




"""simple docstring"""




import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class    UpperCAmelCase_  (       _lowercase						):
		UpperCamelCase						 =(DDPMParallelScheduler,)
		def      _lowerCamelCase				(		self							,		**UpperCamelCase_    )		->    int:
					__lowercase       :   str        =    {
					    """num_train_timesteps""": 10_00,
					    """beta_start""": 0.0_0_0_1,
					    """beta_end""": 0.0_2,
					    """beta_schedule""": """linear""",
					    """variance_type""": """fixed_small""",
					    """clip_sample""": True,
					}

					config.update(**UpperCamelCase__    )
					return config
		def      _lowerCamelCase				(		self    )		->    Union[str, Any]:
					for timesteps in [1, 5, 1_00, 10_00]:
								self.check_over_configs(num_train_timesteps=UpperCamelCase__    )
		def      _lowerCamelCase				(		self    )		->    Dict:
					for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]							,		[0.0_0_2, 0.0_2, 0.2, 2]    ):
								self.check_over_configs(beta_start=UpperCamelCase__							,		beta_end=UpperCamelCase__    )
		def      _lowerCamelCase				(		self    )		->    List[str]:
					for schedule in ["linear", "squaredcos_cap_v2"]:
								self.check_over_configs(beta_schedule=UpperCamelCase__    )
		def      _lowerCamelCase				(		self    )		->    List[str]:
					for variance in ["fixed_small", "fixed_large", "other"]:
								self.check_over_configs(variance_type=UpperCamelCase__    )
		def      _lowerCamelCase				(		self    )		->    Any:
					for clip_sample in [True, False]:
								self.check_over_configs(clip_sample=UpperCamelCase__    )
		def      _lowerCamelCase				(		self    )		->    Any:
					self.check_over_configs(thresholding=UpperCamelCase__    )
					for threshold in [0.5, 1.0, 2.0]:
								for prediction_type in ["epsilon", "sample", "v_prediction"]:
											self.check_over_configs(
											    thresholding=UpperCamelCase__							,		prediction_type=UpperCamelCase__							,		sample_max_value=UpperCamelCase__							,		)
		def      _lowerCamelCase				(		self    )		->    Any:
					for prediction_type in ["epsilon", "sample", "v_prediction"]:
								self.check_over_configs(prediction_type=UpperCamelCase__    )
		def      _lowerCamelCase				(		self    )		->    Union[str, Any]:
					for t in [0, 5_00, 9_99]:
								self.check_over_forward(time_step=UpperCamelCase__    )
		def      _lowerCamelCase				(		self    )		->    List[str]:
					__lowercase       :   int        =    self.scheduler_classes[0]
					__lowercase       :   Union[str, Any]        =    self.get_scheduler_config()
					__lowercase       :   Union[str, Any]        =    scheduler_class(**UpperCamelCase__    )

					assert torch.sum(torch.abs(scheduler._get_variance(0    ) - 0.0    )    ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(4_87    ) - 0.0_0_9_7_9    )    ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(9_99    ) - 0.0_2    )    ) < 1E-5
		def      _lowerCamelCase				(		self    )		->    Dict:
					__lowercase       :   int        =    self.scheduler_classes[0]
					__lowercase       :   List[Any]        =    self.get_scheduler_config()
					__lowercase       :   List[str]        =    scheduler_class(**UpperCamelCase__    )

					__lowercase       :   str        =    len(UpperCamelCase__    )

					__lowercase       :   str        =    self.dummy_model()
					__lowercase       :   int        =    self.dummy_sample_deter
					__lowercase       :   Optional[int]        =    self.dummy_sample_deter + 0.1
					__lowercase       :   Optional[int]        =    self.dummy_sample_deter - 0.1

					__lowercase       :   Union[str, Any]        =    samplea.shape[0]
					__lowercase       :   Union[str, Any]        =    torch.stack([samplea, samplea, samplea]							,		dim=0    )
					__lowercase       :   str        =    torch.arange(UpperCamelCase__    )[0:3, None].repeat(1							,		UpperCamelCase__    )

