
"""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  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      List[Any]          =    (DPMSolverSDEScheduler,)
				a__:      Any          =    10




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							lowerCamelCase_					=  {
							    '''num_train_timesteps''': 1100,
							    '''beta_start''': 0.0_0_0_1,
							    '''beta_end''': 0.0_2,
							    '''beta_schedule''': '''linear''',
							    '''noise_sampler_seed''': 0,
							}

							config.update(**UpperCAmelCase   )
							return config




				def        UpperCAmelCase__						(				self   ):
							for timesteps in [10, 50, 100, 1000]:
										self.check_over_configs(num_train_timesteps=UpperCAmelCase   )




				def        UpperCAmelCase__						(				self   ):
							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=UpperCAmelCase							,						beta_end=UpperCAmelCase   )




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




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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							scheduler.set_timesteps(self.num_inference_steps   )

							lowerCamelCase_					=  self.dummy_model()
							lowerCamelCase_					=  self.dummy_sample_deter * scheduler.init_noise_sigma
							lowerCamelCase_					=  sample.to(UpperCAmelCase   )

							for i, t in enumerate(scheduler.timesteps   ):
										lowerCamelCase_					=  scheduler.scale_model_input(UpperCAmelCase							,						UpperCAmelCase   )

										lowerCamelCase_					=  model(UpperCAmelCase							,						UpperCAmelCase   )

										lowerCamelCase_					=  scheduler.step(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )
										lowerCamelCase_					=  output.prev_sample

							lowerCamelCase_					=  torch.sum(torch.abs(UpperCAmelCase   )   )
							lowerCamelCase_					=  torch.mean(torch.abs(UpperCAmelCase   )   )

							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        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config(prediction_type='''v_prediction'''   )
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

							scheduler.set_timesteps(self.num_inference_steps   )

							lowerCamelCase_					=  self.dummy_model()
							lowerCamelCase_					=  self.dummy_sample_deter * scheduler.init_noise_sigma
							lowerCamelCase_					=  sample.to(UpperCAmelCase   )

							for i, t in enumerate(scheduler.timesteps   ):
										lowerCamelCase_					=  scheduler.scale_model_input(UpperCAmelCase							,						UpperCAmelCase   )

										lowerCamelCase_					=  model(UpperCAmelCase							,						UpperCAmelCase   )

										lowerCamelCase_					=  scheduler.step(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )
										lowerCamelCase_					=  output.prev_sample

							lowerCamelCase_					=  torch.sum(torch.abs(UpperCAmelCase   )   )
							lowerCamelCase_					=  torch.mean(torch.abs(UpperCAmelCase   )   )

							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        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase   )

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

							lowerCamelCase_					=  self.dummy_model()
							lowerCamelCase_					=  self.dummy_sample_deter.to(UpperCAmelCase   ) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
										lowerCamelCase_					=  scheduler.scale_model_input(UpperCAmelCase							,						UpperCAmelCase   )

										lowerCamelCase_					=  model(UpperCAmelCase							,						UpperCAmelCase   )

										lowerCamelCase_					=  scheduler.step(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )
										lowerCamelCase_					=  output.prev_sample

							lowerCamelCase_					=  torch.sum(torch.abs(UpperCAmelCase   )   )
							lowerCamelCase_					=  torch.mean(torch.abs(UpperCAmelCase   )   )

							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        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.scheduler_classes[0]
							lowerCamelCase_					=  self.get_scheduler_config()
							lowerCamelCase_					=  scheduler_class(**UpperCAmelCase							,						use_karras_sigmas=UpperCAmelCase   )

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

							lowerCamelCase_					=  self.dummy_model()
							lowerCamelCase_					=  self.dummy_sample_deter.to(UpperCAmelCase   ) * scheduler.init_noise_sigma
							lowerCamelCase_					=  sample.to(UpperCAmelCase   )

							for t in scheduler.timesteps:
										lowerCamelCase_					=  scheduler.scale_model_input(UpperCAmelCase							,						UpperCAmelCase   )

										lowerCamelCase_					=  model(UpperCAmelCase							,						UpperCAmelCase   )

										lowerCamelCase_					=  scheduler.step(UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   )
										lowerCamelCase_					=  output.prev_sample

							lowerCamelCase_					=  torch.sum(torch.abs(UpperCAmelCase   )   )
							lowerCamelCase_					=  torch.mean(torch.abs(UpperCAmelCase   )   )

							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










"""simple docstring"""





import os
import sys
import tempfile

import torch

from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				Any     ,    SCREAMING_SNAKE_CASE							:				List[str]=()     ,    SCREAMING_SNAKE_CASE							:				Union[str, Any]=None     ,    SCREAMING_SNAKE_CASE							:				Dict="no"     ,    SCREAMING_SNAKE_CASE							:				int="29500" ):


							'''simple docstring'''







