


'''simple docstring'''






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class  a     (     _SCREAMING_SNAKE_CASE       ):
 _lowerCAmelCase 					=	(EulerDiscreteScheduler,)
 _lowerCAmelCase 					=	1_0
 def 							__UpperCAmelCase		( self      ,				**__magic_name__						)					->							List[str]:
      _a			      =							{
          'num_train_timesteps': 11_00,
          'beta_start': 0.0_0_0_1,
          'beta_end': 0.0_2,
          'beta_schedule': 'linear',
      }

      config.update(**__magic_name__						)
      return config
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      for timesteps in [10, 50, 1_00, 10_00]:
           self.check_over_configs(num_train_timesteps=__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							Dict:
      for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]      ,				[0.0_0_0_2, 0.0_0_2, 0.0_2]						):
           self.check_over_configs(beta_start=__magic_name__      ,				beta_end=__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							Dict:
      for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=__magic_name__						)
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      _a			      =							self.scheduler_classes[0]
      _a			      =							self.get_scheduler_config()
      _a			      =							scheduler_class(**__magic_name__						)

      scheduler.set_timesteps(self.num_inference_steps						)

      _a			      =							torch.manual_seed(0						)

      _a			      =							self.dummy_model()
      _a			      =							self.dummy_sample_deter * scheduler.init_noise_sigma
      _a			      =							sample.to(__magic_name__						)

      for i, t in enumerate(scheduler.timesteps						):
           _a			      =							scheduler.scale_model_input(__magic_name__      ,				__magic_name__						)

           _a			      =							model(__magic_name__      ,				__magic_name__						)

           _a			      =							scheduler.step(__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				generator=__magic_name__						)
           _a			      =							output.prev_sample

      _a			      =							torch.sum(torch.abs(__magic_name__						)						)
      _a			      =							torch.mean(torch.abs(__magic_name__						)						)

      assert abs(result_sum.item() - 1_0.0_8_0_7						) < 1e-2
      assert abs(result_mean.item() - 0.0_1_3_1						) < 1e-3
 def 							__UpperCAmelCase		( self						)					->							str:
      _a			      =							self.scheduler_classes[0]
      _a			      =							self.get_scheduler_config(prediction_type='v_prediction'						)
      _a			      =							scheduler_class(**__magic_name__						)

      scheduler.set_timesteps(self.num_inference_steps						)

      _a			      =							torch.manual_seed(0						)

      _a			      =							self.dummy_model()
      _a			      =							self.dummy_sample_deter * scheduler.init_noise_sigma
      _a			      =							sample.to(__magic_name__						)

      for i, t in enumerate(scheduler.timesteps						):
           _a			      =							scheduler.scale_model_input(__magic_name__      ,				__magic_name__						)

           _a			      =							model(__magic_name__      ,				__magic_name__						)

           _a			      =							scheduler.step(__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				generator=__magic_name__						)
           _a			      =							output.prev_sample

      _a			      =							torch.sum(torch.abs(__magic_name__						)						)
      _a			      =							torch.mean(torch.abs(__magic_name__						)						)

      assert abs(result_sum.item() - 0.0_0_0_2						) < 1e-2
      assert abs(result_mean.item() - 2.2676e-06						) < 1e-3
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      _a			      =							self.scheduler_classes[0]
      _a			      =							self.get_scheduler_config()
      _a			      =							scheduler_class(**__magic_name__						)

      scheduler.set_timesteps(self.num_inference_steps      ,				device=__magic_name__						)

      _a			      =							torch.manual_seed(0						)

      _a			      =							self.dummy_model()
      _a			      =							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      _a			      =							sample.to(__magic_name__						)

      for t in scheduler.timesteps:
           _a			      =							scheduler.scale_model_input(__magic_name__      ,				__magic_name__						)

           _a			      =							model(__magic_name__      ,				__magic_name__						)

           _a			      =							scheduler.step(__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				generator=__magic_name__						)
           _a			      =							output.prev_sample

