



'''simple docstring'''







from __future__ import annotations

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

def  lowerCamelCase_					(       ):
	__lowerCamelCase		       =		[randint(-10_00    ,				10_00				) for i in range(10				)]
	__lowerCamelCase		       =		randint(-50_00    ,				50_00				)
	return (arr, r)


_UpperCamelCase					:  Any        =make_dataset()

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

def  lowerCamelCase_					(       A_    ,				A_				):
	arr.sort()
	__lowerCamelCase		       =		len(A_				)
	for i in range(n - 1				):
		__lowerCamelCase       ,	__lowerCamelCase		       =		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_					(       ):
	__lowerCamelCase		       =		'''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
	__lowerCamelCase		       =		'''
triplet_sum1(*dataset)
'''
	__lowerCamelCase		       =		'''
triplet_sum2(*dataset)
'''
	__lowerCamelCase		       =		repeat(setup=A_    ,				stmt=A_    ,				repeat=5    ,				number=1_00_00				)
	__lowerCamelCase		       =		repeat(setup=A_    ,				stmt=A_    ,				repeat=5    ,				number=1_00_00				)
	return (min(A_				), min(A_				))


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

				testmod()
				_UpperCamelCase					:  Optional[Any]        =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


UpperCAmelCase__							  =						{
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__							  =						[
         "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BioGptForCausalLM",
         "BioGptForTokenClassification",
         "BioGptForSequenceClassification",
         "BioGptModel",
         "BioGptPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

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


