






import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest





class     _lowerCAmelCase(	_lowerCAmelCase ):





       """simple docstring"""







       a    :		Optional[int]									=(DDPMParallelScheduler,)





       def       _a   ( self  ,      **_lowerCamelCase							):
         UpperCamelCase_: List[Any]   				=    {
             'num_train_timesteps': 1_0_0_0,
             'beta_start': 0.0_0_0_1,
             'beta_end': 0.0_2,
             'beta_schedule': 'linear',
             'variance_type': 'fixed_small',
             'clip_sample': True,
         }

         config.update(**_lowercase							)
         return config





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





       def       _a   ( self							):
         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=_lowercase  ,      beta_end=_lowercase							)





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





       def       _a   ( self							):
         for variance in ["fixed_small", "fixed_large", "other"]:
           self.check_over_configs(variance_type=_lowercase							)





       def       _a   ( self							):
         for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=_lowercase							)





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





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





       def       _a   ( self							):
         for t in [0, 5_0_0, 9_9_9]:
           self.check_over_forward(time_step=_lowercase							)





       def       _a   ( self							):
         UpperCamelCase_: Any   				=    self.scheduler_classes[0]
         UpperCamelCase_: Dict   				=    self.get_scheduler_config()
         UpperCamelCase_: Union[str, Any]   				=    scheduler_class(**_lowercase							)

         assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_8_7							) - 0.0_0_9_7_9							)							) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.0_2							)							) < 1e-5





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    self.scheduler_classes[0]
         UpperCamelCase_: Dict   				=    self.get_scheduler_config()
         UpperCamelCase_: Union[str, Any]   				=    scheduler_class(**_lowercase							)

         UpperCamelCase_: Dict   				=    len(_lowercase							)

         UpperCamelCase_: Optional[int]   				=    self.dummy_model()
         UpperCamelCase_: Any   				=    self.dummy_sample_deter
         UpperCamelCase_: Optional[Any]   				=    self.dummy_sample_deter + 0.1
         UpperCamelCase_: str   				=    self.dummy_sample_deter - 0.1

         UpperCamelCase_: str   				=    samplea.shape[0]
         UpperCamelCase_: List[str]   				=    torch.stack([samplea, samplea, samplea]  ,      dim=0							)
         UpperCamelCase_: Optional[int]   				=    torch.arange(_lowercase							)[0:3, None].repeat(1  ,      _lowercase							)

         UpperCamelCase_: Any   				=    model(samples.flatten(0  ,      1							)  ,      timesteps.flatten(0  ,      1							)							)
         UpperCamelCase_: Tuple   				=    scheduler.batch_step_no_noise(_lowercase  ,      timesteps.flatten(0  ,      1							)  ,      samples.flatten(0  ,      1							)							)

         UpperCamelCase_: Tuple   				=    torch.sum(torch.abs(_lowercase							)							)
         UpperCamelCase_: Optional[Any]   				=    torch.mean(torch.abs(_lowercase							)							)

         assert abs(result_sum.item() - 1_1_5_3.1_8_3_3							) < 1e-2
         assert abs(result_mean.item() - 0.5_0_0_5							) < 1e-3





       def       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    self.scheduler_classes[0]
         UpperCamelCase_: List[Any]   				=    self.get_scheduler_config()
         UpperCamelCase_: List[Any]   				=    scheduler_class(**_lowercase							)

         UpperCamelCase_: List[str]   				=    len(_lowercase							)

         UpperCamelCase_: Tuple   				=    self.dummy_model()
         UpperCamelCase_: Tuple   				=    self.dummy_sample_deter
         UpperCamelCase_: Optional[int]   				=    torch.manual_seed(0							)

         for t in reversed(range(_lowercase							)							):
           # 1. predict noise residual
           UpperCamelCase_: Optional[int]   				=    model(_lowercase  ,      _lowercase							)

           # 2. predict previous mean of sample x_t-1
           UpperCamelCase_: Optional[int]   				=    scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase  ,      generator=_lowercase							).prev_sample

           UpperCamelCase_: List[Any]   				=    pred_prev_sample

         UpperCamelCase_: str   				=    torch.sum(torch.abs(_lowercase							)							)
         UpperCamelCase_: Optional[Any]   				=    torch.mean(torch.abs(_lowercase							)							)

         assert abs(result_sum.item() - 2_5_8.9_6_0_6							) < 1e-2
         assert abs(result_mean.item() - 0.3_3_7_2							) < 1e-3





       def       _a   ( self							):
         UpperCamelCase_: List[Any]   				=    self.scheduler_classes[0]
         UpperCamelCase_: int   				=    self.get_scheduler_config(prediction_type='v_prediction'							)
         UpperCamelCase_: Dict   				=    scheduler_class(**_lowercase							)

