
"""simple docstring"""

import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser






def 							A						(    snake_case				:Optional[Any]		)				->     Union[str, Any]:
 __UpperCamelCase      =	{}
 __UpperCamelCase      =	tokenizer(example['content']    , truncation=snake_case		)['input_ids']
 __UpperCamelCase      =	len(example['content']		) / len(output['input_ids']		)
 return output


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

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

UpperCamelCase     :    List[Any]       =			time.time()
UpperCamelCase     :    Tuple       =			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     :    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''')





"""simple docstring"""

UpperCamelCase     :    Union[str, Any]       =			[
    [0, 1_6, 1_3, 0, 0, 0],
    [0, 0, 1_0, 1_2, 0, 0],
    [0, 4, 0, 0, 1_4, 0],
    [0, 0, 9, 0, 0, 2_0],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]






def 							A						(    snake_case				:Dict    , snake_case				:Tuple    , snake_case				:str    , snake_case				:Optional[int]		)				->     Union[str, Any]:
 # Return True if there is node that has not iterated.
 __UpperCamelCase      =	[False] * len(snake_case		)
 __UpperCamelCase      =	[s]
 __UpperCamelCase      =	True

 while queue:
  __UpperCamelCase      =	queue.pop(0		)
  for ind in range(len(graph[u]		)		):
   if visited[ind] is False and graph[u][ind] > 0:
    queue.append(snake_case		)
    __UpperCamelCase      =	True
    __UpperCamelCase      =	u

 return visited[t]






def 							A						(    snake_case				:int    , snake_case				:Any    , snake_case				:Union[str, Any]		)				->     Optional[int]:
 __UpperCamelCase      =	[-1] * (len(snake_case		))
 __UpperCamelCase      =	0
 __UpperCamelCase      =	[]
 __UpperCamelCase      =	[i[:] for i in graph]  # Record original cut, copy.
 while bfs(snake_case    , snake_case    , snake_case    , snake_case		):
  __UpperCamelCase      =	float('Inf'		)
  __UpperCamelCase      =	sink

  while s != source:
   # Find the minimum value in select path
   __UpperCamelCase      =	min(snake_case    , graph[parent[s]][s]		)
   __UpperCamelCase      =	parent[s]

  max_flow += path_flow
  __UpperCamelCase      =	sink

  while v != source:
   __UpperCamelCase      =	parent[v]
   graph[u][v] -= path_flow
   graph[v][u] += path_flow
   __UpperCamelCase      =	parent[v]

 for i in range(len(snake_case		)		):
  for j in range(len(graph[0]		)		):
   if graph[i][j] == 0 and temp[i][j] > 0:
    res.append((i, j)		)

 return res


if __name__ == "__main__":
      print(mincut(test_graph, source=0, sink=5))



