
"""simple docstring"""




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler




class 					UpperCamelCase     (	unittest.TestCase				):

				"""simple docstring"""



				UpperCAmelCase_    :				Tuple						  =    (ScoreSdeVeScheduler,)
				UpperCAmelCase_    :				List[Any]						  =    ()


				@property
				def 							A (    self      )   ->   str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     4
									SCREAMING_SNAKE_CASE           =     3
									SCREAMING_SNAKE_CASE           =     8
									SCREAMING_SNAKE_CASE           =     8

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

									return sample


				@property
				def 							A (    self      )   ->   Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     4
									SCREAMING_SNAKE_CASE           =     3
									SCREAMING_SNAKE_CASE           =     8
									SCREAMING_SNAKE_CASE           =     8

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

									return sample


				def 							A (    self      )   ->   Optional[int]:
									"""simple docstring"""







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

									return model


				def 							A (    self		,     **lowercase__      )   ->   Any:
									"""simple docstring"""







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

									config.update(**lowercase__      )
									return config


				def 							A (    self		,     lowercase__=0		,     **lowercase__      )   ->   Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     dict(self.forward_default_kwargs      )

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

														SCREAMING_SNAKE_CASE           =     self.get_scheduler_config(**lowercase__      )
														SCREAMING_SNAKE_CASE           =     scheduler_class(**lowercase__      )

														with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     scheduler_class.from_pretrained(lowercase__      )

														SCREAMING_SNAKE_CASE           =     scheduler.step_pred(
														    lowercase__		,     lowercase__		,     lowercase__		,     generator=torch.manual_seed(0      )		,     **lowercase__      ).prev_sample
														SCREAMING_SNAKE_CASE           =     new_scheduler.step_pred(
														    lowercase__		,     lowercase__		,     lowercase__		,     generator=torch.manual_seed(0      )		,     **lowercase__      ).prev_sample

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

														SCREAMING_SNAKE_CASE           =     scheduler.step_correct(lowercase__		,     lowercase__		,     generator=torch.manual_seed(0      )		,     **lowercase__      ).prev_sample
														SCREAMING_SNAKE_CASE           =     new_scheduler.step_correct(
														    lowercase__		,     lowercase__		,     generator=torch.manual_seed(0      )		,     **lowercase__      ).prev_sample

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


				def 							A (    self		,     lowercase__=0		,     **lowercase__      )   ->   List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     dict(self.forward_default_kwargs      )
									kwargs.update(lowercase__      )

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

														SCREAMING_SNAKE_CASE           =     self.get_scheduler_config()
														SCREAMING_SNAKE_CASE           =     scheduler_class(**lowercase__      )

														with tempfile.TemporaryDirectory() as tmpdirname:
																			scheduler.save_config(lowercase__      )
																			SCREAMING_SNAKE_CASE           =     scheduler_class.from_pretrained(lowercase__      )

														SCREAMING_SNAKE_CASE           =     scheduler.step_pred(
														    lowercase__		,     lowercase__		,     lowercase__		,     generator=torch.manual_seed(0      )		,     **lowercase__      ).prev_sample
														SCREAMING_SNAKE_CASE           =     new_scheduler.step_pred(
														    lowercase__		,     lowercase__		,     lowercase__		,     generator=torch.manual_seed(0      )		,     **lowercase__      ).prev_sample

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

														SCREAMING_SNAKE_CASE           =     scheduler.step_correct(lowercase__		,     lowercase__		,     generator=torch.manual_seed(0      )		,     **lowercase__      ).prev_sample
														SCREAMING_SNAKE_CASE           =     new_scheduler.step_correct(
														    lowercase__		,     lowercase__		,     generator=torch.manual_seed(0      )		,     **lowercase__      ).prev_sample

