


import shutil
import tempfile
import unittest
from unittest.mock import patch

from transformers import (
    DefaultFlowCallback,
    IntervalStrategy,
    PrinterCallback,
    ProgressCallback,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    is_torch_available,
)
from transformers.testing_utils import require_torch


if is_torch_available():
   from transformers.trainer import DEFAULT_CALLBACKS

   from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel






class   UpperCamelCase       (		_SCREAMING_SNAKE_CASE  ):


   '''simple docstring'''







   def __init__(  self		):
        lowercase_							:Optional[int]			     =							[]
   def 		UpperCamelCase	(  self ,				UpperCamelCase_ ,				UpperCamelCase_ ,				UpperCamelCase_ ,				**UpperCamelCase_		):
        self.events.append('''on_init_end'''		)
   def 		UpperCamelCase	(  self ,				UpperCamelCase_ ,				UpperCamelCase_ ,				UpperCamelCase_ ,				**UpperCamelCase_		):
        self.events.append('''on_train_begin'''		)
   def 		UpperCamelCase	(  self ,				UpperCamelCase_ ,				UpperCamelCase_ ,				UpperCamelCase_ ,				**UpperCamelCase_		):
        self.events.append('''on_train_end'''		)
   def 		UpperCamelCase	(  self ,				UpperCamelCase_ ,				UpperCamelCase_ ,				UpperCamelCase_ ,				**UpperCamelCase_		):
        self.events.append('''on_epoch_begin'''		)
   def 		UpperCamelCase	(  self ,				UpperCamelCase_ ,				UpperCamelCase_ ,				UpperCamelCase_ ,				**UpperCamelCase_		):
        self.events.append('''on_epoch_end'''		)
   def 		UpperCamelCase	(  self ,				UpperCamelCase_ ,				UpperCamelCase_ ,				UpperCamelCase_ ,				**UpperCamelCase_		):
        self.events.append('''on_step_begin'''		)
   def 		UpperCamelCase	(  self ,				UpperCamelCase_ ,				UpperCamelCase_ ,				UpperCamelCase_ ,				**UpperCamelCase_		):
        self.events.append('''on_step_end'''		)
   def 		UpperCamelCase	(  self ,				UpperCamelCase_ ,				UpperCamelCase_ ,				UpperCamelCase_ ,				**UpperCamelCase_		):
        self.events.append('''on_evaluate'''		)
   def 		UpperCamelCase	(  self ,				UpperCamelCase_ ,				UpperCamelCase_ ,				UpperCamelCase_ ,				**UpperCamelCase_		):
        self.events.append('''on_predict'''		)
   def 		UpperCamelCase	(  self ,				UpperCamelCase_ ,				UpperCamelCase_ ,				UpperCamelCase_ ,				**UpperCamelCase_		):
        self.events.append('''on_save'''		)
   def 		UpperCamelCase	(  self ,				UpperCamelCase_ ,				UpperCamelCase_ ,				UpperCamelCase_ ,				**UpperCamelCase_		):
        self.events.append('''on_log'''		)




   def 		UpperCamelCase	(  self ,				UpperCamelCase_ ,				UpperCamelCase_ ,				UpperCamelCase_ ,				**UpperCamelCase_		):
        self.events.append('''on_prediction_step'''		)






@require_torch
class   UpperCamelCase       (		unittest.TestCase  ):


   '''simple docstring'''







   def 		UpperCamelCase	(  self		):
        lowercase_							:Optional[int]			     =							tempfile.mkdtemp()
   def 		UpperCamelCase	(  self		):
        shutil.rmtree(self.output_dir		)
   def 		UpperCamelCase	(  self ,				UpperCamelCase_=0 ,				UpperCamelCase_=0 ,				UpperCamelCase_=64 ,				UpperCamelCase_=64 ,				UpperCamelCase_=None ,				UpperCamelCase_=False ,				**UpperCamelCase_		):
        # disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
        # its set to False since the tests later on depend on its value.
        lowercase_							:Optional[Any]			     =							RegressionDataset(length=lowerCAmelCase_		)
        lowercase_							:Union[str, Any]			     =							RegressionDataset(length=lowerCAmelCase_		)
        lowercase_							:str			     =							RegressionModelConfig(a=lowerCAmelCase_ ,				b=lowerCAmelCase_		)
        lowercase_							:List[str]			     =							RegressionPreTrainedModel(lowerCAmelCase_		)

        lowercase_							:List[Any]			     =							TrainingArguments(self.output_dir ,				disable_tqdm=lowerCAmelCase_ ,				report_to=[] ,				**lowerCAmelCase_		)
        return Trainer(
            lowerCAmelCase_ ,				lowerCAmelCase_ ,				train_dataset=lowerCAmelCase_ ,				eval_dataset=lowerCAmelCase_ ,				callbacks=lowerCAmelCase_ ,				)
   def 		UpperCamelCase	(  self ,				UpperCamelCase_ ,				UpperCamelCase_		):
        self.assertEqual(len(lowerCAmelCase_		) ,				len(lowerCAmelCase_		)		)

