



"""simple docstring"""






import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			(DDPMParallelScheduler,)

	def 							_SCREAMING_SNAKE_CASE     (  self:	str ,    **_SCREAMING_SNAKE_CASE:	Any)       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						Any				= {
				    "num_train_timesteps": 1000,
				    "beta_start": 0.0001,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				    "variance_type": "fixed_small",
				    "clip_sample": True,
				}

				config.update(**_SCREAMING_SNAKE_CASE)
				return config

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

				"""simple docstring"""

				for timesteps in [1, 5, 100, 1000]:
							self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> Tuple:

				"""simple docstring"""

				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=_SCREAMING_SNAKE_CASE ,    beta_end=_SCREAMING_SNAKE_CASE)

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

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> Any:

				"""simple docstring"""

				for variance in ["fixed_small", "fixed_large", "other"]:
							self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Tuple:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> Tuple:

				"""simple docstring"""

				self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE)
				for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "sample", "v_prediction"]:
										self.check_over_configs(
										    thresholding=_SCREAMING_SNAKE_CASE ,    prediction_type=_SCREAMING_SNAKE_CASE ,    sample_max_value=_SCREAMING_SNAKE_CASE ,    )

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> Tuple:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> Optional[int]:

				"""simple docstring"""

				for t in [0, 500, 999]:
							self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= self.scheduler_classes[0]
				__lowerCAmelCase   :						List[str]				= self.get_scheduler_config()
				__lowerCAmelCase   :						Optional[Any]				= scheduler_class(**_SCREAMING_SNAKE_CASE)

				assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(487) - 0.0_0979)) < 1e-5
				assert torch.sum(torch.abs(scheduler._get_variance(999) - 0.02)) < 1e-5

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= self.scheduler_classes[0]
				__lowerCAmelCase   :						Dict				= self.get_scheduler_config()
				__lowerCAmelCase   :						List[Any]				= scheduler_class(**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Tuple				= len(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Optional[Any]				= self.dummy_model()
				__lowerCAmelCase   :						Union[str, Any]				= self.dummy_sample_deter
				__lowerCAmelCase   :						List[Any]				= self.dummy_sample_deter + 0.1
				__lowerCAmelCase   :						Dict				= self.dummy_sample_deter - 0.1

				__lowerCAmelCase   :						Tuple				= samplea.shape[0]
				__lowerCAmelCase   :						int				= torch.stack([samplea, samplea, samplea] ,    dim=0)
				__lowerCAmelCase   :						Tuple				= torch.arange(_SCREAMING_SNAKE_CASE)[0:3, None].repeat(1 ,    _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Dict				= model(samples.flatten(0 ,    1) ,    timesteps.flatten(0 ,    1))
				__lowerCAmelCase   :						int				= scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE ,    timesteps.flatten(0 ,    1) ,    samples.flatten(0 ,    1))

				__lowerCAmelCase   :						Optional[Any]				= torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
				__lowerCAmelCase   :						List[str]				= torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

				assert abs(result_sum.item() - 1153.1833) < 1e-2
				assert abs(result_mean.item() - 0.5005) < 1e-3

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= self.scheduler_classes[0]
				__lowerCAmelCase   :						Tuple				= self.get_scheduler_config()
				__lowerCAmelCase   :						List[Any]				= scheduler_class(**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Tuple				= len(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Any				= self.dummy_model()
				__lowerCAmelCase   :						Dict				= self.dummy_sample_deter
				__lowerCAmelCase   :						Optional[Any]				= torch.manual_seed(0)

				for t in reversed(range(_SCREAMING_SNAKE_CASE)):
							# 1. predict noise residual
							__lowerCAmelCase   :						Optional[int]				= model(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

							# 2. predict previous mean of sample x_t-1
							__lowerCAmelCase   :						Optional[Any]				= scheduler.step(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    generator=_SCREAMING_SNAKE_CASE).prev_sample

