
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  (DDIMParallelScheduler,)
     SCREAMING_SNAKE_CASE_		   =  (('''eta''', 0.0), ('''num_inference_steps''', 50))






     def    __SCREAMING_SNAKE_CASE			(							self						,						**__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   int					=				{
               '''num_train_timesteps''': 1_0_0_0,
               '''beta_start''': 0.0001,
               '''beta_end''': 0.02,
               '''beta_schedule''': '''linear''',
               '''clip_sample''': True,
           }

           config.update(**__SCREAMING_SNAKE_CASE  )
           return config






     def    __SCREAMING_SNAKE_CASE			(							self						,						**__SCREAMING_SNAKE_CASE  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				self.scheduler_classes[0]
           UpperCamelCase__    :   List[str]					=				self.get_scheduler_config(**__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Dict					=				scheduler_class(**__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__				,UpperCamelCase__    :   Union[str, Any]					=				1_0, 0.0

           UpperCamelCase__    :   Optional[Any]					=				self.dummy_model()
           UpperCamelCase__    :   Tuple					=				self.dummy_sample_deter

           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  )

           for t in scheduler.timesteps:
                 UpperCamelCase__    :   Optional[int]					=				model(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
                 UpperCamelCase__    :   List[str]					=				scheduler.step(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ).prev_sample

           return sample






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           for timesteps in [1_0_0, 5_0_0, 1_0_0_0]:
                 self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[int]:




           """simple docstring"""







           for steps_offset in [0, 1]:
                 self.check_over_configs(steps_offset=__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   str					=				self.scheduler_classes[0]
           UpperCamelCase__    :   str					=				self.get_scheduler_config(steps_offset=1  )
           UpperCamelCase__    :   Tuple					=				scheduler_class(**__SCREAMING_SNAKE_CASE  )
           scheduler.set_timesteps(5  )
           assert torch.equal(scheduler.timesteps						,						torch.LongTensor([8_0_1, 6_0_1, 4_0_1, 2_0_1, 1]  )  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[str]:




           """simple docstring"""







           for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]						,						[0.002, 0.02, 0.2, 2]  ):
                 self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE						,						beta_end=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




           """simple docstring"""







           for prediction_type in ["epsilon", "v_prediction"]:
                 self.check_over_configs(prediction_type=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           for timestep_spacing in ["trailing", "leading"]:
                 self.check_over_configs(timestep_spacing=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           for rescale_betas_zero_snr in [True, False]:
                 self.check_over_configs(rescale_betas_zero_snr=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           self.check_over_configs(thresholding=__SCREAMING_SNAKE_CASE  )
           for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "v_prediction"]:
                       self.check_over_configs(
                           thresholding=__SCREAMING_SNAKE_CASE						,						prediction_type=__SCREAMING_SNAKE_CASE						,						sample_max_value=__SCREAMING_SNAKE_CASE						,						)






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           for t in [1, 1_0, 4_9]:
                 self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







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






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[int]:




           """simple docstring"""







           for t, eta in zip([1, 1_0, 4_9]						,						[0.0, 0.5, 1.0]  ):
                 self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE						,						eta=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Union[str, Any]:




           """simple docstring"""







           UpperCamelCase__    :   str					=				self.scheduler_classes[0]
           UpperCamelCase__    :   Optional[int]					=				self.get_scheduler_config()
           UpperCamelCase__    :   Any					=				scheduler_class(**__SCREAMING_SNAKE_CASE  )

           assert torch.sum(torch.abs(scheduler._get_variance(0						,						0  ) - 0.0  )  ) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(4_2_0						,						4_0_0  ) - 0.14771  )  ) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(9_8_0						,						9_6_0  ) - 0.32460  )  ) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(0						,						0  ) - 0.0  )  ) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(4_8_7						,						4_8_6  ) - 0.00979  )  ) < 1e-5
           assert torch.sum(torch.abs(scheduler._get_variance(9_9_9						,						9_9_8  ) - 0.02  )  ) < 1e-5






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				self.scheduler_classes[0]
           UpperCamelCase__    :   Union[str, Any]					=				self.get_scheduler_config()
           UpperCamelCase__    :   Any					=				scheduler_class(**__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__				,UpperCamelCase__    :   Optional[Any]					=				1_0, 0.0
           scheduler.set_timesteps(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Union[str, Any]					=				self.dummy_model()
           UpperCamelCase__    :   List[Any]					=				self.dummy_sample_deter
           UpperCamelCase__    :   List[Any]					=				self.dummy_sample_deter + 0.1
           UpperCamelCase__    :   Dict					=				self.dummy_sample_deter - 0.1

