




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)
@flax.struct.dataclass
class        __snake_case  :
    lowerCAmelCase__				=     4_2

    # setable values
    lowerCAmelCase__				=     4_2
    lowerCAmelCase__				=     4_2
    lowerCAmelCase__				=     None



    @classmethod
    def       SCREAMING_SNAKE_CASE       (      cls      :		str		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Dict      )  ->    Optional[Any]:





        '''simple docstring'''




        return cls(common=A__		,	init_noise_sigma=A__		,	timesteps=A__      )






@dataclass
class        __snake_case  (__a		):
    lowerCAmelCase__				=     4_2






class        __snake_case  (__a						,	__a		):
    lowerCAmelCase__				=     [e.name for e in FlaxKarrasDiffusionSchedulers]

    lowerCAmelCase__				=     4_2



    @property
    def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    List[Any]:





        '''simple docstring'''




        return True



    @register_to_config
    def __init__(      self      :		Optional[Any]		,	_UpperCAmelCase      :		Any = 1000		,	_UpperCAmelCase      :		str = 0.0001		,	_UpperCAmelCase      :		List[str] = 0.02		,	_UpperCAmelCase      :		Any = "linear"		,	_UpperCAmelCase      :		Optional[int] = None		,	_UpperCAmelCase      :		List[Any] = "fixed_small"		,	_UpperCAmelCase      :		Any = True		,	_UpperCAmelCase      :		Dict = "epsilon"		,	_UpperCAmelCase      :		str = jnp.floataa		,	)  ->    str:





        '''simple docstring'''




        _lowerCAmelCase    :    Any										=      dtype



    def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		Union[str, Any] = None      )  ->    DDPMSchedulerState:





        '''simple docstring'''




        if common is None:
            _lowerCAmelCase    :    int										=      CommonSchedulerState.create(self      )

        # standard deviation of the initial noise distribution
        _lowerCAmelCase    :    Any										=      jnp.array(1.0		,	dtype=self.dtype      )

        _lowerCAmelCase    :    Union[str, Any]										=      jnp.arange(0		,	self.config.num_train_timesteps      ).round()[::-1]

        return DDPMSchedulerState.create(
            common=A__		,	init_noise_sigma=A__		,	timesteps=A__		,	)



    def       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		Tuple		,	_UpperCAmelCase      :		Union[str, Any]		,	_UpperCAmelCase      :		Optional[int] = None      )  ->    jnp.ndarray:





        '''simple docstring'''




        return sample



    def       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		List[Any] = ()      )  ->    DDPMSchedulerState:





        '''simple docstring'''




        _lowerCAmelCase    :    Optional[int]										=      self.config.num_train_timesteps // num_inference_steps
        # creates integer timesteps by multiplying by ratio
        # rounding to avoid issues when num_inference_step is power of 3
        _lowerCAmelCase    :    str										=      (jnp.arange(0		,	A__      ) * step_ratio).round()[::-1]

        return state.replace(
            num_inference_steps=A__		,	timesteps=A__		,	)



    def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		Dict		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		int=None		,	_UpperCAmelCase      :		int=None      )  ->    List[str]:





        '''simple docstring'''




        _lowerCAmelCase    :    Any										=      state.common.alphas_cumprod[t]
        _lowerCAmelCase    :    List[str]										=      jnp.where(t > 0		,	state.common.alphas_cumprod[t - 1]		,	jnp.array(1.0		,	dtype=self.dtype      )      )

        # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
        # and sample from it to get previous sample
        # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
        _lowerCAmelCase    :    Tuple										=      (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

        if variance_type is None:
            _lowerCAmelCase    :    Optional[int]										=      self.config.variance_type

