


'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a_					:    Optional[int]          =    logging.get_logger(__name__)

a_					:    List[str]          =    {
    "andreasmadsen/efficient_mlm_m0.40": (
        "https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
    ),
}




class  a     (     _SCREAMING_SNAKE_CASE       ):
       _lowerCAmelCase 					=	"""roberta-prelayernorm"""
       def __init__( self      ,				__magic_name__=5_02_65      ,				__magic_name__=7_68      ,				__magic_name__=12      ,				__magic_name__=12      ,				__magic_name__=30_72      ,				__magic_name__="gelu"      ,				__magic_name__=0.1      ,				__magic_name__=0.1      ,				__magic_name__=5_12      ,				__magic_name__=2      ,				__magic_name__=0.0_2      ,				__magic_name__=1e-12      ,				__magic_name__=1      ,				__magic_name__=0      ,				__magic_name__=2      ,				__magic_name__="absolute"      ,				__magic_name__=True      ,				__magic_name__=None      ,				**__magic_name__      ,				)					->							int:
            super().__init__(pad_token_id=__magic_name__      ,				bos_token_id=__magic_name__      ,				eos_token_id=__magic_name__      ,				**__magic_name__						)

            _a			      =							vocab_size
            _a			      =							hidden_size
            _a			      =							num_hidden_layers
            _a			      =							num_attention_heads
            _a			      =							hidden_act
            _a			      =							intermediate_size
            _a			      =							hidden_dropout_prob
            _a			      =							attention_probs_dropout_prob
            _a			      =							max_position_embeddings
            _a			      =							type_vocab_size
            _a			      =							initializer_range
            _a			      =							layer_norm_eps
            _a			      =							position_embedding_type
            _a			      =							use_cache
            _a			      =							classifier_dropout





class  a     (     _SCREAMING_SNAKE_CASE       ):
       @property
       def 							__UpperCAmelCase		( self						)					->							Mapping[str, Mapping[int, str]]:
            if self.task == "multiple-choice":
                 _a			      =							{0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                 _a			      =							{0: 'batch', 1: 'sequence'}
            return OrderedDict(
                [
                    ('input_ids', dynamic_axis),
                    ('attention_mask', dynamic_axis),
                ]						)





'''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 ***************")

