


"""simple docstring"""




import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest


@require_torchsde
class 	_a							(      _a      ):
							_lowercase     :      List[str]  				=			(DPMSolverSDEScheduler,)
							_lowercase     :      List[str]  				=			10

							def 				lowerCamelCase_			(							self:     List[str]   ,						**UpperCamelCase_:     Dict							)      ->       Tuple:





									"""simple docstring"""







									lowercase__						=				{
									    "num_train_timesteps": 1_100,
									    "beta_start": 0.0001,
									    "beta_end": 0.02,
									    "beta_schedule": "linear",
									    "noise_sampler_seed": 0,
									}

									config.update(**_a							)
									return config

							def 				lowerCamelCase_			(							self:     List[Any]							)      ->       Dict:





									"""simple docstring"""







									for timesteps in [10, 50, 100, 1_000]:
											self.check_over_configs(num_train_timesteps=_a							)

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       int:





									"""simple docstring"""







									for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]   ,						[0.0002, 0.002, 0.02]							):
											self.check_over_configs(beta_start=_a   ,						beta_end=_a							)

							def 				lowerCamelCase_			(							self:     Tuple							)      ->       Optional[int]:





									"""simple docstring"""







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

							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       Tuple:





									"""simple docstring"""







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

							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       Optional[int]:





									"""simple docstring"""







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

									scheduler.set_timesteps(self.num_inference_steps							)

									lowercase__						=				self.dummy_model()
									lowercase__						=				self.dummy_sample_deter * scheduler.init_noise_sigma
									lowercase__						=				sample.to(_a							)

									for i, t in enumerate(scheduler.timesteps							):
											lowercase__						=				scheduler.scale_model_input(_a   ,						_a							)

											lowercase__						=				model(_a   ,						_a							)

											lowercase__						=				scheduler.step(_a   ,						_a   ,						_a							)
											lowercase__						=				output.prev_sample

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

									if torch_device in ["mps"]:
											assert abs(result_sum.item() - 167.47_821_044_921_875							) < 1E-2
											assert abs(result_mean.item() - 0.2178705964565277							) < 1E-3
									elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 171.59_352_111_816_406							) < 1E-2
											assert abs(result_mean.item() - 0.22342906892299652							) < 1E-3
									else:
											assert abs(result_sum.item() - 162.52_383_422_851_562							) < 1E-2
											assert abs(result_mean.item() - 0.211619570851326							) < 1E-3

							def 				lowerCamelCase_			(							self:     int							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				self.scheduler_classes[0]
									lowercase__						=				self.get_scheduler_config(prediction_type='''v_prediction'''							)
									lowercase__						=				scheduler_class(**_a							)

									scheduler.set_timesteps(self.num_inference_steps							)

									lowercase__						=				self.dummy_model()
									lowercase__						=				self.dummy_sample_deter * scheduler.init_noise_sigma
									lowercase__						=				sample.to(_a							)

									for i, t in enumerate(scheduler.timesteps							):
											lowercase__						=				scheduler.scale_model_input(_a   ,						_a							)

											lowercase__						=				model(_a   ,						_a							)

											lowercase__						=				scheduler.step(_a   ,						_a   ,						_a							)
											lowercase__						=				output.prev_sample

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

									if torch_device in ["mps"]:
											assert abs(result_sum.item() - 124.77_149_200_439_453							) < 1E-2
											assert abs(result_mean.item() - 0.16226289014816284							) < 1E-3
									elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 128.1_663_360_595_703							) < 1E-2
											assert abs(result_mean.item() - 0.16688326001167297							) < 1E-3
									else:
											assert abs(result_sum.item() - 119.8_487_548_828_125							) < 1E-2
											assert abs(result_mean.item() - 0.1560530662536621							) < 1E-3

							def 				lowerCamelCase_			(							self:     str							)      ->       Any:





									"""simple docstring"""







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

									scheduler.set_timesteps(self.num_inference_steps   ,						device=_a							)

									lowercase__						=				self.dummy_model()
									lowercase__						=				self.dummy_sample_deter.to(_a							) * scheduler.init_noise_sigma

									for t in scheduler.timesteps:
											lowercase__						=				scheduler.scale_model_input(_a   ,						_a							)

											lowercase__						=				model(_a   ,						_a							)

											lowercase__						=				scheduler.step(_a   ,						_a   ,						_a							)
											lowercase__						=				output.prev_sample

