





'''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"""


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


__UpperCamelCase					    =	make_dataset()







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				tuple[int, ...]:



			"""simple docstring"""


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







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase ) ->				tuple[int, int, int]:



			"""simple docstring"""


			arr.sort()
			__snake_case					:      Any							      =     len(_lowerCamelCase )
			for i in range(n - 1 ):
						__snake_case      ,     __snake_case					:      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 					_a						(       ) ->				tuple[float, float]:



			"""simple docstring"""


			__snake_case					:      Tuple							      =     """
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
			__snake_case					:      int							      =     """
triplet_sum1(*dataset)
"""
			__snake_case					:      List[Any]							      =     """
triplet_sum2(*dataset)
"""
			__snake_case					:      str							      =     repeat(setup=_lowerCamelCase    ,	stmt=_lowerCamelCase    ,	repeat=5    ,	number=1_0000 )
			__snake_case					:      int							      =     repeat(setup=_lowerCamelCase    ,	stmt=_lowerCamelCase    ,	repeat=5    ,	number=1_0000 )
			return (min(_lowerCamelCase ), min(_lowerCamelCase ))


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

	testmod()
	__UpperCamelCase					    =	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


_UpperCAmelCase    :     Optional[Any]         =       logging.get_logger(__name__)

_UpperCAmelCase    :     List[Any]         =       {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   "roberta-prelayernorm"
   def __init__(      self      :			Optional[int], UpperCamelCase__      :			List[Any]=5_02_65, UpperCamelCase__      :			int=7_68, UpperCamelCase__      :			str=12, UpperCamelCase__      :			Union[str, Any]=12, UpperCamelCase__      :			Tuple=30_72, UpperCamelCase__      :			Tuple="gelu", UpperCamelCase__      :			List[Any]=0.1, UpperCamelCase__      :			Union[str, Any]=0.1, UpperCamelCase__      :			Any=5_12, UpperCamelCase__      :			Union[str, Any]=2, UpperCamelCase__      :			Optional[Any]=0.02, UpperCamelCase__      :			str=1e-12, UpperCamelCase__      :			str=1, UpperCamelCase__      :			List[str]=0, UpperCamelCase__      :			Optional[int]=2, UpperCamelCase__      :			Optional[Any]="absolute", UpperCamelCase__      :			Optional[Any]=True, UpperCamelCase__      :			List[str]=None, **UpperCamelCase__      :			Any, )  ->			Dict:
        super().__init__(pad_token_id=UpperCamelCase__, bos_token_id=UpperCamelCase__, eos_token_id=UpperCamelCase__, **UpperCamelCase__     )

        _A    		=  vocab_size
        _A    		=  hidden_size
        _A    		=  num_hidden_layers
        _A    		=  num_attention_heads
        _A    		=  hidden_act
        _A    		=  intermediate_size
        _A    		=  hidden_dropout_prob
        _A    		=  attention_probs_dropout_prob
        _A    		=  max_position_embeddings
        _A    		=  type_vocab_size
        _A    		=  initializer_range
        _A    		=  layer_norm_eps
        _A    		=  position_embedding_type
        _A    		=  use_cache
        _A    		=  classifier_dropout







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   @property
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
             _A    		=  {0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
             _A    		=  {0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ]     )



