



from typing import List, Optional

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


_snake_case           =			logging.get_logger(__name__)

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





class  lowerCAmelCase							(       SCREAMING_SNAKE_CASE__ ):
  __lowerCamelCase		=      'autoformer'
  __lowerCamelCase		=      {
      'hidden_size': 'd_model',
      'num_attention_heads': 'encoder_attention_heads',
      'num_hidden_layers': 'encoder_layers',
  }







  def __init__(      self  :Dict   ,							_lowercase  :Optional[int] = None   ,							_lowercase  :Optional[int] = None   ,							_lowercase  :str = "student_t"   ,							_lowercase  :str = "nll"   ,							_lowercase  :int = 1   ,							_lowercase  :List[int] = [1, 2, 3, 4, 5, 6, 7]   ,							_lowercase  :bool = True   ,							_lowercase  :int = 0   ,							_lowercase  :int = 0   ,							_lowercase  :int = 0   ,							_lowercase  :int = 0   ,							_lowercase  :Optional[List[int]] = None   ,							_lowercase  :Optional[List[int]] = None   ,							_lowercase  :int = 64   ,							_lowercase  :int = 2   ,							_lowercase  :int = 2   ,							_lowercase  :int = 2   ,							_lowercase  :int = 2   ,							_lowercase  :int = 32   ,							_lowercase  :int = 32   ,							_lowercase  :str = "gelu"   ,							_lowercase  :float = 0.1   ,							_lowercase  :float = 0.1   ,							_lowercase  :float = 0.1   ,							_lowercase  :float = 0.1   ,							_lowercase  :float = 0.1   ,							_lowercase  :int = 1_00   ,							_lowercase  :float = 0.02   ,							_lowercase  :bool = True   ,							_lowercase  :List[Any]=True   ,							_lowercase  :int = 10   ,							_lowercase  :int = 25   ,							_lowercase  :int = 3   ,							**_lowercase  :Optional[int]   ,							):






     '''simple docstring'''





     lowercase__			       =  prediction_length
     lowercase__			       =  context_length if context_length is not None else prediction_length
     lowercase__			       =  distribution_output
     lowercase__			       =  loss
     lowercase__			       =  input_size
     lowercase__			       =  num_time_features
     lowercase__			       =  lags_sequence
     lowercase__			       =  scaling
     lowercase__			       =  num_dynamic_real_features
     lowercase__			       =  num_static_real_features
     lowercase__			       =  num_static_categorical_features
     if cardinality is not None and num_static_categorical_features > 0:
        if len(a_ ) != num_static_categorical_features:
           raise ValueError(
               "The cardinality should be a list of the same length as `num_static_categorical_features`" )
        lowercase__			       =  cardinality
     else:
        lowercase__			       =  [0]
     if embedding_dimension is not None and num_static_categorical_features > 0:
        if len(a_ ) != num_static_categorical_features:
           raise ValueError(
               "The embedding dimension should be a list of the same length as `num_static_categorical_features`" )
        lowercase__			       =  embedding_dimension
     else:
        lowercase__			       =  [min(50   ,							(cat + 1) // 2 ) for cat in self.cardinality]
     lowercase__			       =  num_parallel_samples

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

     lowercase__			       =  dropout
     lowercase__			       =  attention_dropout
     lowercase__			       =  activation_dropout
     lowercase__			       =  encoder_layerdrop
     lowercase__			       =  decoder_layerdrop

     lowercase__			       =  activation_function
     lowercase__			       =  init_std

     lowercase__			       =  use_cache

     # Autoformer
     lowercase__			       =  label_length
     lowercase__			       =  moving_average
     lowercase__			       =  autocorrelation_factor

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







  @property
  def   UpperCAmelCase					(      self  :Any ):






     '''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
     )













def _A       (   __magic_name__	):
   try:
      lowercase__			       =  float(__magic_name__	)
   except ValueError:
      raise ValueError("Please enter a valid number"	)
   lowercase__			       =  decimal - int(__magic_name__	)
   if fractional_part == 0:
      return int(__magic_name__	), 1
   else:
      lowercase__			       =  len(str(__magic_name__	).split("."	)[1]	)
      lowercase__			       =  int(decimal * (10**number_of_frac_digits)	)
      lowercase__			       =  10**number_of_frac_digits
      lowercase__       ,							lowercase__			       =  denominator, numerator
      while True:
         lowercase__			       =  dividend % divisor
         if remainder == 0:
            break
         lowercase__       ,							lowercase__			       =  divisor, remainder
      lowercase__       ,							lowercase__			       =  numerator / divisor, denominator / divisor
      return int(__magic_name__	), int(__magic_name__	)


if __name__ == "__main__":
  print(F"""{decimal_to_fraction(2) = }""")
  print(F"""{decimal_to_fraction(89.0) = }""")
  print(F"""{decimal_to_fraction("67") = }""")
  print(F"""{decimal_to_fraction("45.0") = }""")
  print(F"""{decimal_to_fraction(1.5) = }""")
  print(F"""{decimal_to_fraction("6.25") = }""")
  print(F"""{decimal_to_fraction("78td") = }""")






