


"""simple docstring"""







class 				__A    :






 """simple docstring"""




 def __init__(		self			,   __A						) ->					str:
     # we need a list not a string, so do something to change the type
     a					=arr.split(''','''						)





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     a					=[int(self.array[0]						)] * len(self.array						)
     a					=[int(self.array[0]						)] * len(self.array						)
     for i in range(1			,   len(self.array						)						):
         a					=max(
             int(self.array[i]						) + sum_value[i - 1]			,   int(self.array[i]						)						)
         a					=max(sum_value[i]			,   rear[i - 1]						)
     return rear[len(self.array						) - 1]


if __name__ == "__main__":
 lowerCamelCase_       :       List[str]      						=  input("""please input some numbers:""")
 lowerCamelCase_       :       Tuple      						=  SubArray(whole_array)
 lowerCamelCase_       :       List[Any]      						=  array.solve_sub_array()
 print(("""the results is:""", re))



"""simple docstring"""







import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser
def _A						(						lowercase		):







    """simple docstring"""
    a					={}
    a					=tokenizer(example['''content''']	,   truncation=lowercase		)['''input_ids''']
    a					=len(example['''content''']		) / len(output['''input_ids''']		)
    return output


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

lowerCamelCase_       :       Any      						=  time.time()
lowerCamelCase_       :       int      						=  load_dataset(args.dataset_name, split="""train""")
print(F'Dataset loaded in {time.time()-t_start:.2f}s')

lowerCamelCase_       :       List[str]      						=  time.time()
lowerCamelCase_       :       str      						=  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_       :       Union[str, Any]      						=  time.time()
ds.push_to_hub(args.tokenized_data_repo)





print(F'Data pushed to the hub in {time.time()-t_start:.2f}s')