           UpperCamelCase__    :   Optional[int]					=				samplea.shape[0]
           UpperCamelCase__    :   Optional[int]					=				torch.stack([samplea, samplea, samplea]						,						dim=0  )
           UpperCamelCase__    :   List[Any]					=				torch.arange(__SCREAMING_SNAKE_CASE  )[0:3, None].repeat(1						,						__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Union[str, Any]					=				model(samples.flatten(0						,						1  )						,						timesteps.flatten(0						,						1  )  )
           UpperCamelCase__    :   Tuple					=				scheduler.batch_step_no_noise(__SCREAMING_SNAKE_CASE						,						timesteps.flatten(0						,						1  )						,						samples.flatten(0						,						1  )						,						__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Optional[int]					=				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE  )  )
           UpperCamelCase__    :   Optional[Any]					=				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE  )  )

           assert abs(result_sum.item() - 1147.7904  ) < 1e-2
           assert abs(result_mean.item() - 0.4982  ) < 1e-3






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[Any]:




           """simple docstring"""







           UpperCamelCase__    :   str					=				self.full_loop()

           UpperCamelCase__    :   str					=				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE  )  )
           UpperCamelCase__    :   List[str]					=				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE  )  )

           assert abs(result_sum.item() - 172.0067  ) < 1e-2
           assert abs(result_mean.item() - 0.223967  ) < 1e-3






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[int]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				self.full_loop(prediction_type='''v_prediction'''  )

           UpperCamelCase__    :   Dict					=				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE  )  )
           UpperCamelCase__    :   List[str]					=				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE  )  )

           assert abs(result_sum.item() - 52.5302  ) < 1e-2
           assert abs(result_mean.item() - 0.0684  ) < 1e-3






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   str					=				self.full_loop(set_alpha_to_one=__SCREAMING_SNAKE_CASE						,						beta_start=0.01  )
           UpperCamelCase__    :   int					=				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE  )  )
           UpperCamelCase__    :   List[str]					=				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE  )  )

           assert abs(result_sum.item() - 149.8295  ) < 1e-2
           assert abs(result_mean.item() - 0.1951  ) < 1e-3






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				self.full_loop(set_alpha_to_one=__SCREAMING_SNAKE_CASE						,						beta_start=0.01  )
           UpperCamelCase__    :   Optional[Any]					=				torch.sum(torch.abs(__SCREAMING_SNAKE_CASE  )  )
           UpperCamelCase__    :   str					=				torch.mean(torch.abs(__SCREAMING_SNAKE_CASE  )  )

           assert abs(result_sum.item() - 149.0784  ) < 1e-2
           assert abs(result_mean.item() - 0.1941  ) < 1e-3




class 				_lowerCamelCase :





     """simple docstring"""







     def __init__(							self  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				''''''
           UpperCamelCase__    :   int					=				''''''
           UpperCamelCase__    :   Optional[int]					=				[]






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   int:




           """simple docstring"""







           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]:
                       UpperCamelCase__    :   Union[str, Any]					=				self.__min_dist_top_down_dp(m - 1						,						n - 1  )
                 else:
                       UpperCamelCase__    :   int					=				self.__min_dist_top_down_dp(__SCREAMING_SNAKE_CASE						,						n - 1  )
                       UpperCamelCase__    :   Tuple					=				self.__min_dist_top_down_dp(m - 1						,						__SCREAMING_SNAKE_CASE  )
                       UpperCamelCase__    :   Optional[Any]					=				self.__min_dist_top_down_dp(m - 1						,						n - 1  )
                       UpperCamelCase__    :   Optional[int]					=				1 + min(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )

                 return self.dp[m][n]






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				worda
           UpperCamelCase__    :   Any					=				worda
           UpperCamelCase__    :   List[str]					=				[[-1 for _ in range(len(__SCREAMING_SNAKE_CASE  )  )] for _ in range(len(__SCREAMING_SNAKE_CASE  )  )]

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






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   int					=				worda
           UpperCamelCase__    :   List[str]					=				worda
           UpperCamelCase__    :   int					=				len(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   Optional[Any]					=				len(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   int					=				[[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
                             UpperCamelCase__    :   List[Any]					=				j
                       elif j == 0:  # second string is empty
                             UpperCamelCase__    :   Union[str, Any]					=				i
                       elif worda[i - 1] == worda[j - 1]:  # last characters are equal
                             UpperCamelCase__    :   List[Any]					=				self.dp[i - 1][j - 1]
                       else:
                             UpperCamelCase__    :   Union[str, Any]					=				self.dp[i][j - 1]
                             UpperCamelCase__    :   str					=				self.dp[i - 1][j]
                             UpperCamelCase__    :   Union[str, Any]					=				self.dp[i - 1][j - 1]
                             UpperCamelCase__    :   List[Any]					=				1 + min(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )
           return self.dp[m][n]


if __name__ == "__main__":
    lowerCamelCase      =EditDistance()

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

    lowerCamelCase      =input("Enter the first string: ").strip()
    lowerCamelCase      =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 ***************")


