
'''simple docstring'''


import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   _snake_case		   =							(DDIMParallelScheduler,)
   _snake_case		   =							(('''eta''', 0.0), ('''num_inference_steps''', 50))







   def       UpperCAmelCase				(			self ,   **lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  {
          '''num_train_timesteps''': 1_0_0_0,
          '''beta_start''': 0.0001,
          '''beta_end''': 0.02,
          '''beta_schedule''': '''linear''',
          '''clip_sample''': True,
      }

      config.update(**lowerCamelCase__   )
      return config







   def       UpperCAmelCase				(			self ,   **lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  self.scheduler_classes[0]
      UpperCamelCase       =  self.get_scheduler_config(**lowerCamelCase__   )
      UpperCamelCase       =  scheduler_class(**lowerCamelCase__   )

      UpperCamelCase     , UpperCamelCase       =  1_0, 0.0

      UpperCamelCase       =  self.dummy_model()
      UpperCamelCase       =  self.dummy_sample_deter

      scheduler.set_timesteps(lowerCamelCase__   )

      for t in scheduler.timesteps:
         UpperCamelCase       =  model(lowerCamelCase__ ,   lowerCamelCase__   )
         UpperCamelCase       =  scheduler.step(lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ).prev_sample

      return sample







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=lowerCamelCase__   )

      UpperCamelCase       =  self.scheduler_classes[0]
      UpperCamelCase       =  self.get_scheduler_config(steps_offset=1   )
      UpperCamelCase       =  scheduler_class(**lowerCamelCase__   )
      scheduler.set_timesteps(5   )
      assert torch.equal(scheduler.timesteps ,   torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]   )   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] ,   [0.002, 0.02, 0.2, 2]   ):
         self.check_over_configs(beta_start=lowerCamelCase__ ,   beta_end=lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      for timestep_spacing in ["trailing", "leading"]:
         self.check_over_configs(timestep_spacing=lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      for rescale_betas_zero_snr in [True, False]:
         self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      self.check_over_configs(thresholding=lowerCamelCase__   )
      for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(
                thresholding=lowerCamelCase__ ,   prediction_type=lowerCamelCase__ ,   sample_max_value=lowerCamelCase__ ,   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      for t in [1, 1_0, 4_9]:
         self.check_over_forward(time_step=lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      for t, num_inference_steps in zip([1, 1_0, 5_0] ,   [1_0, 5_0, 5_0_0]   ):
         self.check_over_forward(time_step=lowerCamelCase__ ,   num_inference_steps=lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      for t, eta in zip([1, 1_0, 4_9] ,   [0.0, 0.5, 1.0]   ):
         self.check_over_forward(time_step=lowerCamelCase__ ,   eta=lowerCamelCase__   )







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.scheduler_classes[0]
      UpperCamelCase       =  self.get_scheduler_config()
      UpperCamelCase       =  scheduler_class(**lowerCamelCase__   )

      assert torch.sum(torch.abs(scheduler._get_variance(0 ,   0   ) - 0.0   )   ) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_2_0 ,   4_0_0   ) - 0.14771   )   ) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_8_0 ,   9_6_0   ) - 0.32460   )   ) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(0 ,   0   ) - 0.0   )   ) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_8_7 ,   4_8_6   ) - 0.00979   )   ) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_9_9 ,   9_9_8   ) - 0.02   )   ) < 1e-5







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.scheduler_classes[0]
      UpperCamelCase       =  self.get_scheduler_config()
      UpperCamelCase       =  scheduler_class(**lowerCamelCase__   )

      UpperCamelCase     , UpperCamelCase       =  1_0, 0.0
      scheduler.set_timesteps(lowerCamelCase__   )

      UpperCamelCase       =  self.dummy_model()
      UpperCamelCase       =  self.dummy_sample_deter
      UpperCamelCase       =  self.dummy_sample_deter + 0.1
      UpperCamelCase       =  self.dummy_sample_deter - 0.1

      UpperCamelCase       =  samplea.shape[0]
      UpperCamelCase       =  torch.stack([samplea, samplea, samplea] ,   dim=0   )
      UpperCamelCase       =  torch.arange(lowerCamelCase__   )[0:3, None].repeat(1 ,   lowerCamelCase__   )

      UpperCamelCase       =  model(samples.flatten(0 ,   1   ) ,   timesteps.flatten(0 ,   1   )   )
      UpperCamelCase       =  scheduler.batch_step_no_noise(lowerCamelCase__ ,   timesteps.flatten(0 ,   1   ) ,   samples.flatten(0 ,   1   ) ,   lowerCamelCase__   )

      UpperCamelCase       =  torch.sum(torch.abs(lowerCamelCase__   )   )
      UpperCamelCase       =  torch.mean(torch.abs(lowerCamelCase__   )   )

      assert abs(result_sum.item() - 1147.7904   ) < 1e-2
      assert abs(result_mean.item() - 0.4982   ) < 1e-3







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.full_loop()

      UpperCamelCase       =  torch.sum(torch.abs(lowerCamelCase__   )   )
      UpperCamelCase       =  torch.mean(torch.abs(lowerCamelCase__   )   )

      assert abs(result_sum.item() - 172.0067   ) < 1e-2
      assert abs(result_mean.item() - 0.223967   ) < 1e-3







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.full_loop(prediction_type='''v_prediction'''   )

      UpperCamelCase       =  torch.sum(torch.abs(lowerCamelCase__   )   )
      UpperCamelCase       =  torch.mean(torch.abs(lowerCamelCase__   )   )

      assert abs(result_sum.item() - 52.5302   ) < 1e-2
      assert abs(result_mean.item() - 0.0684   ) < 1e-3







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.full_loop(set_alpha_to_one=lowerCamelCase__ ,   beta_start=0.01   )
      UpperCamelCase       =  torch.sum(torch.abs(lowerCamelCase__   )   )
      UpperCamelCase       =  torch.mean(torch.abs(lowerCamelCase__   )   )

      assert abs(result_sum.item() - 149.8295   ) < 1e-2
      assert abs(result_mean.item() - 0.1951   ) < 1e-3







   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  self.full_loop(set_alpha_to_one=lowerCamelCase__ ,   beta_start=0.01   )
      UpperCamelCase       =  torch.sum(torch.abs(lowerCamelCase__   )   )
      UpperCamelCase       =  torch.mean(torch.abs(lowerCamelCase__   )   )

      assert abs(result_sum.item() - 149.0784   ) < 1e-2
      assert abs(result_mean.item() - 0.1941   ) < 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  __snake_case     (    ):
   UpperCamelCase       =  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  __snake_case     (    ):
   UpperCamelCase       =  parse_args()

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

   # Patch sys.argv
   UpperCamelCase       =  [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()