        # hacks - were probably added for training stability
        if variance_type == "fixed_small":
            _lowerCAmelCase    :    Dict										=      jnp.clip(A__		,	a_min=1E-20      )
        # for rl-diffuser https://arxiv.org/abs/2205.09991
        elif variance_type == "fixed_small_log":
            _lowerCAmelCase    :    List[str]										=      jnp.log(jnp.clip(A__		,	a_min=1E-20      )      )
        elif variance_type == "fixed_large":
            _lowerCAmelCase    :    int										=      state.common.betas[t]
        elif variance_type == "fixed_large_log":
            # Glide max_log
            _lowerCAmelCase    :    Optional[int]										=      jnp.log(state.common.betas[t]      )
        elif variance_type == "learned":
            return predicted_variance
        elif variance_type == "learned_range":
            _lowerCAmelCase    :    str										=      variance
            _lowerCAmelCase    :    Tuple										=      state.common.betas[t]
            _lowerCAmelCase    :    Optional[Any]										=      (predicted_variance + 1) / 2
            _lowerCAmelCase    :    Dict										=      frac * max_log + (1 - frac) * min_log

        return variance



    def       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		str		,	_UpperCAmelCase      :		List[str]		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		Optional[Any] = None		,	_UpperCAmelCase      :		Union[str, Any] = True		,	)  ->    Union[FlaxDDPMSchedulerOutput, Tuple]:





        '''simple docstring'''




        _lowerCAmelCase    :    Dict										=      timestep

        if key is None:
            _lowerCAmelCase    :    Tuple										=      jax.random.PRNGKey(0      )

        if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
            _lowerCAmelCase					,    _lowerCAmelCase    :    Dict										=      jnp.split(A__		,	sample.shape[1]		,	axis=1      )
        else:
            _lowerCAmelCase    :    str										=      None

        # 1. compute alphas, betas
        _lowerCAmelCase    :    Optional[int]										=      state.common.alphas_cumprod[t]
        _lowerCAmelCase    :    Union[str, Any]										=      jnp.where(t > 0		,	state.common.alphas_cumprod[t - 1]		,	jnp.array(1.0		,	dtype=self.dtype      )      )
        _lowerCAmelCase    :    Any										=      1 - alpha_prod_t
        _lowerCAmelCase    :    int										=      1 - alpha_prod_t_prev

        # 2. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
        if self.config.prediction_type == "epsilon":
            _lowerCAmelCase    :    List[Any]										=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
        elif self.config.prediction_type == "sample":
            _lowerCAmelCase    :    Union[str, Any]										=      model_output
        elif self.config.prediction_type == "v_prediction":
            _lowerCAmelCase    :    Dict										=      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
        else:
            raise ValueError(
                f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` "
                """ for the FlaxDDPMScheduler."""      )

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
            _lowerCAmelCase    :    Any										=      jnp.clip(A__		,	-1		,	1      )

        # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        _lowerCAmelCase    :    Dict										=      (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
        _lowerCAmelCase    :    str										=      state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

        # 5. Compute predicted previous sample µ_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        _lowerCAmelCase    :    List[str]										=      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

        # 6. Add noise
        def random_variance():
            _lowerCAmelCase    :    Any										=      jax.random.split(A__		,	num=1      )
            _lowerCAmelCase    :    Optional[Any]										=      jax.random.normal(A__		,	shape=model_output.shape		,	dtype=self.dtype      )
            return (self._get_variance(A__		,	A__		,	predicted_variance=A__      ) ** 0.5) * noise

        _lowerCAmelCase    :    List[Any]										=      jnp.where(t > 0		,	random_variance()		,	jnp.zeros(model_output.shape		,	dtype=self.dtype      )      )

        _lowerCAmelCase    :    str										=      pred_prev_sample + variance

        if not return_dict:
            return (pred_prev_sample, state)

        return FlaxDDPMSchedulerOutput(prev_sample=A__		,	state=A__      )



    def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		Union[str, Any]		,	_UpperCAmelCase      :		Union[str, Any]		,	_UpperCAmelCase      :		Optional[int]		,	)  ->    jnp.ndarray:





