


'''simple docstring'''



import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   (DDPMParallelScheduler,)




	def 				__UpperCAmelCase							(       self						,    **__a							):

						'''simple docstring'''





						__a      :	List[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(**__a							)
						return config




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self							):

						'''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=__a						,    beta_end=__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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

						assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487							) - 0.00979							)							) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.02							)							) < 1E-5




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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

						__a      :	Optional[int]    =  len(__a							)

						__a      :	Optional[int]    =  self.dummy_model()
						__a      :	Any    =  self.dummy_sample_deter
						__a      :	Optional[Any]    =  self.dummy_sample_deter + 0.1
						__a      :	Optional[int]    =  self.dummy_sample_deter - 0.1

						__a      :	Tuple    =  samplea.shape[0]
						__a      :	str    =  torch.stack([samplea, samplea, samplea]						,    dim=0							)
						__a      :	Optional[Any]    =  torch.arange(__a							)[0:3, None].repeat(1						,    __a							)

						__a      :	str    =  model(samples.flatten(0						,    1							)						,    timesteps.flatten(0						,    1							)							)
						__a      :	str    =  scheduler.batch_step_no_noise(__a						,    timesteps.flatten(0						,    1							)						,    samples.flatten(0						,    1							)							)

						__a      :	Dict    =  torch.sum(torch.abs(__a							)							)
						__a      :	Optional[Any]    =  torch.mean(torch.abs(__a							)							)

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Tuple    =  self.scheduler_classes[0]
						__a      :	Tuple    =  self.get_scheduler_config()
						__a      :	str    =  scheduler_class(**__a							)

						__a      :	Optional[Any]    =  len(__a							)

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

						for t in reversed(range(__a							)							):
											# 1. predict noise residual
											__a      :	int    =  model(__a						,    __a							)

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

											__a      :	str    =  pred_prev_sample

						__a      :	Optional[int]    =  torch.sum(torch.abs(__a							)							)
						__a      :	str    =  torch.mean(torch.abs(__a							)							)

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  self.scheduler_classes[0]
						__a      :	Optional[Any]    =  self.get_scheduler_config(prediction_type='v_prediction'							)
						__a      :	Optional[Any]    =  scheduler_class(**__a							)

						__a      :	List[str]    =  len(__a							)

						__a      :	Tuple    =  self.dummy_model()
						__a      :	Any    =  self.dummy_sample_deter
						__a      :	List[Any]    =  torch.manual_seed(0							)

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

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

											__a      :	Optional[int]    =  pred_prev_sample

						__a      :	Any    =  torch.sum(torch.abs(__a							)							)
						__a      :	Dict    =  torch.mean(torch.abs(__a							)							)

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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

						__a      :	List[str]    =  [100, 87, 50, 1, 0]

						scheduler.set_timesteps(timesteps=__a							)

						__a      :	str    =  scheduler.timesteps

						for i, timestep in enumerate(__a							):
											if i == len(__a							) - 1:
																__a      :	List[Any]    =  -1
											else:
																__a      :	Optional[int]    =  timesteps[i + 1]

											__a      :	str    =  scheduler.previous_timestep(__a							)
											__a      :	List[Any]    =  prev_t.item()

											self.assertEqual(__a						,    __a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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

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

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  self.scheduler_classes[0]
						__a      :	Optional[Any]    =  self.get_scheduler_config()
						__a      :	Dict    =  scheduler_class(**__a							)

						__a      :	List[str]    =  [100, 87, 50, 1, 0]
						__a      :	List[str]    =  len(__a							)

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





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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

						__a      :	Optional[Any]    =  [scheduler.config.num_train_timesteps]

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







'''simple docstring'''



import re

from filelock import FileLock


try:
	import nltk

	__lowercase     :						Optional[Any]	=     True
except (ImportError, ModuleNotFoundError):
	__lowercase     :						Dict	=     False

if NLTK_AVAILABLE:
	with FileLock('.lock') as lock:
		nltk.download('punkt', quiet=True)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str       ):
					re.sub('<n>'						,				''						,				_SCREAMING_SNAKE_CASE       )  # remove pegasus newline char
					assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
					return "\n".join(nltk.sent_tokenize(_SCREAMING_SNAKE_CASE       )       )



