





"""simple docstring"""




def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					dict      )				->set:






	'''simple docstring'''






	a   :	Tuple			=       set()
	# edges = list of graph's edges
	a   :	str			=       get_edges(_lowercase      )

	# While there are still elements in edges list, take an arbitrary edge
	# (from_node, to_node) and add his extremity to chosen_vertices and then
	# remove all arcs adjacent to the from_node and to_node
	while edges:
		a, a   :	Union[str, Any]			=       edges.pop()
		chosen_vertices.add(_lowercase      )
		chosen_vertices.add(_lowercase      )
		for edge in edges.copy():
			if from_node in edge or to_node in edge:
				edges.discard(_lowercase      )
	return chosen_vertices






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					dict      )				->set:






	'''simple docstring'''






	a   :	Optional[int]			=       set()
	for from_node, to_nodes in graph.items():
		for to_node in to_nodes:
			edges.add((from_node, to_node)      )
	return edges


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
		# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")









"""simple docstring"""




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[str]      )				->int:






	'''simple docstring'''






	a   :	int			=       {}
	a   :	Union[str, Any]			=       tokenizer(example["content"]					, truncation=_lowercase      )["input_ids"]
	a   :	Any			=       len(example["content"]      ) / len(output["input_ids"]      )
	return output


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

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

a		:				Dict  			=    time.time()
a		:				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''')

a		:				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''')


