


'''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_  :				Dict						= {
    """huggingface/time-series-transformer-tourism-monthly""": (
        """https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json"""
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}





class      snake_case    (    lowercase			):





  """simple docstring"""




  _lowerCamelCase		    =  "time_series_transformer"
  _lowerCamelCase		    =  {
      "hidden_size": "d_model",
      "num_attention_heads": "encoder_attention_heads",
      "num_hidden_layers": "encoder_layers",
  }






  def __init__(    self			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = "student_t"			,  UpperCamelCase = "nll"			,  UpperCamelCase = 1			,  UpperCamelCase = [1, 2, 3, 4, 5, 6, 7]			,  UpperCamelCase = "mean"			,  UpperCamelCase = 0			,  UpperCamelCase = 0			,  UpperCamelCase = 0			,  UpperCamelCase = 0			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = 32			,  UpperCamelCase = 32			,  UpperCamelCase = 2			,  UpperCamelCase = 2			,  UpperCamelCase = 2			,  UpperCamelCase = 2			,  UpperCamelCase = True			,  UpperCamelCase = "gelu"			,  UpperCamelCase = 64			,  UpperCamelCase = 0.1			,  UpperCamelCase = 0.1			,  UpperCamelCase = 0.1			,  UpperCamelCase = 0.1			,  UpperCamelCase = 0.1			,  UpperCamelCase = 100			,  UpperCamelCase = 0.02			,  UpperCamelCase=True			,  **UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  prediction_length
   lowerCamelCase_      =  context_length or 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 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`"     )
    lowerCamelCase_      =  cardinality
   else:
    lowerCamelCase_      =  [0]
   if embedding_dimension 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`"     )
    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(__A     ) + 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

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







  @property
  def   snake_case		(    self     ):







   """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 		lowercase_(  SCREAMING_SNAKE_CASE_ = 4000000	):


  '''simple docstring'''

  lowerCamelCase :					Any    =				[0, 1]
  lowerCamelCase :					Union[str, Any]    =				0
  while fib[i] <= n:
    fib.append(fib[i] + fib[i + 1]	)
    if fib[i + 2] > n:
      break
    i += 1
  lowerCamelCase :					Union[str, Any]    =				0
  for j in range(len(SCREAMING_SNAKE_CASE_	) - 1	):
    if fib[j] % 2 == 0:
      total += fib[j]

  return total


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



