
'''simple docstring'''







from __future__ import annotations

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







def 				SCREAMING_SNAKE_CASE__	(   )       ->	tuple[list[int], int]:
	_snake_case   						=		[randint(-1_000			,				1_000	) for i in range(10	)]
	_snake_case   						=		randint(-5_000			,				5_000	)
	return (arr, r)


lowercase    :							Union[str, Any]      =					make_dataset()







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	tuple[int, ...]:
	for triplet in permutations(__A			,				3	):
		if sum(__A	) == target:
			return tuple(sorted(__A	)	)
	return (0, 0, 0)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	tuple[int, int, int]:
	arr.sort()
	_snake_case   						=		len(__A	)
	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 				SCREAMING_SNAKE_CASE__	(   )       ->	tuple[float, float]:
	_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=__A			,				stmt=__A			,				repeat=5			,				number=10_000	)
	_snake_case   						=		repeat(setup=__A			,				stmt=__A			,				repeat=5			,				number=10_000	)
	return (min(__A	), min(__A	))


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

					testmod()
					lowercase    :							Any      =					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_torch_available, is_vision_available


lowercase    :							Any      =					{
    "configuration_chinese_clip": [
        "CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ChineseCLIPConfig",
        "ChineseCLIPOnnxConfig",
        "ChineseCLIPTextConfig",
        "ChineseCLIPVisionConfig",
    ],
    "processing_chinese_clip": ["ChineseCLIPProcessor"],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Optional[Any]      =					["ChineseCLIPFeatureExtractor"]
					lowercase    :							List[Any]      =					["ChineseCLIPImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Any      =					[
					    "CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ChineseCLIPModel",
					    "ChineseCLIPPreTrainedModel",
					    "ChineseCLIPTextModel",
					    "ChineseCLIPVisionModel",
					]

if TYPE_CHECKING:
					from .configuration_chinese_clip import (
					    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    ChineseCLIPConfig,
					    ChineseCLIPOnnxConfig,
					    ChineseCLIPTextConfig,
					    ChineseCLIPVisionConfig,
					)
					from .processing_chinese_clip import ChineseCLIPProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_chinese_clip import (
										    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ChineseCLIPModel,
										    ChineseCLIPPreTrainedModel,
										    ChineseCLIPTextModel,
										    ChineseCLIPVisionModel,
										)

else:
					import sys

					lowercase    :							List[Any]      =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

