



import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: int					=      (EulerDiscreteScheduler,)
    __lowerCamelCase			: int					=      10






    def 			_lowerCAmelCase					(       self,		**lowerCamelCase__		):
        A :       Optional[int]          =   {
            """num_train_timesteps""": 1100,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
        }

        config.update(**lowerCamelCase__		)
        return config






    def 			_lowerCAmelCase					(       self		):
        for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001],		[0.0002, 0.002, 0.02]		):
            self.check_over_configs(beta_start=lowerCamelCase__,		beta_end=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for schedule in ["linear", "scaled_linear"]:
            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		):
        A :       List[Any]          =   self.scheduler_classes[0]
        A :       List[Any]          =   self.get_scheduler_config()
        A :       List[Any]          =   scheduler_class(**lowerCamelCase__		)

        scheduler.set_timesteps(self.num_inference_steps		)

        A :       List[str]          =   torch.manual_seed(0		)

        A :       List[Any]          =   self.dummy_model()
        A :       Optional[Any]          =   self.dummy_sample_deter * scheduler.init_noise_sigma
        A :       str          =   sample.to(lowerCamelCase__		)

        for i, t in enumerate(scheduler.timesteps		):
            A :       List[str]          =   scheduler.scale_model_input(lowerCamelCase__,		lowerCamelCase__		)

            A :       List[str]          =   model(lowerCamelCase__,		lowerCamelCase__		)

            A :       Any          =   scheduler.step(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		generator=lowerCamelCase__		)
            A :       List[str]          =   output.prev_sample

        A :       List[str]          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       int          =   torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_sum.item() - 10.0807		) < 1e-2
        assert abs(result_mean.item() - 0.0131		) < 1e-3






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

        scheduler.set_timesteps(self.num_inference_steps		)

        A :       str          =   torch.manual_seed(0		)

        A :       Tuple          =   self.dummy_model()
        A :       List[str]          =   self.dummy_sample_deter * scheduler.init_noise_sigma
        A :       Dict          =   sample.to(lowerCamelCase__		)

        for i, t in enumerate(scheduler.timesteps		):
            A :       List[str]          =   scheduler.scale_model_input(lowerCamelCase__,		lowerCamelCase__		)

            A :       Any          =   model(lowerCamelCase__,		lowerCamelCase__		)

            A :       Union[str, Any]          =   scheduler.step(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		generator=lowerCamelCase__		)
            A :       Optional[Any]          =   output.prev_sample

        A :       Tuple          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       Any          =   torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_sum.item() - 0.0002		) < 1e-2
        assert abs(result_mean.item() - 2.2676e-06		) < 1e-3






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

        scheduler.set_timesteps(self.num_inference_steps,		device=lowerCamelCase__		)

        A :       List[str]          =   torch.manual_seed(0		)

        A :       List[Any]          =   self.dummy_model()
        A :       Optional[Any]          =   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        A :       Union[str, Any]          =   sample.to(lowerCamelCase__		)

        for t in scheduler.timesteps:
            A :       Tuple          =   scheduler.scale_model_input(lowerCamelCase__,		lowerCamelCase__		)

            A :       Dict          =   model(lowerCamelCase__,		lowerCamelCase__		)

            A :       Tuple          =   scheduler.step(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		generator=lowerCamelCase__		)
            A :       int          =   output.prev_sample

        A :       List[str]          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       int          =   torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_sum.item() - 10.0807		) < 1e-2
        assert abs(result_mean.item() - 0.0131		) < 1e-3






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

        scheduler.set_timesteps(self.num_inference_steps,		device=lowerCamelCase__		)

        A :       Optional[int]          =   torch.manual_seed(0		)

        A :       str          =   self.dummy_model()
        A :       Dict          =   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        A :       int          =   sample.to(lowerCamelCase__		)

        for t in scheduler.timesteps:
            A :       Dict          =   scheduler.scale_model_input(lowerCamelCase__,		lowerCamelCase__		)

            A :       int          =   model(lowerCamelCase__,		lowerCamelCase__		)

            A :       Union[str, Any]          =   scheduler.step(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		generator=lowerCamelCase__		)
            A :       Optional[int]          =   output.prev_sample

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

        assert abs(result_sum.item() - 124.52_2994_9951_1719		) < 1e-2
        assert abs(result_mean.item() - 0.1_6213_9326_3339_9963		) < 1e-3







def 	__UpperCamelCase	(  _lowerCAmelCase = 100_0000		)       ->  int:



    """simple docstring"""






    A :       str          =   limit + 1
    A :       Tuple          =   [0] * limit
    for first_term in range(1      ,     _lowerCAmelCase		):
        for n in range(_lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		):
            A :       Any          =   first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    A :       Optional[int]          =   sum(1 for x in frequency[1:limit] if x == 10		)

    return count


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


