





from typing import List, Optional

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


UpperCAmelCase_     							= logging.get_logger(__name__)

UpperCAmelCase_     							= {
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							Any   		=		"autoformer"
   a						:							Tuple   		=		{
       "hidden_size": "d_model",
       "num_attention_heads": "encoder_attention_heads",
       "num_hidden_layers": "encoder_layers",
   }




   def __init__(    self,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = "student_t",		__magic_name__ = "nll",		__magic_name__ = 1,		__magic_name__ = [1, 2, 3, 4, 5, 6, 7],		__magic_name__ = True,		__magic_name__ = 0,		__magic_name__ = 0,		__magic_name__ = 0,		__magic_name__ = 0,		__magic_name__ = None,		__magic_name__ = None,		__magic_name__ = 64,		__magic_name__ = 2,		__magic_name__ = 2,		__magic_name__ = 2,		__magic_name__ = 2,		__magic_name__ = 32,		__magic_name__ = 32,		__magic_name__ = "gelu",		__magic_name__ = 0.1,		__magic_name__ = 0.1,		__magic_name__ = 0.1,		__magic_name__ = 0.1,		__magic_name__ = 0.1,		__magic_name__ = 100,		__magic_name__ = 0.02,		__magic_name__ = True,		__magic_name__=True,		__magic_name__ = 10,		__magic_name__ = 25,		__magic_name__ = 3,		**__magic_name__,		) ->      Dict:






       """simple docstring"""
       # time series specific configuration
       UpperCamelCase__							:				int          =      prediction_length
       UpperCamelCase__							:				Dict          =      context_length if context_length is not None else prediction_length
       UpperCamelCase__							:				List[Any]          =      distribution_output
       UpperCamelCase__							:				Optional[Any]          =      loss
       UpperCamelCase__							:				Tuple          =      input_size
       UpperCamelCase__							:				Dict          =      num_time_features
       UpperCamelCase__							:				Tuple          =      lags_sequence
       UpperCamelCase__							:				Dict          =      scaling
       UpperCamelCase__							:				List[str]          =      num_dynamic_real_features
       UpperCamelCase__							:				Dict          =      num_static_real_features
       UpperCamelCase__							:				str          =      num_static_categorical_features
       if cardinality is not None and num_static_categorical_features > 0:
           if len(__magic_name__							) != num_static_categorical_features:
               raise ValueError(
                   '''The cardinality should be a list of the same length as `num_static_categorical_features`'''							)
           UpperCamelCase__							:				Tuple          =      cardinality
       else:
           UpperCamelCase__							:				Optional[Any]          =      [0]
       if embedding_dimension is not None and num_static_categorical_features > 0:
           if len(__magic_name__							) != num_static_categorical_features:
               raise ValueError(
                   '''The embedding dimension should be a list of the same length as `num_static_categorical_features`'''							)
           UpperCamelCase__							:				str          =      embedding_dimension
       else:
           UpperCamelCase__							:				Union[str, Any]          =      [min(50,		(cat + 1) // 2							) for cat in self.cardinality]
       UpperCamelCase__							:				Tuple          =      num_parallel_samples

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

       UpperCamelCase__							:				Optional[Any]          =      dropout
       UpperCamelCase__							:				Union[str, Any]          =      attention_dropout
       UpperCamelCase__							:				str          =      activation_dropout
       UpperCamelCase__							:				Optional[int]          =      encoder_layerdrop
       UpperCamelCase__							:				Optional[Any]          =      decoder_layerdrop

       UpperCamelCase__							:				Any          =      activation_function
       UpperCamelCase__							:				Optional[int]          =      init_std

       UpperCamelCase__							:				Union[str, Any]          =      use_cache

       # Autoformer
       UpperCamelCase__							:				Optional[Any]          =      label_length
       UpperCamelCase__							:				Tuple          =      moving_average
       UpperCamelCase__							:				Tuple          =      autocorrelation_factor

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




   @property
   def       UpperCamelCase__  (    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
       )








UpperCAmelCase_     							= 'Input must be a string of 8 numbers plus letter'
UpperCAmelCase_     							= 'TRWAGMYFPDXBNJZSQVHLCKE'




def 		lowerCAmelCase_    (  __UpperCAmelCase:			str      )      ->	bool:

    if not isinstance(__UpperCAmelCase		,   __UpperCAmelCase      ):
        UpperCamelCase__							:				Any          =      f"Expected string as input, found {type(__UpperCAmelCase      ).__name__}"
        raise TypeError(__UpperCAmelCase      )

    UpperCamelCase__							:				int          =      spanish_id.replace('''-'''		,   ''''''      ).upper()
    if len(__UpperCAmelCase      ) != 9:
        raise ValueError(__UpperCAmelCase      )

    try:
        UpperCamelCase__							:				List[str]          =      int(spanish_id_clean[0:8]      )
        UpperCamelCase__							:				Optional[int]          =      spanish_id_clean[8]
    except ValueError as ex:
        raise ValueError(__UpperCAmelCase      ) from ex

    if letter.isdigit():
        raise ValueError(__UpperCAmelCase      )

    return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
  import doctest

  doctest.testmod()

