



"""simple docstring"""






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest



class     UpperCAmelCase    (	_a  ):
   A__		:	Dict           =  (IPNDMScheduler,)
   A__		:	List[Any]           =  (('''num_inference_steps''', 50),)



   def    __UpperCAmelCase    (       self    :	List[Any]							,       **__lowerCamelCase    :	Any				):



     """simple docstring"""
     _snake_case   	=      {"num_train_timesteps": 1_0_0_0}
     config.update(**lowerCamelCase__				)
     return config



   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	Union[str, Any]=0							,       **__lowerCamelCase    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      dict(self.forward_default_kwargs				)
     _snake_case   	=      kwargs.pop('''num_inference_steps'''							,       lowerCamelCase__				)
     _snake_case   	=      self.dummy_sample
     _snake_case   	=      0.1 * sample
     _snake_case   	=      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

     for scheduler_class in self.scheduler_classes:
       _snake_case   	=      self.get_scheduler_config(**lowerCamelCase__				)
       _snake_case   	=      scheduler_class(**lowerCamelCase__				)
       scheduler.set_timesteps(lowerCamelCase__				)
       # copy over dummy past residuals
       _snake_case   	=      dummy_past_residuals[:]

       if time_step is None:
         _snake_case   	=      scheduler.timesteps[len(scheduler.timesteps				) // 2]

       with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(lowerCamelCase__				)
         _snake_case   	=      scheduler_class.from_pretrained(lowerCamelCase__				)
         new_scheduler.set_timesteps(lowerCamelCase__				)
         # copy over dummy past residuals
         _snake_case   	=      dummy_past_residuals[:]

       _snake_case   	=      scheduler.step(lowerCamelCase__							,       lowerCamelCase__							,       lowerCamelCase__							,       **lowerCamelCase__				).prev_sample
       _snake_case   	=      new_scheduler.step(lowerCamelCase__							,       lowerCamelCase__							,       lowerCamelCase__							,       **lowerCamelCase__				).prev_sample

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

       _snake_case   	=      scheduler.step(lowerCamelCase__							,       lowerCamelCase__							,       lowerCamelCase__							,       **lowerCamelCase__				).prev_sample
       _snake_case   	=      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    __UpperCAmelCase    (       self    :	Optional[int]				):



     """simple docstring"""
     pass



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	Optional[Any]=0							,       **__lowerCamelCase    :	Tuple				):



     """simple docstring"""
     _snake_case   	=      dict(self.forward_default_kwargs				)
     _snake_case   	=      kwargs.pop('''num_inference_steps'''							,       lowerCamelCase__				)
     _snake_case   	=      self.dummy_sample
     _snake_case   	=      0.1 * sample
     _snake_case   	=      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]

     for scheduler_class in self.scheduler_classes:
       _snake_case   	=      self.get_scheduler_config()
       _snake_case   	=      scheduler_class(**lowerCamelCase__				)
       scheduler.set_timesteps(lowerCamelCase__				)

       # copy over dummy past residuals (must be after setting timesteps)
       _snake_case   	=      dummy_past_residuals[:]

       if time_step is None:
         _snake_case   	=      scheduler.timesteps[len(scheduler.timesteps				) // 2]

       with tempfile.TemporaryDirectory() as tmpdirname:
         scheduler.save_config(lowerCamelCase__				)
         _snake_case   	=      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)
         _snake_case   	=      dummy_past_residuals[:]

       _snake_case   	=      scheduler.step(lowerCamelCase__							,       lowerCamelCase__							,       lowerCamelCase__							,       **lowerCamelCase__				).prev_sample
       _snake_case   	=      new_scheduler.step(lowerCamelCase__							,       lowerCamelCase__							,       lowerCamelCase__							,       **lowerCamelCase__				).prev_sample

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

       _snake_case   	=      scheduler.step(lowerCamelCase__							,       lowerCamelCase__							,       lowerCamelCase__							,       **lowerCamelCase__				).prev_sample
       _snake_case   	=      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    __UpperCAmelCase    (       self    :	Optional[Any]							,       **__lowerCamelCase    :	Tuple				):



