






import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class  __lowercase (		SCREAMING_SNAKE_CASE_					):







					'''simple docstring'''
					SCREAMING_SNAKE_CASE				  =			(EulerAncestralDiscreteScheduler,)
					SCREAMING_SNAKE_CASE				  =			10




					def 						lowerCAmelCase_       (			self    :     Optional[int]	,		**UpperCamelCase_    :     Any			):


									"""simple docstring"""



									__A             =		{
									    """num_train_timesteps""": 1_100,
									    """beta_start""": 0.0001,
									    """beta_end""": 0.02,
									    """beta_schedule""": """linear""",
									}

									config.update(**lowercase_			)
									return config




					def 						lowerCAmelCase_       (			self    :     List[Any]			):


									"""simple docstring"""



									for timesteps in [10, 50, 100, 1_000]:
													self.check_over_configs(num_train_timesteps=lowercase_			)




					def 						lowerCAmelCase_       (			self    :     Tuple			):


									"""simple docstring"""



									for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]	,		[0.0002, 0.002, 0.02]			):
													self.check_over_configs(beta_start=lowercase_	,		beta_end=lowercase_			)




					def 						lowerCAmelCase_       (			self    :     Union[str, Any]			):


									"""simple docstring"""



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




					def 						lowerCAmelCase_       (			self    :     Optional[Any]			):


									"""simple docstring"""



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




					def 						lowerCAmelCase_       (			self    :     str			):


									"""simple docstring"""



									__A             =		self.scheduler_classes[0]
									__A             =		self.get_scheduler_config()
									__A             =		scheduler_class(**lowercase_			)

									scheduler.set_timesteps(self.num_inference_steps			)

									__A             =		torch.manual_seed(0			)

									__A             =		self.dummy_model()
									__A             =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									__A             =		sample.to(lowercase_			)

									for i, t in enumerate(scheduler.timesteps			):
													__A             =		scheduler.scale_model_input(lowercase_	,		lowercase_			)

													__A             =		model(lowercase_	,		lowercase_			)

													__A             =		scheduler.step(lowercase_	,		lowercase_	,		lowercase_	,		generator=lowercase_			)
													__A             =		output.prev_sample

									__A             =		torch.sum(torch.abs(lowercase_			)			)
									__A             =		torch.mean(torch.abs(lowercase_			)			)

									assert abs(result_sum.item() - 152.3_192			) < 1e-2
									assert abs(result_mean.item() - 0.1983			) < 1e-3




					def 						lowerCAmelCase_       (			self    :     Optional[int]			):


									"""simple docstring"""



									__A             =		self.scheduler_classes[0]
									__A             =		self.get_scheduler_config(prediction_type="""v_prediction"""			)
									__A             =		scheduler_class(**lowercase_			)

									scheduler.set_timesteps(self.num_inference_steps			)

									__A             =		torch.manual_seed(0			)

									__A             =		self.dummy_model()
									__A             =		self.dummy_sample_deter * scheduler.init_noise_sigma
									__A             =		sample.to(lowercase_			)

									for i, t in enumerate(scheduler.timesteps			):
													__A             =		scheduler.scale_model_input(lowercase_	,		lowercase_			)

													__A             =		model(lowercase_	,		lowercase_			)

													__A             =		scheduler.step(lowercase_	,		lowercase_	,		lowercase_	,		generator=lowercase_			)
													__A             =		output.prev_sample

									__A             =		torch.sum(torch.abs(lowercase_			)			)
									__A             =		torch.mean(torch.abs(lowercase_			)			)

									assert abs(result_sum.item() - 108.4_439			) < 1e-2
									assert abs(result_mean.item() - 0.1412			) < 1e-3





					def 						lowerCAmelCase_       (			self    :     Tuple			):


									"""simple docstring"""



									__A             =		self.scheduler_classes[0]
									__A             =		self.get_scheduler_config()
									__A             =		scheduler_class(**lowercase_			)

									scheduler.set_timesteps(self.num_inference_steps	,		device=lowercase_			)
									__A             =		torch.manual_seed(0			)

									__A             =		self.dummy_model()
									__A             =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
									__A             =		sample.to(lowercase_			)

									for t in scheduler.timesteps:
													__A             =		scheduler.scale_model_input(lowercase_	,		lowercase_			)

													__A             =		model(lowercase_	,		lowercase_			)

													__A             =		scheduler.step(lowercase_	,		lowercase_	,		lowercase_	,		generator=lowercase_			)
													__A             =		output.prev_sample

									__A             =		torch.sum(torch.abs(lowercase_			)			)
									__A             =		torch.mean(torch.abs(lowercase_			)			)

									assert abs(result_sum.item() - 152.3_192			) < 1e-2
									assert abs(result_mean.item() - 0.1983			) < 1e-3












def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int     ,			__lowercase				:     int      )  -> int:





    """simple docstring"""

    __A             =		1.0
    for i in range(1     ,			r + 1      ):
        ret *= (n + 1 - i) / i
    return round(__lowercase      )



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int      )  -> int:





    """simple docstring"""

    return choose(8 + n     ,			__lowercase      ) + choose(9 + n     ,			__lowercase      ) - 1_0



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int      )  -> int:





    """simple docstring"""

    return sum(non_bouncy_exact(__lowercase      ) for i in range(1     ,			n + 1      )      )



def      _SCREAMING_SNAKE_CASE  (	__lowercase				:     int = 1_0_0      )  -> int:





    """simple docstring"""

    return non_bouncy_upto(__lowercase      )


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




