
from typing import List, Optional, Union

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


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

UpperCAmelCase__		:     Any     			=      {
    '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 UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    List[Any] 				=    '''informer'''
   __UpperCamelCase		:    List[str] 				=    {
       '''hidden_size''': '''d_model''',
       '''num_attention_heads''': '''encoder_attention_heads''',
       '''num_hidden_layers''': '''encoder_layers''',
   }


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

      """simple docstring"""
      # time series specific configuration
      _A:  Optional[Any]					=       prediction_length
      _A:  Optional[Any]					=       context_length or prediction_length
      _A:  Dict					=       distribution_output
      _A:  List[str]					=       loss
      _A:  int					=       input_size
      _A:  List[str]					=       num_time_features
      _A:  Optional[Any]					=       lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
      _A:  str					=       scaling
      _A:  Optional[Any]					=       num_dynamic_real_features
      _A:  List[Any]					=       num_static_real_features
      _A:  Tuple					=       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`'''     )
         _A:  str					=       cardinality
      else:
         _A:  Union[str, Any]					=       [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`'''     )
         _A:  List[str]					=       embedding_dimension
      else:
         _A:  Union[str, Any]					=       [min(5_0			,			(cat + 1) // 2     ) for cat in self.cardinality]

      _A:  int					=       num_parallel_samples

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

      _A:  List[str]					=       dropout
      _A:  Any					=       attention_dropout
      _A:  Optional[int]					=       activation_dropout
      _A:  List[Any]					=       encoder_layerdrop
      _A:  str					=       decoder_layerdrop

      _A:  int					=       activation_function
      _A:  Tuple					=       init_std

      _A:  Union[str, Any]					=       use_cache

      # Informer
      _A:  Union[str, Any]					=       attention_type
      _A:  str					=       sampling_factor
      _A:  List[str]					=       distil

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


   @property
   def  __magic_name__				(					self     :							List[str]     ):

      """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
      )





import random

from .binary_exp_mod import bin_exp_mod






def   lowerCamelCase__							(					a		,		a=10_00      )      ->   Optional[int]:
   if n < 2:
      return False

   if n % 2 == 0:
      return n == 2

   # this means n is odd
   _A:  List[Any]					=       n - 1
   _A:  Dict					=       0
   while d % 2 == 0:
      d /= 2
      exp += 1

   # n - 1=d*(2**exp)
   _A:  List[str]					=       0
   while count < prec:
      _A:  Optional[int]					=       random.randint(2		,		n - 1      )
      _A:  Union[str, Any]					=       bin_exp_mod(a		,		a		,		a      )
      if b != 1:
         _A:  Optional[Any]					=       True
         for _ in range(a      ):
            if b == n - 1:
               _A:  int					=       False
               break
            _A:  Optional[Any]					=       b * b
            b %= n
         if flag:
            return False
         count += 1
   return True


if __name__ == "__main__":
   UpperCAmelCase__		:     Dict     			=      abs(int(input('Enter bound : ').strip()))
   print('Here\'s the list of primes:')
   print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))



