





'''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
)
_SCREAMING_SNAKE_CASE   =      logging.getLogger(__name__)

if __name__ == "__main__":
	_SCREAMING_SNAKE_CASE   =      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_0_5_2_2, type=int)
	_SCREAMING_SNAKE_CASE   =      parser.parse_args()

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

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












'''simple docstring'''



_SCREAMING_SNAKE_CASE   =      {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}




def 				_lowerCAmelCase  (			lowerCamelCase_     :					dict						,    lowerCamelCase_     :					Optional[Any]						,    lowerCamelCase_     :					List[str]			):
			__lowercase			  =							set()
			# keep track of all the paths to be checked
			__lowercase			  =							[[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			  =							queue.pop(0			)
						# get the last node from the path
						__lowercase			  =							path[-1]
						if node not in explored:
									__lowercase			  =							graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												__lowercase			  =							list(lowerCamelCase_			)
												new_path.append(lowerCamelCase_			)
												queue.append(lowerCamelCase_			)
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(lowerCamelCase_			)

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




def 				_lowerCAmelCase  (			lowerCamelCase_     :					dict						,    lowerCamelCase_     :					str						,    lowerCamelCase_     :					str			):
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			__lowercase			  =							[start]
			__lowercase			  =							set(lowerCamelCase_			)
			# Keep tab on distances from `start` node.
			__lowercase			  =							{start: 0, target: -1}
			while queue:
						__lowercase			  =							queue.pop(0			)
						if node == target:
									__lowercase			  =							(
									    dist[node] if dist[target] == -1 else min(dist[target]						,    dist[node]			)
									)
						for adjacent in graph[node]:
									if adjacent not in visited:
												visited.add(lowerCamelCase_			)
												queue.append(lowerCamelCase_			)
												__lowercase			  =							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





