






import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class 		lowerCAmelCase_				(  UpperCamelCase ):
     __lowerCamelCase       :    Dict 	=     (DDIMScheduler,)
     __lowerCamelCase       :    Tuple 	=     (('eta', 0.0), ('num_inference_steps', 50))





     def 						lowerCamelCase							(	self							, **_UpperCAmelCase  )		->				Dict:
        lowerCamelCase									={
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """clip_sample""": True,
        }

        config.update(**_UpperCAmelCase  )
        return config





     def 						lowerCamelCase							(	self							, **_UpperCAmelCase  )		->				Dict:
        lowerCamelCase									=self.scheduler_classes[0]
        lowerCamelCase									=self.get_scheduler_config(**_UpperCAmelCase  )
        lowerCamelCase									=scheduler_class(**_UpperCAmelCase  )

        lowerCamelCase				,       lowerCamelCase									=10, 0.0

        lowerCamelCase									=self.dummy_model()
        lowerCamelCase									=self.dummy_sample_deter

        scheduler.set_timesteps(_UpperCAmelCase  )

        for t in scheduler.timesteps:
           lowerCamelCase									=model(_UpperCAmelCase							, _UpperCAmelCase  )
           lowerCamelCase									=scheduler.step(_UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase							, _UpperCAmelCase  ).prev_sample

        return sample





     def 						lowerCamelCase							(	self  )		->				Any:






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





     def 						lowerCamelCase							(	self  )		->				str:






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

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





     def 						lowerCamelCase							(	self  )		->				List[Any]:






        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=_UpperCAmelCase							, beta_end=_UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:






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





     def 						lowerCamelCase							(	self  )		->				str:






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





     def 						lowerCamelCase							(	self  )		->				Optional[int]:






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





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






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





     def 						lowerCamelCase							(	self  )		->				List[str]:






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





     def 						lowerCamelCase							(	self  )		->				int:
        self.check_over_configs(thresholding=_UpperCAmelCase  )
        for threshold in [0.5, 1.0, 2.0]:
           for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(
                  thresholding=_UpperCAmelCase							, prediction_type=_UpperCAmelCase							, sample_max_value=_UpperCAmelCase							, )





     def 						lowerCamelCase							(	self  )		->				List[str]:






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





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:






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





     def 						lowerCamelCase							(	self  )		->				Any:






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





     def 						lowerCamelCase							(	self  )		->				str:
        lowerCamelCase									=self.scheduler_classes[0]
        lowerCamelCase									=self.get_scheduler_config()
        lowerCamelCase									=scheduler_class(**_UpperCAmelCase  )

        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 						lowerCamelCase							(	self  )		->				Optional[Any]:
        lowerCamelCase									=self.full_loop()

        lowerCamelCase									=torch.sum(torch.abs(_UpperCAmelCase  )  )
        lowerCamelCase									=torch.mean(torch.abs(_UpperCAmelCase  )  )

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





     def 						lowerCamelCase							(	self  )		->				Tuple:
        lowerCamelCase									=self.full_loop(prediction_type="""v_prediction"""  )

        lowerCamelCase									=torch.sum(torch.abs(_UpperCAmelCase  )  )
        lowerCamelCase									=torch.mean(torch.abs(_UpperCAmelCase  )  )

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





     def 						lowerCamelCase							(	self  )		->				str:
        lowerCamelCase									=self.full_loop(set_alpha_to_one=_UpperCAmelCase							, beta_start=0.01  )
        lowerCamelCase									=torch.sum(torch.abs(_UpperCAmelCase  )  )
        lowerCamelCase									=torch.mean(torch.abs(_UpperCAmelCase  )  )

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





     def 						lowerCamelCase							(	self  )		->				Dict:
        lowerCamelCase									=self.full_loop(set_alpha_to_one=_UpperCAmelCase							, beta_start=0.01  )
        lowerCamelCase									=torch.sum(torch.abs(_UpperCAmelCase  )  )
        lowerCamelCase									=torch.mean(torch.abs(_UpperCAmelCase  )  )

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

"""simple docstring"""




__lowerCAmelCase							:  Any 				=		{
    '''0''': 1,
    '''1''': 1,
    '''2''': 2,
    '''3''': 6,
    '''4''': 24,
    '''5''': 120,
    '''6''': 720,
    '''7''': 5040,
    '''8''': 40320,
    '''9''': 362880,
}

__lowerCAmelCase							:  List[Any] 				=		{145: 145}

__lowerCAmelCase							:  Any 				=		{
    145: 0,
    169: 3,
    36301: 3,
    1454: 3,
    871: 2,
    45361: 2,
    872: 2,
}

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if n in CACHE_SUM_DIGIT_FACTORIALS:
				return CACHE_SUM_DIGIT_FACTORIALS[n]
		a__   		=     sum(DIGIT_FACTORIALS[let] for let in str(UpperCamelCase    )    )
		a__   		=     ret
		return ret

def __snake_case (      UpperCamelCase						,					UpperCamelCase = None    )       ->    int:


		"""simple docstring"""





		a__   		=     previous or set()
		if n in CHAIN_LENGTH_CACHE:
				return CHAIN_LENGTH_CACHE[n]
		a__   		=     sum_digit_factorials(UpperCamelCase    )
		if next_number in previous:
				a__   		=     0
				return 0
		else:
				previous.add(UpperCamelCase    )
				a__   		=     1 + chain_length(UpperCamelCase						,					UpperCamelCase    )
				a__   		=     ret
				return ret

def __snake_case (      UpperCamelCase = 60						,					UpperCamelCase = 1_000_000    )       ->    int:


		"""simple docstring"""





		return sum(1 for i in range(1						,					UpperCamelCase    ) if chain_length(UpperCamelCase    ) == num_terms    )


if __name__ == "__main__":
					print(f"{solution() = }")




