
import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class       _snake_case   (	UpperCAmelCase_							):
	__lowerCAmelCase  :     int        =			(DDPMParallelScheduler,)


	def      lowercase__     (					self					,  **SCREAMING_SNAKE_CASE_):


			'''simple docstring'''




			lowercase__  :   List[Any]    		=      {
			    """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(**SCREAMING_SNAKE_CASE_)
			return config


	def      lowercase__     (					self):


			'''simple docstring'''




			for timesteps in [1, 5, 1_00, 10_00]:
					self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_)


	def      lowercase__     (					self):


			'''simple docstring'''




			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=SCREAMING_SNAKE_CASE_					,  beta_end=SCREAMING_SNAKE_CASE_)


	def      lowercase__     (					self):


			'''simple docstring'''




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


	def      lowercase__     (					self):


			'''simple docstring'''




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


	def      lowercase__     (					self):


			'''simple docstring'''




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


	def      lowercase__     (					self):


			'''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      lowercase__     (					self):


			'''simple docstring'''




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


	def      lowercase__     (					self):


			'''simple docstring'''




			for t in [0, 5_00, 9_99]:
					self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_)


	def      lowercase__     (					self):


			'''simple docstring'''




			lowercase__  :   int    		=      self.scheduler_classes[0]
			lowercase__  :   Union[str, Any]    		=      self.get_scheduler_config()
			lowercase__  :   Union[str, 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(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      lowercase__     (					self):


			'''simple docstring'''




			lowercase__  :   List[Any]    		=      self.scheduler_classes[0]
			lowercase__  :   List[Any]    		=      self.get_scheduler_config()
			lowercase__  :   Optional[Any]    		=      scheduler_class(**SCREAMING_SNAKE_CASE_)

			lowercase__  :   Any    		=      len(SCREAMING_SNAKE_CASE_)

			lowercase__  :   List[str]    		=      self.dummy_model()
			lowercase__  :   Union[str, Any]    		=      self.dummy_sample_deter
			lowercase__  :   int    		=      self.dummy_sample_deter + 0.1
			lowercase__  :   List[Any]    		=      self.dummy_sample_deter - 0.1

			lowercase__  :   List[str]    		=      samplea.shape[0]
			lowercase__  :   Any    		=      torch.stack([samplea, samplea, samplea]					,  dim=0)
			lowercase__  :   str    		=      torch.arange(SCREAMING_SNAKE_CASE_)[0:3, None].repeat(1					,  SCREAMING_SNAKE_CASE_)

			lowercase__  :   Optional[Any]    		=      model(samples.flatten(0					,  1)					,  timesteps.flatten(0					,  1))
			lowercase__  :   List[Any]    		=      scheduler.batch_step_no_noise(SCREAMING_SNAKE_CASE_					,  timesteps.flatten(0					,  1)					,  samples.flatten(0					,  1))

			lowercase__  :   str    		=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
			lowercase__  :   Optional[Any]    		=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

			assert abs(result_sum.item() - 11_53.18_33) < 1E-2
			assert abs(result_mean.item() - 0.5_0_0_5) < 1E-3


	def      lowercase__     (					self):


			'''simple docstring'''




			lowercase__  :   Tuple    		=      self.scheduler_classes[0]
			lowercase__  :   Optional[int]    		=      self.get_scheduler_config()
			lowercase__  :   Tuple    		=      scheduler_class(**SCREAMING_SNAKE_CASE_)

			lowercase__  :   Union[str, Any]    		=      len(SCREAMING_SNAKE_CASE_)

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

			for t in reversed(range(SCREAMING_SNAKE_CASE_)):
					# 1. predict noise residual
					lowercase__  :   Union[str, Any]    		=      model(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)

