





from __future__ import annotations

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



def A_     (		)  ->		tuple[list[int], int]:
       a__						:     Tuple			=    [randint(-1000		,				1000    ) for i in range(10    )]
       a__						:     Dict			=    randint(-5000		,				5000    )
       return (arr, r)


lowercase				:	Dict      						=  make_dataset()



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



def A_     (		A__		,				A__    )  ->		tuple[int, int, int]:
       arr.sort()
       a__						:     Optional[Any]			=    len(A__    )
       for i in range(n - 1    ):
              a__			,							a__						:     Dict			=    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]:
       a__						:     int			=    '\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
       a__						:     List[Any]			=    '\ntriplet_sum1(*dataset)\n'
       a__						:     Optional[Any]			=    '\ntriplet_sum2(*dataset)\n'
       a__						:     Union[str, Any]			=    repeat(setup=A__		,				stmt=A__		,				repeat=5		,				number=1_0000    )
       a__						:     Optional[int]			=    repeat(setup=A__		,				stmt=A__		,				repeat=5		,				number=1_0000    )
       return (min(A__    ), min(A__    ))


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

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









import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
    from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
    import torch_xla.core.xla_model as xm



def A_     (		A__    )  ->		Optional[int]:
       if is_torch_version('<'		,				'2.0.0'    ) or not hasattr(A__		,				'_dynamo'    ):
              return False
       return isinstance(A__		,				torch._dynamo.eval_frame.OptimizedModule    )



def A_     (		A__		,				A__ = True    )  ->		int:
       a__						:     Optional[Any]			=    (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

       a__						:     Union[str, Any]			=    is_compiled_module(A__    )
       if is_compiled:
              a__						:     List[str]			=    model
              a__						:     Dict			=    model._orig_mod

       if is_deepspeed_available():
              options += (DeepSpeedEngine,)

       while isinstance(A__		,				A__    ):
              a__						:     str			=    model.module

       if not keep_fpaa_wrapper:
              a__						:     Union[str, Any]			=    getattr(A__		,				'forward'    )
              a__						:     List[Any]			=    model.__dict__.pop('_original_forward'		,				A__    )
              if original_forward is not None:
                     while hasattr(A__		,				'__wrapped__'    ):
                            a__						:     int			=    forward.__wrapped__
                            if forward == original_forward:
                                   break
                     a__						:     List[Any]			=    forward
              if getattr(A__		,				'_converted_to_transformer_engine'		,				A__    ):
                     convert_model(A__		,				to_transformer_engine=A__    )

       if is_compiled:
              a__						:     List[str]			=    model
              a__						:     Any			=    compiled_model

       return model



def A_     (		)  ->		int:
       PartialState().wait_for_everyone()



def A_     (		A__		,				A__    )  ->		Dict:
       if PartialState().distributed_type == DistributedType.TPU:
              xm.save(A__		,				A__    )
       elif PartialState().local_process_index == 0:
              torch.save(A__		,				A__    )



@contextmanager
def A_     (		**A__    )  ->		Any:
       for key, value in kwargs.items():
              a__						:     Optional[int]			=    str(A__    )

       yield

       for key in kwargs:
              if key.upper() in os.environ:
                     del os.environ[key.upper()]



def A_     (		A__    )  ->		List[str]:
       if not hasattr(A__		,				'__qualname__'    ) and not hasattr(A__		,				'__name__'    ):
              a__						:     Dict			=    getattr(A__		,				'__class__'		,				A__    )
       if hasattr(A__		,				'__qualname__'    ):
              return obj.__qualname__
       if hasattr(A__		,				'__name__'    ):
              return obj.__name__
       return str(A__    )



def A_     (		A__		,				A__    )  ->		Dict:
       for key, value in source.items():
              if isinstance(A__		,				A__    ):
                     a__						:     Optional[Any]			=    destination.setdefault(A__		,				{}    )
                     merge_dicts(A__		,				A__    )
              else:
                     a__						:     Optional[int]			=    value

       return destination



def A_     (		A__ = None    )  ->		bool:
       if port is None:
              a__						:     List[Any]			=    2_9500
       with socket.socket(socket.AF_INET		,				socket.SOCK_STREAM    ) as s:
              return s.connect_ex(('localhost', port)    ) == 0


