




def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Tuple     ,							lowercase     :	str					):


      '''simple docstring'''



      lowerCamelCase_							=   len(lowercase					) + 1
      lowerCamelCase_							=   len(lowercase					) + 1

      # dp is a 2d matrix where dp[i][j] denotes whether prefix string of
      # length i of input_string matches with prefix string of length j of
      # given pattern.
      # "dp" stands for dynamic programming.
      lowerCamelCase_							=   [[0 for i in range(lowercase					)] for j in range(lowercase					)]

      # since string of zero length match pattern of zero length
      lowerCamelCase_							=   1

      # since pattern of zero length will never match with string of non-zero length
      for i in range(1     ,							lowercase					):
            lowerCamelCase_							=   0

      # since string of zero length will match with pattern where there
      # is at least one * alternatively
      for j in range(1     ,							lowercase					):
            lowerCamelCase_							=   dp[0][j - 2] if pattern[j - 1] == '*' else 0

      # now using bottom-up approach to find for all remaining lengths
      for i in range(1     ,							lowercase					):
            for j in range(1     ,							lowercase					):
                  if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
                        lowerCamelCase_							=   dp[i - 1][j - 1]

                  elif pattern[j - 1] == "*":
                        if dp[i][j - 2] == 1:
                              lowerCamelCase_							=   1
                        elif pattern[j - 2] in (input_string[i - 1], "."):
                              lowerCamelCase_							=   dp[i - 1][j]
                        else:
                              lowerCamelCase_							=   0
                  else:
                        lowerCamelCase_							=   0

      return bool(dp[-1][-1]					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 # inputing the strings
 # input_string = input("input a string :")
 # pattern = input("input a pattern :")

 lowerCamelCase    :		Dict			   =       "aab"
 lowerCamelCase    :		int			   =       "c*a*b"

 # using function to check whether given string matches the given pattern
 if match_pattern(input_string, pattern):
  print(F"""{input_string} matches the given pattern {pattern}""")
 else:
  print(F"""{input_string} does not match with the given pattern {pattern}""")









from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase    :		Optional[Any]			   =       logging.get_logger(__name__)

lowerCamelCase    :		Tuple			   =       {
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 UpperCamelCase						       =						'''transfo-xl'''
 UpperCamelCase						       =						['''mems''']
 UpperCamelCase						       =						{
     '''n_token''': '''vocab_size''',
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''n_head''',
     '''num_hidden_layers''': '''n_layer''',
 }

 def __init__(    self :  Any						,							A_ :  Optional[Any]=267735						,							A_ :  Optional[Any]=[20000, 40000, 200000]						,							A_ :  Union[str, Any]=1024						,							A_ :  Optional[Any]=1024						,							A_ :  Optional[int]=16						,							A_ :  Any=64						,							A_ :  List[Any]=4096						,							A_ :  str=4						,							A_ :  int=False						,							A_ :  List[Any]=18						,							A_ :  Optional[int]=1600						,							A_ :  Union[str, Any]=1000						,							A_ :  Optional[Any]=True						,							A_ :  Optional[int]=True						,							A_ :  List[str]=0						,							A_ :  int=-1						,							A_ :  List[Any]=True						,							A_ :  List[Any]=0.1						,							A_ :  str=0.0						,							A_ :  Dict=True						,							A_ :  Dict="normal"						,							A_ :  Dict=0.01						,							A_ :  Optional[Any]=0.01						,							A_ :  Any=0.02						,							A_ :  int=1E-5						,							A_ :  List[str]=0						,							**A_ :  Optional[Any]						,							)       ->     Optional[int]:

       """simple docstring"""






       lowerCamelCase_							=   vocab_size
       lowerCamelCase_							=   []
       self.cutoffs.extend(A_  )
       if proj_share_all_but_first:
             lowerCamelCase_							=   [False] + [True] * len(self.cutoffs  )
       else:
             lowerCamelCase_							=   [False] + [False] * len(self.cutoffs  )
       lowerCamelCase_							=   d_model
       lowerCamelCase_							=   d_embed
       lowerCamelCase_							=   d_head
       lowerCamelCase_							=   d_inner
       lowerCamelCase_							=   div_val
       lowerCamelCase_							=   pre_lnorm
       lowerCamelCase_							=   n_layer
       lowerCamelCase_							=   n_head
       lowerCamelCase_							=   mem_len
       lowerCamelCase_							=   same_length
       lowerCamelCase_							=   attn_type
       lowerCamelCase_							=   clamp_len
       lowerCamelCase_							=   sample_softmax
       lowerCamelCase_							=   adaptive
       lowerCamelCase_							=   dropout
       lowerCamelCase_							=   dropatt
       lowerCamelCase_							=   untie_r
       lowerCamelCase_							=   init
       lowerCamelCase_							=   init_range
       lowerCamelCase_							=   proj_init_std
       lowerCamelCase_							=   init_std
       lowerCamelCase_							=   layer_norm_epsilon
       super().__init__(eos_token_id=A_						,							**A_  )

 @property
 def     a__			(    self :  Tuple  )       ->     Any:

       """simple docstring"""






       logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit."""  )
       return -1





 @max_position_embeddings.setter
 def     a__			(    self :  Dict						,							A_ :  Optional[int]  )       ->     List[Any]:

       """simple docstring"""






       raise NotImplementedError(
           f"""The model {self.model_type} is one of the few models that has no sequence length limit."""  )



