






"""simple docstring"""




import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       Dict			=					(DDPMParallelScheduler,)


 def _lowercase	(     self		:						Optional[int]							,						**UpperCAmelCase__		:						str							)					->       List[str]:
   _a  :       Optional[int]             =	{
       """num_train_timesteps""": 1000,
       """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 _lowercase	(     self		:						Any							)					->       Union[str, Any]:
   for timesteps in [1, 5, 100, 1000]:
     self.check_over_configs(num_train_timesteps=UpperCAmelCase__							)


 def _lowercase	(     self		:						List[Any]							)					->       List[str]:
   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 _lowercase	(     self		:						Any							)					->       Dict:
   for schedule in ["linear", "squaredcos_cap_v2"]:
     self.check_over_configs(beta_schedule=UpperCAmelCase__							)


 def _lowercase	(     self		:						str							)					->       int:
   for variance in ["fixed_small", "fixed_large", "other"]:
     self.check_over_configs(variance_type=UpperCAmelCase__							)


 def _lowercase	(     self		:						Union[str, Any]							)					->       Union[str, Any]:
   for clip_sample in [True, False]:
     self.check_over_configs(clip_sample=UpperCAmelCase__							)


 def _lowercase	(     self		:						Tuple							)					->       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 _lowercase	(     self		:						Optional[Any]							)					->       Union[str, Any]:
   for prediction_type in ["epsilon", "sample", "v_prediction"]:
     self.check_over_configs(prediction_type=UpperCAmelCase__							)


 def _lowercase	(     self		:						Optional[int]							)					->       Optional[Any]:
   for t in [0, 500, 999]:
     self.check_over_forward(time_step=UpperCAmelCase__							)


 def _lowercase	(     self		:						Union[str, Any]							)					->       Tuple:
   _a  :       int             =	self.scheduler_classes[0]
   _a  :       str             =	self.get_scheduler_config()
   _a  :       int             =	scheduler_class(**UpperCAmelCase__							)

   assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(487							) - 0.0_0_9_7_9							)							) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.0_2							)							) < 1E-5


 def _lowercase	(     self		:						str							)					->       List[str]:
   _a  :       List[str]             =	self.scheduler_classes[0]
   _a  :       List[str]             =	self.get_scheduler_config()
   _a  :       Union[str, Any]             =	scheduler_class(**UpperCAmelCase__							)

   _a  :       Dict             =	len(UpperCAmelCase__							)

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

   _a  :       List[Any]             =	samplea.shape[0]
   _a  :       Tuple             =	torch.stack([samplea, samplea, samplea]							,						dim=0							)
   _a  :       Dict             =	torch.arange(UpperCAmelCase__							)[0:3, None].repeat(1							,						UpperCAmelCase__							)

   _a  :       Optional[int]             =	model(samples.flatten(0							,						1							)							,						timesteps.flatten(0							,						1							)							)
   _a  :       Optional[Any]             =	scheduler.batch_step_no_noise(UpperCAmelCase__							,						timesteps.flatten(0							,						1							)							,						samples.flatten(0							,						1							)							)

   _a  :       List[str]             =	torch.sum(torch.abs(UpperCAmelCase__							)							)
   _a  :       int             =	torch.mean(torch.abs(UpperCAmelCase__							)							)

   assert abs(result_sum.item() - 1_1_5_3.1_8_3_3							) < 1E-2
   assert abs(result_mean.item() - 0.5_0_0_5							) < 1E-3


 def _lowercase	(     self		:						List[Any]							)					->       Union[str, Any]:
   _a  :       Dict             =	self.scheduler_classes[0]
   _a  :       Optional[Any]             =	self.get_scheduler_config()
   _a  :       Optional[int]             =	scheduler_class(**UpperCAmelCase__							)

   _a  :       Optional[Any]             =	len(UpperCAmelCase__							)

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

   for t in reversed(range(UpperCAmelCase__							)							):
     # 1. predict noise residual
     _a  :       str             =	model(UpperCAmelCase__							,						UpperCAmelCase__							)

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

     _a  :       List[str]             =	pred_prev_sample

   _a  :       List[Any]             =	torch.sum(torch.abs(UpperCAmelCase__							)							)
   _a  :       Dict             =	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 _lowercase	(     self		:						str							)					->       int:
   _a  :       List[Any]             =	self.scheduler_classes[0]
   _a  :       List[Any]             =	self.get_scheduler_config(prediction_type="""v_prediction"""							)
   _a  :       int             =	scheduler_class(**UpperCAmelCase__							)

   _a  :       Dict             =	len(UpperCAmelCase__							)

   _a  :       List[str]             =	self.dummy_model()
   _a  :       List[str]             =	self.dummy_sample_deter
   _a  :       str             =	torch.manual_seed(0							)

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

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

     _a  :       Any             =	pred_prev_sample

   _a  :       List[str]             =	torch.sum(torch.abs(UpperCAmelCase__							)							)
   _a  :       Tuple             =	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 _lowercase	(     self		:						Union[str, Any]							)					->       Dict:
   _a  :       int             =	self.scheduler_classes[0]
   _a  :       List[Any]             =	self.get_scheduler_config()
   _a  :       Optional[Any]             =	scheduler_class(**UpperCAmelCase__							)

   _a  :       Dict             =	[100, 87, 50, 1, 0]

   scheduler.set_timesteps(timesteps=UpperCAmelCase__							)

   _a  :       Any             =	scheduler.timesteps

   for i, timestep in enumerate(UpperCAmelCase__							):
     if i == len(UpperCAmelCase__							) - 1:
       _a  :       Dict             =	-1
     else:
       _a  :       Tuple             =	timesteps[i + 1]

     _a  :       Union[str, Any]             =	scheduler.previous_timestep(UpperCAmelCase__							)
     _a  :       Tuple             =	prev_t.item()

     self.assertEqual(UpperCAmelCase__							,						UpperCAmelCase__							)


 def _lowercase	(     self		:						Any							)					->       Optional[int]:
   _a  :       Dict             =	self.scheduler_classes[0]
   _a  :       Optional[Any]             =	self.get_scheduler_config()
   _a  :       Optional[Any]             =	scheduler_class(**UpperCAmelCase__							)

   _a  :       Union[str, Any]             =	[100, 87, 50, 51, 0]

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


 def _lowercase	(     self		:						Dict							)					->       List[Any]:
   _a  :       Dict             =	self.scheduler_classes[0]
   _a  :       int             =	self.get_scheduler_config()
   _a  :       List[str]             =	scheduler_class(**UpperCAmelCase__							)

   _a  :       Dict             =	[100, 87, 50, 1, 0]
   _a  :       Optional[int]             =	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 _lowercase	(     self		:						Union[str, Any]							)					->       Any:
   _a  :       Union[str, Any]             =	self.scheduler_classes[0]
   _a  :       Optional[Any]             =	self.get_scheduler_config()
   _a  :       Union[str, Any]             =	scheduler_class(**UpperCAmelCase__							)

   _a  :       int             =	[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"""




import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       int			=					(IPNDMScheduler,)
 UpperCamelCase					:       int			=					(('''num_inference_steps''', 50),)


 def _lowercase	(     self		:						Union[str, Any]							,						**UpperCAmelCase__		:						Tuple							)					->       int:
   _a  :       Optional[int]             =	{"""num_train_timesteps""": 1000}
   config.update(**UpperCAmelCase__							)
   return config


 def _lowercase	(     self		:						Dict							,						UpperCAmelCase__		:						Any=0							,						**UpperCAmelCase__		:						Optional[Any]							)					->       Union[str, Any]:
   _a  :       Optional[int]             =	dict(self.forward_default_kwargs							)
   _a  :       Dict             =	kwargs.pop("""num_inference_steps"""							,						UpperCAmelCase__							)
   _a  :       Optional[Any]             =	self.dummy_sample
   _a  :       Union[str, Any]             =	0.1 * sample
   _a  :       Union[str, Any]             =	[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

   for scheduler_class in self.scheduler_classes:
     _a  :       Optional[int]             =	self.get_scheduler_config(**UpperCAmelCase__							)
     _a  :       Union[str, Any]             =	scheduler_class(**UpperCAmelCase__							)
     scheduler.set_timesteps(UpperCAmelCase__							)
     # copy over dummy past residuals
     _a  :       Any             =	dummy_past_residuals[:]

     if time_step is None:
       _a  :       str             =	scheduler.timesteps[len(scheduler.timesteps							) // 2]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(UpperCAmelCase__							)
       _a  :       Union[str, Any]             =	scheduler_class.from_pretrained(UpperCAmelCase__							)
       new_scheduler.set_timesteps(UpperCAmelCase__							)
       # copy over dummy past residuals
       _a  :       Optional[Any]             =	dummy_past_residuals[:]

     _a  :       List[Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       str             =	new_scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

     _a  :       Optional[int]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       Tuple             =	new_scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"


