


from ..utils import DummyObject, requires_backends
class        _lowercase      (			metaclass=A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			str						=		['''flax''', '''transformers''']


				def __init__(       self			:Tuple			,			*lowerCAmelCase__			:Optional[Any]			,			**lowerCAmelCase__			:Optional[Any]	)		->			Dict:
										requires_backends(self			,			['''flax''', '''transformers''']	)


				@classmethod
				def 		__magic_name__(       cls			:List[Any]			,			*lowerCAmelCase__			:Any			,			**lowerCAmelCase__			:Union[str, Any]	)		->			Any:
										requires_backends(cls			,			['''flax''', '''transformers''']	)



				@classmethod
				def 		__magic_name__(       cls			:Union[str, Any]			,			*lowerCAmelCase__			:List[Any]			,			**lowerCAmelCase__			:str	)		->			Any:
										requires_backends(cls			,			['''flax''', '''transformers''']	)




class        _lowercase      (			metaclass=A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Optional[int]						=		['''flax''', '''transformers''']


				def __init__(       self			:List[str]			,			*lowerCAmelCase__			:Optional[Any]			,			**lowerCAmelCase__			:List[Any]	)		->			Optional[int]:
										requires_backends(self			,			['''flax''', '''transformers''']	)


				@classmethod
				def 		__magic_name__(       cls			:Any			,			*lowerCAmelCase__			:str			,			**lowerCAmelCase__			:str	)		->			Dict:
										requires_backends(cls			,			['''flax''', '''transformers''']	)



				@classmethod
				def 		__magic_name__(       cls			:str			,			*lowerCAmelCase__			:Optional[Any]			,			**lowerCAmelCase__			:List[str]	)		->			Tuple:
										requires_backends(cls			,			['''flax''', '''transformers''']	)




class        _lowercase      (			metaclass=A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Dict						=		['''flax''', '''transformers''']


				def __init__(       self			:Any			,			*lowerCAmelCase__			:List[str]			,			**lowerCAmelCase__			:List[str]	)		->			int:
										requires_backends(self			,			['''flax''', '''transformers''']	)


				@classmethod
				def 		__magic_name__(       cls			:Optional[Any]			,			*lowerCAmelCase__			:str			,			**lowerCAmelCase__			:int	)		->			Optional[Any]:
										requires_backends(cls			,			['''flax''', '''transformers''']	)



				@classmethod
				def 		__magic_name__(       cls			:Dict			,			*lowerCAmelCase__			:List[Any]			,			**lowerCAmelCase__			:Tuple	)		->			Dict:
										requires_backends(cls			,			['''flax''', '''transformers''']	)




class        _lowercase      (			metaclass=A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Union[str, Any]						=		['''flax''', '''transformers''']


				def __init__(       self			:Dict			,			*lowerCAmelCase__			:Dict			,			**lowerCAmelCase__			:List[str]	)		->			Tuple:
										requires_backends(self			,			['''flax''', '''transformers''']	)


				@classmethod
				def 		__magic_name__(       cls			:Tuple			,			*lowerCAmelCase__			:int			,			**lowerCAmelCase__			:int	)		->			List[Any]:
										requires_backends(cls			,			['''flax''', '''transformers''']	)



				@classmethod
				def 		__magic_name__(       cls			:Tuple			,			*lowerCAmelCase__			:Optional[Any]			,			**lowerCAmelCase__			:List[str]	)		->			str:
										requires_backends(cls			,			['''flax''', '''transformers''']	)





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: List[str]					=      (DDIMParallelScheduler,)
    __lowerCamelCase			: int					=      (("eta", 0.0), ("num_inference_steps", 50))






    def 			_lowerCAmelCase					(       self,		**lowerCamelCase__		):
        A :       int          =   {
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """clip_sample""": True,
        }

        config.update(**lowerCamelCase__		)
        return config






    def 			_lowerCAmelCase					(       self,		**lowerCamelCase__		):
        A :       Dict          =   self.scheduler_classes[0]
        A :       str          =   self.get_scheduler_config(**lowerCamelCase__		)
        A :       Tuple          =   scheduler_class(**lowerCamelCase__		)

