






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Any									=(UniPCMultistepScheduler,)
       a    :		Optional[Any]									=(('''num_inference_steps''', 25),)





       def       _a   ( self  ,      **_lowerCamelCase							):
         UpperCamelCase_: int   				=    {
             'num_train_timesteps': 1_0_0_0,
             'beta_start': 0.0_0_0_1,
             'beta_end': 0.0_2,
             'beta_schedule': 'linear',
             'solver_order': 2,
             'solver_type': 'bh2',
         }

         config.update(**_lowerCamelCase							)
         return config





       def       _a   ( self  ,      _lowerCamelCase=0  ,      **_lowerCamelCase							):
         UpperCamelCase_: str   				=    dict(self.forward_default_kwargs							)
         UpperCamelCase_: List[str]   				=    kwargs.pop('num_inference_steps'  ,      _lowerCamelCase							)
         UpperCamelCase_: Tuple   				=    self.dummy_sample
         UpperCamelCase_: str   				=    0.1 * sample
         UpperCamelCase_: str   				=    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

         for scheduler_class in self.scheduler_classes:
           UpperCamelCase_: Tuple   				=    self.get_scheduler_config(**_lowerCamelCase							)
           UpperCamelCase_: Union[str, Any]   				=    scheduler_class(**_lowerCamelCase							)
           scheduler.set_timesteps(_lowerCamelCase							)
           # copy over dummy past residuals
           UpperCamelCase_: List[Any]   				=    dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(_lowerCamelCase							)
             UpperCamelCase_: Union[str, Any]   				=    scheduler_class.from_pretrained(_lowerCamelCase							)
             new_scheduler.set_timesteps(_lowerCamelCase							)
             # copy over dummy past residuals
             UpperCamelCase_: Tuple   				=    dummy_past_residuals[: new_scheduler.config.solver_order]

           UpperCamelCase_				,UpperCamelCase_: int   				=    sample, sample
           for t in range(_lowerCamelCase  ,      time_step + scheduler.config.solver_order + 1							):
             UpperCamelCase_: Dict   				=    scheduler.step(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      **_lowerCamelCase							).prev_sample
             UpperCamelCase_: Union[str, Any]   				=    new_scheduler.step(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      **_lowerCamelCase							).prev_sample

             assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"





       def       _a   ( self  ,      _lowerCamelCase=0  ,      **_lowerCamelCase							):
         UpperCamelCase_: List[Any]   				=    dict(self.forward_default_kwargs							)
         UpperCamelCase_: List[Any]   				=    kwargs.pop('num_inference_steps'  ,      _lowerCamelCase							)
         UpperCamelCase_: Optional[int]   				=    self.dummy_sample
         UpperCamelCase_: int   				=    0.1 * sample
         UpperCamelCase_: Optional[int]   				=    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

         for scheduler_class in self.scheduler_classes:
           UpperCamelCase_: Tuple   				=    self.get_scheduler_config()
           UpperCamelCase_: Any   				=    scheduler_class(**_lowerCamelCase							)
           scheduler.set_timesteps(_lowerCamelCase							)

           # copy over dummy past residuals (must be after setting timesteps)
           UpperCamelCase_: List[Any]   				=    dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
             scheduler.save_config(_lowerCamelCase							)
             UpperCamelCase_: List[str]   				=    scheduler_class.from_pretrained(_lowerCamelCase							)
             # copy over dummy past residuals
             new_scheduler.set_timesteps(_lowerCamelCase							)

             # copy over dummy past residual (must be after setting timesteps)
             UpperCamelCase_: List[Any]   				=    dummy_past_residuals[: new_scheduler.config.solver_order]

