




from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat




def     __magic_name__       (				):






			'''simple docstring'''







			a					=    [randint(-1000,						1000     ) for i in range(10     )]
			a					=    randint(-5000,						5000     )
			return (arr, r)


__lowerCAmelCase	:       Any     =	make_dataset()




def     __magic_name__       (				A	:      list[int],						A	:      int     ):






			'''simple docstring'''







			for triplet in permutations(A,						3     ):
						if sum(A     ) == target:
									return tuple(sorted(A     )     )
			return (0, 0, 0)




def     __magic_name__       (				A	:      list[int],						A	:      int     ):






			'''simple docstring'''







			arr.sort()
			a					=    len(A     )
			for i in range(n - 1     ):
						a					,	a					=    i + 1, n - 1
						while left < right:
									if arr[i] + arr[left] + arr[right] == target:
												return (arr[i], arr[left], arr[right])
									elif arr[i] + arr[left] + arr[right] < target:
												left += 1
									elif arr[i] + arr[left] + arr[right] > target:
												right -= 1
			return (0, 0, 0)




def     __magic_name__       (				):






			'''simple docstring'''







			a					=    "\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n"
			a					=    "\ntriplet_sum1(*dataset)\n"
			a					=    "\ntriplet_sum2(*dataset)\n"
			a					=    repeat(setup=A,						stmt=A,						repeat=5,						number=10000     )
			a					=    repeat(setup=A,						stmt=A,						repeat=5,						number=10000     )
			return (min(A     ), min(A     ))


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		__lowerCAmelCase	:       Optional[int]     =	solution_times()
		print(F'''The time for naive implementation is {times[0]}.''')
		print(F'''The time for optimized implementation is {times[1]}.''')












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__lowerCAmelCase	:       Any     =	{
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       Optional[Any]     =	[
		    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'RoCBertForCausalLM',
		    'RoCBertForMaskedLM',
		    'RoCBertForMultipleChoice',
		    'RoCBertForPreTraining',
		    'RoCBertForQuestionAnswering',
		    'RoCBertForSequenceClassification',
		    'RoCBertForTokenClassification',
		    'RoCBertLayer',
		    'RoCBertModel',
		    'RoCBertPreTrainedModel',
		    'load_tf_weights_in_roc_bert',
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		__lowerCAmelCase	:       Union[str, Any]     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






