
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin




@dataclass
class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  42
     SCREAMING_SNAKE_CASE_		   =  42
     SCREAMING_SNAKE_CASE_		   =  None




class 				_lowerCamelCase (      UpperCamelCase_	,							UpperCamelCase_ ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  2






     @register_to_config
     def __init__(							self						,						__SCREAMING_SNAKE_CASE = 0.02						,						__SCREAMING_SNAKE_CASE = 1_0_0						,						__SCREAMING_SNAKE_CASE = 1.007						,						__SCREAMING_SNAKE_CASE = 8_0						,						__SCREAMING_SNAKE_CASE = 0.05						,						__SCREAMING_SNAKE_CASE = 5_0						,						)  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				sigma_max

           # setable values
           UpperCamelCase__    :   int					=				None
           UpperCamelCase__    :   np.IntTensor					=				None
           UpperCamelCase__    :   torch.FloatTensor					=				None  # sigma(t_i)






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None  )  ->   torch.FloatTensor:




           """simple docstring"""







           return sample






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				num_inference_steps
           UpperCamelCase__    :   List[Any]					=				np.arange(0						,						self.num_inference_steps  )[::-1].copy()
           UpperCamelCase__    :   Optional[int]					=				torch.from_numpy(__SCREAMING_SNAKE_CASE  ).to(__SCREAMING_SNAKE_CASE  )
           UpperCamelCase__    :   int					=				[
               (
                   self.config.sigma_max**2
                   * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
               )
               for i in self.timesteps
           ]
           UpperCamelCase__    :   int					=				torch.tensor(__SCREAMING_SNAKE_CASE						,						dtype=torch.floataa						,						device=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = None  )  ->   Tuple[torch.FloatTensor, float]:




           """simple docstring"""







           if self.config.s_min <= sigma <= self.config.s_max:
                 UpperCamelCase__    :   str					=				min(self.config.s_churn / self.num_inference_steps						,						2**0.5 - 1  )
           else:
                 UpperCamelCase__    :   List[str]					=				0

           # sample eps ~ N(0, S_noise^2 * I)
           UpperCamelCase__    :   str					=				self.config.s_noise * randn_tensor(sample.shape						,						generator=__SCREAMING_SNAKE_CASE  ).to(sample.device  )
           UpperCamelCase__    :   Optional[int]					=				sigma + gamma * sigma
           UpperCamelCase__    :   List[str]					=				sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

           return sample_hat, sigma_hat






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = True						,						)  ->   Union[KarrasVeOutput, Tuple]:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				sample_hat + sigma_hat * model_output
           UpperCamelCase__    :   List[Any]					=				(sample_hat - pred_original_sample) / sigma_hat
           UpperCamelCase__    :   Any					=				sample_hat + (sigma_prev - sigma_hat) * derivative

           if not return_dict:
                 return (sample_prev, derivative)

           return KarrasVeOutput(
               prev_sample=__SCREAMING_SNAKE_CASE						,						derivative=__SCREAMING_SNAKE_CASE						,						pred_original_sample=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE = True						,						)  ->   Union[KarrasVeOutput, Tuple]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				sample_prev + sigma_prev * model_output
           UpperCamelCase__    :   List[str]					=				(sample_prev - pred_original_sample) / sigma_prev
           UpperCamelCase__    :   Optional[int]					=				sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

           if not return_dict:
                 return (sample_prev, derivative)

           return KarrasVeOutput(
               prev_sample=__SCREAMING_SNAKE_CASE						,						derivative=__SCREAMING_SNAKE_CASE						,						pred_original_sample=__SCREAMING_SNAKE_CASE  )






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




           """simple docstring"""







           raise NotImplementedError()




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 ***************")


