code: 
  
  
  import torch
  
  from diffusers import DDIMScheduler
  
  from .test_schedulers import SchedulerCommonTest
  
  
  
  
  
  class        lowercase_				( UpperCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		(DDIMScheduler,)
  							snake_case_        =		(('''eta''', 0.0), ('''num_inference_steps''', 50))
  
  
  							def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		{
  													    """num_train_timesteps""": 1_000,
  													    """beta_start""": 0.0_0_0_1,
  													    """beta_end""": 0.0_2,
  													    """beta_schedule""": """linear""",
  													    """clip_sample""": True,
  													}
  
  													config.update(**_UpperCAmelCase		)
  													return config
  
  
  							def 						lowercase__     (   self			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.scheduler_classes[0]
  													a_					=		self.get_scheduler_config(**_UpperCAmelCase		)
  													a_					=		scheduler_class(**_UpperCAmelCase		)
  
  													a_			,   a_					=		10, 0.0
  
  													a_					=		self.dummy_model()
  													a_					=		self.dummy_sample_deter
  
  													scheduler.set_timesteps(_UpperCAmelCase		)
  
  													for t in scheduler.timesteps:
  																			a_					=		model(_UpperCAmelCase			,    _UpperCAmelCase		)
  																			a_					=		scheduler.step(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase		).prev_sample
  
  													return sample
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for timesteps in [100, 500, 1_000]:
  																			self.check_over_configs(num_train_timesteps=_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for steps_offset in [0, 1]:
  																			self.check_over_configs(steps_offset=_UpperCAmelCase		)
  
  													a_					=		self.scheduler_classes[0]
  													a_					=		self.get_scheduler_config(steps_offset=1		)
  													a_					=		scheduler_class(**_UpperCAmelCase		)
  													scheduler.set_timesteps(5		)
  													assert torch.equal(scheduler.timesteps			,    torch.LongTensor([801, 601, 401, 201, 1]		)		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]			,    [0.0_0_2, 0.0_2, 0.2, 2]		):
  																			self.check_over_configs(beta_start=_UpperCAmelCase			,    beta_end=_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for schedule in ["linear", "squaredcos_cap_v2"]:
  																			self.check_over_configs(beta_schedule=_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for prediction_type in ["epsilon", "v_prediction"]:
  																			self.check_over_configs(prediction_type=_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for clip_sample in [True, False]:
  																			self.check_over_configs(clip_sample=_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for timestep_spacing in ["trailing", "leading"]:
  																			self.check_over_configs(timestep_spacing=_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for rescale_betas_zero_snr in [True, False]:
  																			self.check_over_configs(rescale_betas_zero_snr=_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													self.check_over_configs(thresholding=_UpperCAmelCase		)
  													for threshold in [0.5, 1.0, 2.0]:
  																			for prediction_type in ["epsilon", "v_prediction"]:
  																									self.check_over_configs(
  																									    thresholding=_UpperCAmelCase			,    prediction_type=_UpperCAmelCase			,    sample_max_value=_UpperCAmelCase			,    )
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for t in [1, 10, 49]:
  																			self.check_over_forward(time_step=_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for t, num_inference_steps in zip([1, 10, 50]			,    [10, 50, 500]		):
  																			self.check_over_forward(time_step=_UpperCAmelCase			,    num_inference_steps=_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													for t, eta in zip([1, 10, 49]			,    [0.0, 0.5, 1.0]		):
  																			self.check_over_forward(time_step=_UpperCAmelCase			,    eta=_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.scheduler_classes[0]
  													a_					=		self.get_scheduler_config()
  													a_					=		scheduler_class(**_UpperCAmelCase		)
  
  													assert torch.sum(torch.abs(scheduler._get_variance(0			,    0		) - 0.0		)		) < 1e-5
  													assert torch.sum(torch.abs(scheduler._get_variance(420			,    400		) - 0.1_4_7_7_1		)		) < 1e-5
  													assert torch.sum(torch.abs(scheduler._get_variance(980			,    960		) - 0.3_2_4_6_0		)		) < 1e-5
  													assert torch.sum(torch.abs(scheduler._get_variance(0			,    0		) - 0.0		)		) < 1e-5
  													assert torch.sum(torch.abs(scheduler._get_variance(487			,    486		) - 0.0_0_9_7_9		)		) < 1e-5
  													assert torch.sum(torch.abs(scheduler._get_variance(999			,    998		) - 0.0_2		)		) < 1e-5
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.full_loop()
  
  													a_					=		torch.sum(torch.abs(_UpperCAmelCase		)		)
  													a_					=		torch.mean(torch.abs(_UpperCAmelCase		)		)
  
  													assert abs(result_sum.item() - 1_7_2.0_0_6_7		) < 1e-2
  													assert abs(result_mean.item() - 0.2_2_3_9_6_7		) < 1e-3
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.full_loop(prediction_type="""v_prediction"""		)
  
  													a_					=		torch.sum(torch.abs(_UpperCAmelCase		)		)
  													a_					=		torch.mean(torch.abs(_UpperCAmelCase		)		)
  
  													assert abs(result_sum.item() - 5_2.5_3_0_2		) < 1e-2
  													assert abs(result_mean.item() - 0.0_6_8_4		) < 1e-3
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.full_loop(set_alpha_to_one=_UpperCAmelCase			,    beta_start=0.0_1		)
  													a_					=		torch.sum(torch.abs(_UpperCAmelCase		)		)
  													a_					=		torch.mean(torch.abs(_UpperCAmelCase		)		)
  
  													assert abs(result_sum.item() - 1_4_9.8_2_9_5		) < 1e-2
  													assert abs(result_mean.item() - 0.1_9_5_1		) < 1e-3
  
  
  
  
  
  
  							def 						lowercase__     (   self		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													a_					=		self.full_loop(set_alpha_to_one=_UpperCAmelCase			,    beta_start=0.0_1		)
  													a_					=		torch.sum(torch.abs(_UpperCAmelCase		)		)
  													a_					=		torch.mean(torch.abs(_UpperCAmelCase		)		)
  
  													assert abs(result_sum.item() - 1_4_9.0_7_8_4		) < 1e-2
  													assert abs(result_mean.item() - 0.1_9_4_1		) < 1e-3
style_context: 
  
  
  import importlib
  import sys
  from argparse import REMAINDER, ArgumentParser
  from pathlib import Path
  
  import torch_xla.distributed.xla_multiprocessing as xmp
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		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=UpperCAmelCase__       ,    default=1       ,    help="""Number of TPU cores to use (1 or 8)."""      )
  
  						# positional
  						parser.add_argument(
  						    """training_script"""       ,    type=UpperCAmelCase__       ,    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=UpperCAmelCase__      )
  
  						return parser.parse_args()
  
  
  
  
  def   lowerCamelCase_       (					):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		parse_args()
  
  						# Import training_script as a module.
  						a_					=		Path(args.training_script      )
  						sys.path.append(str(script_fpath.parent.resolve()      )      )
  						a_					=		script_fpath.stem
  						a_					=		importlib.import_module(UpperCAmelCase__      )
  
  						# Patch sys.argv
  						a_					=		[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()