





'''simple docstring'''




import copy
import tempfile
import unittest

from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError

from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test




class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					@parameterized.expand([(None,), ("""foo.json""",)]						)
					def lowerCamelCase_			(						self				:			Tuple      ,						lowerCamelCase_				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Dict		=			GenerationConfig(
							    do_sample=lowerCamelCase_      ,						temperature=0.7      ,						length_penalty=1.0      ,						bad_words_ids=[[1, 2, 3], [4, 5]]      ,						)
							with tempfile.TemporaryDirectory() as tmp_dir:
									config.save_pretrained(lowerCamelCase_      ,						config_name=lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Tuple		=			GenerationConfig.from_pretrained(lowerCamelCase_      ,						config_name=lowerCamelCase_						)

							# Checks parameters that were specified
							self.assertEqual(loaded_config.do_sample      ,						lowerCamelCase_						)
							self.assertEqual(loaded_config.temperature      ,						0.7						)
							self.assertEqual(loaded_config.length_penalty      ,						1.0						)
							self.assertEqual(loaded_config.bad_words_ids      ,						[[1, 2, 3], [4, 5]]						)

							# Checks parameters that were not specified (defaults)
							self.assertEqual(loaded_config.top_k      ,						50						)
							self.assertEqual(loaded_config.max_length      ,						20						)
							self.assertEqual(loaded_config.max_time      ,						lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			AutoConfig.from_pretrained("""gpt2"""						)
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			GenerationConfig.from_model_config(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				str		=			GenerationConfig()

							# The generation config has loaded a few non-default parameters from the model config
							self.assertNotEqual(lowerCamelCase_      ,						lowerCamelCase_						)

							# One of those parameters is eos_token_id -- check if it matches
							self.assertNotEqual(generation_config_from_model.eos_token_id      ,						default_generation_config.eos_token_id						)
							self.assertEqual(generation_config_from_model.eos_token_id      ,						model_config.eos_token_id						)


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			GenerationConfig()
							SCREAMING_SNAKE_CASE :				List[Any]		=			{
							    """max_new_tokens""": 10_24,
							    """foo""": """bar""",
							}
							SCREAMING_SNAKE_CASE :				str		=			copy.deepcopy(lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				List[str]		=			generation_config.update(**lowerCamelCase_						)

							# update_kwargs was not modified (no side effects)
							self.assertEqual(lowerCamelCase_      ,						lowerCamelCase_						)

							# update_kwargs was used to update the config on valid attributes
							self.assertEqual(generation_config.max_new_tokens      ,						10_24						)

							# `.update()` returns a dictionary of unused kwargs
							self.assertEqual(lowerCamelCase_      ,						{"""foo""": """bar"""}						)


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

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			GenerationConfig()
							SCREAMING_SNAKE_CASE :				List[Any]		=			"""bar"""

							with tempfile.TemporaryDirectory("""test-generation-config"""						) as tmp_dir:
									generation_config.save_pretrained(lowerCamelCase_						)

									SCREAMING_SNAKE_CASE :				str		=			GenerationConfig.from_pretrained(lowerCamelCase_						)
							# update_kwargs was used to update the config on valid attributes
							self.assertEqual(new_config.foo      ,						"""bar"""						)

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			GenerationConfig.from_model_config(lowerCamelCase_						)
							assert not hasattr(lowerCamelCase_      ,						"""foo"""						)  # no new kwargs should be initialized if from config



					def lowerCamelCase_			(						self				:			int						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				int		=			GenerationConfig()
							self.assertEqual(default_config.temperature      ,						1.0						)
							self.assertEqual(default_config.do_sample      ,						lowerCamelCase_						)
							self.assertEqual(default_config.num_beams      ,						1						)

