

from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

__snake_case							:     Optional[int] 						=	{
    """A""": ["""B""", """C"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F"""],
    """D""": ["""B"""],
    """E""": ["""B""", """F"""],
    """F""": ["""C""", """E"""],
}


def 			_UpperCAmelCase							(		a__       ,					a__) ->    Optional[int]:

			'''simple docstring'''



			a_		:		List[Any]				    =				{start}
			a_		:		Optional[Any]				    =				[start]
			a_		:		Queue				    =				Queue()
			queue.put(a__)
			while not queue.empty():
						a_		:		List[str]				    =				queue.get()
						for w in graph[v]:
									if w not in explored:
												explored.add(a__)
												result.append(a__)
												queue.put(a__)
			return result


def 			_UpperCAmelCase							(		a__       ,					a__) ->    Union[str, Any]:

			'''simple docstring'''



			a_		:		List[str]				    =				{start}
			a_		:		Dict				    =				[start]
			a_		:		str				    =				deque([start])
			while queue:
						a_		:		List[Any]				    =				queue.popleft()
						for child in graph[v]:
									if child not in visited:
												visited.add(a__)
												result.append(a__)
												queue.append(a__)
			return result


def 			_UpperCAmelCase							(		a__) ->    Any:

			'''simple docstring'''



			a_		:		str				    =				f'''from __main__ import G, {name}'''
			a_		:		Union[str, Any]				    =				1_0_0_0_0
			a_		:		List[str]				    =				timeit(f'''{name}(G, \'A\')'''       ,					setup=a__       ,					number=a__)
			print(f'''{name:<35} finished {number} runs in {res:.5f} seconds''')


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	benchmark_function("""breadth_first_search""")
	benchmark_function("""breadth_first_search_with_deque""")
	# breadth_first_search                finished 10000 runs in 0.20999 seconds
	# breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds





import sys

from transformers import AutoTokenizer


__snake_case							:     int 						=	sys.argv[1]
__snake_case							:     Dict 						=	sys.argv[2]
__snake_case							:     Dict 						=	int(sys.argv[3])

__snake_case							:     Union[str, Any] 						=	0

__snake_case							:     Any 						=	AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, """rt""") as f_p:
	for line in f_p:
		__snake_case							:     str 						=	line.rstrip()

		if not line:
			print(line)
			__snake_case							:     Optional[int] 						=	0
			continue

		__snake_case							:     Dict 						=	line.split()[0]

		__snake_case							:     str 						=	len(tokenizer.tokenize(token))

		# Token contains strange control characters like \x96 or \x95
		# Just filter out the complete line
		if current_subwords_len == 0:
			continue

		if (subword_len_counter + current_subwords_len) > max_len:
			print("""""")
			print(line)
			__snake_case							:     Optional[int] 						=	current_subwords_len
			continue

		subword_len_counter += current_subwords_len

		print(line)


