






"""simple docstring"""





from typing import List, Optional

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


_SCREAMING_SNAKE_CASE					:				Any					    =						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:				Union[str, Any]					    =						{
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}






class      a						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     str						= """autoformer"""
    SCREAMING_SNAKE_CASE						:     int						= {
        """hidden_size""": """d_model""",
        """num_attention_heads""": """encoder_attention_heads""",
        """num_hidden_layers""": """encoder_layers""",
    }






    def __init__(       self     :							int       ,						__SCREAMING_SNAKE_CASE     :							Optional[int] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[int] = None       ,						__SCREAMING_SNAKE_CASE     :							str = "student_t"       ,						__SCREAMING_SNAKE_CASE     :							str = "nll"       ,						__SCREAMING_SNAKE_CASE     :							int = 1       ,						__SCREAMING_SNAKE_CASE     :							List[int] = [1, 2, 3, 4, 5, 6, 7]       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						__SCREAMING_SNAKE_CASE     :							int = 0       ,						__SCREAMING_SNAKE_CASE     :							int = 0       ,						__SCREAMING_SNAKE_CASE     :							int = 0       ,						__SCREAMING_SNAKE_CASE     :							int = 0       ,						__SCREAMING_SNAKE_CASE     :							Optional[List[int]] = None       ,						__SCREAMING_SNAKE_CASE     :							Optional[List[int]] = None       ,						__SCREAMING_SNAKE_CASE     :							int = 64       ,						__SCREAMING_SNAKE_CASE     :							int = 2       ,						__SCREAMING_SNAKE_CASE     :							int = 2       ,						__SCREAMING_SNAKE_CASE     :							int = 2       ,						__SCREAMING_SNAKE_CASE     :							int = 2       ,						__SCREAMING_SNAKE_CASE     :							int = 32       ,						__SCREAMING_SNAKE_CASE     :							int = 32       ,						__SCREAMING_SNAKE_CASE     :							str = "gelu"       ,						__SCREAMING_SNAKE_CASE     :							float = 0.1       ,						__SCREAMING_SNAKE_CASE     :							float = 0.1       ,						__SCREAMING_SNAKE_CASE     :							float = 0.1       ,						__SCREAMING_SNAKE_CASE     :							float = 0.1       ,						__SCREAMING_SNAKE_CASE     :							float = 0.1       ,						__SCREAMING_SNAKE_CASE     :							int = 100       ,						__SCREAMING_SNAKE_CASE     :							float = 0.02       ,						__SCREAMING_SNAKE_CASE     :							bool = True       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=True       ,						__SCREAMING_SNAKE_CASE     :							int = 10       ,						__SCREAMING_SNAKE_CASE     :							int = 25       ,						__SCREAMING_SNAKE_CASE     :							int = 3       ,						**__SCREAMING_SNAKE_CASE     :							Any       ,						)     ->		Dict:
      # time series specific configuration
      lowerCamelCase_         =						prediction_length
      lowerCamelCase_         =						context_length if context_length is not None else prediction_length
      lowerCamelCase_         =						distribution_output
      lowerCamelCase_         =						loss
      lowerCamelCase_         =						input_size
      lowerCamelCase_         =						num_time_features
      lowerCamelCase_         =						lags_sequence
      lowerCamelCase_         =						scaling
      lowerCamelCase_         =						num_dynamic_real_features
      lowerCamelCase_         =						num_static_real_features
      lowerCamelCase_         =						num_static_categorical_features
      if cardinality is not None 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`'					)
        lowerCamelCase_         =						cardinality
      else:
        lowerCamelCase_         =						[0]
      if embedding_dimension is not None 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`'					)
        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

      # Autoformer
      lowerCamelCase_         =						label_length
      lowerCamelCase_         =						moving_average
      lowerCamelCase_         =						autocorrelation_factor

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






    @property
    def    UpperCamelCase       (       self     :							str					)     ->		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"""





from __future__ import annotations







def      lowerCamelCase__       (							_lowerCamelCase       :		int )      ->				list[int]:
  lowerCamelCase_         =						[True] * limit
  lowerCamelCase_         =						False
  lowerCamelCase_         =						False
  lowerCamelCase_         =						True

  for i in range(3		,				int(limit**0.5 + 1 )		,				2 ):
    lowerCamelCase_         =						i * 2
    while index < limit:
      lowerCamelCase_         =						False
      lowerCamelCase_         =						index + i

  lowerCamelCase_         =						[2]

  for i in range(3		,				_lowerCamelCase		,				2 ):
    if is_prime[i]:
      primes.append(_lowerCamelCase )

  return primes







def      lowerCamelCase__       (							_lowerCamelCase       :		int = 1000000 )      ->				int:
  lowerCamelCase_         =						prime_sieve(_lowerCamelCase )
  lowerCamelCase_         =						0
  lowerCamelCase_         =						0

  for i in range(len(_lowerCamelCase ) ):
    for j in range(i + length		,				len(_lowerCamelCase ) ):
      lowerCamelCase_         =						sum(primes[i:j] )
      if sol >= ceiling:
        break

      if sol in primes:
        lowerCamelCase_         =						j - i
        lowerCamelCase_         =						sol

  return largest


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




