'''simple docstring'''




import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  int						=						(CMStochasticIterativeScheduler,)
      __UpperCamelCase:  Optional[int]						=						1_0




      def      _A	(       self						:				int						,					**A						:				int       ):
             _UpperCAmelCase     :  Any							      =    {
                 "num_train_timesteps": 201,
                 "sigma_min": 0.002,
                 "sigma_max": 80.0,
             }

             config.update(**A       )
             return config




      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  Dict							      =    10

             _UpperCAmelCase     :  Optional[Any]							      =    self.get_scheduler_config()
             _UpperCAmelCase     :  str							      =    self.scheduler_classes[0](**A       )

             scheduler.set_timesteps(A       )

             _UpperCAmelCase     :  Tuple							      =    scheduler.timesteps[0]
             _UpperCAmelCase     :  Tuple							      =    scheduler.timesteps[1]

             _UpperCAmelCase     :  List[Any]							      =    self.dummy_sample
             _UpperCAmelCase     :  Optional[int]							      =    0.1 * sample

             _UpperCAmelCase     :  List[Any]							      =    scheduler.step(A						,					A						,					A       ).prev_sample
             _UpperCAmelCase     :  List[str]							      =    scheduler.step(A						,					A						,					A       ).prev_sample

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




      def      _A	(       self						:				Any       ):
             for timesteps in [10, 50, 100, 1000]:
                    self.check_over_configs(num_train_timesteps=A       )




      def      _A	(       self						:				Dict       ):
             for clip_denoised in [True, False]:
                    self.check_over_configs(clip_denoised=A       )




      def      _A	(       self						:				str       ):
             _UpperCAmelCase     :  Optional[int]							      =    self.scheduler_classes[0]
             _UpperCAmelCase     :  str							      =    self.get_scheduler_config()
             _UpperCAmelCase     :  Tuple							      =    scheduler_class(**A       )

             _UpperCAmelCase     :  Tuple							      =    1
             scheduler.set_timesteps(A       )
             _UpperCAmelCase     :  Any							      =    scheduler.timesteps

             _UpperCAmelCase     :  List[str]							      =    torch.manual_seed(0       )

             _UpperCAmelCase     :  Union[str, Any]							      =    self.dummy_model()
             _UpperCAmelCase     :  str							      =    self.dummy_sample_deter * scheduler.init_noise_sigma

             for i, t in enumerate(A       ):
                    # 1. scale model input
                    _UpperCAmelCase     :  str							      =    scheduler.scale_model_input(A						,					A       )

                    # 2. predict noise residual
                    _UpperCAmelCase     :  List[Any]							      =    model(A						,					A       )

                    # 3. predict previous sample x_t-1
                    _UpperCAmelCase     :  Any							      =    scheduler.step(A						,					A						,					A						,					generator=A       ).prev_sample

                    _UpperCAmelCase     :  List[Any]							      =    pred_prev_sample

             _UpperCAmelCase     :  int							      =    torch.sum(torch.abs(A       )       )
             _UpperCAmelCase     :  str							      =    torch.mean(torch.abs(A       )       )

             assert abs(result_sum.item() - 192.7_614       ) < 1E-2
             assert abs(result_mean.item() - 0.2_510       ) < 1E-3




      def      _A	(       self						:				str       ):
             _UpperCAmelCase     :  str							      =    self.scheduler_classes[0]
             _UpperCAmelCase     :  Dict							      =    self.get_scheduler_config()
             _UpperCAmelCase     :  Any							      =    scheduler_class(**A       )

             _UpperCAmelCase     :  List[str]							      =    [106, 0]
             scheduler.set_timesteps(timesteps=A       )
             _UpperCAmelCase     :  Tuple							      =    scheduler.timesteps

             _UpperCAmelCase     :  Optional[Any]							      =    torch.manual_seed(0       )

             _UpperCAmelCase     :  Optional[Any]							      =    self.dummy_model()
             _UpperCAmelCase     :  Dict							      =    self.dummy_sample_deter * scheduler.init_noise_sigma

             for t in timesteps:
                    # 1. scale model input
                    _UpperCAmelCase     :  str							      =    scheduler.scale_model_input(A						,					A       )