							__lowerCamelCase   :   Optional[int]       =False
							__lowerCamelCase   :   Tuple       =False
							if any(key.startswith('''KAGGLE''' ) for key in os.environ.keys() ):
														__lowerCamelCase   :   Optional[int]       =True
							elif "IPython" in sys.modules:
														__lowerCamelCase   :   int       ='''google.colab''' in str(sys.modules['''IPython'''].get_ipython() )

							try:
														__lowerCamelCase   :   str       =PrecisionType(mixed_precision.lower() )
							except ValueError:
														raise ValueError(
														    F'Unknown mixed_precision mode: {args.mixed_precision.lower()}. Choose between {PrecisionType.list()}.' )

							if (in_colab or in_kaggle) and (os.environ.get('''TPU_NAME'''     ,    SCREAMING_SNAKE_CASE ) is not None):
														# TPU launch
														import torch_xla.distributed.xla_multiprocessing as xmp

														if len(AcceleratorState._shared_state ) > 0:
																					raise ValueError(
																					    '''To train on TPU in Colab or Kaggle Kernel, the `Accelerator` should only be initialized inside '''
																					    '''your training function. Restart your notebook and make sure no cells initializes an '''
																					    '''`Accelerator`.''' )
														if num_processes is None:
																					__lowerCamelCase   :   int       =8

														__lowerCamelCase   :   Optional[Any]       =PrepareForLaunch(SCREAMING_SNAKE_CASE     ,    distributed_type='''TPU''' )
														print(F'Launching a training on {num_processes} TPU cores.' )
														xmp.spawn(SCREAMING_SNAKE_CASE     ,    args=SCREAMING_SNAKE_CASE     ,    nprocs=SCREAMING_SNAKE_CASE     ,    start_method='''fork''' )
							elif in_colab:
														# No need for a distributed launch otherwise as it's either CPU or one GPU.
														if torch.cuda.is_available():
																					print('''Launching training on one GPU.''' )
														else:
																					print('''Launching training on one CPU.''' )
														function(*SCREAMING_SNAKE_CASE )
							else:
														if num_processes is None:
																					raise ValueError(
																					    '''You have to specify the number of GPUs you would like to use, add `num_processes=...` to your call.''' )

														if num_processes > 1:
																					# Multi-GPU launch
																					from torch.multiprocessing import start_processes
																					from torch.multiprocessing.spawn import ProcessRaisedException

																					if len(AcceleratorState._shared_state ) > 0:
																												raise ValueError(
																												    '''To launch a multi-GPU training from your notebook, the `Accelerator` should only be initialized '''
																												    '''inside your training function. Restart your notebook and make sure no cells initializes an '''
																												    '''`Accelerator`.''' )

																					if torch.cuda.is_initialized():
																												raise ValueError(
																												    '''To launch a multi-GPU training from your notebook, you need to avoid running any instruction '''
																												    '''using `torch.cuda` in any cell. Restart your notebook and make sure no cells use any CUDA '''
																												    '''function.''' )

																					# torch.distributed will expect a few environment variable to be here. We set the ones common to each
																					# process here (the other ones will be set be the launcher).
																					with patch_environment(
																					    world_size=SCREAMING_SNAKE_CASE     ,    master_addr='''127.0.01'''     ,    master_port=SCREAMING_SNAKE_CASE     ,    mixed_precision=SCREAMING_SNAKE_CASE ):
																												__lowerCamelCase   :   List[str]       =PrepareForLaunch(SCREAMING_SNAKE_CASE     ,    distributed_type='''MULTI_GPU''' )
																												print(F'Launching training on {num_processes} GPUs.' )
																												try:
																																			start_processes(SCREAMING_SNAKE_CASE     ,    args=SCREAMING_SNAKE_CASE     ,    nprocs=SCREAMING_SNAKE_CASE     ,    start_method='''fork''' )
																												except ProcessRaisedException as e:
																																			if "Cannot re-initialize CUDA in forked subprocess" in e.args[0]:
																																										raise RuntimeError(
																																										    '''CUDA has been initialized before the `notebook_launcher` could create a forked subprocess. '''
																																										    '''This likely stems from an outside import causing issues once the `notebook_launcher()` is called. '''
																																										    '''Please review your imports and test them when running the `notebook_launcher()` to identify '''
																																										    '''which one is problematic.''' ) from e

														else:
																					# No need for a distributed launch otherwise as it's either CPU, GPU or MPS.
																					if is_mps_available():
																												__lowerCamelCase   :   Optional[Any]       ='''1'''
																												print('''Launching training on MPS.''' )
																					elif torch.cuda.is_available():
																												print('''Launching training on one GPU.''' )
																					else:
																												print('''Launching training on CPU.''' )
																					function(*SCREAMING_SNAKE_CASE )



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				Optional[int]=()     ,    SCREAMING_SNAKE_CASE							:				Optional[Any]=2 ):


							'''simple docstring'''







							from torch.multiprocessing import start_processes

							with tempfile.NamedTemporaryFile() as tmp_file:
														# torch.distributed will expect a few environment variable to be here. We set the ones common to each
														# process here (the other ones will be set be the launcher).
														with patch_environment(
														    world_size=SCREAMING_SNAKE_CASE     ,    master_addr='''127.0.01'''     ,    master_port='''29500'''     ,    accelerate_mixed_precision='''no'''     ,    accelerate_debug_rdv_file=tmp_file.name     ,    accelerate_use_cpu='''yes'''     ,    ):
																					__lowerCamelCase   :   Tuple       =PrepareForLaunch(SCREAMING_SNAKE_CASE     ,    debug=SCREAMING_SNAKE_CASE )
																					start_processes(SCREAMING_SNAKE_CASE     ,    args=SCREAMING_SNAKE_CASE     ,    nprocs=SCREAMING_SNAKE_CASE     ,    start_method='''fork''' )