         UpperCamelCase_: str   				=    len(_lowercase							)

         UpperCamelCase_: Optional[int]   				=    self.dummy_model()
         UpperCamelCase_: str   				=    self.dummy_sample_deter
         UpperCamelCase_: Tuple   				=    torch.manual_seed(0							)

         for t in reversed(range(_lowercase							)							):
           # 1. predict noise residual
           UpperCamelCase_: int   				=    model(_lowercase  ,      _lowercase							)

           # 2. predict previous mean of sample x_t-1
           UpperCamelCase_: List[Any]   				=    scheduler.step(_lowercase  ,      _lowercase  ,      _lowercase  ,      generator=_lowercase							).prev_sample

           UpperCamelCase_: Dict   				=    pred_prev_sample

         UpperCamelCase_: str   				=    torch.sum(torch.abs(_lowercase							)							)
         UpperCamelCase_: Optional[int]   				=    torch.mean(torch.abs(_lowercase							)							)

         assert abs(result_sum.item() - 2_0_2.0_2_9_6							) < 1e-2
         assert abs(result_mean.item() - 0.2_6_3_1							) < 1e-3





       def       _a   ( self							):
         UpperCamelCase_: int   				=    self.scheduler_classes[0]
         UpperCamelCase_: Union[str, Any]   				=    self.get_scheduler_config()
         UpperCamelCase_: Optional[Any]   				=    scheduler_class(**_lowercase							)

         UpperCamelCase_: str   				=    [1_0_0, 8_7, 5_0, 1, 0]

         scheduler.set_timesteps(timesteps=_lowercase							)

         UpperCamelCase_: str   				=    scheduler.timesteps

         for i, timestep in enumerate(_lowercase							):
           if i == len(_lowercase							) - 1:
             UpperCamelCase_: Tuple   				=    -1
           else:
             UpperCamelCase_: Optional[int]   				=    timesteps[i + 1]

           UpperCamelCase_: List[Any]   				=    scheduler.previous_timestep(_lowercase							)
           UpperCamelCase_: List[Any]   				=    prev_t.item()

           self.assertEqual(_lowercase  ,      _lowercase							)





       def       _a   ( self							):
         UpperCamelCase_: str   				=    self.scheduler_classes[0]
         UpperCamelCase_: Optional[Any]   				=    self.get_scheduler_config()
         UpperCamelCase_: Dict   				=    scheduler_class(**_lowercase							)

         UpperCamelCase_: Tuple   				=    [1_0_0, 8_7, 5_0, 5_1, 0]

         with self.assertRaises(_lowercase  ,      msg='`custom_timesteps` must be in descending order.'							):
           scheduler.set_timesteps(timesteps=_lowercase							)





       def       _a   ( self							):
         UpperCamelCase_: Any   				=    self.scheduler_classes[0]
         UpperCamelCase_: Dict   				=    self.get_scheduler_config()
         UpperCamelCase_: Dict   				=    scheduler_class(**_lowercase							)

         UpperCamelCase_: Union[str, Any]   				=    [1_0_0, 8_7, 5_0, 1, 0]
         UpperCamelCase_: List[str]   				=    len(_lowercase							)

         with self.assertRaises(_lowercase  ,      msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'							):
           scheduler.set_timesteps(num_inference_steps=_lowercase  ,      timesteps=_lowercase							)





       def       _a   ( self							):
         UpperCamelCase_: Any   				=    self.scheduler_classes[0]
         UpperCamelCase_: Dict   				=    self.get_scheduler_config()
         UpperCamelCase_: Union[str, Any]   				=    scheduler_class(**_lowercase							)

         UpperCamelCase_: Dict   				=    [scheduler.config.num_train_timesteps]

         with self.assertRaises(
             _lowercase  ,      msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'  ,      ):
           scheduler.set_timesteps(timesteps=_lowercase							)







import os


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_doctest_list.py
A_		:		List[str]		     =			'.'


if __name__ == "__main__":
   A_		:		Dict		     =			os.path.join(REPO_PATH, 'utils/documentation_tests.txt')
   A_		:		Dict		     =			[]
   A_		:		Optional[Any]		     =			[]
   with open(doctest_file_path) as fp:
      for line in fp:
         A_		:		Tuple		     =			line.strip()
         A_		:		Any		     =			os.path.join(REPO_PATH, line)
         if not (os.path.isfile(path) or os.path.isdir(path)):
            non_existent_paths.append(line)
         all_paths.append(path)
   if len(non_existent_paths) > 0:
      A_		:		str		     =			'\n'.join(non_existent_paths)
      raise ValueError(F'''`utils/documentation_tests.txt` contains non-existent paths:\n{non_existent_paths}''')
   if all_paths != sorted(all_paths):
      raise ValueError('Files in `utils/documentation_tests.txt` are not in alphabetical order.')