



"""simple docstring"""
from typing import List, Optional, Union

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


A:   Union[str, Any]								=		logging.get_logger(__name__)

A:   str								=		{
    "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       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Any						=						'informer'
   __lowerCAmelCase  :   str						=						{
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
       'num_hidden_layers': 'encoder_layers',
   }

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = "student_t"      ,     _SCREAMING_SNAKE_CASE = "nll"      ,     _SCREAMING_SNAKE_CASE = 1      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = "mean"      ,     _SCREAMING_SNAKE_CASE = 0      ,     _SCREAMING_SNAKE_CASE = 0      ,     _SCREAMING_SNAKE_CASE = 0      ,     _SCREAMING_SNAKE_CASE = 0      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = 64      ,     _SCREAMING_SNAKE_CASE = 32      ,     _SCREAMING_SNAKE_CASE = 32      ,     _SCREAMING_SNAKE_CASE = 2      ,     _SCREAMING_SNAKE_CASE = 2      ,     _SCREAMING_SNAKE_CASE = 2      ,     _SCREAMING_SNAKE_CASE = 2      ,     _SCREAMING_SNAKE_CASE = True      ,     _SCREAMING_SNAKE_CASE = "gelu"      ,     _SCREAMING_SNAKE_CASE = 0.05      ,     _SCREAMING_SNAKE_CASE = 0.1      ,     _SCREAMING_SNAKE_CASE = 0.1      ,     _SCREAMING_SNAKE_CASE = 0.1      ,     _SCREAMING_SNAKE_CASE = 0.1      ,     _SCREAMING_SNAKE_CASE = 100      ,     _SCREAMING_SNAKE_CASE = 0.02      ,     _SCREAMING_SNAKE_CASE=True      ,     _SCREAMING_SNAKE_CASE = "prob"      ,     _SCREAMING_SNAKE_CASE = 5      ,     _SCREAMING_SNAKE_CASE = True      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      List[str]:





    '''simple docstring'''




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

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

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

    UpperCAmelCase	:					Optional[Any]	=	num_parallel_samples

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

    UpperCAmelCase	:					List[Any]	=	dropout
    UpperCAmelCase	:					Tuple	=	attention_dropout
    UpperCAmelCase	:					Any	=	activation_dropout
    UpperCAmelCase	:					List[str]	=	encoder_layerdrop
    UpperCAmelCase	:					List[Any]	=	decoder_layerdrop

    UpperCAmelCase	:					str	=	activation_function
    UpperCAmelCase	:					List[str]	=	init_std

    UpperCAmelCase	:					Any	=	use_cache

    # Informer
    UpperCAmelCase	:					Any	=	attention_type
    UpperCAmelCase	:					Tuple	=	sampling_factor
    UpperCAmelCase	:					int	=	distil

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




   @property
   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      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 collections import defaultdict
from math import ceil, sqrt

def       _snake_case						(						UpperCamelCase						:		int = 1000000			,		UpperCamelCase						:		int = 10						):
 UpperCAmelCase	:					defaultdict	=	defaultdict(UpperCamelCase						)

 for outer_width in range(3			,		(t_limit // 4) + 2						):
  if outer_width * outer_width > t_limit:
   UpperCAmelCase	:					str	=	max(
       ceil(sqrt(outer_width * outer_width - t_limit						)						)			,		1						)
  else:
   UpperCAmelCase	:					Optional[Any]	=	1

  hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

  for hole_width in range(UpperCamelCase			,		outer_width - 1			,		2						):
   count[outer_width * outer_width - hole_width * hole_width] += 1

 return sum(1 for n in count.values() if 1 <= n <= 10						)


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