'''simple docstring'''

a_						:       Union[str, Any]      			=				[
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]





def __snake_case      (   UpperCAmelCase_		:		Dict      ,       UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		Any      ,       UpperCAmelCase_		:		Any	):
 lowerCamelCase_  				=						[False] * len(__UpperCamelCase	)
 lowerCamelCase_  				=						[s]
 lowerCamelCase_  				=						True

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

 return visited[t]





def __snake_case      (   UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		Dict	):
 lowerCamelCase_  				=						[-1] * (len(__UpperCamelCase	))
 lowerCamelCase_  				=						0
 lowerCamelCase_  				=						[]
 lowerCamelCase_  				=						[i[:] for i in graph]  # Record original cut, copy.
 while bfs(__UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase	):
  lowerCamelCase_  				=						float("Inf"	)
  lowerCamelCase_  				=						sink

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

  max_flow += path_flow
  lowerCamelCase_  				=						sink

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

 for i in range(len(__UpperCamelCase	)	):
  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))







'''simple docstring'''

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save





def __snake_case      (   UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		Optional[int]=1024      ,       UpperCAmelCase_		:		int=1024      ,       UpperCAmelCase_		:		Optional[Any]=False      ,       **UpperCAmelCase_		:		Optional[Any]	):
 lowerCamelCase_  				=						AutoTokenizer.from_pretrained(UpperCAmelCase_	)
 lowerCamelCase_  				=						SeqaSeqDataset(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       type_path="train"      ,       **UpperCAmelCase_	)
 lowerCamelCase_  				=						tok.pad_token_id

 def get_lens(UpperCAmelCase_		:		str	):
  lowerCamelCase_  				=						tqdm(
      DataLoader(UpperCAmelCase_      ,       batch_size=512      ,       num_workers=8      ,       shuffle=UpperCAmelCase_      ,       collate_fn=ds.collate_fn	)      ,       desc=str(ds.len_file	)      ,       )
  lowerCamelCase_  				=						[]
  for batch in dl:
   lowerCamelCase_  				=						batch["input_ids"].ne(UpperCAmelCase_	).sum(1	).tolist()
   lowerCamelCase_  				=						batch["labels"].ne(UpperCAmelCase_	).sum(1	).tolist()
   if consider_target:
    for src, tgt in zip(UpperCAmelCase_      ,       UpperCAmelCase_	):
     max_lens.append(max(UpperCAmelCase_      ,       UpperCAmelCase_	)	)
   else:
    max_lens.extend(UpperCAmelCase_	)
  return max_lens

 lowerCamelCase_  				=						get_lens(UpperCAmelCase_	)
 lowerCamelCase_  				=						SeqaSeqDataset(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       type_path="val"      ,       **UpperCAmelCase_	)
 lowerCamelCase_  				=						get_lens(UpperCAmelCase_	)
 pickle_save(UpperCAmelCase_      ,       train_ds.len_file	)
 pickle_save(UpperCAmelCase_      ,       val_ds.len_file	)


if __name__ == "__main__":
     fire.Fire(save_len_file)



