

'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np







def   a__			(    a__ ,       a__ ,       a__ ,       a__ ,       a__ ,       a__ ,       a__ ,       a__ ,       a__ ,       ):




     """simple docstring"""


     for nxt, d in graph[v]:
          if nxt in visited_forward:
               continue
          __SCREAMING_SNAKE_CASE      = cst_fwd.get(a__ ,       np.inf		)
          __SCREAMING_SNAKE_CASE      = cst_fwd[v] + d
          if new_cost_f < old_cost_f:
               queue.put((new_cost_f, nxt)		)
               __SCREAMING_SNAKE_CASE      = new_cost_f
               __SCREAMING_SNAKE_CASE      = v
          if nxt in visited_backward:
               if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
                    __SCREAMING_SNAKE_CASE      = cst_fwd[v] + d + cst_bwd[nxt]
     return shortest_distance







def   a__			(    a__ ,       a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = -1

     __SCREAMING_SNAKE_CASE      = set()
     __SCREAMING_SNAKE_CASE      = set()
     __SCREAMING_SNAKE_CASE      = {source: 0}
     __SCREAMING_SNAKE_CASE      = {destination: 0}
     __SCREAMING_SNAKE_CASE      = {source: None}
     __SCREAMING_SNAKE_CASE      = {destination: None}
     __SCREAMING_SNAKE_CASE      = PriorityQueue()
     __SCREAMING_SNAKE_CASE      = PriorityQueue()

     __SCREAMING_SNAKE_CASE      = np.inf

     queue_forward.put((0, source)		)
     queue_backward.put((0, destination)		)

     if source == destination:
          return 0

     while not queue_forward.empty() and not queue_backward.empty():
          __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = queue_forward.get()
          visited_forward.add(a__		)

          __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = queue_backward.get()
          visited_backward.add(a__		)

          __SCREAMING_SNAKE_CASE      = pass_and_relaxation(
              a__ ,       a__ ,       a__ ,       a__ ,       a__ ,       a__ ,       a__ ,       a__ ,       a__ ,       )

          __SCREAMING_SNAKE_CASE      = pass_and_relaxation(
              a__ ,       a__ ,       a__ ,       a__ ,       a__ ,       a__ ,       a__ ,       a__ ,       a__ ,       )

          if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
               break

     if shortest_distance != np.inf:
          __SCREAMING_SNAKE_CASE      = shortest_distance
     return shortest_path_distance


UpperCAmelCase					:			Optional[Any]       =	{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
UpperCAmelCase					:			Any       =	{
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
  import doctest

  doctest.testmod()









'''simple docstring'''



import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = {}
     __SCREAMING_SNAKE_CASE      = tokenizer(example["""content"""] ,       truncation=a__		)["""input_ids"""]
     __SCREAMING_SNAKE_CASE      = len(example["""content"""]		) / len(output["""input_ids"""]		)
     return output


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

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

UpperCAmelCase					:			List[str]       =	time.time()
UpperCAmelCase					:			List[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""")

UpperCAmelCase					:			List[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""")