							SCREAMING_SNAKE_CASE :				Optional[Any]		=			GenerationConfig(
							    do_sample=lowerCamelCase_      ,						temperature=0.7      ,						length_penalty=1.0      ,						bad_words_ids=[[1, 2, 3], [4, 5]]      ,						)
							self.assertEqual(config.temperature      ,						0.7						)
							self.assertEqual(config.do_sample      ,						lowerCamelCase_						)
							self.assertEqual(config.num_beams      ,						1						)

							with tempfile.TemporaryDirectory() as tmp_dir:
									config.save_pretrained(lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				int		=			GenerationConfig.from_pretrained(lowerCamelCase_      ,						temperature=1.0						)

							self.assertEqual(loaded_config.temperature      ,						1.0						)
							self.assertEqual(loaded_config.do_sample      ,						lowerCamelCase_						)
							self.assertEqual(loaded_config.num_beams      ,						1						)  # default value







@is_staging_test
class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					@classmethod
					def lowerCamelCase_			(						cls				:			List[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				List[Any]		=			TOKEN
							HfFolder.save_token(lowerCamelCase_						)


					@classmethod
					def lowerCamelCase_			(						cls				:			List[str]						):

							'''simple docstring'''



							try:
									delete_repo(token=cls._token      ,						repo_id="""test-generation-config"""						)
							except HTTPError:
									pass

							try:
									delete_repo(token=cls._token      ,						repo_id="""valid_org/test-generation-config-org"""						)
							except HTTPError:
									pass


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

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			GenerationConfig(
							    do_sample=lowerCamelCase_      ,						temperature=0.7      ,						length_penalty=1.0      ,						)
							config.push_to_hub("""test-generation-config"""      ,						use_auth_token=self._token						)

							SCREAMING_SNAKE_CASE :				Any		=			GenerationConfig.from_pretrained(f'''{USER}/test-generation-config'''						)
							for k, v in config.to_dict().items():
									if k != "transformers_version":
											self.assertEqual(lowerCamelCase_      ,						getattr(lowerCamelCase_      ,						lowerCamelCase_						)						)

        # Reset repo
							delete_repo(token=self._token      ,						repo_id="""test-generation-config"""						)

							# Push to hub via save_pretrained
							with tempfile.TemporaryDirectory() as tmp_dir:
									config.save_pretrained(
									    lowerCamelCase_      ,						repo_id="""test-generation-config"""      ,						push_to_hub=lowerCamelCase_      ,						use_auth_token=self._token						)

							SCREAMING_SNAKE_CASE :				Union[str, Any]		=			GenerationConfig.from_pretrained(f'''{USER}/test-generation-config'''						)
							for k, v in config.to_dict().items():
									if k != "transformers_version":
											self.assertEqual(lowerCamelCase_      ,						getattr(lowerCamelCase_      ,						lowerCamelCase_						)						)



					def lowerCamelCase_			(						self				:			Optional[Any]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				str		=			GenerationConfig(
							    do_sample=lowerCamelCase_      ,						temperature=0.7      ,						length_penalty=1.0      ,						)
							config.push_to_hub("""valid_org/test-generation-config-org"""      ,						use_auth_token=self._token						)

							SCREAMING_SNAKE_CASE :				List[Any]		=			GenerationConfig.from_pretrained("""valid_org/test-generation-config-org"""						)
							for k, v in config.to_dict().items():
									if k != "transformers_version":
											self.assertEqual(lowerCamelCase_      ,						getattr(lowerCamelCase_      ,						lowerCamelCase_						)						)

        # Reset repo
							delete_repo(token=self._token      ,						repo_id="""valid_org/test-generation-config-org"""						)

							# Push to hub via save_pretrained
							with tempfile.TemporaryDirectory() as tmp_dir:
									config.save_pretrained(
									    lowerCamelCase_      ,						repo_id="""valid_org/test-generation-config-org"""      ,						push_to_hub=lowerCamelCase_      ,						use_auth_token=self._token						)

							SCREAMING_SNAKE_CASE :				str		=			GenerationConfig.from_pretrained("""valid_org/test-generation-config-org"""						)
							for k, v in config.to_dict().items():
									if k != "transformers_version":
											self.assertEqual(lowerCamelCase_      ,						getattr(lowerCamelCase_      ,						lowerCamelCase_						)						)










'''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



