

'''simple docstring'''
import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
					from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
					from tensorflow.keras.optimizers import Adam
class 					_snake_case		(tf.keras.optimizers.schedules.LearningRateSchedule):






			def __init__(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case = 1.0			,_snake_case = None			,):
				super().__init__()
				UpperCAmelCase_     :      int						=  initial_learning_rate
				UpperCAmelCase_     :      Any						=  warmup_steps
				UpperCAmelCase_     :      Tuple						=  power
				UpperCAmelCase_     :      Any						=  decay_schedule_fn
				UpperCAmelCase_     :      Union[str, Any]						=  name






			def __call__(  self			,_snake_case     ):
				with tf.name_scope(self.name or "WarmUp"     ) as name:
					# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
					# learning rate will be `global_step/num_warmup_steps * init_lr`.
					UpperCAmelCase_     :      Tuple						=  tf.cast(_snake_case			,tf.floataa     )
					UpperCAmelCase_     :      Optional[Any]						=  tf.cast(self.warmup_steps			,tf.floataa     )
					UpperCAmelCase_     :      List[str]						=  global_step_float / warmup_steps_float
					UpperCAmelCase_     :      List[str]						=  self.initial_learning_rate * tf.math.pow(_snake_case			,self.power     )
					return tf.cond(
					    global_step_float < warmup_steps_float			,lambda: warmup_learning_rate			,lambda: self.decay_schedule_fn(step - self.warmup_steps     )			,name=_snake_case			,)






			def 		UpperCamelCase__							(  self     ):
				return {
				    "initial_learning_rate": self.initial_learning_rate,
				    "decay_schedule_fn": self.decay_schedule_fn,
				    "warmup_steps": self.warmup_steps,
				    "power": self.power,
				    "name": self.name,
				}




def  a__       (    _SCREAMING_SNAKE_CASE   :       float	,				_SCREAMING_SNAKE_CASE   :       int	,				_SCREAMING_SNAKE_CASE   :       int	,				_SCREAMING_SNAKE_CASE   :       float = 0.0	,				_SCREAMING_SNAKE_CASE   :       float = 0.9	,				_SCREAMING_SNAKE_CASE   :       float = 0.999	,				_SCREAMING_SNAKE_CASE   :       float = 1E-8	,				_SCREAMING_SNAKE_CASE   :       Optional[float] = None	,				_SCREAMING_SNAKE_CASE   :       Optional[float] = None	,				_SCREAMING_SNAKE_CASE   :       float = 0.0	,				_SCREAMING_SNAKE_CASE   :       float = 1.0	,				_SCREAMING_SNAKE_CASE   :       Optional[List[str]] = None	,				)     ->					Optional[int]:


	"""simple docstring"""





	UpperCAmelCase_     :      List[str]						=  tf.keras.optimizers.schedules.PolynomialDecay(
	    initial_learning_rate=_SCREAMING_SNAKE_CASE	,				decay_steps=num_train_steps - num_warmup_steps	,				end_learning_rate=init_lr * min_lr_ratio	,				power=_SCREAMING_SNAKE_CASE	,				)
	if num_warmup_steps:
		UpperCAmelCase_     :      Tuple						=  WarmUp(
		    initial_learning_rate=_SCREAMING_SNAKE_CASE	,				decay_schedule_fn=_SCREAMING_SNAKE_CASE	,				warmup_steps=_SCREAMING_SNAKE_CASE	,				)
	if weight_decay_rate > 0.0:
		UpperCAmelCase_     :      Any						=  AdamWeightDecay(
		    learning_rate=_SCREAMING_SNAKE_CASE	,				weight_decay_rate=_SCREAMING_SNAKE_CASE	,				beta_a=_SCREAMING_SNAKE_CASE	,				beta_a=_SCREAMING_SNAKE_CASE	,				epsilon=_SCREAMING_SNAKE_CASE	,				clipnorm=_SCREAMING_SNAKE_CASE	,				global_clipnorm=_SCREAMING_SNAKE_CASE	,				exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]	,				include_in_weight_decay=_SCREAMING_SNAKE_CASE	,				)
	else:
		UpperCAmelCase_     :      Tuple						=  tf.keras.optimizers.Adam(
		    learning_rate=_SCREAMING_SNAKE_CASE	,				beta_a=_SCREAMING_SNAKE_CASE	,				beta_a=_SCREAMING_SNAKE_CASE	,				epsilon=_SCREAMING_SNAKE_CASE	,				clipnorm=_SCREAMING_SNAKE_CASE	,				global_clipnorm=_SCREAMING_SNAKE_CASE	,				)
	# We return the optimizer and the LR scheduler in order to better track the
	# evolution of the LR independently of the optimizer.
	return optimizer, lr_schedule
class 					_snake_case		(__SCREAMING_SNAKE_CASE):






