
'''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 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       (DPMSolverSDEScheduler,)
							__lowercase      		=       10


							def  lowerCamelCase				(       self    ,   **lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		{
								    'num_train_timesteps': 11_00,
								    'beta_start': 0.0001,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								    'noise_sampler_seed': 0,
								}

								config.update(**lowerCAmelCase_  )
								return config


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								for timesteps in [10, 50, 1_00, 10_00]:
									self.check_over_configs(num_train_timesteps=lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""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=lowerCAmelCase_    ,   beta_end=lowerCAmelCase_  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.scheduler_classes[0]
								_snake_case   						=		self.get_scheduler_config()
								_snake_case   						=		scheduler_class(**lowerCAmelCase_  )

								scheduler.set_timesteps(self.num_inference_steps  )

								_snake_case   						=		self.dummy_model()
								_snake_case   						=		self.dummy_sample_deter * scheduler.init_noise_sigma
								_snake_case   						=		sample.to(lowerCAmelCase_  )

								for i, t in enumerate(scheduler.timesteps  ):
									_snake_case   						=		scheduler.scale_model_input(lowerCAmelCase_    ,   lowerCAmelCase_  )

									_snake_case   						=		model(lowerCAmelCase_    ,   lowerCAmelCase_  )

									_snake_case   						=		scheduler.step(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  )
									_snake_case   						=		output.prev_sample

								_snake_case   						=		torch.sum(torch.abs(lowerCAmelCase_  )  )
								_snake_case   						=		torch.mean(torch.abs(lowerCAmelCase_  )  )

								if torch_device in ["mps"]:
									assert abs(result_sum.item() - 167.47821044921875  ) < 1E-2
									assert abs(result_mean.item() - 0.2178705964565277  ) < 1E-3
								elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 171.59352111816406  ) < 1E-2
									assert abs(result_mean.item() - 0.22342906892299652  ) < 1E-3
								else:
									assert abs(result_sum.item() - 162.52383422851562  ) < 1E-2
									assert abs(result_mean.item() - 0.211619570851326  ) < 1E-3


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.scheduler_classes[0]
								_snake_case   						=		self.get_scheduler_config(prediction_type='v_prediction'  )
								_snake_case   						=		scheduler_class(**lowerCAmelCase_  )

								scheduler.set_timesteps(self.num_inference_steps  )

								_snake_case   						=		self.dummy_model()
								_snake_case   						=		self.dummy_sample_deter * scheduler.init_noise_sigma
								_snake_case   						=		sample.to(lowerCAmelCase_  )

								for i, t in enumerate(scheduler.timesteps  ):
									_snake_case   						=		scheduler.scale_model_input(lowerCAmelCase_    ,   lowerCAmelCase_  )

									_snake_case   						=		model(lowerCAmelCase_    ,   lowerCAmelCase_  )

									_snake_case   						=		scheduler.step(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  )
									_snake_case   						=		output.prev_sample

								_snake_case   						=		torch.sum(torch.abs(lowerCAmelCase_  )  )
								_snake_case   						=		torch.mean(torch.abs(lowerCAmelCase_  )  )

								if torch_device in ["mps"]:
									assert abs(result_sum.item() - 124.77149200439453  ) < 1E-2
									assert abs(result_mean.item() - 0.16226289014816284  ) < 1E-3
								elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 128.1663360595703  ) < 1E-2
									assert abs(result_mean.item() - 0.16688326001167297  ) < 1E-3
								else:
									assert abs(result_sum.item() - 119.8487548828125  ) < 1E-2
									assert abs(result_mean.item() - 0.1560530662536621  ) < 1E-3


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.scheduler_classes[0]
								_snake_case   						=		self.get_scheduler_config()
								_snake_case   						=		scheduler_class(**lowerCAmelCase_  )

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

								_snake_case   						=		self.dummy_model()
								_snake_case   						=		self.dummy_sample_deter.to(lowerCAmelCase_  ) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
									_snake_case   						=		scheduler.scale_model_input(lowerCAmelCase_    ,   lowerCAmelCase_  )

									_snake_case   						=		model(lowerCAmelCase_    ,   lowerCAmelCase_  )

									_snake_case   						=		scheduler.step(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  )
									_snake_case   						=		output.prev_sample

								_snake_case   						=		torch.sum(torch.abs(lowerCAmelCase_  )  )
								_snake_case   						=		torch.mean(torch.abs(lowerCAmelCase_  )  )

								if torch_device in ["mps"]:
									assert abs(result_sum.item() - 167.46957397460938  ) < 1E-2
									assert abs(result_mean.item() - 0.21805934607982635  ) < 1E-3
								elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 171.59353637695312  ) < 1E-2
									assert abs(result_mean.item() - 0.22342908382415771  ) < 1E-3
								else:
									assert abs(result_sum.item() - 162.52383422851562  ) < 1E-2
									assert abs(result_mean.item() - 0.211619570851326  ) < 1E-3




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.scheduler_classes[0]
								_snake_case   						=		self.get_scheduler_config()
								_snake_case   						=		scheduler_class(**lowerCAmelCase_    ,   use_karras_sigmas=lowerCAmelCase_  )

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

								_snake_case   						=		self.dummy_model()
								_snake_case   						=		self.dummy_sample_deter.to(lowerCAmelCase_  ) * scheduler.init_noise_sigma
								_snake_case   						=		sample.to(lowerCAmelCase_  )

								for t in scheduler.timesteps:
									_snake_case   						=		scheduler.scale_model_input(lowerCAmelCase_    ,   lowerCAmelCase_  )

									_snake_case   						=		model(lowerCAmelCase_    ,   lowerCAmelCase_  )

									_snake_case   						=		scheduler.step(lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  )
									_snake_case   						=		output.prev_sample

								_snake_case   						=		torch.sum(torch.abs(lowerCAmelCase_  )  )
								_snake_case   						=		torch.mean(torch.abs(lowerCAmelCase_  )  )

								if torch_device in ["mps"]:
									assert abs(result_sum.item() - 176.66974135742188  ) < 1E-2
									assert abs(result_mean.item() - 0.23003872730981811  ) < 1E-2
								elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 177.63653564453125  ) < 1E-2
									assert abs(result_mean.item() - 0.23003872730981811  ) < 1E-2
								else:
									assert abs(result_sum.item() - 170.3135223388672  ) < 1E-2
									assert abs(result_mean.item() - 0.23003872730981811  ) < 1E-2




import warnings

from ...utils import logging
from .image_processing_clip import CLIPImageProcessor


_lowerCAmelCase					:			str  				=			logging.get_logger(__name__)






class 	__magic_name__    (			lowerCAmelCase_      ):





			def __init__(		self					,  *__snake_case					,  **__snake_case					)   -> None:




						'''simple docstring'''
						warnings.warn(
						    'The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
						    ' use CLIPImageProcessor instead.'					,  __snake_case					,  )
						super().__init__(*__snake_case					,  **__snake_case					)






