from queue import PriorityQueue
from typing import Any

import numpy as np






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				List[Any]		, _SCREAMING_SNAKE_CASE     :				List[str]		, _SCREAMING_SNAKE_CASE     :				Dict		, _SCREAMING_SNAKE_CASE     :				Any		, _SCREAMING_SNAKE_CASE     :				int		, _SCREAMING_SNAKE_CASE     :				Optional[int]		, _SCREAMING_SNAKE_CASE     :				Tuple		, _SCREAMING_SNAKE_CASE     :				List[Any]		, _SCREAMING_SNAKE_CASE     :				Optional[Any]		, ):


						for nxt, d in graph[v]:
												if nxt in visited_forward:
																		continue
												UpperCamelCase_		:							List[Any]  =		cst_fwd.get(a__		, np.inf			)
												UpperCamelCase_		:							Any  =		cst_fwd[v] + d
												if new_cost_f < old_cost_f:
																		queue.put((new_cost_f, nxt)			)
																		UpperCamelCase_		:							List[str]  =		new_cost_f
																		UpperCamelCase_		:							List[str]  =		v
												if nxt in visited_backward:
																		if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																								UpperCamelCase_		:							Union[str, Any]  =		cst_fwd[v] + d + cst_bwd[nxt]
						return shortest_distance






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Any		, _SCREAMING_SNAKE_CASE     :				Any		, _SCREAMING_SNAKE_CASE     :				Optional[Any]		, _SCREAMING_SNAKE_CASE     :				Any			):
						UpperCamelCase_		:							Union[str, Any]  =		-1

						UpperCamelCase_		:							Any  =		set()
						UpperCamelCase_		:							Optional[int]  =		set()
						UpperCamelCase_		:							Optional[Any]  =		{source: 0}
						UpperCamelCase_		:							Optional[Any]  =		{destination: 0}
						UpperCamelCase_		:							Optional[Any]  =		{source: None}
						UpperCamelCase_		:							Any  =		{destination: None}
						UpperCamelCase_		:							Dict  =		PriorityQueue()
						UpperCamelCase_		:							str  =		PriorityQueue()

						UpperCamelCase_		:							int  =		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_		:							List[str]  =		queue_forward.get()
												visited_forward.add(a__			)

												UpperCamelCase_,UpperCamelCase_		:							List[str]  =		queue_backward.get()
												visited_backward.add(a__			)

												UpperCamelCase_		:							str  =		pass_and_relaxation(
												    a__		, a__		, a__		, a__		, a__		, a__		, a__		, a__		, a__		, )

												UpperCamelCase_		:							str  =		pass_and_relaxation(
												    a__		, a__		, a__		, a__		, a__		, a__		, a__		, a__		, a__		, )

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

						if shortest_distance != np.inf:
												UpperCamelCase_		:							Optional[Any]  =		shortest_distance
						return shortest_path_distance


SCREAMING_SNAKE_CASE		:     Optional[Any]         =	{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
SCREAMING_SNAKE_CASE		:     Any         =	{
    '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()









'''simple docstring'''



import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = {}
     __SCREAMING_SNAKE_CASE      = tokenizer(example["""content"""] ,       truncation=a__		)["""input_ids"""]
     __SCREAMING_SNAKE_CASE      = len(example["""content"""]		) / len(output["""input_ids"""]		)
     return output


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

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

UpperCAmelCase					:			List[str]       =	time.time()
UpperCAmelCase					:			List[str]       =	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					:			List[Any]       =	time.time()
ds.push_to_hub(args.tokenized_data_repo)



print(f"""Data pushed to the hub in {time.time()-t_start:.2f}s""")






