


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 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				(DPMSolverSDEScheduler,)
					__UpperCAmelCase            =				10






					def A      (				self ,       **snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

								__lowercase 					=   {
								    '''num_train_timesteps''': 1_1_0_0,
								    '''beta_start''': 0.0_0_0_1,
								    '''beta_end''': 0.0_2,
								    '''beta_schedule''': '''linear''',
								    '''noise_sampler_seed''': 0,
								}

								config.update(**snake_case_						)
								return config






					def A      (				self						)   ->			Dict:







								'''simple docstring'''

								for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
											self.check_over_configs(num_train_timesteps=snake_case_						)






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







								'''simple docstring'''

								for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1] ,       [0.0_0_0_2, 0.0_0_2, 0.0_2]						):
											self.check_over_configs(beta_start=snake_case_ ,       beta_end=snake_case_						)






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







								'''simple docstring'''

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






					def A      (				self						)   ->			Tuple:







								'''simple docstring'''

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






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







								'''simple docstring'''

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config()
								__lowercase 					=   scheduler_class(**snake_case_						)

								scheduler.set_timesteps(self.num_inference_steps						)

								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter * scheduler.init_noise_sigma
								__lowercase 					=   sample.to(snake_case_						)

								for i, t in enumerate(scheduler.timesteps						):
											__lowercase 					=   scheduler.scale_model_input(snake_case_ ,       snake_case_						)

											__lowercase 					=   model(snake_case_ ,       snake_case_						)

											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_						)
											__lowercase 					=   output.prev_sample

								__lowercase 					=   torch.sum(torch.abs(snake_case_						)						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								if torch_device in ["mps"]:
											assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5						) < 1e-2
											assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7						) < 1e-3
								elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6						) < 1e-2
											assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2						) < 1e-3
								else:
											assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2						) < 1e-2
											assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6						) < 1e-3






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config(prediction_type='''v_prediction'''						)
								__lowercase 					=   scheduler_class(**snake_case_						)

								scheduler.set_timesteps(self.num_inference_steps						)

								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter * scheduler.init_noise_sigma
								__lowercase 					=   sample.to(snake_case_						)

								for i, t in enumerate(scheduler.timesteps						):
											__lowercase 					=   scheduler.scale_model_input(snake_case_ ,       snake_case_						)

											__lowercase 					=   model(snake_case_ ,       snake_case_						)

											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_						)
											__lowercase 					=   output.prev_sample

								__lowercase 					=   torch.sum(torch.abs(snake_case_						)						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								if torch_device in ["mps"]:
											assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3						) < 1e-2
											assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4						) < 1e-3
								elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3						) < 1e-2
											assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7						) < 1e-3
								else:
											assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5						) < 1e-2
											assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1						) < 1e-3






					def A      (				self						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config()
								__lowercase 					=   scheduler_class(**snake_case_						)

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

								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter.to(snake_case_						) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
											__lowercase 					=   scheduler.scale_model_input(snake_case_ ,       snake_case_						)

											__lowercase 					=   model(snake_case_ ,       snake_case_						)

											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_						)
											__lowercase 					=   output.prev_sample

								__lowercase 					=   torch.sum(torch.abs(snake_case_						)						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								if torch_device in ["mps"]:
											assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8						) < 1e-2
											assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5						) < 1e-3
								elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2						) < 1e-2
											assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1						) < 1e-3
								else:
											assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2						) < 1e-2
											assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6						) < 1e-3






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







								'''simple docstring'''

								__lowercase 					=   self.scheduler_classes[0]
								__lowercase 					=   self.get_scheduler_config()
								__lowercase 					=   scheduler_class(**snake_case_ ,       use_karras_sigmas=snake_case_						)

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

								__lowercase 					=   self.dummy_model()
								__lowercase 					=   self.dummy_sample_deter.to(snake_case_						) * scheduler.init_noise_sigma
								__lowercase 					=   sample.to(snake_case_						)

								for t in scheduler.timesteps:
											__lowercase 					=   scheduler.scale_model_input(snake_case_ ,       snake_case_						)

											__lowercase 					=   model(snake_case_ ,       snake_case_						)

											__lowercase 					=   scheduler.step(snake_case_ ,       snake_case_ ,       snake_case_						)
											__lowercase 					=   output.prev_sample

								__lowercase 					=   torch.sum(torch.abs(snake_case_						)						)
								__lowercase 					=   torch.mean(torch.abs(snake_case_						)						)

								if torch_device in ["mps"]:
											assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8						) < 1e-2
											assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1						) < 1e-2
								elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5						) < 1e-2
											assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1						) < 1e-2
								else:
											assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2						) < 1e-2
											assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1						) < 1e-2






import functools
import gc
import inspect

import torch

from .imports import is_npu_available, is_xpu_available


def 		lowercase_  (     *_UpperCamelCase  ):


			'''simple docstring'''







			if not isinstance(_UpperCamelCase       ,			_UpperCamelCase  ):
						__lowercase 					=   list(_UpperCamelCase  )
			for i in range(len(_UpperCamelCase  )  ):
						__lowercase 					=   None
			gc.collect()
			if is_xpu_available():
						torch.xpu.empty_cache()
			elif is_npu_available():
						torch.npu.empty_cache()
			else:
						torch.cuda.empty_cache()
			return objects


def 		lowercase_  (     _UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   [
			    '''CUDA out of memory.''',  # CUDA OOM
			    '''cuDNN error: CUDNN_STATUS_NOT_SUPPORTED.''',  # CUDNN SNAFU
			    '''DefaultCPUAllocator: can\'t allocate memory''',  # CPU OOM
			]
			if isinstance(_UpperCamelCase       ,			_UpperCamelCase  ) and len(exception.args  ) == 1:
						return any(err in exception.args[0] for err in _statements  )
			return False





def 		lowercase_  (     _UpperCamelCase = None       ,			_UpperCamelCase = 1_28  ):


			'''simple docstring'''







			if function is None:
						return functools.partial(_UpperCamelCase       ,			starting_batch_size=_UpperCamelCase  )

			__lowercase 					=   starting_batch_size

			def decorator(*_UpperCamelCase       ,			**_UpperCamelCase  ):
						nonlocal batch_size
						gc.collect()
						if is_xpu_available():
									torch.xpu.empty_cache()
						elif is_npu_available():
									torch.npu.empty_cache()
						else:
									torch.cuda.empty_cache()
						__lowercase 					=   list(inspect.signature(_UpperCamelCase  ).parameters.keys()  )
						# Guard against user error
						if len(_UpperCamelCase  ) < (len(_UpperCamelCase  ) + 1):
									__lowercase 					=   ''', '''.join([F'{arg}={value}' for arg, value in zip(params[1:]       ,			args[1:]  )]  )
									raise TypeError(
									    F'Batch size was passed into `{function.__name__}` as the first argument when called.'
									    F'Remove this as the decorator already does so: `{function.__name__}({arg_str})`'  )
						while True:
									if batch_size == 0:
												raise RuntimeError('''No executable batch size found, reached zero.'''  )
									try:
												return function(_UpperCamelCase       ,			*_UpperCamelCase       ,			**_UpperCamelCase  )
									except Exception as e:
												if should_reduce_batch_size(_UpperCamelCase  ):
															gc.collect()
															if is_xpu_available():
																		torch.xpu.empty_cache()
															elif is_npu_available():
																		torch.npu.empty_cache()
															else:
																		torch.cuda.empty_cache()
															batch_size //= 2
												else:
															raise

			return decorator


