from __future__ import annotations

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



def A__  (			)				->		tuple[list[int], int]:
       A						:     Optional[int]         =[randint(-1_000,		1_000					) for i in range(10					)]
       A						:     List[Any]         =randint(-5_000,		5_000					)
       return (arr, r)


_lowercase							:      int      							=make_dataset()



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



def A__  (			lowercase:     list[int],		lowercase:     int					)				->		tuple[int, int, int]:
       arr.sort()
       A						:     Optional[int]         =len(lowercase					)
       for i in range(n - 1					):
              A						:     Union[str, Any]         =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						:     Optional[Any]         ='\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
       A						:     Tuple         ='\ntriplet_sum1(*dataset)\n'
       A						:     Optional[int]         ='\ntriplet_sum2(*dataset)\n'
       A						:     Dict         =repeat(setup=lowercase,		stmt=lowercase,		repeat=5,		number=10_000					)
       A						:     Optional[int]         =repeat(setup=lowercase,		stmt=lowercase,		repeat=5,		number=10_000					)
       return (min(lowercase					), min(lowercase					))


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

    testmod()
    _lowercase							:      List[Any]      							=solution_times()
    print(f'''The time for naive implementation is {times[0]}.''')
    print(f'''The time for optimized implementation is {times[1]}.''')







import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase							:      Any      							=logging.getLogger(__name__)



@dataclass
class  SCREAMING_SNAKE_CASE_ (					lowerCAmelCase_			):
  '''simple docstring'''


  lowercase		:     Optional[float]            =				field(
      default=0.0   ,						metadata={"help": "The label smoothing epsilon to apply (if not zero)."}			)
  lowercase		:     bool            =				field(default=lowerCAmelCase_   ,						metadata={"help": "Whether to SortishSamler or not."}			)
  lowercase		:     bool            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}			)
  lowercase		:     bool            =				field(default=lowerCAmelCase_   ,						metadata={"help": "whether to use adafactor"}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Encoder layer dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Decoder layer dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(default=lowerCAmelCase_   ,						metadata={"help": "Dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[float]            =				field(
      default=lowerCAmelCase_   ,						metadata={"help": "Attention dropout probability. Goes into model.config."}			)
  lowercase		:     Optional[str]            =				field(
      default="linear"   ,						metadata={"help": f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()			)}'}   ,						)






