward_default_kwargs )
								__lowerCAmelCase         =							kwargs.pop("num_inference_steps"      ,				snake_case__ )
								__lowerCAmelCase         =							self.dummy_sample
								__lowerCAmelCase         =							0.1 * sample
								__lowerCAmelCase         =							[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

								for scheduler_class in self.scheduler_classes:
									__lowerCAmelCase         =							self.get_scheduler_config()
									__lowerCAmelCase         =							scheduler_class(**snake_case__ )
									scheduler.set_timesteps(snake_case__ )

									# copy over dummy past residuals (must be after setting timesteps)
									__lowerCAmelCase         =							dummy_past_residuals[: scheduler.config.solver_order]

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

										# copy over dummy past residual (must be after setting timesteps)
										__lowerCAmelCase         =							dummy_past_residuals[: new_scheduler.config.solver_order]

									__lowerCAmelCase         =							scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__      ,				**snake_case__ ).prev_sample
									__lowerCAmelCase         =							new_scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__      ,				**snake_case__ ).prev_sample

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







							def       UpperCAmelCase__					(						self  :  Optional[Any]      ,				snake_case__  :  Optional[Any]=None      ,				**snake_case__  :  Optional[int] ):
								"""simple docstring"""







								if scheduler is None:
									__lowerCAmelCase         =							self.scheduler_classes[0]
									__lowerCAmelCase         =							self.get_scheduler_config(**snake_case__ )
									__lowerCAmelCase         =							scheduler_class(**snake_case__ )

								__lowerCAmelCase         =							self.scheduler_classes[0]
								__lowerCAmelCase         =							self.get_scheduler_config(**snake_case__ )
								__lowerCAmelCase         =							scheduler_class(**snake_case__ )

								__lowerCAmelCase         =							10
								__lowerCAmelCase         =							self.dummy_model()
								__lowerCAmelCase         =							self.dummy_sample_deter
								scheduler.set_timesteps(snake_case__ )

								for i, t in enumerate(scheduler.timesteps ):
									__lowerCAmelCase         =							model(snake_case__      ,				snake_case__ )
									__lowerCAmelCase         =							scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__ ).prev_sample

								return sample







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
								__lowerCAmelCase         =							50
								__lowerCAmelCase         =							self.dummy_model()
								__lowerCAmelCase         =							self.dummy_sample_deter
								scheduler.set_timesteps(snake_case__ )

								# make sure that the first t is uneven
								for i, t in enumerate(scheduler.timesteps[3:] ):
									__lowerCAmelCase         =							model(snake_case__      ,				snake_case__ )
									__lowerCAmelCase         =							scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__ ).prev_sample

								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_5_7_4 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								for timesteps in [25, 50, 100, 999, 1_000]:
									self.check_over_configs(num_train_timesteps=snake_case__ )







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
								__lowerCAmelCase         =							self.full_loop(scheduler=snake_case__ )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_7_9_1 ) < 1E-3

								__lowerCAmelCase         =							DEISMultistepScheduler.from_config(scheduler.config )
								__lowerCAmelCase         =							DPMSolverMultistepScheduler.from_config(scheduler.config )
								__lowerCAmelCase         =							UniPCMultistepScheduler.from_config(scheduler.config )
								__lowerCAmelCase         =							DPMSolverSinglestepScheduler.from_config(scheduler.config )

								__lowerCAmelCase         =							self.full_loop(scheduler=snake_case__ )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_7_9_1 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								self.check_over_configs(thresholding=snake_case__ )
								for order in [1, 2, 3]:
									for solver_type in ["midpoint", "heun"]:
										for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "sample"]:
												self.check_over_configs(
												    thresholding=snake_case__      ,				prediction_type=snake_case__      ,				sample_max_value=snake_case__      ,				algorithm_type="dpmsolver++"      ,				solver_order=snake_case__      ,				solver_type=snake_case__      ,				)







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







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







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								for algorithm_type in ["dpmsolver", "dpmsolver++"]:
									for solver_type in ["midpoint", "heun"]:
										for order in [1, 2, 3]:
											for prediction_type in ["epsilon", "sample"]:
												self.check_over_configs(
												    solver_order=snake_case__      ,				solver_type=snake_case__      ,				prediction_type=snake_case__      ,				algorithm_type=snake_case__      ,				)
												__lowerCAmelCase         =							self.full_loop(
												    solver_order=snake_case__      ,				solver_type=snake_case__      ,				prediction_type=snake_case__      ,				algorithm_type=snake_case__      ,				)
												assert not torch.isnan(snake_case__ ).any(), "Samples have nan numbers"







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								self.check_over_configs(lower_order_final=snake_case__ )
								self.check_over_configs(lower_order_final=snake_case__ )







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								self.check_over_configs(lambda_min_clipped=-float("inf" ) )
								self.check_over_configs(lambda_min_clipped=-5.1 )







							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								self.check_over_configs(variance_type=snake_case__ )
								self.check_over_configs(variance_type="learned_range" )







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
									self.check_over_forward(num_inference_steps=snake_case__      ,				time_step=0 )







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.full_loop()
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_7_9_1 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.full_loop(use_karras_sigmas=snake_case__ )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.2_2_4_8 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.full_loop(prediction_type="v_prediction" )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.1_4_5_3 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.full_loop(prediction_type="v_prediction"      ,				use_karras_sigmas=snake_case__ )
								__lowerCAmelCase         =							torch.mean(torch.abs(snake_case__ ) )

								assert abs(result_mean.item() - 0.0_6_4_9 ) < 1E-3







							def       UpperCAmelCase__					(						self  :  int ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.scheduler_classes[0]
								__lowerCAmelCase         =							self.get_scheduler_config(thresholding=snake_case__      ,				dynamic_thresholding_ratio=0 )
								__lowerCAmelCase         =							scheduler_class(**snake_case__ )

								__lowerCAmelCase         =							10
								__lowerCAmelCase         =							self.dummy_model()
								__lowerCAmelCase         =							self.dummy_sample_deter.half()
								scheduler.set_timesteps(snake_case__ )

								for i, t in enumerate(scheduler.timesteps ):
									__lowerCAmelCase         =							model(snake_case__      ,				snake_case__ )
									__lowerCAmelCase         =							scheduler.step(snake_case__      ,				snake_case__      ,				snake_case__ ).prev_sample

								assert sample.dtype == torch.floataa






