





from __future__ import annotations

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







def        __lowerCamelCase (							)		->    Optional[int]:






  """simple docstring"""

  A__  				=       [randint(-1_0_0_0   ,					1_0_0_0   ) for i in range(1_0   )]
  A__  				=       randint(-5_0_0_0   ,					5_0_0_0   )
  return (arr, r)


A   : Dict			=				make_dataset()







def        __lowerCamelCase (							__a       :List[str]   ,					__a       :int   )		->    List[Any]:






  """simple docstring"""

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







def        __lowerCamelCase (							__a       :Union[str, Any]   ,					__a       :List[Any]   )		->    Optional[Any]:






  """simple docstring"""

  arr.sort()
  A__  				=       len(__a   )
  for i in range(n - 1   ):
    A__				,      A__  				=       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 (							)		->    List[str]:






  """simple docstring"""

  A__  				=       """
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
  A__  				=       """
triplet_sum1(*dataset)
"""
  A__  				=       """
triplet_sum2(*dataset)
"""
  A__  				=       repeat(setup=__a   ,					stmt=__a   ,					repeat=5   ,					number=1_0_0_0_0   )
  A__  				=       repeat(setup=__a   ,					stmt=__a   ,					repeat=5   ,					number=1_0_0_0_0   )
  return (min(__a   ), min(__a   ))


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

     testmod()
     A   : Union[str, Any]			=				solution_times()
     print(F'''The time for naive implementation is {times[0]}.''')
     print(F'''The time for optimized implementation is {times[1]}.''')










from ...configuration_utils import PretrainedConfig
from ...utils import logging


A   : Dict			=				logging.get_logger(__name__)

A   : Dict			=				{
    '''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Tuple          =   '''pegasus'''
			__lowerCamelCase     :	Any          =   ['''past_key_values''']
			__lowerCamelCase     :	List[str]          =   {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






			def __init__(      self       :   List[Any]							,     __lowerCAmelCase       :   str=5_02_65							,     __lowerCAmelCase       :   Dict=10_24							,     __lowerCAmelCase       :   Union[str, Any]=12							,     __lowerCAmelCase       :   Any=40_96							,     __lowerCAmelCase       :   Optional[Any]=16							,     __lowerCAmelCase       :   Dict=12							,     __lowerCAmelCase       :   Optional[int]=40_96							,     __lowerCAmelCase       :   Dict=16							,     __lowerCAmelCase       :   Optional[int]=0.0							,     __lowerCAmelCase       :   Optional[Any]=0.0							,     __lowerCAmelCase       :   Union[str, Any]=True							,     __lowerCAmelCase       :   List[str]=True							,     __lowerCAmelCase       :   str="gelu"							,     __lowerCAmelCase       :   Union[str, Any]=10_24							,     __lowerCAmelCase       :   Union[str, Any]=0.1							,     __lowerCAmelCase       :   Dict=0.0							,     __lowerCAmelCase       :   Union[str, Any]=0.0							,     __lowerCAmelCase       :   Union[str, Any]=0.0_2							,     __lowerCAmelCase       :   List[Any]=0							,     __lowerCAmelCase       :   str=False							,     __lowerCAmelCase       :   int=0							,     __lowerCAmelCase       :   Optional[Any]=1							,     __lowerCAmelCase       :   Optional[int]=1							,     **__lowerCAmelCase       :   Dict							,     )     ->     Dict:







					"""simple docstring"""






					A__  				=       vocab_size
					A__  				=       max_position_embeddings
					A__  				=       d_model
					A__  				=       encoder_ffn_dim
					A__  				=       encoder_layers
					A__  				=       encoder_attention_heads
					A__  				=       decoder_ffn_dim
					A__  				=       decoder_layers
					A__  				=       decoder_attention_heads
					A__  				=       dropout
					A__  				=       attention_dropout
					A__  				=       activation_dropout
					A__  				=       activation_function
					A__  				=       init_std
					A__  				=       encoder_layerdrop
					A__  				=       decoder_layerdrop
					A__  				=       use_cache
					A__  				=       encoder_layers
					A__  				=       scale_embedding  # scale factor will be sqrt(d_model) if True
					super().__init__(
					    pad_token_id=__lowerCAmelCase							,     eos_token_id=__lowerCAmelCase							,     is_encoder_decoder=__lowerCAmelCase							,     decoder_start_token_id=__lowerCAmelCase							,     forced_eos_token_id=__lowerCAmelCase							,     **__lowerCAmelCase							,     )






			@property
			def        a_	(      self       :   Dict       )     ->     int:







					"""simple docstring"""






					return self.encoder_attention_heads







			@property
			def        a_	(      self       :   Optional[Any]       )     ->     int:







					"""simple docstring"""






					return self.d_model