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


 def _lowercase	(     self		:						Optional[int]							,						UpperCAmelCase__		:						List[str]=0							,						**UpperCAmelCase__		:						Optional[Any]							)					->       List[Any]:
   _a  :       Optional[Any]             =	dict(self.forward_default_kwargs							)
   _a  :       Optional[Any]             =	kwargs.pop("""num_inference_steps"""							,						UpperCAmelCase__							)
   _a  :       Optional[Any]             =	self.dummy_sample
   _a  :       List[Any]             =	0.1 * sample
   _a  :       Optional[Any]             =	[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

   for scheduler_class in self.scheduler_classes:
     _a  :       Union[str, Any]             =	self.get_scheduler_config()
     _a  :       Optional[Any]             =	scheduler_class(**UpperCAmelCase__							)
     scheduler.set_timesteps(UpperCAmelCase__							)

     # copy over dummy past residuals (must be after setting timesteps)
     _a  :       Any             =	dummy_past_residuals[:]

     if time_step is None:
       _a  :       List[Any]             =	scheduler.timesteps[len(scheduler.timesteps							) // 2]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(UpperCAmelCase__							)
       _a  :       Any             =	scheduler_class.from_pretrained(UpperCAmelCase__							)
       # copy over dummy past residuals
       new_scheduler.set_timesteps(UpperCAmelCase__							)

       # copy over dummy past residual (must be after setting timesteps)
       _a  :       Optional[Any]             =	dummy_past_residuals[:]

     _a  :       List[str]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       Tuple             =	new_scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"

     _a  :       Union[str, Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       int             =	new_scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

     assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"


 def _lowercase	(     self		:						str							,						**UpperCAmelCase__		:						Any							)					->       List[str]:
   _a  :       Optional[int]             =	self.scheduler_classes[0]
   _a  :       Optional[Any]             =	self.get_scheduler_config(**UpperCAmelCase__							)
   _a  :       Union[str, Any]             =	scheduler_class(**UpperCAmelCase__							)

   _a  :       int             =	10
   _a  :       List[Any]             =	self.dummy_model()
   _a  :       str             =	self.dummy_sample_deter
   scheduler.set_timesteps(UpperCAmelCase__							)

   for i, t in enumerate(scheduler.timesteps							):
     _a  :       str             =	model(UpperCAmelCase__							,						UpperCAmelCase__							)
     _a  :       List[Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							).prev_sample

   for i, t in enumerate(scheduler.timesteps							):
     _a  :       Union[str, Any]             =	model(UpperCAmelCase__							,						UpperCAmelCase__							)
     _a  :       Any             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							).prev_sample

   return sample


 def _lowercase	(     self		:						int							)					->       str:
   _a  :       Dict             =	dict(self.forward_default_kwargs							)

   _a  :       int             =	kwargs.pop("""num_inference_steps"""							,						UpperCAmelCase__							)

   for scheduler_class in self.scheduler_classes:
     _a  :       Optional[int]             =	self.get_scheduler_config()
     _a  :       Tuple             =	scheduler_class(**UpperCAmelCase__							)

     _a  :       Tuple             =	self.dummy_sample
     _a  :       Optional[Any]             =	0.1 * sample

     if num_inference_steps is not None and hasattr(UpperCAmelCase__							,						"""set_timesteps"""							):
       scheduler.set_timesteps(UpperCAmelCase__							)
     elif num_inference_steps is not None and not hasattr(UpperCAmelCase__							,						"""set_timesteps"""							):
       _a  :       List[str]             =	num_inference_steps

     # copy over dummy past residuals (must be done after set_timesteps)
     _a  :       Union[str, Any]             =	[residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
     _a  :       Optional[Any]             =	dummy_past_residuals[:]

     _a  :       Optional[Any]             =	scheduler.timesteps[5]
     _a  :       str             =	scheduler.timesteps[6]

     _a  :       Optional[int]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       Union[str, Any]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

     self.assertEqual(output_a.shape							,						sample.shape							)
     self.assertEqual(output_a.shape							,						output_a.shape							)

     _a  :       Tuple             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample
     _a  :       List[str]             =	scheduler.step(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							,						**UpperCAmelCase__							).prev_sample

     self.assertEqual(output_a.shape							,						sample.shape							)
     self.assertEqual(output_a.shape							,						output_a.shape							)


 def _lowercase	(     self		:						List[str]							)					->       List[str]:
   for timesteps in [100, 1000]:
     self.check_over_configs(num_train_timesteps=UpperCAmelCase__							,						time_step=UpperCAmelCase__							)


 def _lowercase	(     self		:						List[str]							)					->       List[str]:
   for t, num_inference_steps in zip([1, 5, 10]							,						[10, 50, 100]							):
     self.check_over_forward(num_inference_steps=UpperCAmelCase__							,						time_step=UpperCAmelCase__							)







 def _lowercase	(     self		:						int							)					->       List[Any]:
   _a  :       str             =	self.full_loop()
   _a  :       List[Any]             =	torch.mean(torch.abs(UpperCAmelCase__							)							)

   assert abs(result_mean.item() - 2540529							) < 10

