






class 	__snake_case	:





      '''simple docstring'''






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


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						""""""
          _lowerCamelCase     :		int 						=						""""""
          _lowerCamelCase     :		Union[str, Any] 						=						[]



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				Any)				->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]:
                  _lowerCamelCase     :		str 						=						self.__min_dist_top_down_dp(m - 1    ,  n - 1)
              else:
                  _lowerCamelCase     :		int 						=						self.__min_dist_top_down_dp(__A    ,  n - 1)
                  _lowerCamelCase     :		Optional[Any] 						=						self.__min_dist_top_down_dp(m - 1    ,  __A)
                  _lowerCamelCase     :		int 						=						self.__min_dist_top_down_dp(m - 1    ,  n - 1)
                  _lowerCamelCase     :		int 						=						1 + min(__A    ,  __A    ,  __A)

              return self.dp[m][n]



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				Any)				->int:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						worda
          _lowerCamelCase     :		List[str] 						=						worda
          _lowerCamelCase     :		Optional[Any] 						=						[[-1 for _ in range(len(__A))] for _ in range(len(__A))]

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




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				List[Any])				->int:


          """simple docstring"""


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


if __name__ == "__main__":
    lowerCAmelCase			:							Any     =EditDistance()

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

    lowerCAmelCase			:							Union[str, Any]     =input("Enter the first string: ").strip()
    lowerCAmelCase			:							List[Any]     =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 ***************")





import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						{}
    _lowerCamelCase     :		List[Any] 						=						tokenizer(example["""content"""]   , truncation=__A	)["""input_ids"""]
    _lowerCamelCase     :		Tuple 						=						len(example["""content"""]	) / len(output["""input_ids"""]	)
    return output


lowerCAmelCase			:							int     =HfArgumentParser(PretokenizationArguments)
lowerCAmelCase			:							int     =parser.parse_args()
if args.num_workers is None:
    lowerCAmelCase			:							Any     =multiprocessing.cpu_count()
lowerCAmelCase			:							Optional[Any]     =AutoTokenizer.from_pretrained(args.tokenizer_dir)

lowerCAmelCase			:							str     =time.time()
lowerCAmelCase			:							Union[str, Any]     =load_dataset(args.dataset_name, split="train")
print(F"""Dataset loaded in {time.time()-t_start:.2f}s""")

lowerCAmelCase			:							Dict     =time.time()
lowerCAmelCase			:							Dict     =ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        "repo_name",
        "path",
        "copies",
        "size",
        "content",
        "license",
        "hash",
        "line_mean",
        "line_max",
        "alpha_frac",
        "autogenerated",
    ],
)
print(F"""Dataset tokenized in {time.time()-t_start:.2f}s""")

lowerCAmelCase			:							Tuple     =time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(F"""Data pushed to the hub in {time.time()-t_start:.2f}s""")