							__lowerCAmelCase   :						str				= pred_prev_sample

				__lowerCAmelCase   :						List[str]				= torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
				__lowerCAmelCase   :						List[Any]				= torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

				assert abs(result_sum.item() - 258.9606) < 1e-2
				assert abs(result_mean.item() - 0.3372) < 1e-3

	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= self.scheduler_classes[0]
				__lowerCAmelCase   :						List[Any]				= self.get_scheduler_config(prediction_type="v_prediction")
				__lowerCAmelCase   :						Optional[Any]				= scheduler_class(**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						str				= len(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Optional[int]				= self.dummy_model()
				__lowerCAmelCase   :						Tuple				= self.dummy_sample_deter
				__lowerCAmelCase   :						Union[str, Any]				= torch.manual_seed(0)

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

							# 2. predict previous mean of sample x_t-1
							__lowerCAmelCase   :						Union[str, Any]				= scheduler.step(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    generator=_SCREAMING_SNAKE_CASE).prev_sample

							__lowerCAmelCase   :						Dict				= pred_prev_sample

				__lowerCAmelCase   :						Optional[int]				= torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
				__lowerCAmelCase   :						Dict				= torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

				assert abs(result_sum.item() - 202.0296) < 1e-2
				assert abs(result_mean.item() - 0.2631) < 1e-3

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> List[str]:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= self.scheduler_classes[0]
				__lowerCAmelCase   :						List[str]				= self.get_scheduler_config()
				__lowerCAmelCase   :						Any				= scheduler_class(**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						int				= [100, 87, 50, 1, 0]

				scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Optional[int]				= scheduler.timesteps

				for i, timestep in enumerate(_SCREAMING_SNAKE_CASE):
							if i == len(_SCREAMING_SNAKE_CASE) - 1:
										__lowerCAmelCase   :						List[str]				= -1
							else:
										__lowerCAmelCase   :						Tuple				= timesteps[i + 1]

							__lowerCAmelCase   :						Optional[int]				= scheduler.previous_timestep(_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase   :						Dict				= prev_t.item()

							self.assertEqual(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= self.scheduler_classes[0]
				__lowerCAmelCase   :						Dict				= self.get_scheduler_config()
				__lowerCAmelCase   :						Dict				= scheduler_class(**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						Any				= [100, 87, 50, 51, 0]

				with self.assertRaises(_SCREAMING_SNAKE_CASE ,    msg="`custom_timesteps` must be in descending order."):
							scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE)

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= self.scheduler_classes[0]
				__lowerCAmelCase   :						Any				= self.get_scheduler_config()
				__lowerCAmelCase   :						Dict				= scheduler_class(**_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						List[Any]				= [100, 87, 50, 1, 0]
				__lowerCAmelCase   :						Union[str, Any]				= len(_SCREAMING_SNAKE_CASE)

				with self.assertRaises(_SCREAMING_SNAKE_CASE ,    msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."):
							scheduler.set_timesteps(num_inference_steps=_SCREAMING_SNAKE_CASE ,    timesteps=_SCREAMING_SNAKE_CASE)







	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						str				= self.scheduler_classes[0]
				__lowerCAmelCase   :						str				= self.get_scheduler_config()
				__lowerCAmelCase   :						List[str]				= scheduler_class(**_SCREAMING_SNAKE_CASE)

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

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




"""simple docstring"""






def     _lowercase       (						__snake_case      ,__snake_case			)		->     list:
			__lowerCAmelCase   :						List[str]				= len(__snake_case			)
			__lowerCAmelCase   :						Dict				= []
			for i in range(len(__snake_case			) - pat_len + 1			):
						__lowerCAmelCase   :						List[Any]				= True
						for j in range(__snake_case			):
									if s[i + j] != pattern[j]:
												__lowerCAmelCase   :						Union[str, Any]				= False
												break
						if match_found:
									position.append(__snake_case			)
			return position


if __name__ == "__main__":
	assert naive_pattern_search('ABCDEFG', 'DE') == [3]
	print(naive_pattern_search('ABAAABCDBBABCDDEBCABC', 'ABC'))