        '''simple docstring'''




        return add_noise_common(state.common		,	A__		,	A__		,	A__      )



    def       SCREAMING_SNAKE_CASE       (      self      :		int		,	_UpperCAmelCase      :		Optional[int]		,	_UpperCAmelCase      :		int		,	_UpperCAmelCase      :		Any		,	_UpperCAmelCase      :		int		,	)  ->    jnp.ndarray:





        '''simple docstring'''




        return get_velocity_common(state.common		,	A__		,	A__		,	A__      )



    def __len__(      self      :		Optional[int]      )  ->    Tuple:





        '''simple docstring'''




        return self.config.num_train_timesteps







import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest
class        __snake_case  (_a		):
				lowerCAmelCase__				=     (PNDMScheduler,)
				lowerCAmelCase__				=     (("num_inference_steps", 5_0),)



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]		,	**_UpperCAmelCase      :		Optional[int]      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[Any]										=      {
								    """num_train_timesteps""": 1000,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								}

								config.update(**_UpperCAmelCase      )
								return config



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		Union[str, Any]=0		,	**_UpperCAmelCase      :		str      )  ->    Any:





								'''simple docstring'''




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

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Any										=      self.get_scheduler_config(**_UpperCAmelCase      )
												_lowerCAmelCase    :    Optional[Any]										=      scheduler_class(**_UpperCAmelCase      )
												scheduler.set_timesteps(_UpperCAmelCase      )
												# copy over dummy past residuals
												_lowerCAmelCase    :    Tuple										=      dummy_past_residuals[:]

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(_UpperCAmelCase      )
																_lowerCAmelCase    :    str										=      scheduler_class.from_pretrained(_UpperCAmelCase      )
																new_scheduler.set_timesteps(_UpperCAmelCase      )
																# copy over dummy past residuals
																_lowerCAmelCase    :    Any										=      dummy_past_residuals[:]

												_lowerCAmelCase    :    int										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    List[str]										=      new_scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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

												_lowerCAmelCase    :    Tuple										=      scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Optional[int]										=      new_scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    str:





								'''simple docstring'''




								pass



				def       SCREAMING_SNAKE_CASE       (      self      :		int		,	_UpperCAmelCase      :		Any=0		,	**_UpperCAmelCase      :		Optional[int]      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      dict(self.forward_default_kwargs      )
								_lowerCAmelCase    :    Any										=      kwargs.pop("""num_inference_steps"""		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    List[Any]										=      self.dummy_sample
								_lowerCAmelCase    :    Dict										=      0.1 * sample
								_lowerCAmelCase    :    Dict										=      [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Any										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Optional[Any]										=      scheduler_class(**_UpperCAmelCase      )
												scheduler.set_timesteps(_UpperCAmelCase      )

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

												with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(_UpperCAmelCase      )
																_lowerCAmelCase    :    List[Any]										=      scheduler_class.from_pretrained(_UpperCAmelCase      )
																# copy over dummy past residuals
																new_scheduler.set_timesteps(_UpperCAmelCase      )

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

												_lowerCAmelCase    :    Tuple										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    str										=      new_scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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

												_lowerCAmelCase    :    Any										=      scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Any										=      new_scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	**_UpperCAmelCase      :		Dict      )  ->    List[str]:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      self.scheduler_classes[0]
								_lowerCAmelCase    :    int										=      self.get_scheduler_config(**_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      scheduler_class(**_UpperCAmelCase      )

								_lowerCAmelCase    :    List[Any]										=      10
								_lowerCAmelCase    :    Any										=      self.dummy_model()
								_lowerCAmelCase    :    Tuple										=      self.dummy_sample_deter
								scheduler.set_timesteps(_UpperCAmelCase      )

								for i, t in enumerate(scheduler.prk_timesteps      ):
												_lowerCAmelCase    :    List[Any]										=      model(_UpperCAmelCase		,	_UpperCAmelCase      )
												_lowerCAmelCase    :    Tuple										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ).prev_sample

								for i, t in enumerate(scheduler.plms_timesteps      ):
												_lowerCAmelCase    :    List[Any]										=      model(_UpperCAmelCase		,	_UpperCAmelCase      )
												_lowerCAmelCase    :    Dict										=      scheduler.step_plms(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ).prev_sample

								return sample



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    List[str]:





								'''simple docstring'''




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

								_lowerCAmelCase    :    int										=      kwargs.pop("""num_inference_steps"""		,	_UpperCAmelCase      )

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Tuple										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Union[str, Any]										=      scheduler_class(**_UpperCAmelCase      )

												_lowerCAmelCase    :    str										=      self.dummy_sample
												_lowerCAmelCase    :    str										=      0.1 * sample

												if num_inference_steps is not None and hasattr(_UpperCAmelCase		,	"""set_timesteps"""      ):
																scheduler.set_timesteps(_UpperCAmelCase      )
												elif num_inference_steps is not None and not hasattr(_UpperCAmelCase		,	"""set_timesteps"""      ):
																_lowerCAmelCase    :    Tuple										=      num_inference_steps

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

												_lowerCAmelCase    :    Optional[int]										=      scheduler.step_prk(_UpperCAmelCase		,	0		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Optional[int]										=      scheduler.step_prk(_UpperCAmelCase		,	1		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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

												_lowerCAmelCase    :    Tuple										=      scheduler.step_plms(_UpperCAmelCase		,	0		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample
												_lowerCAmelCase    :    Optional[Any]										=      scheduler.step_plms(_UpperCAmelCase		,	1		,	_UpperCAmelCase		,	**_UpperCAmelCase      ).prev_sample

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



				def       SCREAMING_SNAKE_CASE       (      self      :		int      )  ->    List[Any]:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[Any]      )  ->    Optional[int]:





								'''simple docstring'''




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

								_lowerCAmelCase    :    Dict										=      self.scheduler_classes[0]
								_lowerCAmelCase    :    Any										=      self.get_scheduler_config(steps_offset=1      )
								_lowerCAmelCase    :    Any										=      scheduler_class(**_UpperCAmelCase      )
								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       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    List[Any]:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    int:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    Union[str, Any]:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    List[str]:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    List[Any]:





								'''simple docstring'''




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



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      27

								for scheduler_class in self.scheduler_classes:
												_lowerCAmelCase    :    Optional[Any]										=      self.dummy_sample
												_lowerCAmelCase    :    Any										=      0.1 * sample

												_lowerCAmelCase    :    List[str]										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Any										=      scheduler_class(**_UpperCAmelCase      )

												scheduler.set_timesteps(_UpperCAmelCase      )

												# 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]      ):
																_lowerCAmelCase    :    List[Any]										=      scheduler.step_prk(_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase      ).prev_sample



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict      )  ->    Union[str, Any]:





								'''simple docstring'''




								with self.assertRaises(_UpperCAmelCase      ):
												_lowerCAmelCase    :    Tuple										=      self.scheduler_classes[0]
												_lowerCAmelCase    :    List[str]										=      self.get_scheduler_config()
												_lowerCAmelCase    :    Any										=      scheduler_class(**_UpperCAmelCase      )

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple      )  ->    Any:





								'''simple docstring'''




								_lowerCAmelCase    :    Tuple										=      self.full_loop()
								_lowerCAmelCase    :    str										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    List[Any]										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

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



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      self.full_loop(prediction_type="""v_prediction"""      )
								_lowerCAmelCase    :    Optional[int]										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Optional[int]										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

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



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    str										=      self.full_loop(set_alpha_to_one=_UpperCAmelCase		,	beta_start=0.01      )
								_lowerCAmelCase    :    Any										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Optional[Any]										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

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



				def       SCREAMING_SNAKE_CASE       (      self      :		str      )  ->    Union[str, Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      self.full_loop(set_alpha_to_one=_UpperCAmelCase		,	beta_start=0.01      )
								_lowerCAmelCase    :    List[Any]										=      torch.sum(torch.abs(_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Tuple										=      torch.mean(torch.abs(_UpperCAmelCase      )      )

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