        # Order doesn't matter
        lowercase_							:Any			     =							sorted(lowerCAmelCase_ ,				key=lambda UpperCamelCase_		: cb.__name__ if isinstance(lowerCAmelCase_ ,				lowerCAmelCase_		) else cb.__class__.__name__		)
        lowercase_							:Union[str, Any]			     =							sorted(lowerCAmelCase_ ,				key=lambda UpperCamelCase_		: cb.__name__ if isinstance(lowerCAmelCase_ ,				lowerCAmelCase_		) else cb.__class__.__name__		)

        for cba, cba in zip(lowerCAmelCase_ ,				lowerCAmelCase_		):
             if isinstance(lowerCAmelCase_ ,				lowerCAmelCase_		) and isinstance(lowerCAmelCase_ ,				lowerCAmelCase_		):
                  self.assertEqual(lowerCAmelCase_ ,				lowerCAmelCase_		)
             elif isinstance(lowerCAmelCase_ ,				lowerCAmelCase_		) and not isinstance(lowerCAmelCase_ ,				lowerCAmelCase_		):
                  self.assertEqual(lowerCAmelCase_ ,				cba.__class__		)
             elif not isinstance(lowerCAmelCase_ ,				lowerCAmelCase_		) and isinstance(lowerCAmelCase_ ,				lowerCAmelCase_		):
                  self.assertEqual(cba.__class__ ,				lowerCAmelCase_		)
             else:
                  self.assertEqual(lowerCAmelCase_ ,				lowerCAmelCase_		)
   def 		UpperCamelCase	(  self ,				UpperCamelCase_		):
        lowercase_							:List[str]			     =							["""on_init_end""", """on_train_begin"""]
        lowercase_							:List[Any]			     =							0
        lowercase_							:List[Any]			     =							len(trainer.get_eval_dataloader()		)
        lowercase_							:Dict			     =							["""on_prediction_step"""] * len(trainer.get_eval_dataloader()		) + ["""on_log""", """on_evaluate"""]
        for _ in range(trainer.state.num_train_epochs		):
             expected_events.append('''on_epoch_begin'''		)
             for _ in range(lowerCAmelCase_		):
                  step += 1
                  expected_events += ["on_step_begin", "on_step_end"]
                  if step % trainer.args.logging_steps == 0:
                       expected_events.append('''on_log'''		)
                  if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
                       expected_events += evaluation_events.copy()
                  if step % trainer.args.save_steps == 0:
                       expected_events.append('''on_save'''		)
             expected_events.append('''on_epoch_end'''		)
             if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
                  expected_events += evaluation_events.copy()
        expected_events += ["on_log", "on_train_end"]
        return expected_events
   def 		UpperCamelCase	(  self		):
        lowercase_							:int			     =							self.get_trainer()
        lowercase_							:List[Any]			     =							DEFAULT_CALLBACKS.copy() + [ProgressCallback]
        self.check_callbacks_equality(trainer.callback_handler.callbacks ,				lowerCAmelCase_		)

        # Callbacks passed at init are added to the default callbacks
        lowercase_							:int			     =							self.get_trainer(callbacks=[MyTestTrainerCallback]		)
        expected_callbacks.append(lowerCAmelCase_		)
        self.check_callbacks_equality(trainer.callback_handler.callbacks ,				lowerCAmelCase_		)

        # TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
        lowercase_							:int			     =							self.get_trainer(disable_tqdm=lowerCAmelCase_		)
        lowercase_							:Dict			     =							DEFAULT_CALLBACKS.copy() + [PrinterCallback]
        self.check_callbacks_equality(trainer.callback_handler.callbacks ,				lowerCAmelCase_		)
   def 		UpperCamelCase	(  self		):
        lowercase_							:List[str]			     =							DEFAULT_CALLBACKS.copy() + [ProgressCallback]
        lowercase_							:List[str]			     =							self.get_trainer()