			def __init__(  self			,_snake_case = 0.001			,_snake_case = 0.9			,_snake_case = 0.999			,_snake_case = 1E-7			,_snake_case = False			,_snake_case = 0.0			,_snake_case = None			,_snake_case = None			,_snake_case = "AdamWeightDecay"			,**_snake_case			,):
				super().__init__(_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,_snake_case			,**_snake_case     )
				UpperCAmelCase_     :      List[str]						=  weight_decay_rate
				UpperCAmelCase_     :      Optional[int]						=  include_in_weight_decay
				UpperCAmelCase_     :      Optional[Any]						=  exclude_from_weight_decay






			@classmethod
			def 		UpperCamelCase__							(  cls			,_snake_case     ):
				UpperCAmelCase_     :      Union[str, Any]						=  {"WarmUp": WarmUp}
				return super(_snake_case			,cls     ).from_config(_snake_case			,custom_objects=_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case     ):
				super(_snake_case			,self     )._prepare_local(_snake_case			,_snake_case			,_snake_case     )
				UpperCAmelCase_     :      int						=  tf.constant(
				    self.weight_decay_rate			,name="adam_weight_decay_rate"     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      Tuple						=  self._do_use_weight_decay(var.name     )
				if do_decay:
					return var.assign_sub(
					    learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["weight_decay_rate"]			,use_locking=self._use_locking			,)
				return tf.no_op()






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case=None			,**_snake_case     ):
				UpperCAmelCase_					,					UpperCAmelCase_     :      str						=  list(zip(*_snake_case     )     )
				return super(_snake_case			,self     ).apply_gradients(zip(_snake_case			,_snake_case     )			,name=_snake_case			,**_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case     ):
				if apply_state is None:
					return self._decayed_lr_t[var_dtype], {}

				UpperCAmelCase_     :      str						=  apply_state or {}
				UpperCAmelCase_     :      Any						=  apply_state.get((var_device, var_dtype)     )
				if coefficients is None:
					UpperCAmelCase_     :      List[str]						=  self._fallback_apply_state(_snake_case			,_snake_case     )
					UpperCAmelCase_     :      str						=  coefficients

				return coefficients["lr_t"], {"apply_state": apply_state}






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case=None     ):
				UpperCAmelCase_					,					UpperCAmelCase_     :      Any						=  self._get_lr(var.device			,var.dtype.base_dtype			,_snake_case     )
				UpperCAmelCase_     :      List[str]						=  self._decay_weights_op(_snake_case			,_snake_case			,_snake_case     )
				with tf.control_dependencies([decay]     ):
					return super(_snake_case			,self     )._resource_apply_dense(_snake_case			,_snake_case			,**_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case=None     ):
				UpperCAmelCase_					,					UpperCAmelCase_     :      Dict						=  self._get_lr(var.device			,var.dtype.base_dtype			,_snake_case     )
				UpperCAmelCase_     :      Any						=  self._decay_weights_op(_snake_case			,_snake_case			,_snake_case     )
				with tf.control_dependencies([decay]     ):
					return super(_snake_case			,self     )._resource_apply_sparse(_snake_case			,_snake_case			,_snake_case			,**_snake_case     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      int						=  super().get_config()
				config.update({"weight_decay_rate": self.weight_decay_rate}     )
				return config






			def 		UpperCamelCase__							(  self			,_snake_case     ):
				if self.weight_decay_rate == 0:
					return False

				if self._include_in_weight_decay:
					for r in self._include_in_weight_decay:
						if re.search(_snake_case			,_snake_case     ) is not None:
							return True

				if self._exclude_from_weight_decay:
					for r in self._exclude_from_weight_decay:
						if re.search(_snake_case			,_snake_case     ) is not None:
							return False
				return True




class 					_snake_case		(__SCREAMING_SNAKE_CASE):






			def __init__(  self     ):
				UpperCAmelCase_     :      Any						=  []
				UpperCAmelCase_     :      Union[str, Any]						=  None






