






'''simple docstring'''


import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 							lowerCAmelCase					(							lowerCamelCase__   ):







   """simple docstring"""







   _A						=   (HeunDiscreteScheduler,)
   _A						=   10



   def    __magic_name__    ( self   ,   **_A			)    ->   Optional[int]:
    __a    :	List[Any]									=						{
        'num_train_timesteps': 1100,
        'beta_start': 0.0_001,
        'beta_end': 0.02,
        'beta_schedule': 'linear',
    }

    config.update(**_A			)
    return config



   def    __magic_name__    ( self			)    ->   Union[str, Any]:
    for timesteps in [10, 50, 100, 1000]:
     self.check_over_configs(num_train_timesteps=_A			)



   def    __magic_name__    ( self			)    ->   Union[str, Any]:
    for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]   ,   [0.0_002, 0.002, 0.02]			):
     self.check_over_configs(beta_start=_A   ,   beta_end=_A			)



   def    __magic_name__    ( self			)    ->   Optional[int]:
    for schedule in ["linear", "scaled_linear", "exp"]:
     self.check_over_configs(beta_schedule=_A			)



   def    __magic_name__    ( self			)    ->   Union[str, Any]:
    for clip_sample_range in [1.0, 2.0, 3.0]:
     self.check_over_configs(clip_sample_range=_A   ,   clip_sample=_A			)



   def    __magic_name__    ( self			)    ->   Optional[int]:
    for prediction_type in ["epsilon", "v_prediction", "sample"]:
     self.check_over_configs(prediction_type=_A			)



   def    __magic_name__    ( self			)    ->   Union[str, Any]:
    __a    :	List[Any]									=						self.scheduler_classes[0]
    __a    :	Any									=						self.get_scheduler_config()
    __a    :	Optional[int]									=						scheduler_class(**_A			)

    scheduler.set_timesteps(self.num_inference_steps			)

    __a    :	Dict									=						self.dummy_model()
    __a    :	Optional[Any]									=						self.dummy_sample_deter * scheduler.init_noise_sigma
    __a    :	Tuple									=						sample.to(_A			)

    for i, t in enumerate(scheduler.timesteps			):
     __a    :	Optional[int]									=						scheduler.scale_model_input(_A   ,   _A			)

     __a    :	str									=						model(_A   ,   _A			)

     __a    :	Tuple									=						scheduler.step(_A   ,   _A   ,   _A			)
     __a    :	Optional[int]									=						output.prev_sample

    __a    :	Any									=						torch.sum(torch.abs(_A			)			)
    __a    :	Union[str, Any]									=						torch.mean(torch.abs(_A			)			)

    if torch_device in ["cpu", "mps"]:
     assert abs(result_sum.item() - 0.1_233			) < 1E-2
     assert abs(result_mean.item() - 0.0_002			) < 1E-3
    else:
     # CUDA
     assert abs(result_sum.item() - 0.1_233			) < 1E-2
     assert abs(result_mean.item() - 0.0_002			) < 1E-3



   def    __magic_name__    ( self			)    ->   str:
    __a    :	str									=						self.scheduler_classes[0]
    __a    :	Union[str, Any]									=						self.get_scheduler_config(prediction_type='v_prediction'			)
    __a    :	Optional[Any]									=						scheduler_class(**_A			)

    scheduler.set_timesteps(self.num_inference_steps			)

    __a    :	List[str]									=						self.dummy_model()
    __a    :	int									=						self.dummy_sample_deter * scheduler.init_noise_sigma
    __a    :	Tuple									=						sample.to(_A			)

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

     __a    :	Optional[Any]									=						model(_A   ,   _A			)

     __a    :	Optional[Any]									=						scheduler.step(_A   ,   _A   ,   _A			)
     __a    :	int									=						output.prev_sample

    __a    :	Tuple									=						torch.sum(torch.abs(_A			)			)
    __a    :	str									=						torch.mean(torch.abs(_A			)			)

    if torch_device in ["cpu", "mps"]:
     assert abs(result_sum.item() - 4.6_9_3_4E-0_7			) < 1E-2
     assert abs(result_mean.item() - 6.1_1_1_2E-1_0			) < 1E-3
    else:
     # CUDA
     assert abs(result_sum.item() - 4.6_9_3_4_2_8_6_5_0_1_7_0_9_7_2E-0_7			) < 1E-2
     assert abs(result_mean.item() - 0.0_002			) < 1E-3



   def    __magic_name__    ( self			)    ->   Optional[int]:
    __a    :	Tuple									=						self.scheduler_classes[0]
    __a    :	Tuple									=						self.get_scheduler_config()
    __a    :	Tuple									=						scheduler_class(**_A			)

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

    __a    :	Union[str, Any]									=						self.dummy_model()
    __a    :	Tuple									=						self.dummy_sample_deter.to(_A			) * scheduler.init_noise_sigma

    for t in scheduler.timesteps:
     __a    :	Any									=						scheduler.scale_model_input(_A   ,   _A			)

     __a    :	Optional[Any]									=						model(_A   ,   _A			)

     __a    :	Union[str, Any]									=						scheduler.step(_A   ,   _A   ,   _A			)
     __a    :	Optional[Any]									=						output.prev_sample

    __a    :	int									=						torch.sum(torch.abs(_A			)			)
    __a    :	Union[str, Any]									=						torch.mean(torch.abs(_A			)			)

    if str(_A			).startswith('cpu'			):
     # The following sum varies between 148 and 156 on mps. Why?
     assert abs(result_sum.item() - 0.1_233			) < 1E-2
     assert abs(result_mean.item() - 0.0_002			) < 1E-3
    elif str(_A			).startswith('mps'			):
     # Larger tolerance on mps
     assert abs(result_mean.item() - 0.0_002			) < 1E-2
    else:
     # CUDA
     assert abs(result_sum.item() - 0.1_233			) < 1E-2
     assert abs(result_mean.item() - 0.0_002			) < 1E-3



   def    __magic_name__    ( self			)    ->   Union[str, Any]:
    __a    :	Dict									=						self.scheduler_classes[0]
    __a    :	Tuple									=						self.get_scheduler_config()
    __a    :	str									=						scheduler_class(**_A   ,   use_karras_sigmas=_A			)

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

    __a    :	str									=						self.dummy_model()
    __a    :	str									=						self.dummy_sample_deter.to(_A			) * scheduler.init_noise_sigma
    __a    :	Optional[int]									=						sample.to(_A			)

    for t in scheduler.timesteps:
     __a    :	Tuple									=						scheduler.scale_model_input(_A   ,   _A			)

     __a    :	int									=						model(_A   ,   _A			)

     __a    :	Optional[int]									=						scheduler.step(_A   ,   _A   ,   _A			)
     __a    :	Dict									=						output.prev_sample

    __a    :	List[Any]									=						torch.sum(torch.abs(_A			)			)
    __a    :	Optional[int]									=						torch.mean(torch.abs(_A			)			)

    assert abs(result_sum.item() - 0.00_015			) < 1E-2
    assert abs(result_mean.item() - 1.9_8_6_9_5_5_4_5_3_5_0_3_4_6_9_5E-0_7			) < 1E-2












'''simple docstring'''


def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__ = 30		):
 __a    :	Tuple									=						exponent + 2
 __a    :	str									=						(1 + 5**0.5) / 2
 __a    :	Any									=						(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5

 return int(SCREAMING_SNAKE_CASE__		)


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