           UpperCamelCase_: int   				=    scheduler.step(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      **_lowerCamelCase							).prev_sample
           UpperCamelCase_: List[str]   				=    new_scheduler.step(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      **_lowerCamelCase							).prev_sample

           assert torch.sum(torch.abs(output - new_output							)							) < 1e-5, "Scheduler outputs are not identical"





       def       _a   ( self  ,      _lowerCamelCase=None  ,      **_lowerCamelCase							):
         if scheduler is None:
           UpperCamelCase_: Union[str, Any]   				=    self.scheduler_classes[0]
           UpperCamelCase_: Dict   				=    self.get_scheduler_config(**_lowerCamelCase							)
           UpperCamelCase_: Any   				=    scheduler_class(**_lowerCamelCase							)

         UpperCamelCase_: List[str]   				=    self.scheduler_classes[0]
         UpperCamelCase_: str   				=    self.get_scheduler_config(**_lowerCamelCase							)
         UpperCamelCase_: int   				=    scheduler_class(**_lowerCamelCase							)

         UpperCamelCase_: int   				=    1_0
         UpperCamelCase_: List[str]   				=    self.dummy_model()
         UpperCamelCase_: Tuple   				=    self.dummy_sample_deter
         scheduler.set_timesteps(_lowerCamelCase							)

         for i, t in enumerate(scheduler.timesteps							):
           UpperCamelCase_: int   				=    model(_lowerCamelCase  ,      _lowerCamelCase							)
           UpperCamelCase_: int   				=    scheduler.step(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							).prev_sample

         return sample





       def       _a   ( self							):
         UpperCamelCase_: Any   				=    dict(self.forward_default_kwargs							)

         UpperCamelCase_: int   				=    kwargs.pop('num_inference_steps'  ,      _lowerCamelCase							)

         for scheduler_class in self.scheduler_classes:
           UpperCamelCase_: Optional[int]   				=    self.get_scheduler_config()
           UpperCamelCase_: Dict   				=    scheduler_class(**_lowerCamelCase							)

           UpperCamelCase_: Optional[Any]   				=    self.dummy_sample
           UpperCamelCase_: Union[str, Any]   				=    0.1 * sample

           if num_inference_steps is not None and hasattr(_lowerCamelCase  ,      'set_timesteps'							):
             scheduler.set_timesteps(_lowerCamelCase							)
           elif num_inference_steps is not None and not hasattr(_lowerCamelCase  ,      'set_timesteps'							):
             UpperCamelCase_: List[Any]   				=    num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           UpperCamelCase_: Optional[Any]   				=    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
           UpperCamelCase_: List[Any]   				=    dummy_past_residuals[: scheduler.config.solver_order]

           UpperCamelCase_: Tuple   				=    scheduler.timesteps[5]
           UpperCamelCase_: int   				=    scheduler.timesteps[6]

           UpperCamelCase_: Optional[int]   				=    scheduler.step(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      **_lowerCamelCase							).prev_sample
           UpperCamelCase_: Union[str, Any]   				=    scheduler.step(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      **_lowerCamelCase							).prev_sample

           self.assertEqual(output_a.shape  ,      sample.shape							)
           self.assertEqual(output_a.shape  ,      output_a.shape							)





       def       _a   ( self							):
         # make sure that iterating over schedulers with same config names gives same results
         # for defaults
         UpperCamelCase_: Tuple   				=    UniPCMultistepScheduler(**self.get_scheduler_config()							)
         UpperCamelCase_: Union[str, Any]   				=    self.full_loop(scheduler=_lowerCamelCase							)
         UpperCamelCase_: Union[str, Any]   				=    torch.mean(torch.abs(_lowerCamelCase							)							)

         assert abs(result_mean.item() - 0.2_4_6_4							) < 1e-3

         UpperCamelCase_: Union[str, Any]   				=    DPMSolverSinglestepScheduler.from_config(scheduler.config							)
         UpperCamelCase_: Optional[Any]   				=    DEISMultistepScheduler.from_config(scheduler.config							)
         UpperCamelCase_: List[Any]   				=    DPMSolverMultistepScheduler.from_config(scheduler.config							)
         UpperCamelCase_: int   				=    UniPCMultistepScheduler.from_config(scheduler.config							)