        # We can add, pop, or remove by class name
        trainer.remove_callback(lowerCAmelCase_		)
        expected_callbacks.remove(lowerCAmelCase_		)
        self.check_callbacks_equality(trainer.callback_handler.callbacks ,				lowerCAmelCase_		)

        lowercase_							:Tuple			     =							self.get_trainer()
        lowercase_							:int			     =							trainer.pop_callback(lowerCAmelCase_		)
        self.assertEqual(cb.__class__ ,				lowerCAmelCase_		)
        self.check_callbacks_equality(trainer.callback_handler.callbacks ,				lowerCAmelCase_		)

        trainer.add_callback(lowerCAmelCase_		)
        expected_callbacks.insert(0 ,				lowerCAmelCase_		)
        self.check_callbacks_equality(trainer.callback_handler.callbacks ,				lowerCAmelCase_		)

        # We can also add, pop, or remove by instance
        lowercase_							:Any			     =							self.get_trainer()
        lowercase_							:str			     =							trainer.callback_handler.callbacks[0]
        trainer.remove_callback(lowerCAmelCase_		)
        expected_callbacks.remove(lowerCAmelCase_		)
        self.check_callbacks_equality(trainer.callback_handler.callbacks ,				lowerCAmelCase_		)

        lowercase_							:List[Any]			     =							self.get_trainer()
        lowercase_							:Tuple			     =							trainer.callback_handler.callbacks[0]
        lowercase_							:Dict			     =							trainer.pop_callback(lowerCAmelCase_		)
        self.assertEqual(lowerCAmelCase_ ,				lowerCAmelCase_		)
        self.check_callbacks_equality(trainer.callback_handler.callbacks ,				lowerCAmelCase_		)

        trainer.add_callback(lowerCAmelCase_		)
        expected_callbacks.insert(0 ,				lowerCAmelCase_		)
        self.check_callbacks_equality(trainer.callback_handler.callbacks ,				lowerCAmelCase_		)




   def 		UpperCamelCase	(  self		):
        import warnings

        # XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
        warnings.simplefilter(action='''ignore''' ,				category=lowerCAmelCase_		)

        lowercase_							:List[str]			     =							self.get_trainer(callbacks=[MyTestTrainerCallback]		)
        trainer.train()
        lowercase_							:Optional[Any]			     =							trainer.callback_handler.callbacks[-2].events
        self.assertEqual(lowerCAmelCase_ ,				self.get_expected_events(lowerCAmelCase_		)		)

        # Independent log/save/eval
        lowercase_							:Optional[int]			     =							self.get_trainer(callbacks=[MyTestTrainerCallback] ,				logging_steps=5		)
        trainer.train()
        lowercase_							:Dict			     =							trainer.callback_handler.callbacks[-2].events
        self.assertEqual(lowerCAmelCase_ ,				self.get_expected_events(lowerCAmelCase_		)		)

        lowercase_							:Optional[Any]			     =							self.get_trainer(callbacks=[MyTestTrainerCallback] ,				save_steps=5		)
        trainer.train()
        lowercase_							:Tuple			     =							trainer.callback_handler.callbacks[-2].events
        self.assertEqual(lowerCAmelCase_ ,				self.get_expected_events(lowerCAmelCase_		)		)

        lowercase_							:List[str]			     =							self.get_trainer(callbacks=[MyTestTrainerCallback] ,				eval_steps=5 ,				evaluation_strategy='''steps'''		)
        trainer.train()
        lowercase_							:str			     =							trainer.callback_handler.callbacks[-2].events
        self.assertEqual(lowerCAmelCase_ ,				self.get_expected_events(lowerCAmelCase_		)		)

        lowercase_							:Tuple			     =							self.get_trainer(callbacks=[MyTestTrainerCallback] ,				evaluation_strategy='''epoch'''		)
        trainer.train()
        lowercase_							:List[Any]			     =							trainer.callback_handler.callbacks[-2].events
        self.assertEqual(lowerCAmelCase_ ,				self.get_expected_events(lowerCAmelCase_		)		)

        # A bit of everything
        lowercase_							:Dict			     =							self.get_trainer(
            callbacks=[MyTestTrainerCallback] ,				logging_steps=3 ,				save_steps=10 ,				eval_steps=5 ,				evaluation_strategy='''steps''' ,				)
        trainer.train()
        lowercase_							:Optional[Any]			     =							trainer.callback_handler.callbacks[-2].events
        self.assertEqual(lowerCAmelCase_ ,				self.get_expected_events(lowerCAmelCase_		)		)

