
"""simple docstring"""


import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 						lowerCamelCase__   (   __magic_name__							):





			'''simple docstring'''
			lowerCamelCase       =						(HeunDiscreteScheduler,)
			lowerCamelCase       =						10



			def    _lowerCAmelCase						(				self	,	**__UpperCAmelCase    )					->	Optional[Any]:
				_lowerCAmelCase								={
				    """num_train_timesteps""": 11_00,
				    """beta_start""": 0.0_0_0_1,
				    """beta_end""": 0.0_2,
				    """beta_schedule""": """linear""",
				}

				config.update(**lowercase__    )
				return config



			def    _lowerCAmelCase						(				self    )					->	str:



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



			def    _lowerCAmelCase						(				self    )					->	List[str]:



				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=lowercase__	,	beta_end=lowercase__    )



			def    _lowerCAmelCase						(				self    )					->	int:



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



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



				for clip_sample_range in [1.0, 2.0, 3.0]:
					self.check_over_configs(clip_sample_range=lowercase__	,	clip_sample=lowercase__    )



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



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



			def    _lowerCAmelCase						(				self    )					->	Any:
				_lowerCAmelCase								=self.scheduler_classes[0]
				_lowerCAmelCase								=self.get_scheduler_config()
				_lowerCAmelCase								=scheduler_class(**lowercase__    )

				scheduler.set_timesteps(self.num_inference_steps    )

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

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

					_lowerCAmelCase								=model(lowercase__	,	lowercase__    )

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

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

				if torch_device in ["cpu", "mps"]:
					assert abs(result_sum.item() - 0.1_2_3_3    ) < 1e-2
					assert abs(result_mean.item() - 0.0_0_0_2    ) < 1e-3
				else:
					# CUDA
					assert abs(result_sum.item() - 0.1_2_3_3    ) < 1e-2
					assert abs(result_mean.item() - 0.0_0_0_2    ) < 1e-3



			def    _lowerCAmelCase						(				self    )					->	Dict:
				_lowerCAmelCase								=self.scheduler_classes[0]
				_lowerCAmelCase								=self.get_scheduler_config(prediction_type="""v_prediction"""    )
				_lowerCAmelCase								=scheduler_class(**lowercase__    )

				scheduler.set_timesteps(self.num_inference_steps    )

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

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

					_lowerCAmelCase								=model(lowercase__	,	lowercase__    )

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

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

				if torch_device in ["cpu", "mps"]:
					assert abs(result_sum.item() - 4.69_34e-07    ) < 1e-2
					assert abs(result_mean.item() - 6.11_12e-10    ) < 1e-3
				else:
					# CUDA
					assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07    ) < 1e-2
					assert abs(result_mean.item() - 0.0_0_0_2    ) < 1e-3



			def    _lowerCAmelCase						(				self    )					->	List[str]:
				_lowerCAmelCase								=self.scheduler_classes[0]
				_lowerCAmelCase								=self.get_scheduler_config()
				_lowerCAmelCase								=scheduler_class(**lowercase__    )

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

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

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

					_lowerCAmelCase								=model(lowercase__	,	lowercase__    )

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

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

				if str(lowercase__    ).startswith("""cpu"""    ):
					# The following sum varies between 148 and 156 on mps. Why?
					assert abs(result_sum.item() - 0.1_2_3_3    ) < 1e-2
					assert abs(result_mean.item() - 0.0_0_0_2    ) < 1e-3
				elif str(lowercase__    ).startswith("""mps"""    ):
					# Larger tolerance on mps
					assert abs(result_mean.item() - 0.0_0_0_2    ) < 1e-2
				else:
					# CUDA
					assert abs(result_sum.item() - 0.1_2_3_3    ) < 1e-2
					assert abs(result_mean.item() - 0.0_0_0_2    ) < 1e-3



			def    _lowerCAmelCase						(				self    )					->	str:
				_lowerCAmelCase								=self.scheduler_classes[0]
				_lowerCAmelCase								=self.get_scheduler_config()
				_lowerCAmelCase								=scheduler_class(**lowercase__	,	use_karras_sigmas=lowercase__    )

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

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

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

					_lowerCAmelCase								=model(lowercase__	,	lowercase__    )

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

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

				assert abs(result_sum.item() - 0.0_0_0_1_5    ) < 1e-2
				assert abs(result_mean.item() - 1.98_69_55_45_35_03_46_95e-07    ) < 1e-2




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

import torch_xla.distributed.xla_multiprocessing as xmp






def  __lowerCamelCase						(		):
       lowerCAmelCase__	      =   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=lowerCAmelCase__      ,    default=1      ,    help='Number of TPU cores to use (1 or 8).'      )

       # positional
       parser.add_argument(
           'training_script'      ,    type=lowerCAmelCase__      ,    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=lowerCAmelCase__      )

       return parser.parse_args()






def  __lowerCamelCase						(		):
       lowerCAmelCase__	      =   parse_args()

       # Import training_script as a module.
       lowerCAmelCase__	      =   Path(args.training_script      )
       sys.path.append(str(script_fpath.parent.resolve()      )      )
       lowerCAmelCase__	      =   script_fpath.stem
       lowerCAmelCase__	      =   importlib.import_module(lowerCAmelCase__      )

       # Patch sys.argv
       lowerCAmelCase__	      =   [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()
