


from __future__ import annotations

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




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







      """simple docstring"""

      lowercase	:      int          =						[randint(-1000,			1000   ) for i in range(10   )]
      lowercase	:      List[str]          =						randint(-5000,			5000   )
      return (arr, r)


__a       =					make_dataset()




def 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase   )			->tuple[int, ...]:







      """simple docstring"""

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




def 	__lowercase		(  _UpperCamelCase,			_UpperCamelCase   )			->tuple[int, int, int]:







      """simple docstring"""

      arr.sort()
      lowercase	:      List[Any]          =						len(__snake_case   )
      for i in range(n - 1   ):
            lowercase	:      Optional[int]          =						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 	__lowercase		(  )			->tuple[float, float]:







      """simple docstring"""

      lowercase	:      Optional[int]          =						'\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
      lowercase	:      List[Any]          =						'\ntriplet_sum1(*dataset)\n'
      lowercase	:      str          =						'\ntriplet_sum2(*dataset)\n'
      lowercase	:      str          =						repeat(setup=__snake_case,			stmt=__snake_case,			repeat=5,			number=10000   )
      lowercase	:      int          =						repeat(setup=__snake_case,			stmt=__snake_case,			repeat=5,			number=10000   )
      return (min(__snake_case   ), min(__snake_case   ))


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

  testmod()
  __a       =					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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__    :      List[str]						=    logging.get_logger(__name__)

lowercase__    :      Dict						=    {
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}


class  SCREAMING_SNAKE_CASE    (a__							):
     lowerCAmelCase					=       '''roberta'''


     def __init__(						self						,	_UpperCAmelCase=5_0265						,	_UpperCAmelCase=768						,	_UpperCAmelCase=12						,	_UpperCAmelCase=12						,	_UpperCAmelCase=3072						,	_UpperCAmelCase="gelu"						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=0.1						,	_UpperCAmelCase=512						,	_UpperCAmelCase=2						,	_UpperCAmelCase=0.02						,	_UpperCAmelCase=1e-1_2						,	_UpperCAmelCase=1						,	_UpperCAmelCase=0						,	_UpperCAmelCase=2						,	_UpperCAmelCase="absolute"						,	_UpperCAmelCase=True						,	_UpperCAmelCase=None						,	**_UpperCAmelCase						,	):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase						,	bos_token_id=_UpperCAmelCase						,	eos_token_id=_UpperCAmelCase						,	**_UpperCAmelCase)

          __A						: Optional[int]      =       vocab_size
          __A						: int      =       hidden_size
          __A						: Union[str, Any]      =       num_hidden_layers
          __A						: List[str]      =       num_attention_heads
          __A						: Optional[int]      =       hidden_act
          __A						: str      =       intermediate_size
          __A						: Union[str, Any]      =       hidden_dropout_prob
          __A						: Dict      =       attention_probs_dropout_prob
          __A						: int      =       max_position_embeddings
          __A						: str      =       type_vocab_size
          __A						: Any      =       initializer_range
          __A						: int      =       layer_norm_eps
          __A						: Optional[int]      =       position_embedding_type
          __A						: int      =       use_cache
          __A						: Union[str, Any]      =       classifier_dropout




class  SCREAMING_SNAKE_CASE    (a__							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if self.task == "multiple-choice":
               __A						: str      =       {0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
               __A						: List[str]      =       {0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ])