


'''simple docstring'''






from __future__ import annotations

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






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




     '''simple docstring'''



     _a      =							[randint(-10_00			,  10_00						) for i in range(10						)]
     _a      =							randint(-50_00			,  50_00						)
     return (arr, r)


a_   :      List[str]				    =      make_dataset()






def 		_A			(lowerCAmelCase__					:List[str]			,  lowerCAmelCase__					:Optional[Any]						)				->					tuple[int, ...]:




     '''simple docstring'''



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






def 		_A			(lowerCAmelCase__					:Dict			,  lowerCAmelCase__					:Optional[int]						)				->					tuple[int, int, int]:




     '''simple docstring'''



     arr.sort()
     _a      =							len(UpperCAmelCase__						)
     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 		_A			()				->					tuple[float, float]:




     '''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=UpperCAmelCase__			,  stmt=UpperCAmelCase__			,  repeat=5			,  number=1_00_00						)
     _a      =							repeat(setup=UpperCAmelCase__			,  stmt=UpperCAmelCase__			,  repeat=5			,  number=1_00_00						)
     return (min(UpperCAmelCase__						), min(UpperCAmelCase__						))


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

 testmod()
 a_   :      Tuple				    =      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 ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_   :      List[Any]				    =      {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[str]				    =      ["GPTNeoXTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[Any]				    =      [
     "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPTNeoXForCausalLM",
     "GPTNeoXForQuestionAnswering",
     "GPTNeoXForSequenceClassification",
     "GPTNeoXForTokenClassification",
     "GPTNeoXLayer",
     "GPTNeoXModel",
     "GPTNeoXPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox import (
      GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXForCausalLM,
      GPTNeoXForQuestionAnswering,
      GPTNeoXForSequenceClassification,
      GPTNeoXForTokenClassification,
      GPTNeoXLayer,
      GPTNeoXModel,
      GPTNeoXPreTrainedModel,
  )


else:
 import sys

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

