


from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


lowerCAmelCase__						:Optional[int]         =							TypeVar('''DatasetType''', Dataset, IterableDataset)

def  lowerCAmelCase__				(    a__:	int    ,						a__:	Any    ,						a__:	Optional[Any] )						->	DatasetType:


							'''simple docstring'''





							if isinstance(a__    ,						a__ ):
														return _split_by_node_map_style_dataset(a__    ,						rank=a__    ,						world_size=a__ )
							else:
														return _split_by_node_iterable_dataset(a__    ,						rank=a__    ,						world_size=a__ )




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler

class 	__a   (       unittest.TestCase    ):
		# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
		_a			:						List[Any]    			=			(ScoreSdeVeScheduler,)
		_a			:						Any    			=			()
		@property
		def 			UpperCAmelCase__       (    self		)				-> List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=    4
									_UpperCAmelCase					=    3
									_UpperCAmelCase					=    8
									_UpperCAmelCase					=    8

									_UpperCAmelCase					=    torch.rand((batch_size, num_channels, height, width)		)

									return sample
		@property
		def 			UpperCAmelCase__       (    self		)				-> Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    4
									_UpperCAmelCase					=    3
									_UpperCAmelCase					=    8
									_UpperCAmelCase					=    8

									_UpperCAmelCase					=    batch_size * num_channels * height * width
									_UpperCAmelCase					=    torch.arange(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    sample.reshape(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    sample / num_elems
									_UpperCAmelCase					=    sample.permute(3    ,					0    ,					1    ,					2		)

									return sample
		def 			UpperCAmelCase__       (    self		)				-> str:




									"""simple docstring"""





									def model(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					*_SCREAMING_SNAKE_CASE		):
																return sample * t / (t + 1)

									return model
		def 			UpperCAmelCase__       (    self    ,					**_SCREAMING_SNAKE_CASE		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    {
									    'num_train_timesteps': 2000,
									    'snr': 0.15,
									    'sigma_min': 0.01,
									    'sigma_max': 1348,
									    'sampling_eps': 1e-5,
									}

									config.update(**_SCREAMING_SNAKE_CASE		)
									return config
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE=0    ,					**_SCREAMING_SNAKE_CASE		)				-> List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    dict(self.forward_default_kwargs		)

									for scheduler_class in self.scheduler_classes:
																_UpperCAmelCase					=    self.dummy_sample
																_UpperCAmelCase					=    0.1 * sample

																_UpperCAmelCase					=    self.get_scheduler_config(**_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

																with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(_SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase					=    scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    scheduler.step_pred(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    new_scheduler.step_pred(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

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

																_UpperCAmelCase					=    scheduler.step_correct(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    new_scheduler.step_correct(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler correction are not identical"
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE=0    ,					**_SCREAMING_SNAKE_CASE		)				-> List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=    dict(self.forward_default_kwargs		)
									kwargs.update(_SCREAMING_SNAKE_CASE		)

									for scheduler_class in self.scheduler_classes:
																_UpperCAmelCase					=    self.dummy_sample
																_UpperCAmelCase					=    0.1 * sample

																_UpperCAmelCase					=    self.get_scheduler_config()
																_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

																with tempfile.TemporaryDirectory() as tmpdirname:
																							scheduler.save_config(_SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase					=    scheduler_class.from_pretrained(_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    scheduler.step_pred(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    new_scheduler.step_pred(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

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

																_UpperCAmelCase					=    scheduler.step_correct(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    new_scheduler.step_correct(
																    _SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler correction are not identical"
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									for timesteps in [10, 100, 1000]:
																self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> List[Any]:




									"""simple docstring"""





									for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]    ,					[1, 100, 1000]		):
																self.check_over_configs(sigma_min=_SCREAMING_SNAKE_CASE    ,					sigma_max=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> str:




									"""simple docstring"""





									for t in [0.1, 0.5, 0.75]:
																self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE		)
		def 			UpperCAmelCase__       (    self		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    dict(self.forward_default_kwargs		)

									_UpperCAmelCase					=    self.scheduler_classes[0]
									_UpperCAmelCase					=    self.get_scheduler_config()
									_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase					=    3

									_UpperCAmelCase					=    self.dummy_model()
									_UpperCAmelCase					=    self.dummy_sample_deter

									scheduler.set_sigmas(_SCREAMING_SNAKE_CASE		)
									scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase					=    torch.manual_seed(0		)

									for i, t in enumerate(scheduler.timesteps		):
																_UpperCAmelCase					=    scheduler.sigmas[i]

																for _ in range(scheduler.config.correct_steps		):
																							with torch.no_grad():
																														_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase					=    scheduler.step_correct(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

																with torch.no_grad():
																							_UpperCAmelCase					=    model(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    scheduler.step_pred(_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					generator=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase	, _UpperCAmelCase					=    output.prev_sample, output.prev_sample_mean

									_UpperCAmelCase					=    torch.sum(torch.abs(_SCREAMING_SNAKE_CASE		)		)
									_UpperCAmelCase					=    torch.mean(torch.abs(_SCREAMING_SNAKE_CASE		)		)

									assert np.isclose(result_sum.item()    ,					14372758528.0		)
									assert np.isclose(result_mean.item()    ,					18714530.0		)






		def 			UpperCAmelCase__       (    self		)				-> Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=    dict(self.forward_default_kwargs		)

									_UpperCAmelCase					=    kwargs.pop('num_inference_steps'    ,					_SCREAMING_SNAKE_CASE		)

									for scheduler_class in self.scheduler_classes:
																_UpperCAmelCase					=    self.get_scheduler_config()
																_UpperCAmelCase					=    scheduler_class(**_SCREAMING_SNAKE_CASE		)

																_UpperCAmelCase					=    self.dummy_sample
																_UpperCAmelCase					=    0.1 * sample

																if num_inference_steps is not None and hasattr(_SCREAMING_SNAKE_CASE    ,					'set_timesteps'		):
																							scheduler.set_timesteps(_SCREAMING_SNAKE_CASE		)
																elif num_inference_steps is not None and not hasattr(_SCREAMING_SNAKE_CASE    ,					'set_timesteps'		):
																							_UpperCAmelCase					=    num_inference_steps

																_UpperCAmelCase					=    scheduler.step_pred(_SCREAMING_SNAKE_CASE    ,					0    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample
																_UpperCAmelCase					=    scheduler.step_pred(_SCREAMING_SNAKE_CASE    ,					1    ,					_SCREAMING_SNAKE_CASE    ,					generator=torch.manual_seed(0		)    ,					**_SCREAMING_SNAKE_CASE		).prev_sample

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