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


				def 							A (    self      )   ->   Dict:
									"""simple docstring"""







									for timesteps in [10, 100, 1000]:
														self.check_over_configs(num_train_timesteps=lowercase__      )


				def 							A (    self      )   ->   Union[str, Any]:
									"""simple docstring"""







									for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]		,     [1, 100, 1000]      ):
														self.check_over_configs(sigma_min=lowercase__		,     sigma_max=lowercase__      )


				def 							A (    self      )   ->   List[Any]:
									"""simple docstring"""







									for t in [0.1, 0.5, 0.75]:
														self.check_over_forward(time_step=lowercase__      )


				def 							A (    self      )   ->   Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     dict(self.forward_default_kwargs      )

									SCREAMING_SNAKE_CASE           =     self.scheduler_classes[0]
									SCREAMING_SNAKE_CASE           =     self.get_scheduler_config()
									SCREAMING_SNAKE_CASE           =     scheduler_class(**lowercase__      )

									SCREAMING_SNAKE_CASE           =     3

									SCREAMING_SNAKE_CASE           =     self.dummy_model()
									SCREAMING_SNAKE_CASE           =     self.dummy_sample_deter

									scheduler.set_sigmas(lowercase__      )
									scheduler.set_timesteps(lowercase__      )
									SCREAMING_SNAKE_CASE           =     torch.manual_seed(0      )

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

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

														with torch.no_grad():
																			SCREAMING_SNAKE_CASE           =     model(lowercase__		,     lowercase__      )

														SCREAMING_SNAKE_CASE           =     scheduler.step_pred(lowercase__		,     lowercase__		,     lowercase__		,     generator=lowercase__		,     **lowercase__      )
														SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE           =     output.prev_sample, output.prev_sample_mean

									SCREAMING_SNAKE_CASE           =     torch.sum(torch.abs(lowercase__      )      )
									SCREAMING_SNAKE_CASE           =     torch.mean(torch.abs(lowercase__      )      )

									assert np.isclose(result_sum.item()		,     14_372_758_528.0      )
									assert np.isclose(result_mean.item()		,     18_714_530.0      )




				def 							A (    self      )   ->   Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     dict(self.forward_default_kwargs      )

									SCREAMING_SNAKE_CASE           =     kwargs.pop('num_inference_steps'		,     lowercase__      )

									for scheduler_class in self.scheduler_classes:
														SCREAMING_SNAKE_CASE           =     self.get_scheduler_config()
														SCREAMING_SNAKE_CASE           =     scheduler_class(**lowercase__      )

														SCREAMING_SNAKE_CASE           =     self.dummy_sample
														SCREAMING_SNAKE_CASE           =     0.1 * sample

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

														SCREAMING_SNAKE_CASE           =     scheduler.step_pred(lowercase__		,     0		,     lowercase__		,     generator=torch.manual_seed(0      )		,     **lowercase__      ).prev_sample
														SCREAMING_SNAKE_CASE           =     scheduler.step_pred(lowercase__		,     1		,     lowercase__		,     generator=torch.manual_seed(0      )		,     **lowercase__      ).prev_sample

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


"""simple docstring"""




import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


snake_case			       =		TypeVar('T', bound='TaskTemplate')




@dataclass(frozen=__magic_name__				)
class 					UpperCamelCase     (	abc.ABC				):

				"""simple docstring"""



				UpperCAmelCase_    :				str
				UpperCAmelCase_    :				ClassVar[Features]
				UpperCAmelCase_    :				ClassVar[Features]


				def 							A (    self		,     lowercase__      )   ->   T:
									"""simple docstring"""







									return copy.deepcopy(self      )


				@property
				def 							A (    self      )   ->   Features:
									"""simple docstring"""







									return Features(**self.input_schema		,     **self.label_schema      )


				@property
				@abc.abstractmethod
				def 							A (    self      )   ->   Dict[str, str]:
									"""simple docstring"""







									raise NotImplementedError




				@classmethod
				def 							A (    cls		,     lowercase__      )   ->   T:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =     {f.name for f in dataclasses.fields(cls      )}
									return cls(**{k: v for k, v in template_dict.items() if k in field_names}      )
