





'''simple docstring'''


from typing import List, Optional, Union

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


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							{
    "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    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							Union[str, Any]         =					"informer"
 __lowerCamelCase						:							List[str]         =					{
     "hidden_size": "d_model",
     "num_attention_heads": "encoder_attention_heads",
     "num_hidden_layers": "encoder_layers",
 }




 def __init__(	self			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = "student_t"			,     _lowerCAmelCase = "nll"			,     _lowerCAmelCase = 1			,     _lowerCAmelCase = None			,     _lowerCAmelCase = "mean"			,     _lowerCAmelCase = 0			,     _lowerCAmelCase = 0			,     _lowerCAmelCase = 0			,     _lowerCAmelCase = 0			,     _lowerCAmelCase = None			,     _lowerCAmelCase = None			,     _lowerCAmelCase = 64			,     _lowerCAmelCase = 32			,     _lowerCAmelCase = 32			,     _lowerCAmelCase = 2			,     _lowerCAmelCase = 2			,     _lowerCAmelCase = 2			,     _lowerCAmelCase = 2			,     _lowerCAmelCase = True			,     _lowerCAmelCase = "gelu"			,     _lowerCAmelCase = 0.05			,     _lowerCAmelCase = 0.1			,     _lowerCAmelCase = 0.1			,     _lowerCAmelCase = 0.1			,     _lowerCAmelCase = 0.1			,     _lowerCAmelCase = 100			,     _lowerCAmelCase = 0.02			,     _lowerCAmelCase=True			,     _lowerCAmelCase = "prob"			,     _lowerCAmelCase = 5			,     _lowerCAmelCase = True			,     **_lowerCAmelCase			,     )    ->							Any:
     # time series specific configuration
     _lowerCAmelCase				=      prediction_length
     _lowerCAmelCase				=      context_length or prediction_length
     _lowerCAmelCase				=      distribution_output
     _lowerCAmelCase				=      loss
     _lowerCAmelCase				=      input_size
     _lowerCAmelCase				=      num_time_features
     _lowerCAmelCase				=      lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
     _lowerCAmelCase				=      scaling
     _lowerCAmelCase				=      num_dynamic_real_features
     _lowerCAmelCase				=      num_static_real_features
     _lowerCAmelCase				=      num_static_categorical_features

     # set cardinality
     if cardinality and num_static_categorical_features > 0:
         if len(_lowerCAmelCase		) != num_static_categorical_features:
             raise ValueError(
                 "The cardinality should be a list of the same length as `num_static_categorical_features`"		)
         _lowerCAmelCase				=      cardinality
     else:
         _lowerCAmelCase				=      [0]

     # set embedding_dimension
     if embedding_dimension and num_static_categorical_features > 0:
         if len(_lowerCAmelCase		) != num_static_categorical_features:
             raise ValueError(
                 "The embedding dimension should be a list of the same length as `num_static_categorical_features`"		)
         _lowerCAmelCase				=      embedding_dimension
     else:
         _lowerCAmelCase				=      [min(50			,     (cat + 1) // 2		) for cat in self.cardinality]

     _lowerCAmelCase				=      num_parallel_samples

     # Transformer architecture configuration
     _lowerCAmelCase				=      input_size * len(self.lags_sequence		) + self._number_of_features
     _lowerCAmelCase				=      d_model
     _lowerCAmelCase				=      encoder_attention_heads
     _lowerCAmelCase				=      decoder_attention_heads
     _lowerCAmelCase				=      encoder_ffn_dim
     _lowerCAmelCase				=      decoder_ffn_dim
     _lowerCAmelCase				=      encoder_layers
     _lowerCAmelCase				=      decoder_layers

     _lowerCAmelCase				=      dropout
     _lowerCAmelCase				=      attention_dropout
     _lowerCAmelCase				=      activation_dropout
     _lowerCAmelCase				=      encoder_layerdrop
     _lowerCAmelCase				=      decoder_layerdrop

     _lowerCAmelCase				=      activation_function
     _lowerCAmelCase				=      init_std

     _lowerCAmelCase				=      use_cache

     # Informer
     _lowerCAmelCase				=      attention_type
     _lowerCAmelCase				=      sampling_factor
     _lowerCAmelCase				=      distil

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





 @property
 def 							_snake_case   (	self		)    ->							int:
     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'''


import math


def 	__a(SCREAMING_SNAKE_CASE_  :					int = 100    ):



    '''simple docstring'''





    _lowerCAmelCase				=      sum(i * i for i in range(1		,       n + 1    )    )
    _lowerCAmelCase				=      int(math.pow(sum(range(1		,       n + 1    )    )		,       2    )    )
    return square_of_sum - sum_of_squares


if __name__ == "__main__":
       print(f'''{solution() = }''')


