




import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class     A(		_UpperCamelCase  ):



 '''simple docstring'''


 UpperCamelCase		  =		(DDIMParallelScheduler,)
 UpperCamelCase		  =		(("eta", 0.0), ("num_inference_steps", 50))

 def 							a__					(							self   :    str		,				**A_   :    Optional[int]    )  ->      List[Any]:

       """simple docstring"""






       lowerCamelCase_							=       {
           "num_train_timesteps": 1000,
           "beta_start": 0.0001,
           "beta_end": 0.02,
           "beta_schedule": "linear",
           "clip_sample": True,
       }

       config.update(**a_    )
       return config

 def 							a__					(							self   :    Optional[Any]		,				**A_   :    Union[str, Any]    )  ->      Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=       self.scheduler_classes[0]
       lowerCamelCase_							=       self.get_scheduler_config(**a_    )
       lowerCamelCase_							=       scheduler_class(**a_    )

       lowerCamelCase_							=       10, 0.0

       lowerCamelCase_							=       self.dummy_model()
       lowerCamelCase_							=       self.dummy_sample_deter

       scheduler.set_timesteps(a_    )

       for t in scheduler.timesteps:
             lowerCamelCase_							=       model(a_		,				a_    )
             lowerCamelCase_							=       scheduler.step(a_		,				a_		,				a_		,				a_    ).prev_sample

       return sample

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

       """simple docstring"""






       for timesteps in [100, 500, 1000]:
             self.check_over_configs(num_train_timesteps=a_    )

 def 							a__					(							self   :    List[str]    )  ->      List[Any]:

       """simple docstring"""






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

       lowerCamelCase_							=       self.scheduler_classes[0]
       lowerCamelCase_							=       self.get_scheduler_config(steps_offset=1    )
       lowerCamelCase_							=       scheduler_class(**a_    )
       scheduler.set_timesteps(5    )
       assert torch.equal(scheduler.timesteps		,				torch.LongTensor([801, 601, 401, 201, 1]    )    )

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

       """simple docstring"""






       for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]		,				[0.002, 0.02, 0.2, 2]    ):
             self.check_over_configs(beta_start=a_		,				beta_end=a_    )

 def 							a__					(							self   :    str    )  ->      Dict:

       """simple docstring"""






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

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

       """simple docstring"""






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

 def 							a__					(							self   :    Any    )  ->      Tuple:

       """simple docstring"""






       for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=a_    )

 def 							a__					(							self   :    Dict    )  ->      Optional[int]:

       """simple docstring"""






       for timestep_spacing in ["trailing", "leading"]:
             self.check_over_configs(timestep_spacing=a_    )

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

       """simple docstring"""






       for rescale_betas_zero_snr in [True, False]:
             self.check_over_configs(rescale_betas_zero_snr=a_    )

 def 							a__					(							self   :    str    )  ->      Any:

       """simple docstring"""






       self.check_over_configs(thresholding=a_    )
       for threshold in [0.5, 1.0, 2.0]:
             for prediction_type in ["epsilon", "v_prediction"]:
                   self.check_over_configs(
                       thresholding=a_		,				prediction_type=a_		,				sample_max_value=a_		,				)

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

       """simple docstring"""






       for t in [1, 10, 49]:
             self.check_over_forward(time_step=a_    )

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

       """simple docstring"""






       for t, num_inference_steps in zip([1, 10, 50]		,				[10, 50, 500]    ):
             self.check_over_forward(time_step=a_		,				num_inference_steps=a_    )

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

       """simple docstring"""






       for t, eta in zip([1, 10, 49]		,				[0.0, 0.5, 1.0]    ):
             self.check_over_forward(time_step=a_		,				eta=a_    )

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

       """simple docstring"""






       lowerCamelCase_							=       self.scheduler_classes[0]
       lowerCamelCase_							=       self.get_scheduler_config()
       lowerCamelCase_							=       scheduler_class(**a_    )

       assert torch.sum(torch.abs(scheduler._get_variance(0		,				0    ) - 0.0    )    ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(420		,				400    ) - 0.14771    )    ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(980		,				960    ) - 0.32460    )    ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(0		,				0    ) - 0.0    )    ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(487		,				486    ) - 0.00979    )    ) < 1E-5
       assert torch.sum(torch.abs(scheduler._get_variance(999		,				998    ) - 0.02    )    ) < 1E-5

 def 							a__					(							self   :    List[str]    )  ->      Tuple:

       """simple docstring"""






       lowerCamelCase_							=       self.scheduler_classes[0]
       lowerCamelCase_							=       self.get_scheduler_config()
       lowerCamelCase_							=       scheduler_class(**a_    )

       lowerCamelCase_							=       10, 0.0
       scheduler.set_timesteps(a_    )

       lowerCamelCase_							=       self.dummy_model()
       lowerCamelCase_							=       self.dummy_sample_deter
       lowerCamelCase_							=       self.dummy_sample_deter + 0.1
       lowerCamelCase_							=       self.dummy_sample_deter - 0.1

       lowerCamelCase_							=       samplea.shape[0]
       lowerCamelCase_							=       torch.stack([samplea, samplea, samplea]		,				dim=0    )
       lowerCamelCase_							=       torch.arange(a_    )[0:3, None].repeat(1		,				a_    )

       lowerCamelCase_							=       model(samples.flatten(0		,				1    )		,				timesteps.flatten(0		,				1    )    )
       lowerCamelCase_							=       scheduler.batch_step_no_noise(a_		,				timesteps.flatten(0		,				1    )		,				samples.flatten(0		,				1    )		,				a_    )

       lowerCamelCase_							=       torch.sum(torch.abs(a_    )    )
       lowerCamelCase_							=       torch.mean(torch.abs(a_    )    )

       assert abs(result_sum.item() - 1147.7904    ) < 1E-2
       assert abs(result_mean.item() - 0.4982    ) < 1E-3

 def 							a__					(							self   :    Dict    )  ->      Tuple:

