


'''simple docstring'''
lowerCAmelCase   : List[str]             =				{
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}




def 	_A  (       A							,A							,A							)  ->				list[str]:
	lowercase						:		Optional[int]   					=		set()
	# keep track of all the paths to be checked
	lowercase						:		Any   					=		[[start]]

	# return path if start is goal
	if start == goal:
		return [start]

	# keeps looping until all possible paths have been checked
	while queue:
		# pop the first path from the queue
		lowercase						:		List[Any]   					=		queue.pop(0							)
		# get the last node from the path
		lowercase						:		Dict   					=		path[-1]
		if node not in explored:
			lowercase						:		Any   					=		graph[node]
			# go through all neighbour nodes, construct a new path and
			# push it into the queue
			for neighbour in neighbours:
				lowercase						:		Union[str, Any]   					=		list(_A							)
				new_path.append(_A							)
				queue.append(_A							)
				# return path if neighbour is goal
				if neighbour == goal:
					return new_path

            # mark node as explored
			explored.add(_A							)

    # in case there's no path between the 2 nodes
	return []




def 	_A  (       A							,A							,A							)  ->				int:
	if not graph or start not in graph or target not in graph:
		return -1
	if start == target:
		return 0
	lowercase						:		List[Any]   					=		[start]
	lowercase						:		Any   					=		set(_A							)
	# Keep tab on distances from `start` node.
	lowercase						:		Tuple   					=		{start: 0, target: -1}
	while queue:
		lowercase						:		List[Any]   					=		queue.pop(0							)
		if node == target:
			lowercase						:		Union[str, Any]   					=		(
			    dist[node] if dist[target] == -1 else min(dist[target]							,dist[node]							)
			)
		for adjacent in graph[node]:
			if adjacent not in visited:
				visited.add(_A							)
				queue.append(_A							)
				lowercase						:		int   					=		dist[node] + 1
	return dist[target]


if __name__ == "__main__":
					print(bfs_shortest_path(demo_graph, """G""", """D"""))  # returns ['G', 'C', 'A', 'B', 'D']
					print(bfs_shortest_path_distance(demo_graph, """G""", """D"""))  # returns 4










"""simple docstring"""







import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
_lowerCAmelCase            =	logging.getLogger(__name__)

if __name__ == "__main__":
				_lowerCAmelCase            =	argparse.ArgumentParser(
				    description='Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'
				)
				parser.add_argument(
				    '--data_file', type=str, default='data/dump.bert-base-uncased.pickle', help='The binarized dataset.'
				)
				parser.add_argument(
				    '--token_counts_dump', type=str, default='data/token_counts.bert-base-uncased.pickle', help='The dump file.'
				)
				parser.add_argument('--vocab_size', default=3_05_22, type=int)
				_lowerCAmelCase            =	parser.parse_args()

				logger.info(F'Loading data from {args.data_file}')
				with open(args.data_file, 'rb') as fp:
								_lowerCAmelCase            =	pickle.load(fp)

				logger.info('Counting occurrences for MLM.')
				_lowerCAmelCase            =	Counter()
				for tk_ids in data:
								counter.update(tk_ids)
				_lowerCAmelCase            =	[0] * args.vocab_size
				for k, v in counter.items():
								_lowerCAmelCase            =	v

				logger.info(F'Dump to {args.token_counts_dump}')
				with open(args.token_counts_dump, 'wb') as handle:
								pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