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

									if torch_device in ["mps"]:
											assert abs(result_sum.item() - 167.46_957_397_460_938							) < 1E-2
											assert abs(result_mean.item() - 0.21805934607982635							) < 1E-3
									elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 171.59_353_637_695_312							) < 1E-2
											assert abs(result_mean.item() - 0.22342908382415771							) < 1E-3
									else:
											assert abs(result_sum.item() - 162.52_383_422_851_562							) < 1E-2
											assert abs(result_mean.item() - 0.211619570851326							) < 1E-3


							def 				lowerCamelCase_			(							self:     Tuple							)      ->       int:





									"""simple docstring"""







									lowercase__						=				self.scheduler_classes[0]
									lowercase__						=				self.get_scheduler_config()
									lowercase__						=				scheduler_class(**_a   ,						use_karras_sigmas=_a							)

									scheduler.set_timesteps(self.num_inference_steps   ,						device=_a							)

									lowercase__						=				self.dummy_model()
									lowercase__						=				self.dummy_sample_deter.to(_a							) * scheduler.init_noise_sigma
									lowercase__						=				sample.to(_a							)

									for t in scheduler.timesteps:
											lowercase__						=				scheduler.scale_model_input(_a   ,						_a							)

											lowercase__						=				model(_a   ,						_a							)

											lowercase__						=				scheduler.step(_a   ,						_a   ,						_a							)
											lowercase__						=				output.prev_sample

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

									if torch_device in ["mps"]:
											assert abs(result_sum.item() - 176.66_974_135_742_188							) < 1E-2
											assert abs(result_mean.item() - 0.23003872730981811							) < 1E-2
									elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 177.63_653_564_453_125							) < 1E-2
											assert abs(result_mean.item() - 0.23003872730981811							) < 1E-2
									else:
											assert abs(result_sum.item() - 170.3_135_223_388_672							) < 1E-2
											assert abs(result_mean.item() - 0.23003872730981811							) < 1E-2






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
			import torch

			from transformers.activations import gelu_new, gelu_python, get_activation


@require_torch
class 	_a							(      unittest.TestCase      ):

							def 				lowerCamelCase_			(							self:     Dict							)      ->       Any:





									"""simple docstring"""







									lowercase__						=				torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]							)
									lowercase__						=				get_activation('''gelu'''							)
									self.assertTrue(torch.allclose(gelu_python(UpperCamelCase_							)   ,						torch_builtin(UpperCamelCase_							)							)							)
									self.assertFalse(torch.allclose(gelu_python(UpperCamelCase_							)   ,						gelu_new(UpperCamelCase_							)							)							)

							def 				lowerCamelCase_			(							self:     Optional[int]							)      ->       str:





									"""simple docstring"""







									lowercase__						=				torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100]							)
									lowercase__						=				get_activation('''gelu'''							)
									lowercase__						=				get_activation('''gelu_10'''							)

									lowercase__						=				torch_builtin(UpperCamelCase_							)
									lowercase__						=				geluaa(UpperCamelCase_							)

									lowercase__						=				torch.where(y_gelu_aa < 10.0   ,						1   ,						0							)

									self.assertTrue(torch.max(UpperCamelCase_							).item() == 10.0							)
									self.assertTrue(torch.allclose(y_gelu * clipped_mask   ,						y_gelu_aa * clipped_mask							)							)

							def 				lowerCamelCase_			(							self:     Union[str, Any]							)      ->       Tuple:





									"""simple docstring"""







									get_activation('''gelu'''							)
									get_activation('''gelu_10'''							)
									get_activation('''gelu_fast'''							)
									get_activation('''gelu_new'''							)
									get_activation('''gelu_python'''							)
									get_activation('''gelu_pytorch_tanh'''							)
									get_activation('''linear'''							)
									get_activation('''mish'''							)
									get_activation('''quick_gelu'''							)
									get_activation('''relu'''							)
									get_activation('''sigmoid'''							)
									get_activation('''silu'''							)
									get_activation('''swish'''							)
									get_activation('''tanh'''							)
									with self.assertRaises(UpperCamelCase_							):
											get_activation('''bogus'''							)
									with self.assertRaises(UpperCamelCase_							):
											get_activation(UpperCamelCase_							)


							def 				lowerCamelCase_			(							self:     Tuple							)      ->       int:





									"""simple docstring"""







									lowercase__						=				get_activation('''gelu'''							)
									lowercase__						=				1
									lowercase__						=				get_activation('''gelu'''							)
									self.assertEqual(acta.a   ,						1							)
									with self.assertRaises(UpperCamelCase_							):
											lowercase__						=				acta.a