        A     ,							A :       Tuple          =   10, 0.0

        A :       Optional[int]          =   self.dummy_model()
        A :       List[str]          =   self.dummy_sample_deter

        scheduler.set_timesteps(lowerCamelCase__		)

        for t in scheduler.timesteps:
            A :       Optional[int]          =   model(lowerCamelCase__,		lowerCamelCase__		)
            A :       str          =   scheduler.step(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		).prev_sample

        return sample






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






    def 			_lowerCAmelCase					(       self		):
        for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=lowerCamelCase__		)

        A :       Tuple          =   self.scheduler_classes[0]
        A :       Optional[Any]          =   self.get_scheduler_config(steps_offset=1		)
        A :       List[Any]          =   scheduler_class(**lowerCamelCase__		)
        scheduler.set_timesteps(5		)
        assert torch.equal(scheduler.timesteps,		torch.LongTensor([801, 601, 401, 201, 1]		)		)






    def 			_lowerCAmelCase					(       self		):
        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=lowerCamelCase__,		beta_end=lowerCamelCase__		)






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






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






    def 			_lowerCAmelCase					(       self		):
        for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=lowerCamelCase__		)






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






    def 			_lowerCAmelCase					(       self		):
        for rescale_betas_zero_snr in [True, False]:
            self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase__		)






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






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






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






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






    def 			_lowerCAmelCase					(       self		):
        A :       List[str]          =   self.scheduler_classes[0]
        A :       List[str]          =   self.get_scheduler_config()
        A :       Dict          =   scheduler_class(**lowerCamelCase__		)

        assert torch.sum(torch.abs(scheduler._get_variance(0,		0		) - 0.0		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(420,		400		) - 0.1_4771		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(980,		960		) - 0.3_2460		)		) < 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		) - 0.0_0979		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999,		998		) - 0.02		)		) < 1e-5






    def 			_lowerCAmelCase					(       self		):
        A :       int          =   self.scheduler_classes[0]
        A :       Any          =   self.get_scheduler_config()
        A :       Union[str, Any]          =   scheduler_class(**lowerCamelCase__		)

        A     ,							A :       List[str]          =   10, 0.0
        scheduler.set_timesteps(lowerCamelCase__		)

        A :       Any          =   self.dummy_model()
        A :       Dict          =   self.dummy_sample_deter
        A :       Dict          =   self.dummy_sample_deter + 0.1
        A :       Tuple          =   self.dummy_sample_deter - 0.1

        A :       Tuple          =   samplea.shape[0]
        A :       List[Any]          =   torch.stack([samplea, samplea, samplea],		dim=0		)
        A :       str          =   torch.arange(lowerCamelCase__		)[0:3, None].repeat(1,		lowerCamelCase__		)

        A :       Any          =   model(samples.flatten(0,		1		),		timesteps.flatten(0,		1		)		)
        A :       Any          =   scheduler.batch_step_no_noise(lowerCamelCase__,		timesteps.flatten(0,		1		),		samples.flatten(0,		1		),		lowerCamelCase__		)

        A :       List[Any]          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       Optional[Any]          =   torch.mean(torch.abs(lowerCamelCase__		)		)

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






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   self.full_loop()

        A :       str          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       Optional[Any]          =   torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_sum.item() - 172.0067		) < 1e-2
        assert abs(result_mean.item() - 0.22_3967		) < 1e-3






    def 			_lowerCAmelCase					(       self		):
        A :       str          =   self.full_loop(prediction_type="""v_prediction"""		)

        A :       Any          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       Optional[Any]          =   torch.mean(torch.abs(lowerCamelCase__		)		)

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






    def 			_lowerCAmelCase					(       self		):
        # We specify different beta, so that the first alpha is 0.99
        A :       Any          =   self.full_loop(set_alpha_to_one=lowerCamelCase__,		beta_start=0.01		)
        A :       Dict          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       str          =   torch.mean(torch.abs(lowerCamelCase__		)		)

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






    def 			_lowerCAmelCase					(       self		):
        # We specify different beta, so that the first alpha is 0.99
        A :       int          =   self.full_loop(set_alpha_to_one=lowerCamelCase__,		beta_start=0.01		)
        A :       List[Any]          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       List[Any]          =   torch.mean(torch.abs(lowerCamelCase__		)		)

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