					__lowercase       :   Tuple        =    model(samples.flatten(0							,		1    )							,		timesteps.flatten(0							,		1    )    )
					__lowercase       :   Dict        =    scheduler.batch_step_no_noise(UpperCamelCase__							,		timesteps.flatten(0							,		1    )							,		samples.flatten(0							,		1    )    )

					__lowercase       :   Union[str, Any]        =    torch.sum(torch.abs(UpperCamelCase__    )    )
					__lowercase       :   List[str]        =    torch.mean(torch.abs(UpperCamelCase__    )    )

					assert abs(result_sum.item() - 11_53.18_33    ) < 1E-2
					assert abs(result_mean.item() - 0.5_0_0_5    ) < 1E-3
		def      _lowerCamelCase				(		self    )		->    Dict:
					__lowercase       :   List[Any]        =    self.scheduler_classes[0]
					__lowercase       :   Dict        =    self.get_scheduler_config()
					__lowercase       :   List[Any]        =    scheduler_class(**UpperCamelCase__    )

					__lowercase       :   List[Any]        =    len(UpperCamelCase__    )

					__lowercase       :   Optional[int]        =    self.dummy_model()
					__lowercase       :   int        =    self.dummy_sample_deter
					__lowercase       :   Optional[int]        =    torch.manual_seed(0    )

					for t in reversed(range(UpperCamelCase__    )    ):
								# 1. predict noise residual
								__lowercase       :   Dict        =    model(UpperCamelCase__							,		UpperCamelCase__    )

								# 2. predict previous mean of sample x_t-1
								__lowercase       :   Any        =    scheduler.step(UpperCamelCase__							,		UpperCamelCase__							,		UpperCamelCase__							,		generator=UpperCamelCase__    ).prev_sample

								__lowercase       :   List[str]        =    pred_prev_sample

					__lowercase       :   List[Any]        =    torch.sum(torch.abs(UpperCamelCase__    )    )
					__lowercase       :   Optional[int]        =    torch.mean(torch.abs(UpperCamelCase__    )    )

					assert abs(result_sum.item() - 2_5_8.9_6_0_6    ) < 1E-2
					assert abs(result_mean.item() - 0.3_3_7_2    ) < 1E-3
		def      _lowerCamelCase				(		self    )		->    Any:
					__lowercase       :   Optional[Any]        =    self.scheduler_classes[0]
					__lowercase       :   Any        =    self.get_scheduler_config(prediction_type='''v_prediction'''    )
					__lowercase       :   Any        =    scheduler_class(**UpperCamelCase__    )

					__lowercase       :   int        =    len(UpperCamelCase__    )

					__lowercase       :   Optional[int]        =    self.dummy_model()
					__lowercase       :   List[Any]        =    self.dummy_sample_deter
					__lowercase       :   List[str]        =    torch.manual_seed(0    )

					for t in reversed(range(UpperCamelCase__    )    ):
								# 1. predict noise residual
								__lowercase       :   Optional[Any]        =    model(UpperCamelCase__							,		UpperCamelCase__    )

								# 2. predict previous mean of sample x_t-1
								__lowercase       :   List[str]        =    scheduler.step(UpperCamelCase__							,		UpperCamelCase__							,		UpperCamelCase__							,		generator=UpperCamelCase__    ).prev_sample

								__lowercase       :   List[Any]        =    pred_prev_sample

					__lowercase       :   Union[str, Any]        =    torch.sum(torch.abs(UpperCamelCase__    )    )
					__lowercase       :   List[str]        =    torch.mean(torch.abs(UpperCamelCase__    )    )

					assert abs(result_sum.item() - 2_0_2.0_2_9_6    ) < 1E-2
					assert abs(result_mean.item() - 0.2_6_3_1    ) < 1E-3
		def      _lowerCamelCase				(		self    )		->    List[Any]:
					__lowercase       :   Dict        =    self.scheduler_classes[0]
					__lowercase       :   List[str]        =    self.get_scheduler_config()
					__lowercase       :   Optional[int]        =    scheduler_class(**UpperCamelCase__    )