        # warning should be emitted for duplicated callbacks
        with patch('''transformers.trainer_callback.logger.warning'''		) as warn_mock:
             lowercase_							:int			     =							self.get_trainer(
                 callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] ,				)
             assert str(lowerCAmelCase_		) in warn_mock.call_args[0][0]









"""simple docstring"""
import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE    ):





	"""simple docstring"""




	_A							:					Optional[int] 			=					(UniPCMultistepScheduler,)
	_A							:					Optional[Any] 			=					(("""num_inference_steps""", 25),)





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		A_		:    str						     = {
		    """num_train_timesteps""": 1000,
		    """beta_start""": 0.0001,
		    """beta_end""": 0.02,
		    """beta_schedule""": """linear""",
		    """solver_order""": 2,
		    """solver_type""": """bh2""",
		}

		config.update(**lowerCAmelCase_				)
		return config





	def  lowerCamelCase(self  ,  lowerCAmelCase_=0  ,  **lowerCAmelCase_				):
		A_		:    Tuple						     = dict(self.forward_default_kwargs				)
		A_		:    Any						     = kwargs.pop("""num_inference_steps"""  ,  lowerCAmelCase_				)
		A_		:    str						     = self.dummy_sample
		A_		:    Dict						     = 0.1 * sample
		A_		:    int						     = [residual + 0.2, residual + 0.15, residual + 0.10]

		for scheduler_class in self.scheduler_classes:
			A_		:    List[Any]						     = self.get_scheduler_config(**lowerCAmelCase_				)
			A_		:    Dict						     = scheduler_class(**lowerCAmelCase_				)
			scheduler.set_timesteps(lowerCAmelCase_				)
			# copy over dummy past residuals
			A_		:    Tuple						     = dummy_past_residuals[: scheduler.config.solver_order]

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

			A_     ,  A_		:    Optional[int]						     = sample, sample
			for t in range(lowerCAmelCase_  ,  time_step + scheduler.config.solver_order + 1				):
				A_		:    Any						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample
				A_		:    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  ,  lowerCAmelCase_=0  ,  **lowerCAmelCase_				):
		A_		:    Optional[int]						     = dict(self.forward_default_kwargs				)
		A_		:    int						     = kwargs.pop("""num_inference_steps"""  ,  lowerCAmelCase_				)
		A_		:    Optional[Any]						     = self.dummy_sample
		A_		:    List[Any]						     = 0.1 * sample
		A_		:    List[Any]						     = [residual + 0.2, residual + 0.15, residual + 0.10]

		for scheduler_class in self.scheduler_classes:
			A_		:    Dict						     = self.get_scheduler_config()
			A_		:    str						     = scheduler_class(**lowerCAmelCase_				)
			scheduler.set_timesteps(lowerCAmelCase_				)

			# copy over dummy past residuals (must be after setting timesteps)
			A_		:    Union[str, Any]						     = dummy_past_residuals[: scheduler.config.solver_order]

			with tempfile.TemporaryDirectory() as tmpdirname:
				scheduler.save_config(lowerCAmelCase_				)
				A_		:    Tuple						     = 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)
				A_		:    Optional[int]						     = dummy_past_residuals[: new_scheduler.config.solver_order]

			A_		:    Tuple						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample
			A_		:    str						     = 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  ,  lowerCAmelCase_=None  ,  **lowerCAmelCase_				):
		if scheduler is None:
			A_		:    int						     = self.scheduler_classes[0]
			A_		:    List[str]						     = self.get_scheduler_config(**lowerCAmelCase_				)
			A_		:    Any						     = scheduler_class(**lowerCAmelCase_				)

		A_		:    int						     = self.scheduler_classes[0]
		A_		:    str						     = self.get_scheduler_config(**lowerCAmelCase_				)
		A_		:    Union[str, Any]						     = scheduler_class(**lowerCAmelCase_				)

		A_		:    Optional[int]						     = 10
		A_		:    Optional[Any]						     = self.dummy_model()
		A_		:    Optional[Any]						     = self.dummy_sample_deter
		scheduler.set_timesteps(lowerCAmelCase_				)

		for i, t in enumerate(scheduler.timesteps				):
			A_		:    Optional[int]						     = model(lowerCAmelCase_  ,  lowerCAmelCase_				)
			A_		:    Optional[int]						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				).prev_sample

		return sample





	def  lowerCamelCase(self				):
		A_		:    Any						     = dict(self.forward_default_kwargs				)

		A_		:    Any						     = kwargs.pop("""num_inference_steps"""  ,  lowerCAmelCase_				)

		for scheduler_class in self.scheduler_classes:
			A_		:    Dict						     = self.get_scheduler_config()
			A_		:    List[Any]						     = scheduler_class(**lowerCAmelCase_				)