       """simple docstring"""






       lowerCamelCase_							=       self.full_loop()

       lowerCamelCase_							=       torch.sum(torch.abs(a_    )    )
       lowerCamelCase_							=       torch.mean(torch.abs(a_    )    )

       assert abs(result_sum.item() - 172.0067    ) < 1E-2
       assert abs(result_mean.item() - 0.223967    ) < 1E-3

 def 							a__					(							self   :    Tuple    )  ->      Dict:

       """simple docstring"""






       lowerCamelCase_							=       self.full_loop(prediction_type='v_prediction'    )

       lowerCamelCase_							=       torch.sum(torch.abs(a_    )    )
       lowerCamelCase_							=       torch.mean(torch.abs(a_    )    )

       assert abs(result_sum.item() - 52.5302    ) < 1E-2
       assert abs(result_mean.item() - 0.0684    ) < 1E-3

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

       """simple docstring"""






       lowerCamelCase_							=       self.full_loop(set_alpha_to_one=a_		,				beta_start=0.01    )
       lowerCamelCase_							=       torch.sum(torch.abs(a_    )    )
       lowerCamelCase_							=       torch.mean(torch.abs(a_    )    )

       assert abs(result_sum.item() - 149.8295    ) < 1E-2
       assert abs(result_mean.item() - 0.1951    ) < 1E-3





 def 							a__					(							self   :    str    )  ->      List[Any]:

       """simple docstring"""






       lowerCamelCase_							=       self.full_loop(set_alpha_to_one=a_		,				beta_start=0.01    )
       lowerCamelCase_							=       torch.sum(torch.abs(a_    )    )
       lowerCamelCase_							=       torch.mean(torch.abs(a_    )    )

       assert abs(result_sum.item() - 149.0784    ) < 1E-2
       assert abs(result_mean.item() - 0.1941    ) < 1E-3










"""simple docstring"""






import pytest

import datasets


# Import fixture modules as plugins
__UpperCAmelCase						=			['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']






def      lowercase__ (					lowerCAmelCase__   :			Optional[Any]							,			lowerCAmelCase__   :			str			)    ->			str:



			'''simple docstring'''
			# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
			for item in items:
						if any(marker in item.keywords for marker in ["integration", "unit"]			):
									continue
						item.add_marker(pytest.mark.unit			)






def      lowercase__ (					lowerCAmelCase__   :			List[Any]			)    ->			Any:



			'''simple docstring'''
			config.addinivalue_line("markers"							,			"torchaudio_latest: mark test to run with torchaudio>=0.12"			)






@pytest.fixture(autouse=lowerCAmelCase__			)
def      lowercase__ (					lowerCAmelCase__   :			Optional[Any]							,			lowerCAmelCase__   :			Dict			)    ->			List[str]:



			'''simple docstring'''
			# test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
			a__  :	Dict		 =			tmp_path_factory.getbasetemp() / "cache"
			a__  :	int		 =			test_hf_cache_home / "datasets"
			a__  :	Tuple		 =			test_hf_cache_home / "metrics"
			a__  :	Any		 =			test_hf_cache_home / "modules"
			monkeypatch.setattr("datasets.config.HF_DATASETS_CACHE"							,			str(lowerCAmelCase__			)			)
			monkeypatch.setattr("datasets.config.HF_METRICS_CACHE"							,			str(lowerCAmelCase__			)			)
			monkeypatch.setattr("datasets.config.HF_MODULES_CACHE"							,			str(lowerCAmelCase__			)			)
			a__  :	Optional[int]		 =			test_hf_datasets_cache / "downloads"
			monkeypatch.setattr("datasets.config.DOWNLOADED_DATASETS_PATH"							,			str(lowerCAmelCase__			)			)
			a__  :	Tuple		 =			test_hf_datasets_cache / "downloads" / "extracted"
			monkeypatch.setattr("datasets.config.EXTRACTED_DATASETS_PATH"							,			str(lowerCAmelCase__			)			)






@pytest.fixture(autouse=lowerCAmelCase__							,			scope="session"			)
def      lowercase__ (					)    ->			Union[str, Any]:



			'''simple docstring'''
			datasets.disable_progress_bar()






@pytest.fixture(autouse=lowerCAmelCase__			)
def      lowercase__ (					lowerCAmelCase__   :			Union[str, Any]			)    ->			Optional[Any]:



			'''simple docstring'''
			# don't take tests into account when counting downloads
			monkeypatch.setattr("datasets.config.HF_UPDATE_DOWNLOAD_COUNTS"							,			lowerCAmelCase__			)






@pytest.fixture
def      lowercase__ (					lowerCAmelCase__   :			Optional[Any]			)    ->			str:



			'''simple docstring'''
			# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
			# To be removed once SQLAlchemy 2.0 supported
			monkeypatch.setattr("sqlalchemy.util.deprecations.SILENCE_UBER_WARNING"							,			lowerCAmelCase__			)