					__lowercase       :   List[str]        =    [1_00, 87, 50, 1, 0]

					scheduler.set_timesteps(timesteps=UpperCamelCase__    )

					__lowercase       :   Any        =    scheduler.timesteps

					for i, timestep in enumerate(UpperCamelCase__    ):
								if i == len(UpperCamelCase__    ) - 1:
											__lowercase       :   List[str]        =    -1
								else:
											__lowercase       :   int        =    timesteps[i + 1]

								__lowercase       :   List[Any]        =    scheduler.previous_timestep(UpperCamelCase__    )
								__lowercase       :   Union[str, Any]        =    prev_t.item()

								self.assertEqual(UpperCamelCase__							,		UpperCamelCase__    )
		def      _lowerCamelCase				(		self    )		->    Optional[int]:
					__lowercase       :   Optional[int]        =    self.scheduler_classes[0]
					__lowercase       :   Union[str, Any]        =    self.get_scheduler_config()
					__lowercase       :   Any        =    scheduler_class(**UpperCamelCase__    )

					__lowercase       :   List[str]        =    [1_00, 87, 50, 51, 0]

					with self.assertRaises(UpperCamelCase__							,		msg='''`custom_timesteps` must be in descending order.'''    ):
								scheduler.set_timesteps(timesteps=UpperCamelCase__    )
		def      _lowerCamelCase				(		self    )		->    List[str]:
					__lowercase       :   Tuple        =    self.scheduler_classes[0]
					__lowercase       :   Dict        =    self.get_scheduler_config()
					__lowercase       :   str        =    scheduler_class(**UpperCamelCase__    )

					__lowercase       :   Dict        =    [1_00, 87, 50, 1, 0]
					__lowercase       :   List[str]        =    len(UpperCamelCase__    )

					with self.assertRaises(UpperCamelCase__							,		msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''    ):
								scheduler.set_timesteps(num_inference_steps=UpperCamelCase__							,		timesteps=UpperCamelCase__    )
		def      _lowerCamelCase				(		self    )		->    List[Any]:
					__lowercase       :   Union[str, Any]        =    self.scheduler_classes[0]
					__lowercase       :   Tuple        =    self.get_scheduler_config()
					__lowercase       :   List[Any]        =    scheduler_class(**UpperCamelCase__    )

					__lowercase       :   Union[str, Any]        =    [scheduler.config.num_train_timesteps]

					with self.assertRaises(
					    UpperCamelCase__							,		msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''							,		):
								scheduler.set_timesteps(timesteps=UpperCamelCase__    )











'''simple docstring'''







class _lowercase	:




							def __init__(							self:				Tuple			,							UpperCamelCase__:				list[int]     ):
														lowerCamelCase__					:	Union[str, Any]         =       len(UpperCamelCase__     )
														lowerCamelCase__					:	Union[str, Any]         =       [0] * len_array

														if len_array > 0:
																					lowerCamelCase__					:	Union[str, Any]         =       array[0]

														for i in range(1			,							UpperCamelCase__     ):
																					lowerCamelCase__					:	Tuple         =       self.prefix_sum[i - 1] + array[i]




							def 		lowerCamelCase_				(							self:				Tuple			,							UpperCamelCase__:				int			,							UpperCamelCase__:				int     ):
														if start == 0:
																					return self.prefix_sum[end]

														return self.prefix_sum[end] - self.prefix_sum[start - 1]




							def 		lowerCamelCase_				(							self:				Optional[int]			,							UpperCamelCase__:				int     ):
														lowerCamelCase__					:	Dict         =       {0}
														for sum_item in self.prefix_sum:
																					if sum_item - target_sum in sums:
																												return True

																					sums.add(UpperCamelCase__     )

														return False


if __name__ == "__main__":
			import doctest

			doctest.testmod()