			A_		:    int						     = self.dummy_sample
			A_		:    str						     = 0.1 * sample

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

			# copy over dummy past residuals (must be done after set_timesteps)
			A_		:    List[Any]						     = [residual + 0.2, residual + 0.15, residual + 0.10]
			A_		:    Dict						     = dummy_past_residuals[: scheduler.config.solver_order]

			A_		:    str						     = scheduler.timesteps[5]
			A_		:    Any						     = scheduler.timesteps[6]

			A_		:    Tuple						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample
			A_		:    Dict						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_  ,  **lowerCAmelCase_				).prev_sample

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





	def  lowerCamelCase(self				):
		# make sure that iterating over schedulers with same config names gives same results
		# for defaults
		A_		:    Tuple						     = UniPCMultistepScheduler(**self.get_scheduler_config()				)
		A_		:    List[str]						     = self.full_loop(scheduler=lowerCAmelCase_				)
		A_		:    int						     = torch.mean(torch.abs(lowerCAmelCase_				)				)

		assert abs(result_mean.item() - 0.2464				) < 1e-3

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

		A_		:    Dict						     = self.full_loop(scheduler=lowerCAmelCase_				)
		A_		:    List[Any]						     = torch.mean(torch.abs(lowerCAmelCase_				)				)

		assert abs(result_mean.item() - 0.2464				) < 1e-3





	def  lowerCamelCase(self				):
		for timesteps in [25, 50, 100, 999, 1000]:
			self.check_over_configs(num_train_timesteps=lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		self.check_over_configs(thresholding=lowerCAmelCase_				)
		for order in [1, 2, 3]:
			for solver_type in ["bh1", "bh2"]:
				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_  ,  solver_order=lowerCAmelCase_  ,  solver_type=lowerCAmelCase_  ,  )





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





	def  lowerCamelCase(self				):
		for solver_type in ["bh1", "bh2"]:
			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_  ,  )
					A_		:    Union[str, Any]						     = self.full_loop(
					    solver_order=lowerCAmelCase_  ,  solver_type=lowerCAmelCase_  ,  prediction_type=lowerCAmelCase_  ,  )
					assert not torch.isnan(lowerCAmelCase_				).any(), "Samples have nan numbers"





	def  lowerCamelCase(self				):
		self.check_over_configs(lower_order_final=lowerCAmelCase_				)
		self.check_over_configs(lower_order_final=lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
			self.check_over_forward(num_inference_steps=lowerCAmelCase_  ,  time_step=0				)





	def  lowerCamelCase(self				):
		A_		:    Optional[int]						     = self.full_loop()
		A_		:    Optional[int]						     = torch.mean(torch.abs(lowerCAmelCase_				)				)

		assert abs(result_mean.item() - 0.2464				) < 1e-3





	def  lowerCamelCase(self				):
		A_		:    str						     = self.full_loop(prediction_type="""v_prediction"""				)
		A_		:    Optional[Any]						     = torch.mean(torch.abs(lowerCAmelCase_				)				)

		assert abs(result_mean.item() - 0.1014				) < 1e-3





	def  lowerCamelCase(self				):
		A_		:    List[str]						     = self.scheduler_classes[0]
		A_		:    str						     = self.get_scheduler_config(thresholding=lowerCAmelCase_  ,  dynamic_thresholding_ratio=0				)
		A_		:    Union[str, Any]						     = scheduler_class(**lowerCAmelCase_				)

		A_		:    Any						     = 10
		A_		:    int						     = self.dummy_model()
		A_		:    Optional[Any]						     = self.dummy_sample_deter.half()
		scheduler.set_timesteps(lowerCAmelCase_				)

		for i, t in enumerate(scheduler.timesteps				):
			A_		:    Dict						     = model(lowerCAmelCase_  ,  lowerCAmelCase_				)
			A_		:    Tuple						     = scheduler.step(lowerCAmelCase_  ,  lowerCAmelCase_  ,  lowerCAmelCase_				).prev_sample

		assert sample.dtype == torch.floataa





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		for scheduler_class in self.scheduler_classes:
			A_		:    Tuple						     = self.get_scheduler_config(**lowerCAmelCase_				)
			A_		:    int						     = scheduler_class(**lowerCAmelCase_				)

			scheduler.set_timesteps(scheduler.config.num_train_timesteps				)
			assert len(scheduler.timesteps.unique()				) == scheduler.num_inference_steps

