




import functools






def 						_snake_case					( _snake_case							: Optional[int]    ,  _snake_case							: List[str]							)   -> int:






      '''simple docstring'''


      _A        =       len(_snake_case							)
      _A        =       len(_snake_case							)

      @functools.cache
      def min_distance(_snake_case							: str    ,  _snake_case							: Any							) -> int:
            # if first word index is overflow - delete all from the second word
            if indexa >= len_worda:
                  return len_worda - indexa
            # if second word index is overflow - delete all from the first word
            if indexa >= len_worda:
                  return len_worda - indexa
            _A        =       int(worda[indexa] != worda[indexa]							)  # current letters not identical
            return min(
                1 + min_distance(indexa + 1    ,  _snake_case							)    ,  1 + min_distance(_snake_case    ,  indexa + 1							)    ,  diff + min_distance(indexa + 1    ,  indexa + 1							)    ,  )

      return min_distance(0    ,  0							)


if __name__ == "__main__":
       import doctest

       doctest.testmod()







"""simple docstring"""

import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Union[str, Any]				  =     (IPNDMScheduler,)
 UpperCAmelCase   :					Optional[Any]				  =     (('''num_inference_steps''', 50),)

 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							**_UpperCAmelCase	:	List[Any]		):
       _A        =       {'num_train_timesteps': 1_000}
       config.update(**_UpperCAmelCase		)
       return config

 def       lowerCAmelCase_							(			self	:	Tuple   ,							_UpperCAmelCase	:	Optional[int]=0   ,							**_UpperCAmelCase	:	Union[str, Any]		):
       _A        =       dict(self.forward_default_kwargs		)
       _A        =       kwargs.pop('num_inference_steps'   ,							_UpperCAmelCase		)
       _A        =       self.dummy_sample
       _A        =       0.1 * sample
       _A        =       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

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

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

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

 def       lowerCAmelCase_							(			self	:	str		):
       pass

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	Any=0   ,							**_UpperCAmelCase	:	Any		):
       _A        =       dict(self.forward_default_kwargs		)
       _A        =       kwargs.pop('num_inference_steps'   ,							_UpperCAmelCase		)
       _A        =       self.dummy_sample
       _A        =       0.1 * sample
       _A        =       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

       for scheduler_class in self.scheduler_classes:
             _A        =       self.get_scheduler_config()
             _A        =       scheduler_class(**_UpperCAmelCase		)
             scheduler.set_timesteps(_UpperCAmelCase		)

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

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

             with tempfile.TemporaryDirectory() as tmpdirname:
                   scheduler.save_config(_UpperCAmelCase		)
                   _A        =       scheduler_class.from_pretrained(_UpperCAmelCase		)
                   # copy over dummy past residuals
                   new_scheduler.set_timesteps(_UpperCAmelCase		)

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

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

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

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       new_scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

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

 def       lowerCAmelCase_							(			self	:	List[str]   ,							**_UpperCAmelCase	:	Optional[int]		):
       _A        =       self.scheduler_classes[0]
       _A        =       self.get_scheduler_config(**_UpperCAmelCase		)
       _A        =       scheduler_class(**_UpperCAmelCase		)

       _A        =       10
       _A        =       self.dummy_model()
       _A        =       self.dummy_sample_deter
       scheduler.set_timesteps(_UpperCAmelCase		)

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

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

       return sample

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       _A        =       dict(self.forward_default_kwargs		)

       _A        =       kwargs.pop('num_inference_steps'   ,							_UpperCAmelCase		)

       for scheduler_class in self.scheduler_classes:
             _A        =       self.get_scheduler_config()
             _A        =       scheduler_class(**_UpperCAmelCase		)

             _A        =       self.dummy_sample
             _A        =       0.1 * sample

             if num_inference_steps is not None and hasattr(_UpperCAmelCase   ,							'set_timesteps'		):
                   scheduler.set_timesteps(_UpperCAmelCase		)
             elif num_inference_steps is not None and not hasattr(_UpperCAmelCase   ,							'set_timesteps'		):
                   _A        =       num_inference_steps

             # copy over dummy past residuals (must be done after set_timesteps)
             _A        =       [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
             _A        =       dummy_past_residuals[:]

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

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

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

             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample
             _A        =       scheduler.step(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							**_UpperCAmelCase		).prev_sample

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

 def       lowerCAmelCase_							(			self	:	Tuple		):
       for timesteps in [100, 1_000]:
             self.check_over_configs(num_train_timesteps=_UpperCAmelCase   ,							time_step=_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       for t, num_inference_steps in zip([1, 5, 10]   ,							[10, 50, 100]		):
             self.check_over_forward(num_inference_steps=_UpperCAmelCase   ,							time_step=_UpperCAmelCase		)



 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       self.full_loop()
       _A        =       torch.mean(torch.abs(_UpperCAmelCase		)		)

       assert abs(result_mean.item() - 2_540_529		) < 10

