


'''simple docstring'''





from math import factorial
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->float:
   if successes > trials:
      raise ValueError("""successes must be lower or equal to trials"""   )
   if trials < 0 or successes < 0:
      raise ValueError("""the function is defined for non-negative integers"""   )
   if not isinstance(lowerCAmelCase_  ,						lowerCAmelCase_   ) or not isinstance(lowerCAmelCase_  ,						lowerCAmelCase_   ):
      raise ValueError("""the function is defined for non-negative integers"""   )
   if not 0 < prob < 1:
      raise ValueError("""prob has to be in range of 1 - 0"""   )
   UpperCAmelCase							=     (prob**successes) * ((1 - prob) ** (trials - successes))
   # Calculate the binomial coefficient: n! / k!(n-k)!
   UpperCAmelCase							=     float(factorial(lowerCAmelCase_   )   )
   coefficient /= factorial(lowerCAmelCase_   ) * factorial(trials - successes   )
   return probability * coefficient


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 print("""Probability of 2 successes out of 4 trails""")
 print("""with probability of 0.75 is:""", end=""" """)
 print(binomial_distribution(2, 4, 0.75))







import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser
def   _UpperCamelCase				(						lowerCAmelCase_   )						->int:
   UpperCAmelCase							=     {}
   UpperCAmelCase							=     tokenizer(example["""content"""]  ,						truncation=lowerCAmelCase_   )["""input_ids"""]
   UpperCAmelCase							=     len(example["""content"""]   ) / len(output["""input_ids"""]   )
   return output


__a     =			HfArgumentParser(PretokenizationArguments)
__a     =			parser.parse_args()
if args.num_workers is None:
 __a     =			multiprocessing.cpu_count()
__a     =			AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

__a     =			time.time()
__a     =			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""")

__a     =			time.time()
ds.push_to_hub(args.tokenized_data_repo)







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



