


from queue import PriorityQueue
from typing import Any

import numpy as np
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						)						->float | int:
   for nxt, d in graph[v]:
      if nxt in visited_forward:
         continue
      UpperCAmelCase							=     cst_fwd.get(lowerCAmelCase_  ,						np.inf   )
      UpperCAmelCase							=     cst_fwd[v] + d
      if new_cost_f < old_cost_f:
         queue.put((new_cost_f, nxt)   )
         UpperCAmelCase							=     new_cost_f
         UpperCAmelCase							=     v
      if nxt in visited_backward:
         if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
            UpperCAmelCase							=     cst_fwd[v] + d + cst_bwd[nxt]
   return shortest_distance
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   )						->int:
   UpperCAmelCase							=     -1

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

   UpperCAmelCase							=     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():
      UpperCAmelCase				,  UpperCAmelCase							=     queue_forward.get()
      visited_forward.add(lowerCAmelCase_   )

      UpperCAmelCase				,  UpperCAmelCase							=     queue_backward.get()
      visited_backward.add(lowerCAmelCase_   )

      UpperCAmelCase							=     pass_and_relaxation(
          lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						)

      UpperCAmelCase							=     pass_and_relaxation(
          lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						)

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

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


__a     =			{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}
__a     =			{
    """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()







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""")



