"""simple docstring"""







import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 							__snake_case(					A_	):


    '''simple docstring'''





    _UpperCAmelCase       =			(EulerAncestralDiscreteScheduler,)
    _UpperCAmelCase       =			1_0
    def 						_a							(  self					,       **__lowerCamelCase	):




         '''simple docstring'''



         __A					:					Optional[int]								=		{
             'num_train_timesteps': 1100,
             'beta_start': 0.00_01,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
         }

         config.update(**__lowerCamelCase	)
         return config
    def 						_a							(  self	):




         '''simple docstring'''



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




         '''simple docstring'''



         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]					,       [0.00_02, 0.0_02, 0.02]	):
              self.check_over_configs(beta_start=__lowerCamelCase					,       beta_end=__lowerCamelCase	)
    def 						_a							(  self	):




         '''simple docstring'''



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




         '''simple docstring'''



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




         '''simple docstring'''



         __A					:					Dict								=		self.scheduler_classes[0]
         __A					:					Optional[Any]								=		self.get_scheduler_config()
         __A					:					Tuple								=		scheduler_class(**__lowerCamelCase	)

         scheduler.set_timesteps(self.num_inference_steps	)

         __A					:					int								=		torch.manual_seed(0	)

         __A					:					str								=		self.dummy_model()
         __A					:					Union[str, Any]								=		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         __A					:					Tuple								=		sample.to(__lowerCamelCase	)

         for i, t in enumerate(scheduler.timesteps	):
              __A					:					Optional[int]								=		scheduler.scale_model_input(__lowerCamelCase					,       __lowerCamelCase	)

              __A					:					Optional[Any]								=		model(__lowerCamelCase					,       __lowerCamelCase	)

              __A					:					Union[str, Any]								=		scheduler.step(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       generator=__lowerCamelCase	)
              __A					:					Optional[Any]								=		output.prev_sample

         __A					:					Optional[int]								=		torch.sum(torch.abs(__lowerCamelCase	)	)
         __A					:					Tuple								=		torch.mean(torch.abs(__lowerCamelCase	)	)

         assert abs(result_sum.item() - 1_52.31_92	) < 1e-2
         assert abs(result_mean.item() - 0.19_83	) < 1e-3
    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					List[str]								=		self.scheduler_classes[0]
         __A					:					Dict								=		self.get_scheduler_config(prediction_type='v_prediction'	)
         __A					:					Tuple								=		scheduler_class(**__lowerCamelCase	)

         scheduler.set_timesteps(self.num_inference_steps	)

         __A					:					List[str]								=		torch.manual_seed(0	)

         __A					:					Optional[int]								=		self.dummy_model()
         __A					:					Optional[int]								=		self.dummy_sample_deter * scheduler.init_noise_sigma
         __A					:					int								=		sample.to(__lowerCamelCase	)

         for i, t in enumerate(scheduler.timesteps	):
              __A					:					Any								=		scheduler.scale_model_input(__lowerCamelCase					,       __lowerCamelCase	)

              __A					:					List[str]								=		model(__lowerCamelCase					,       __lowerCamelCase	)

              __A					:					List[Any]								=		scheduler.step(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       generator=__lowerCamelCase	)
              __A					:					Union[str, Any]								=		output.prev_sample

         __A					:					List[Any]								=		torch.sum(torch.abs(__lowerCamelCase	)	)
         __A					:					Optional[Any]								=		torch.mean(torch.abs(__lowerCamelCase	)	)

         assert abs(result_sum.item() - 1_08.44_39	) < 1e-2
         assert abs(result_mean.item() - 0.14_12	) < 1e-3

    def 						_a							(  self	):




         '''simple docstring'''



         __A					:					Any								=		self.scheduler_classes[0]
         __A					:					str								=		self.get_scheduler_config()
         __A					:					Dict								=		scheduler_class(**__lowerCamelCase	)

         scheduler.set_timesteps(self.num_inference_steps					,       device=__lowerCamelCase	)
         __A					:					str								=		torch.manual_seed(0	)

         __A					:					Tuple								=		self.dummy_model()
         __A					:					List[str]								=		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
         __A					:					List[Any]								=		sample.to(__lowerCamelCase	)

         for t in scheduler.timesteps:
              __A					:					str								=		scheduler.scale_model_input(__lowerCamelCase					,       __lowerCamelCase	)

              __A					:					Dict								=		model(__lowerCamelCase					,       __lowerCamelCase	)

              __A					:					Optional[Any]								=		scheduler.step(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       generator=__lowerCamelCase	)
              __A					:					Dict								=		output.prev_sample

         __A					:					List[str]								=		torch.sum(torch.abs(__lowerCamelCase	)	)
         __A					:					int								=		torch.mean(torch.abs(__lowerCamelCase	)	)

         assert abs(result_sum.item() - 1_52.31_92	) < 1e-2
         assert abs(result_mean.item() - 0.19_83	) < 1e-3




"""simple docstring"""







import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp




def 						_lowercase				(  )    ->     List[str]:







     '''simple docstring'''


     __A					:					int								=		ArgumentParser(
         description=(
             'PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes'
         )						)

     # Optional arguments for the launch helper
     parser.add_argument('--num_cores'  ,       type=_SCREAMING_SNAKE_CASE  ,       default=1  ,       help='Number of TPU cores to use (1 or 8).'						)

     # positional
     parser.add_argument(
         'training_script'  ,       type=_SCREAMING_SNAKE_CASE  ,       help=(
             'The full path to the single TPU training '
             'program/script to be launched in parallel, '
             'followed by all the arguments for the '
             'training script'
         )  ,       )

     # rest from the training program
     parser.add_argument('training_script_args'  ,       nargs=_SCREAMING_SNAKE_CASE						)

     return parser.parse_args()




def 						_lowercase				(  )    ->     List[str]:







     '''simple docstring'''


     __A					:					List[str]								=		parse_args()

     # Import training_script as a module.
     __A					:					Dict								=		Path(args.training_script						)
     sys.path.append(str(script_fpath.parent.resolve()						)						)
     __A					:					List[Any]								=		script_fpath.stem
     __A					:					Optional[Any]								=		importlib.import_module(_SCREAMING_SNAKE_CASE						)

     # Patch sys.argv
     __A					:					Union[str, Any]								=		[args.training_script] + args.training_script_args + ['--tpu_num_cores', str(args.num_cores						)]

     xmp.spawn(mod._mp_fn  ,       args=()  ,       nprocs=args.num_cores						)


if __name__ == "__main__":
       main()