			@property
			def 		UpperCamelCase__							(  self     ):
				if self._accum_steps is None:
					UpperCAmelCase_     :      str						=  tf.Variable(
					    tf.constant(0			,dtype=tf.intaa     )			,trainable=_snake_case			,synchronization=tf.VariableSynchronization.ON_READ			,aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA			,)

				return self._accum_steps.value()






			@property
			def 		UpperCamelCase__							(  self     ):
				if not self._gradients:
					raise ValueError("The accumulator should be called first to initialize the gradients"     )
				return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]






			def __call__(  self			,_snake_case     ):
				if not self._gradients:
					UpperCAmelCase_     :      List[Any]						=  self.step  # Create the step variable.
					self._gradients.extend(
					    [
					        tf.Variable(
					            tf.zeros_like(_snake_case     )			,trainable=_snake_case			,synchronization=tf.VariableSynchronization.ON_READ			,aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA			,)
					        if gradient is not None
					        else gradient
					        for gradient in gradients
					    ]     )
				if len(_snake_case     ) != len(self._gradients     ):
					raise ValueError(f'''Expected {len(self._gradients     )} gradients, but got {len(_snake_case     )}'''     )

				for accum_gradient, gradient in zip(self._gradients			,_snake_case     ):
					if accum_gradient is not None and gradient is not None:
						accum_gradient.assign_add(_snake_case     )

				self._accum_steps.assign_add(1     )






			def 		UpperCamelCase__							(  self     ):
				if not self._gradients:
					return
				self._accum_steps.assign(0     )
				for gradient in self._gradients:
					if gradient is not None:
						gradient.assign(tf.zeros_like(_snake_case     )     )











'''simple docstring'''




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	(DPMSolverSinglestepScheduler,)
					SCREAMING_SNAKE_CASE__				=	(('''num_inference_steps''', 25),)


					def lowerCamelCase_			(						self				:			Any      ,						**lowerCamelCase_				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			{
							    """num_train_timesteps""": 10_00,
							    """beta_start""": 0.0_001,
							    """beta_end""": 0.02,
							    """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(**lowerCamelCase_						)
							return config


					def lowerCamelCase_			(						self				:			Any      ,						lowerCamelCase_				:			List[str]=0      ,						**lowerCamelCase_				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			dict(self.forward_default_kwargs						)
							SCREAMING_SNAKE_CASE :				List[str]		=			kwargs.pop("""num_inference_steps"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.dummy_sample
							SCREAMING_SNAKE_CASE :				str		=			0.1 * sample
							SCREAMING_SNAKE_CASE :				Any		=			[residual + 0.2, residual + 0.15, residual + 0.10]

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

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

									SCREAMING_SNAKE_CASE,							SCREAMING_SNAKE_CASE :				str		=			sample, sample
									for t in range(lowerCamelCase_      ,						time_step + scheduler.config.solver_order + 1						):
											SCREAMING_SNAKE_CASE :				Union[str, Any]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						**lowerCamelCase_						).prev_sample
											SCREAMING_SNAKE_CASE :				Optional[int]		=			new_scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						**lowerCamelCase_						).prev_sample

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


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							pass


					def lowerCamelCase_			(						self				:			str      ,						lowerCamelCase_				:			Union[str, Any]=0      ,						**lowerCamelCase_				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			dict(self.forward_default_kwargs						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			kwargs.pop("""num_inference_steps"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			self.dummy_sample
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			0.1 * sample
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			[residual + 0.2, residual + 0.15, residual + 0.10]

							for scheduler_class in self.scheduler_classes:
									SCREAMING_SNAKE_CASE :				int		=			self.get_scheduler_config()
									SCREAMING_SNAKE_CASE :				Any		=			scheduler_class(**lowerCamelCase_						)
									scheduler.set_timesteps(lowerCamelCase_						)

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

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

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

									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						**lowerCamelCase_						).prev_sample
									SCREAMING_SNAKE_CASE :				Dict		=			new_scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_      ,						**lowerCamelCase_						).prev_sample

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


					def lowerCamelCase_			(						self				:			Dict      ,						lowerCamelCase_				:			int=None      ,						**lowerCamelCase_				:			Union[str, Any]						):

							'''simple docstring'''



							if scheduler is None:
									SCREAMING_SNAKE_CASE :				Tuple		=			self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE :				int		=			self.get_scheduler_config(**lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				List[str]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[int]		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				List[Any]		=			self.get_scheduler_config(**lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Any		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			10
							SCREAMING_SNAKE_CASE :				Any		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				Any		=			self.dummy_sample_deter
							scheduler.set_timesteps(lowerCamelCase_						)

							for i, t in enumerate(scheduler.timesteps						):
									SCREAMING_SNAKE_CASE :				Dict		=			model(lowerCamelCase_      ,						lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_						).prev_sample

							return sample


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			50
							SCREAMING_SNAKE_CASE :				Dict		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				Any		=			self.dummy_sample_deter
							scheduler.set_timesteps(lowerCamelCase_						)

