

'''simple docstring'''
class 					_snake_case		:






			def __init__(  self     ):
				UpperCAmelCase_     :      Tuple						=  ""
				UpperCAmelCase_     :      Tuple						=  ""
				UpperCAmelCase_     :      List[Any]						=  []






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case     ):
				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_     :      int						=  self.__min_dist_top_down_dp(m - 1			,n - 1     )
					else:
						UpperCAmelCase_     :      int						=  self.__min_dist_top_down_dp(__UpperCAmelCase			,n - 1     )
						UpperCAmelCase_     :      Union[str, Any]						=  self.__min_dist_top_down_dp(m - 1			,__UpperCAmelCase     )
						UpperCAmelCase_     :      Union[str, Any]						=  self.__min_dist_top_down_dp(m - 1			,n - 1     )
						UpperCAmelCase_     :      Optional[Any]						=  1 + min(__UpperCAmelCase			,__UpperCAmelCase			,__UpperCAmelCase     )

					return self.dp[m][n]






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      Dict						=  worda
				UpperCAmelCase_     :      str						=  worda
				UpperCAmelCase_     :      Optional[int]						=  [[-1 for _ in range(len(__UpperCAmelCase     )     )] for _ in range(len(__UpperCAmelCase     )     )]

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






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case     ):
				UpperCAmelCase_     :      int						=  worda
				UpperCAmelCase_     :      int						=  worda
				UpperCAmelCase_     :      Union[str, Any]						=  len(__UpperCAmelCase     )
				UpperCAmelCase_     :      Any						=  len(__UpperCAmelCase     )
				UpperCAmelCase_     :      Dict						=  [[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_     :      Dict						=  j
						elif j == 0:  # second string is empty
							UpperCAmelCase_     :      Any						=  i
						elif worda[i - 1] == worda[j - 1]:  # last characters are equal
							UpperCAmelCase_     :      int						=  self.dp[i - 1][j - 1]
						else:
							UpperCAmelCase_     :      Tuple						=  self.dp[i][j - 1]
							UpperCAmelCase_     :      Union[str, Any]						=  self.dp[i - 1][j]
							UpperCAmelCase_     :      Any						=  self.dp[i - 1][j - 1]
							UpperCAmelCase_     :      Optional[Any]						=  1 + min(__UpperCAmelCase			,__UpperCAmelCase			,__UpperCAmelCase     )
				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 ***************""")







"""simple docstring"""




import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A_				:      Optional[Any]           =			logging.getLogger(__name__)





@dataclass
class  lowerCamelCase    (A__      ):
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=0.0      ,metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}      )
			lowerCamelCase__ :		bool       		=				field(default=A__      ,metadata={'help': 'Whether to SortishSamler or not.'}      )
			lowerCamelCase__ :		bool       		=				field(
			    default=A__      ,metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}      )
			lowerCamelCase__ :		bool       		=				field(default=A__      ,metadata={'help': 'whether to use adafactor'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(default=A__      ,metadata={'help': 'Dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[float]       		=				field(
			    default=A__      ,metadata={'help': 'Attention dropout probability. Goes into model.config.'}      )
			lowerCamelCase__ :		Optional[str]       		=				field(
			    default='linear'      ,metadata={'help': f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()      )}"}      ,)