      _a			      =							torch.sum(torch.abs(__magic_name__						)						)
      _a			      =							torch.mean(torch.abs(__magic_name__						)						)

      assert abs(result_sum.item() - 1_0.0_8_0_7						) < 1e-2
      assert abs(result_mean.item() - 0.0_1_3_1						) < 1e-3




 def 							__UpperCAmelCase		( self						)					->							Any:
      _a			      =							self.scheduler_classes[0]
      _a			      =							self.get_scheduler_config()
      _a			      =							scheduler_class(**__magic_name__      ,				use_karras_sigmas=__magic_name__						)

      scheduler.set_timesteps(self.num_inference_steps      ,				device=__magic_name__						)

      _a			      =							torch.manual_seed(0						)

      _a			      =							self.dummy_model()
      _a			      =							self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      _a			      =							sample.to(__magic_name__						)

      for t in scheduler.timesteps:
           _a			      =							scheduler.scale_model_input(__magic_name__      ,				__magic_name__						)

           _a			      =							model(__magic_name__      ,				__magic_name__						)

           _a			      =							scheduler.step(__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				generator=__magic_name__						)
           _a			      =							output.prev_sample

      _a			      =							torch.sum(torch.abs(__magic_name__						)						)
      _a			      =							torch.mean(torch.abs(__magic_name__						)						)

      assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9						) < 1e-2
      assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3						) < 1e-3





'''simple docstring'''






class  a     :
 def __init__( self						)					->							List[Any]:
      _a			      =							''
      _a			      =							''
      _a			      =							[]
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							int:
      if m == -1:
           return n + 1
      elif n == -1:
           return m + 1
      elif self.dp[m][n] > -1:
           return self.dp[m][n]
      else:
           if self.worda[m] == self.worda[n]:
                _a			      =							self.__min_dist_top_down_dp(m - 1      ,				n - 1						)
           else:
                _a			      =							self.__min_dist_top_down_dp(__magic_name__      ,				n - 1						)
                _a			      =							self.__min_dist_top_down_dp(m - 1      ,				__magic_name__						)
                _a			      =							self.__min_dist_top_down_dp(m - 1      ,				n - 1						)
                _a			      =							1 + min(__magic_name__      ,				__magic_name__      ,				__magic_name__						)

           return self.dp[m][n]
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							int:
      _a			      =							worda
      _a			      =							worda
      _a			      =							[[-1 for _ in range(len(__magic_name__						)						)] for _ in range(len(__magic_name__						)						)]

      return self.__min_dist_top_down_dp(len(__magic_name__						) - 1      ,				len(__magic_name__						) - 1						)




 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__						)					->							int:
      _a			      =							worda
      _a			      =							worda
      _a			      =							len(__magic_name__						)
      _a			      =							len(__magic_name__						)
      _a			      =							[[0 for _ in range(n + 1						)] for _ in range(m + 1						)]

      for i in range(m + 1						):
           for j in range(n + 1						):
                if i == 0:  # first string is empty
                     _a			      =							j
                elif j == 0:  # second string is empty
                     _a			      =							i
                elif worda[i - 1] == worda[j - 1]:  # last characters are equal
                     _a			      =							self.dp[i - 1][j - 1]
                else:
                     _a			      =							self.dp[i][j - 1]
                     _a			      =							self.dp[i - 1][j]
                     _a			      =							self.dp[i - 1][j - 1]
                     _a			      =							1 + min(__magic_name__      ,				__magic_name__      ,				__magic_name__						)
      return self.dp[m][n]


if __name__ == "__main__":
 a_					:    Dict          =    EditDistance()

 print("****************** Testing Edit Distance DP Algorithm ******************")
 print()

 a_					:    Tuple          =    input("Enter the first string: ").strip()
 a_					:    Dict          =    input("Enter the second string: ").strip()

 print()
 print(f'''The minimum edit distance is: {solver.min_dist_top_down(Sa, Sa)}''')
 print(f'''The minimum edit distance is: {solver.min_dist_bottom_up(Sa, Sa)}''')
 print()
 print("*************** End of Testing Edit Distance DP Algorithm ***************")