                    # 2. predict noise residual
                    _UpperCAmelCase     :  Optional[Any]							      =    model(A						,					A       )

                    # 3. predict previous sample x_t-1
                    _UpperCAmelCase     :  List[str]							      =    scheduler.step(A						,					A						,					A						,					generator=A       ).prev_sample

                    _UpperCAmelCase     :  int							      =    pred_prev_sample

             _UpperCAmelCase     :  Union[str, Any]							      =    torch.sum(torch.abs(A       )       )
             _UpperCAmelCase     :  Optional[int]							      =    torch.mean(torch.abs(A       )       )

             assert abs(result_sum.item() - 347.6_357       ) < 1E-2
             assert abs(result_mean.item() - 0.4_527       ) < 1E-3




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

             _UpperCAmelCase     :  str							      =    [39, 30, 12, 15, 0]

             with self.assertRaises(A						,					msg="`timesteps` must be in descending order."       ):
                    scheduler.set_timesteps(timesteps=A       )




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  str							      =    self.scheduler_classes[0]
             _UpperCAmelCase     :  List[Any]							      =    self.get_scheduler_config()
             _UpperCAmelCase     :  Any							      =    scheduler_class(**A       )

             _UpperCAmelCase     :  Dict							      =    [39, 30, 12, 1, 0]
             _UpperCAmelCase     :  Dict							      =    len(A       )

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





      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    self.scheduler_classes[0]
             _UpperCAmelCase     :  str							      =    self.get_scheduler_config()
             _UpperCAmelCase     :  Tuple							      =    scheduler_class(**A       )

             _UpperCAmelCase     :  Dict							      =    [scheduler.config.num_train_timesteps]

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


'''simple docstring'''




def 	UpperCamelCase_							(					_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str    )    ->    float:


       """simple docstring"""







       def get_matched_characters(_UpperCAmelCase  :      str			,	_UpperCAmelCase  :      str    ) -> str:
              _UpperCAmelCase     :  Tuple							      =    []
              _UpperCAmelCase     :  Dict							      =    min(len(_stra    )			,	len(_stra    )    ) // 2
              for i, l in enumerate(_stra    ):
                     _UpperCAmelCase     :  int							      =    int(max(0			,	i - limit    )    )
                     _UpperCAmelCase     :  Any							      =    int(min(i + limit + 1			,	len(_stra    )    )    )
                     if l in _stra[left:right]:
                            matched.append(_UpperCAmelCase    )
                            _UpperCAmelCase     :  List[Any]							      =    F"""{_stra[0:_stra.index(_UpperCAmelCase    )]} {_stra[_stra.index(_UpperCAmelCase    ) + 1:]}"""

              return "".join(_UpperCAmelCase    )

       # matching characters
       _UpperCAmelCase     :  Union[str, Any]							      =    get_matched_characters(_UpperCAmelCase			,	_UpperCAmelCase    )
       _UpperCAmelCase     :  Tuple							      =    get_matched_characters(_UpperCAmelCase			,	_UpperCAmelCase    )
       _UpperCAmelCase     :  Tuple							      =    len(_UpperCAmelCase    )

       # transposition
       _UpperCAmelCase     :  Optional[Any]							      =    (
           len([(ca, ca) for ca, ca in zip(_UpperCAmelCase			,	_UpperCAmelCase    ) if ca != ca]    ) // 2
       )

       if not match_count:
              _UpperCAmelCase     :  Dict							      =    0.0
       else:
              _UpperCAmelCase     :  Optional[int]							      =    (
                  1
                  / 3
                  * (
                      match_count / len(_UpperCAmelCase    )
                      + match_count / len(_UpperCAmelCase    )
                      + (match_count - transpositions) / match_count
                  )
              )

       # common prefix up to 4 characters
       _UpperCAmelCase     :  str							      =    0
       for ca, ca in zip(stra[:4]			,	stra[:4]    ):
              if ca == ca:
                     prefix_len += 1
              else:
                     break

       return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(jaro_winkler("""hello""", """world"""))

