


from __future__ import annotations

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



def     __lowerCAmelCase						(							)       ->							tuple[list[int], int]:
   __lowerCamelCase:				str     =	[randint(-1000						,      1000 ) for i in range(10 )]
   __lowerCamelCase:				Optional[Any]     =	randint(-5000						,      5000 )
   return (arr, r)


_A		:				int									=       make_dataset()



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



def     __lowerCAmelCase						(							snake_case	:    list[int]						,      snake_case	:    int )       ->							tuple[int, int, int]:
   arr.sort()
   __lowerCamelCase:				str     =	len(snake_case )
   for i in range(n - 1 ):
      __lowerCamelCase   ,     __lowerCamelCase:				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     __lowerCAmelCase						(							)       ->							tuple[float, float]:
   __lowerCamelCase:				str     =	"""
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
   __lowerCamelCase:				List[Any]     =	"""
triplet_sum1(*dataset)
"""
   __lowerCamelCase:				Optional[Any]     =	"""
triplet_sum2(*dataset)
"""
   __lowerCamelCase:				str     =	repeat(setup=snake_case						,      stmt=snake_case						,      repeat=5						,      number=10000 )
   __lowerCamelCase:				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		:				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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_A		:				List[Any]									=       {
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				int									=       ['''ConvBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				List[Any]									=       [
       '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''ConvBertForMaskedLM''',
       '''ConvBertForMultipleChoice''',
       '''ConvBertForQuestionAnswering''',
       '''ConvBertForSequenceClassification''',
       '''ConvBertForTokenClassification''',
       '''ConvBertLayer''',
       '''ConvBertModel''',
       '''ConvBertPreTrainedModel''',
       '''load_tf_weights_in_convbert''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		:				List[Any]									=       [
       '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFConvBertForMaskedLM''',
       '''TFConvBertForMultipleChoice''',
       '''TFConvBertForQuestionAnswering''',
       '''TFConvBertForSequenceClassification''',
       '''TFConvBertForTokenClassification''',
       '''TFConvBertLayer''',
       '''TFConvBertModel''',
       '''TFConvBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
   from .tokenization_convbert import ConvBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_convbert_fast import ConvBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_convbert import (
          CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ConvBertForMaskedLM,
          ConvBertForMultipleChoice,
          ConvBertForQuestionAnswering,
          ConvBertForSequenceClassification,
          ConvBertForTokenClassification,
          ConvBertLayer,
          ConvBertModel,
          ConvBertPreTrainedModel,
          load_tf_weights_in_convbert,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_convbert import (
          TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFConvBertForMaskedLM,
          TFConvBertForMultipleChoice,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertLayer,
          TFConvBertModel,
          TFConvBertPreTrainedModel,
      )


else:
   import sys

   _A		:				int									=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


