


"""simple docstring"""



from typing import List, Optional, Union

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


UpperCAmelCase      		=			logging.get_logger(__name__)

UpperCAmelCase      		=			{
    '''huggingface/informer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      str        =   "informer"
       __A			:      List[Any]        =   {
           "hidden_size": "d_model",
           "num_attention_heads": "encoder_attention_heads",
           "num_hidden_layers": "encoder_layers",
       }






       def __init__(   self  :    Optional[int]  ,     snake_case__  :    Optional[int] = None  ,     snake_case__  :    Optional[int] = None  ,     snake_case__  :    str = "student_t"  ,     snake_case__  :    str = "nll"  ,     snake_case__  :    int = 1  ,     snake_case__  :    List[int] = None  ,     snake_case__  :    Optional[Union[str, bool]] = "mean"  ,     snake_case__  :    int = 0  ,     snake_case__  :    int = 0  ,     snake_case__  :    int = 0  ,     snake_case__  :    int = 0  ,     snake_case__  :    Optional[List[int]] = None  ,     snake_case__  :    Optional[List[int]] = None  ,     snake_case__  :    int = 6_4  ,     snake_case__  :    int = 3_2  ,     snake_case__  :    int = 3_2  ,     snake_case__  :    int = 2  ,     snake_case__  :    int = 2  ,     snake_case__  :    int = 2  ,     snake_case__  :    int = 2  ,     snake_case__  :    bool = True  ,     snake_case__  :    str = "gelu"  ,     snake_case__  :    float = 0.05  ,     snake_case__  :    float = 0.1  ,     snake_case__  :    float = 0.1  ,     snake_case__  :    float = 0.1  ,     snake_case__  :    float = 0.1  ,     snake_case__  :    int = 1_0_0  ,     snake_case__  :    float = 0.02  ,     snake_case__  :    List[Any]=True  ,     snake_case__  :    str = "prob"  ,     snake_case__  :    int = 5  ,     snake_case__  :    bool = True  ,     **snake_case__  :    List[str]  ,     ):

              '''simple docstring'''






              lowercase		:List[Any]       							=				prediction_length
              lowercase		:Union[str, Any]       							=				context_length or prediction_length
              lowercase		:Any       							=				distribution_output
              lowercase		:Union[str, Any]       							=				loss
              lowercase		:Dict       							=				input_size
              lowercase		:Union[str, Any]       							=				num_time_features
              lowercase		:int       							=				lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
              lowercase		:Any       							=				scaling
              lowercase		:Union[str, Any]       							=				num_dynamic_real_features
              lowercase		:List[Any]       							=				num_static_real_features
              lowercase		:Dict       							=				num_static_categorical_features

              # set cardinality
              if cardinality and num_static_categorical_features > 0:
                     if len(snake_case__				) != num_static_categorical_features:
                            raise ValueError(
                                '''The cardinality should be a list of the same length as `num_static_categorical_features`'''				)
                     lowercase		:List[Any]       							=				cardinality
              else:
                     lowercase		:Dict       							=				[0]

              # set embedding_dimension
              if embedding_dimension and num_static_categorical_features > 0:
                     if len(snake_case__				) != num_static_categorical_features:
                            raise ValueError(
                                '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''				)
                     lowercase		:Any       							=				embedding_dimension
              else:
                     lowercase		:int       							=				[min(5_0  ,     (cat + 1) // 2				) for cat in self.cardinality]

              lowercase		:str       							=				num_parallel_samples

              # Transformer architecture configuration
              lowercase		:int       							=				input_size * len(self.lags_sequence				) + self._number_of_features
              lowercase		:Optional[Any]       							=				d_model
              lowercase		:int       							=				encoder_attention_heads
              lowercase		:str       							=				decoder_attention_heads
              lowercase		:Any       							=				encoder_ffn_dim
              lowercase		:Tuple       							=				decoder_ffn_dim
              lowercase		:Optional[int]       							=				encoder_layers
              lowercase		:int       							=				decoder_layers

              lowercase		:Optional[Any]       							=				dropout
              lowercase		:Tuple       							=				attention_dropout
              lowercase		:Tuple       							=				activation_dropout
              lowercase		:Any       							=				encoder_layerdrop
              lowercase		:List[Any]       							=				decoder_layerdrop

              lowercase		:Optional[Any]       							=				activation_function
              lowercase		:Tuple       							=				init_std

              lowercase		:Optional[Any]       							=				use_cache

              # Informer
              lowercase		:Optional[int]       							=				attention_type
              lowercase		:Optional[int]       							=				sampling_factor
              lowercase		:Union[str, Any]       							=				distil

              super().__init__(is_encoder_decoder=snake_case__  ,     **snake_case__				)







       @property
       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              return (
                  sum(self.embedding_dimension				)
                  + self.num_dynamic_real_features
                  + self.num_time_features
                  + self.num_static_real_features
                  + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
              )





"""simple docstring"""



from __future__ import annotations

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






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


UpperCAmelCase      		=			make_dataset()






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






def       lowerCamelCase					(a_						:list[int]				,				a_						:int)     ->							tuple[int, int, int]:
       arr.sort()
       lowercase		:Union[str, Any]       							=				len(a_)
       for i in range(n - 1):
              lowercase							,  lowercase		: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       lowerCamelCase					()     ->							tuple[float, float]:
       lowercase		:int       							=				'''
from __main__ import dataset, triplet_sum1, triplet_sum2
'''
       lowercase		:Optional[Any]       							=				'''
triplet_sum1(*dataset)
'''
       lowercase		:Union[str, Any]       							=				'''
triplet_sum2(*dataset)
'''
       lowercase		:Dict       							=				repeat(setup=a_				,				stmt=a_				,				repeat=5				,				number=1_0000)
       lowercase		:Optional[int]       							=				repeat(setup=a_				,				stmt=a_				,				repeat=5				,				number=1_0000)
       return (min(a_), min(a_))


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]}.""")