         UpperCamelCase_: Dict   				=    self.full_loop(scheduler=_lowerCamelCase							)
         UpperCamelCase_: Dict   				=    torch.mean(torch.abs(_lowerCamelCase							)							)

         assert abs(result_mean.item() - 0.2_4_6_4							) < 1e-3





       def       _a   ( self							):
         for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
           self.check_over_configs(num_train_timesteps=_lowerCamelCase							)





       def       _a   ( self							):
         self.check_over_configs(thresholding=_lowerCamelCase							)
         for order in [1, 2, 3]:
           for solver_type in ["bh1", "bh2"]:
             for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     thresholding=_lowerCamelCase  ,      prediction_type=_lowerCamelCase  ,      sample_max_value=_lowerCamelCase  ,      solver_order=_lowerCamelCase  ,      solver_type=_lowerCamelCase  ,      )





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





       def       _a   ( self							):
         for solver_type in ["bh1", "bh2"]:
           for order in [1, 2, 3]:
             for prediction_type in ["epsilon", "sample"]:
               self.check_over_configs(
                   solver_order=_lowerCamelCase  ,      solver_type=_lowerCamelCase  ,      prediction_type=_lowerCamelCase  ,      )
               UpperCamelCase_: List[str]   				=    self.full_loop(
                   solver_order=_lowerCamelCase  ,      solver_type=_lowerCamelCase  ,      prediction_type=_lowerCamelCase  ,      )
               assert not torch.isnan(_lowerCamelCase							).any(), "Samples have nan numbers"





       def       _a   ( self							):
         self.check_over_configs(lower_order_final=_lowerCamelCase							)
         self.check_over_configs(lower_order_final=_lowerCamelCase							)





       def       _a   ( self							):
         for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
           self.check_over_forward(num_inference_steps=_lowerCamelCase  ,      time_step=0							)





       def       _a   ( self							):
         UpperCamelCase_: Union[str, Any]   				=    self.full_loop()
         UpperCamelCase_: Any   				=    torch.mean(torch.abs(_lowerCamelCase							)							)

         assert abs(result_mean.item() - 0.2_4_6_4							) < 1e-3





       def       _a   ( self							):
         UpperCamelCase_: Optional[Any]   				=    self.full_loop(prediction_type='v_prediction'							)
         UpperCamelCase_: Dict   				=    torch.mean(torch.abs(_lowerCamelCase							)							)

         assert abs(result_mean.item() - 0.1_0_1_4							) < 1e-3





       def       _a   ( self							):
         UpperCamelCase_: str   				=    self.scheduler_classes[0]
         UpperCamelCase_: List[str]   				=    self.get_scheduler_config(thresholding=_lowerCamelCase  ,      dynamic_thresholding_ratio=0							)
         UpperCamelCase_: Union[str, Any]   				=    scheduler_class(**_lowerCamelCase							)

         UpperCamelCase_: Optional[Any]   				=    1_0
         UpperCamelCase_: str   				=    self.dummy_model()
         UpperCamelCase_: List[str]   				=    self.dummy_sample_deter.half()
         scheduler.set_timesteps(_lowerCamelCase							)

         for i, t in enumerate(scheduler.timesteps							):
           UpperCamelCase_: Any   				=    model(_lowerCamelCase  ,      _lowerCamelCase							)
           UpperCamelCase_: str   				=    scheduler.step(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							).prev_sample

         assert sample.dtype == torch.floataa





       def       _a   ( self  ,      **_lowerCamelCase							):
         for scheduler_class in self.scheduler_classes:
           UpperCamelCase_: Any   				=    self.get_scheduler_config(**_lowerCamelCase							)
           UpperCamelCase_: Optional[Any]   				=    scheduler_class(**_lowerCamelCase							)

           scheduler.set_timesteps(scheduler.config.num_train_timesteps							)
           assert len(scheduler.timesteps.unique()							) == scheduler.num_inference_steps







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.')