							# make sure that the first t is uneven
							for i, t in enumerate(scheduler.timesteps[3:]						):
									SCREAMING_SNAKE_CASE :				str		=			model(lowerCamelCase_      ,						lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_						).prev_sample

							SCREAMING_SNAKE_CASE :				Any		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.2_574						) < 1e-3


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							for timesteps in [25, 50, 1_00, 9_99, 10_00]:
									self.check_over_configs(num_train_timesteps=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			DPMSolverSinglestepScheduler(**self.get_scheduler_config()						)
							SCREAMING_SNAKE_CASE :				int		=			self.full_loop(scheduler=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.2_791						) < 1e-3

							SCREAMING_SNAKE_CASE :				List[str]		=			DEISMultistepScheduler.from_config(scheduler.config						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			DPMSolverMultistepScheduler.from_config(scheduler.config						)
							SCREAMING_SNAKE_CASE :				Any		=			UniPCMultistepScheduler.from_config(scheduler.config						)
							SCREAMING_SNAKE_CASE :				Optional[int]		=			DPMSolverSinglestepScheduler.from_config(scheduler.config						)

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			self.full_loop(scheduler=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				List[str]		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.2_791						) < 1e-3


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							self.check_over_configs(thresholding=lowerCamelCase_						)
							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=lowerCamelCase_      ,						prediction_type=lowerCamelCase_      ,						sample_max_value=lowerCamelCase_      ,						algorithm_type="""dpmsolver++"""      ,						solver_order=lowerCamelCase_      ,						solver_type=lowerCamelCase_      ,						)


					def lowerCamelCase_			(						self				:			Dict						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			Union[str, Any]						):

							'''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=lowerCamelCase_      ,						solver_type=lowerCamelCase_      ,						prediction_type=lowerCamelCase_      ,						algorithm_type=lowerCamelCase_      ,						)
															SCREAMING_SNAKE_CASE :				int		=			self.full_loop(
															    solver_order=lowerCamelCase_      ,						solver_type=lowerCamelCase_      ,						prediction_type=lowerCamelCase_      ,						algorithm_type=lowerCamelCase_      ,						)
															assert not torch.isnan(lowerCamelCase_						).any(), "Samples have nan numbers"


					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							self.check_over_configs(lower_order_final=lowerCamelCase_						)
							self.check_over_configs(lower_order_final=lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



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


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
									self.check_over_forward(num_inference_steps=lowerCamelCase_      ,						time_step=0						)


					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			self.full_loop()
							SCREAMING_SNAKE_CASE :				Any		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.2_791						) < 1e-3


					def lowerCamelCase_			(						self				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			self.full_loop(use_karras_sigmas=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				List[Any]		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.2_248						) < 1e-3


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[str]		=			self.full_loop(prediction_type="""v_prediction"""						)
							SCREAMING_SNAKE_CASE :				int		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.1_453						) < 1e-3


					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Tuple		=			self.full_loop(prediction_type="""v_prediction"""      ,						use_karras_sigmas=lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				str		=			torch.mean(torch.abs(lowerCamelCase_						)						)

							assert abs(result_mean.item() - 0.0_649						) < 1e-3



					def lowerCamelCase_			(						self				:			str						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			self.scheduler_classes[0]
							SCREAMING_SNAKE_CASE :				List[str]		=			self.get_scheduler_config(thresholding=lowerCamelCase_      ,						dynamic_thresholding_ratio=0						)
							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			scheduler_class(**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				Any		=			10
							SCREAMING_SNAKE_CASE :				List[Any]		=			self.dummy_model()
							SCREAMING_SNAKE_CASE :				int		=			self.dummy_sample_deter.half()
							scheduler.set_timesteps(lowerCamelCase_						)

							for i, t in enumerate(scheduler.timesteps						):
									SCREAMING_SNAKE_CASE :				List[Any]		=			model(lowerCamelCase_      ,						lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Any		=			scheduler.step(lowerCamelCase_      ,						lowerCamelCase_      ,						lowerCamelCase_						).prev_sample

							assert sample.dtype == torch.floataa



