"""simple docstring"""
from __future__ import annotations

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





def 				lowerCAmelCase__      (     )   ->			tuple[list[int], int]:





						"""simple docstring"""






						snake_case        =     [randint(-1_0_0_0	,     1_0_0_0   ) for i in range(1_0   )]
						snake_case        =     randint(-5_0_0_0	,     5_0_0_0   )
						return (arr, r)


SCREAMING_SNAKE_CASE__   =					make_dataset()





def 				lowerCAmelCase__      (     _UpperCamelCase    :			list[int]	,     _UpperCamelCase    :			int   )   ->			tuple[int, ...]:





						"""simple docstring"""






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





def 				lowerCAmelCase__      (     _UpperCamelCase    :			list[int]	,     _UpperCamelCase    :			int   )   ->			tuple[int, int, int]:





						"""simple docstring"""






						arr.sort()
						snake_case        =     len(_UpperCamelCase   )
						for i in range(n - 1   ):
												snake_case   ,snake_case        =     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 				lowerCAmelCase__      (     )   ->			tuple[float, float]:





						"""simple docstring"""






						snake_case        =     '\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
						snake_case        =     '\ntriplet_sum1(*dataset)\n'
						snake_case        =     '\ntriplet_sum2(*dataset)\n'
						snake_case        =     repeat(setup=_UpperCamelCase	,     stmt=_UpperCamelCase	,     repeat=5	,     number=1_0_0_0_0   )
						snake_case        =     repeat(setup=_UpperCamelCase	,     stmt=_UpperCamelCase	,     repeat=5	,     number=1_0_0_0_0   )
						return (min(_UpperCamelCase   ), min(_UpperCamelCase   ))


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

					testmod()
					SCREAMING_SNAKE_CASE__   =					solution_times()
					print(f"""The time for naive implementation is {times[0]}.""")
					print(f"""The time for optimized implementation is {times[1]}.""")




"""simple docstring"""
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__   =					{
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "XLMForMultipleChoice",
					    "XLMForQuestionAnswering",
					    "XLMForQuestionAnsweringSimple",
					    "XLMForSequenceClassification",
					    "XLMForTokenClassification",
					    "XLMModel",
					    "XLMPreTrainedModel",
					    "XLMWithLMHeadModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   =					[
					    "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFXLMForMultipleChoice",
					    "TFXLMForQuestionAnsweringSimple",
					    "TFXLMForSequenceClassification",
					    "TFXLMForTokenClassification",
					    "TFXLMMainLayer",
					    "TFXLMModel",
					    "TFXLMPreTrainedModel",
					    "TFXLMWithLMHeadModel",
					]


if TYPE_CHECKING:
					from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
					from .tokenization_xlm import XLMTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm import (
										    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMForMultipleChoice,
										    XLMForQuestionAnswering,
										    XLMForQuestionAnsweringSimple,
										    XLMForSequenceClassification,
										    XLMForTokenClassification,
										    XLMModel,
										    XLMPreTrainedModel,
										    XLMWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm import (
										    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMForMultipleChoice,
										    TFXLMForQuestionAnsweringSimple,
										    TFXLMForSequenceClassification,
										    TFXLMForTokenClassification,
										    TFXLMMainLayer,
										    TFXLMModel,
										    TFXLMPreTrainedModel,
										    TFXLMWithLMHeadModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE__   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


