



def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Dict													=					[False] * len(lowerCamelCase__						)
				lowercase__					:   int													=					[]
				queue.append(lowerCamelCase__						)
				lowercase__					:   int													=					True

				while queue:
								lowercase__					:   Optional[Any]													=					queue.pop(0						)
								for ind in range(len(graph[u]						)						):
												if visited[ind] is False and graph[u][ind] > 0:
																queue.append(lowerCamelCase__						)
																lowercase__					:   Any													=					True
																lowercase__					:   List[Any]													=					u

				return visited[t]


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Optional[Any]													=					[-1] * (len(lowerCamelCase__						))
				lowercase__					:   List[Any]													=					0
				while bfs(lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):
								lowercase__					:   List[str]													=					float("Inf"						)
								lowercase__					:   List[str]													=					sink

								while s != source:
												# Find the minimum value in select path
												lowercase__					:   Optional[int]													=					min(lowerCamelCase__					,			graph[parent[s]][s]						)
												lowercase__					:   str													=					parent[s]

								max_flow += path_flow
								lowercase__					:   str													=					sink

								while v != source:
												lowercase__					:   Optional[int]													=					parent[v]
												graph[u][v] -= path_flow
												graph[v][u] += path_flow
												lowercase__					:   Dict													=					parent[v]
				return max_flow


lowerCAmelCase__					  =  [
    [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],
]

lowerCAmelCase__  ,						lowerCAmelCase__					  =  0, 5
print(ford_fulkerson(graph, source, sink))









import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Optional[Any]													=					{}
				lowercase__					:   Tuple													=					tokenizer(example["content"]					,			truncation=lowerCamelCase__						)["input_ids"]
				lowercase__					:   Optional[int]													=					len(example["content"]						) / len(output["input_ids"]						)
				return output


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

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

lowerCAmelCase__					  =  time.time()
lowerCAmelCase__					  =  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''')

lowerCAmelCase__					  =  time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(f'''Data pushed to the hub in {time.time()-t_start:.2f}s''')




