




import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest


class        UpperCAmelCase__				(							a__				):



     '''simple docstring'''
     _SCREAMING_SNAKE_CASE      :					Tuple     					=    (DDIMScheduler,)
     _SCREAMING_SNAKE_CASE      :					Tuple     					=    (('eta', 0.0), ('num_inference_steps', 50))
     def        lowerCAmelCase__  (				self ,    **_lowerCAmelCase	):
            a			   ={
                """num_train_timesteps""": 1_000,
                """beta_start""": 0.00_01,
                """beta_end""": 0.02,
                """beta_schedule""": """linear""",
                """clip_sample""": True,
            }

            config.update(**__UpperCAmelCase	)
            return config
     def        lowerCAmelCase__  (				self ,    **_lowerCAmelCase	):
            a			   =self.scheduler_classes[0]
            a			   =self.get_scheduler_config(**__UpperCAmelCase	)
            a			   =scheduler_class(**__UpperCAmelCase	)

            a   ,      a			   =10, 0.0

            a			   =self.dummy_model()
            a			   =self.dummy_sample_deter

            scheduler.set_timesteps(__UpperCAmelCase	)

            for t in scheduler.timesteps:
                   a			   =model(__UpperCAmelCase ,    __UpperCAmelCase	)
                   a			   =scheduler.step(__UpperCAmelCase ,    __UpperCAmelCase ,    __UpperCAmelCase ,    __UpperCAmelCase	).prev_sample

            return sample
     def        lowerCAmelCase__  (				self	):
            for timesteps in [100, 500, 1_000]:
                   self.check_over_configs(num_train_timesteps=__UpperCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            for steps_offset in [0, 1]:
                   self.check_over_configs(steps_offset=__UpperCAmelCase	)

            a			   =self.scheduler_classes[0]
            a			   =self.get_scheduler_config(steps_offset=1	)
            a			   =scheduler_class(**__UpperCAmelCase	)
            scheduler.set_timesteps(5	)
            assert torch.equal(scheduler.timesteps ,    torch.LongTensor([801, 601, 401, 201, 1]	)	)
     def        lowerCAmelCase__  (				self	):
            for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1] ,    [0.0_02, 0.02, 0.2, 2]	):
                   self.check_over_configs(beta_start=__UpperCAmelCase ,    beta_end=__UpperCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            for schedule in ["linear", "squaredcos_cap_v2"]:
                   self.check_over_configs(beta_schedule=__UpperCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            for prediction_type in ["epsilon", "v_prediction"]:
                   self.check_over_configs(prediction_type=__UpperCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            for clip_sample in [True, False]:
                   self.check_over_configs(clip_sample=__UpperCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            for timestep_spacing in ["trailing", "leading"]:
                   self.check_over_configs(timestep_spacing=__UpperCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            for rescale_betas_zero_snr in [True, False]:
                   self.check_over_configs(rescale_betas_zero_snr=__UpperCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            self.check_over_configs(thresholding=__UpperCAmelCase	)
            for threshold in [0.5, 1.0, 2.0]:
                   for prediction_type in ["epsilon", "v_prediction"]:
                          self.check_over_configs(
                              thresholding=__UpperCAmelCase ,    prediction_type=__UpperCAmelCase ,    sample_max_value=__UpperCAmelCase ,    )
     def        lowerCAmelCase__  (				self	):
            for t in [1, 10, 49]:
                   self.check_over_forward(time_step=__UpperCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            for t, num_inference_steps in zip([1, 10, 50] ,    [10, 50, 500]	):
                   self.check_over_forward(time_step=__UpperCAmelCase ,    num_inference_steps=__UpperCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            for t, eta in zip([1, 10, 49] ,    [0.0, 0.5, 1.0]	):
                   self.check_over_forward(time_step=__UpperCAmelCase ,    eta=__UpperCAmelCase	)
     def        lowerCAmelCase__  (				self	):
            a			   =self.scheduler_classes[0]
            a			   =self.get_scheduler_config()
            a			   =scheduler_class(**__UpperCAmelCase	)

            assert torch.sum(torch.abs(scheduler._get_variance(0 ,    0	) - 0.0	)	) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(420 ,    400	) - 0.1_47_71	)	) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(980 ,    960	) - 0.3_24_60	)	) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(0 ,    0	) - 0.0	)	) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487 ,    486	) - 0.0_09_79	)	) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999 ,    998	) - 0.02	)	) < 1E-5
     def        lowerCAmelCase__  (				self	):
            a			   =self.full_loop()

            a			   =torch.sum(torch.abs(__UpperCAmelCase	)	)
            a			   =torch.mean(torch.abs(__UpperCAmelCase	)	)

            assert abs(result_sum.item() - 1_72.00_67	) < 1E-2
            assert abs(result_mean.item() - 0.22_39_67	) < 1E-3
     def        lowerCAmelCase__  (				self	):
            a			   =self.full_loop(prediction_type="""v_prediction"""	)

            a			   =torch.sum(torch.abs(__UpperCAmelCase	)	)
            a			   =torch.mean(torch.abs(__UpperCAmelCase	)	)

            assert abs(result_sum.item() - 52.53_02	) < 1E-2
            assert abs(result_mean.item() - 0.06_84	) < 1E-3
     def        lowerCAmelCase__  (				self	):
            # We specify different beta, so that the first alpha is 0.99
            a			   =self.full_loop(set_alpha_to_one=__UpperCAmelCase ,    beta_start=0.01	)
            a			   =torch.sum(torch.abs(__UpperCAmelCase	)	)
            a			   =torch.mean(torch.abs(__UpperCAmelCase	)	)

            assert abs(result_sum.item() - 1_49.82_95	) < 1E-2
            assert abs(result_mean.item() - 0.19_51	) < 1E-3






     def        lowerCAmelCase__  (				self	):
            # We specify different beta, so that the first alpha is 0.99
            a			   =self.full_loop(set_alpha_to_one=__UpperCAmelCase ,    beta_start=0.01	)
            a			   =torch.sum(torch.abs(__UpperCAmelCase	)	)
            a			   =torch.mean(torch.abs(__UpperCAmelCase	)	)

            assert abs(result_sum.item() - 1_49.07_84	) < 1E-2
            assert abs(result_mean.item() - 0.19_41	) < 1E-3




"""simple docstring"""






def    A_ ( _lowerCAmelCase  :       str,  _lowerCAmelCase  :       str							):






     """simple docstring"""

     if len(_lowerCAmelCase							) < len(_lowerCAmelCase							):
          return levenshtein_distance(_lowerCAmelCase,  _lowerCAmelCase							)

     if len(_lowerCAmelCase							) == 0:
          return len(_lowerCAmelCase							)

     _a     						=					list(range(len(_lowerCAmelCase							) + 1							)							)

     for i, ca in enumerate(_lowerCAmelCase							):
          _a     						=					[i + 1]

          for j, ca in enumerate(_lowerCAmelCase							):
               # Calculate insertions, deletions and substitutions
               _a     						=					previous_row[j + 1] + 1
               _a     						=					current_row[j] + 1
               _a     						=					previous_row[j] + (ca != ca)

               # Get the minimum to append to the current row
               current_row.append(min(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)							)

          # Store the previous row
          _a     						=					current_row

     # Returns the last element (distance)
     return previous_row[-1]


if __name__ == "__main__":
   __snake_case												=							input('''Enter the first word:\n''').strip()
   __snake_case												=							input('''Enter the second word:\n''').strip()

   __snake_case												=							levenshtein_distance(first_word, second_word)
   print(f'Levenshtein distance between {first_word} and {second_word} is {result}')