					# 2. predict previous mean of sample x_t-1
					lowercase__  :   Optional[int]    		=      scheduler.step(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  generator=SCREAMING_SNAKE_CASE_).prev_sample

					lowercase__  :   Optional[int]    		=      pred_prev_sample

			lowercase__  :   Optional[Any]    		=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
			lowercase__  :   Union[str, Any]    		=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

			assert abs(result_sum.item() - 2_58.96_06) < 1E-2
			assert abs(result_mean.item() - 0.3_3_7_2) < 1E-3


	def      lowercase__     (					self):


			'''simple docstring'''




			lowercase__  :   Dict    		=      self.scheduler_classes[0]
			lowercase__  :   str    		=      self.get_scheduler_config(prediction_type="""v_prediction""")
			lowercase__  :   List[Any]    		=      scheduler_class(**SCREAMING_SNAKE_CASE_)

			lowercase__  :   Optional[Any]    		=      len(SCREAMING_SNAKE_CASE_)

			lowercase__  :   str    		=      self.dummy_model()
			lowercase__  :   Any    		=      self.dummy_sample_deter
			lowercase__  :   Union[str, Any]    		=      torch.manual_seed(0)

			for t in reversed(range(SCREAMING_SNAKE_CASE_)):
					# 1. predict noise residual
					lowercase__  :   List[Any]    		=      model(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)

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

					lowercase__  :   Optional[int]    		=      pred_prev_sample

			lowercase__  :   Optional[int]    		=      torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
			lowercase__  :   Optional[int]    		=      torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

			assert abs(result_sum.item() - 2_02.02_96) < 1E-2
			assert abs(result_mean.item() - 0.2_6_3_1) < 1E-3


	def      lowercase__     (					self):


			'''simple docstring'''




			lowercase__  :   Optional[int]    		=      self.scheduler_classes[0]
			lowercase__  :   Dict    		=      self.get_scheduler_config()
			lowercase__  :   Tuple    		=      scheduler_class(**SCREAMING_SNAKE_CASE_)

			lowercase__  :   Optional[int]    		=      [1_00, 87, 50, 1, 0]

			scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)

			lowercase__  :   Optional[int]    		=      scheduler.timesteps

			for i, timestep in enumerate(SCREAMING_SNAKE_CASE_):
					if i == len(SCREAMING_SNAKE_CASE_) - 1:
							lowercase__  :   Optional[Any]    		=      -1
					else:
							lowercase__  :   str    		=      timesteps[i + 1]

					lowercase__  :   Dict    		=      scheduler.previous_timestep(SCREAMING_SNAKE_CASE_)
					lowercase__  :   str    		=      prev_t.item()

					self.assertEqual(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)


	def      lowercase__     (					self):


			'''simple docstring'''




			lowercase__  :   Any    		=      self.scheduler_classes[0]
			lowercase__  :   Any    		=      self.get_scheduler_config()
			lowercase__  :   str    		=      scheduler_class(**SCREAMING_SNAKE_CASE_)

			lowercase__  :   List[Any]    		=      [1_00, 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      lowercase__     (					self):


			'''simple docstring'''




			lowercase__  :   Union[str, Any]    		=      self.scheduler_classes[0]
			lowercase__  :   Optional[Any]    		=      self.get_scheduler_config()
			lowercase__  :   int    		=      scheduler_class(**SCREAMING_SNAKE_CASE_)

			lowercase__  :   List[Any]    		=      [1_00, 87, 50, 1, 0]
			lowercase__  :   Tuple    		=      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      lowercase__     (					self):


			'''simple docstring'''




			lowercase__  :   Optional[Any]    		=      self.scheduler_classes[0]
			lowercase__  :   Any    		=      self.get_scheduler_config()
			lowercase__  :   int    		=      scheduler_class(**SCREAMING_SNAKE_CASE_)

			lowercase__  :   List[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_)








from abc import ABC, abstractmethod
from argparse import ArgumentParser



class       _snake_case   (	UpperCAmelCase_							):


 @staticmethod
 @abstractmethod
 def      lowercase__     (					SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   raise NotImplementedError()



 @abstractmethod
 def      lowercase__     (					self):


   '''simple docstring'''




   raise NotImplementedError()