     """simple docstring"""
     _snake_case   	=      self.scheduler_classes[0]
     _snake_case   	=      self.get_scheduler_config(**lowerCamelCase__				)
     _snake_case   	=      scheduler_class(**lowerCamelCase__				)

     _snake_case   	=      1_0
     _snake_case   	=      self.dummy_model()
     _snake_case   	=      self.dummy_sample_deter
     scheduler.set_timesteps(lowerCamelCase__				)

     for i, t in enumerate(scheduler.timesteps				):
       _snake_case   	=      model(lowerCamelCase__							,       lowerCamelCase__				)
       _snake_case   	=      scheduler.step(lowerCamelCase__							,       lowerCamelCase__							,       lowerCamelCase__				).prev_sample

     for i, t in enumerate(scheduler.timesteps				):
       _snake_case   	=      model(lowerCamelCase__							,       lowerCamelCase__				)
       _snake_case   	=      scheduler.step(lowerCamelCase__							,       lowerCamelCase__							,       lowerCamelCase__				).prev_sample

     return sample



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      dict(self.forward_default_kwargs				)

     _snake_case   	=      kwargs.pop('''num_inference_steps'''							,       lowerCamelCase__				)

     for scheduler_class in self.scheduler_classes:
       _snake_case   	=      self.get_scheduler_config()
       _snake_case   	=      scheduler_class(**lowerCamelCase__				)

       _snake_case   	=      self.dummy_sample
       _snake_case   	=      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'''				):
         _snake_case   	=      num_inference_steps

       # copy over dummy past residuals (must be done after set_timesteps)
       _snake_case   	=      [residual + 0.2, residual + 0.1_5, residual + 0.1, residual + 0.0_5]
       _snake_case   	=      dummy_past_residuals[:]

       _snake_case   	=      scheduler.timesteps[5]
       _snake_case   	=      scheduler.timesteps[6]

       _snake_case   	=      scheduler.step(lowerCamelCase__							,       lowerCamelCase__							,       lowerCamelCase__							,       **lowerCamelCase__				).prev_sample
       _snake_case   	=      scheduler.step(lowerCamelCase__							,       lowerCamelCase__							,       lowerCamelCase__							,       **lowerCamelCase__				).prev_sample

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

       _snake_case   	=      scheduler.step(lowerCamelCase__							,       lowerCamelCase__							,       lowerCamelCase__							,       **lowerCamelCase__				).prev_sample
       _snake_case   	=      scheduler.step(lowerCamelCase__							,       lowerCamelCase__							,       lowerCamelCase__							,       **lowerCamelCase__				).prev_sample

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



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     for timesteps in [1_0_0, 1_0_0_0]:
       self.check_over_configs(num_train_timesteps=lowerCamelCase__							,       time_step=lowerCamelCase__				)



   def    __UpperCAmelCase    (       self    :	int				):



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





   def    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     _snake_case   	=      self.full_loop()
     _snake_case   	=      torch.mean(torch.abs(lowerCamelCase__				)				)

     assert abs(result_mean.item() - 2_5_4_0_5_2_9				) < 1_0








import fire

from utils import calculate_rouge, save_json



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE=None	,**SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Tuple									= [x.strip() for x in open(SCREAMING_SNAKE_CASE      ).readlines()]
	UpperCAmelCase__:	Dict									= [x.strip() for x in open(SCREAMING_SNAKE_CASE      ).readlines()][: len(SCREAMING_SNAKE_CASE      )]
	UpperCAmelCase__:	Any									= calculate_rouge(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,**SCREAMING_SNAKE_CASE      )
	if save_path is not None:
		save_json(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,indent=SCREAMING_SNAKE_CASE      )
	return metrics  # these print nicely


if __name__ == "__main__":
					fire.Fire(calculate_rouge_path)