
from typing import List, Optional

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


lowerCamelCase__     :		str        = logging.get_logger(__name__)

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



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  int	   =      'autoformer'
 __lowerCAmelCase	:  str	   =      {
     'hidden_size': 'd_model',
     'num_attention_heads': 'encoder_attention_heads',
     'num_hidden_layers': 'encoder_layers',
 }


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = "student_t"       ,	SCREAMING_SNAKE_CASE_ = "nll"       ,	SCREAMING_SNAKE_CASE_ = 1       ,	SCREAMING_SNAKE_CASE_ = [1, 2, 3, 4, 5, 6, 7]       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_ = 0       ,	SCREAMING_SNAKE_CASE_ = 0       ,	SCREAMING_SNAKE_CASE_ = 0       ,	SCREAMING_SNAKE_CASE_ = 0       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = 64       ,	SCREAMING_SNAKE_CASE_ = 2       ,	SCREAMING_SNAKE_CASE_ = 2       ,	SCREAMING_SNAKE_CASE_ = 2       ,	SCREAMING_SNAKE_CASE_ = 2       ,	SCREAMING_SNAKE_CASE_ = 32       ,	SCREAMING_SNAKE_CASE_ = 32       ,	SCREAMING_SNAKE_CASE_ = "gelu"       ,	SCREAMING_SNAKE_CASE_ = 0.1       ,	SCREAMING_SNAKE_CASE_ = 0.1       ,	SCREAMING_SNAKE_CASE_ = 0.1       ,	SCREAMING_SNAKE_CASE_ = 0.1       ,	SCREAMING_SNAKE_CASE_ = 0.1       ,	SCREAMING_SNAKE_CASE_ = 1_00       ,	SCREAMING_SNAKE_CASE_ = 0.0_2       ,	SCREAMING_SNAKE_CASE_ = True       ,	SCREAMING_SNAKE_CASE_=True       ,	SCREAMING_SNAKE_CASE_ = 10       ,	SCREAMING_SNAKE_CASE_ = 25       ,	SCREAMING_SNAKE_CASE_ = 3       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Any     		=   prediction_length
   lowercase__	: Dict     		=   context_length if context_length is not None else prediction_length
   lowercase__	: List[str]     		=   distribution_output
   lowercase__	: Optional[int]     		=   loss
   lowercase__	: Tuple     		=   input_size
   lowercase__	: Any     		=   num_time_features
   lowercase__	: Optional[Any]     		=   lags_sequence
   lowercase__	: Dict     		=   scaling
   lowercase__	: Dict     		=   num_dynamic_real_features
   lowercase__	: Union[str, Any]     		=   num_static_real_features
   lowercase__	: Any     		=   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`""")
     lowercase__	: str     		=   cardinality
   else:
     lowercase__	: int     		=   [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`""")
     lowercase__	: List[Any]     		=   embedding_dimension
   else:
     lowercase__	: Optional[Any]     		=   [min(50       ,	(cat + 1) // 2) for cat in self.cardinality]
   lowercase__	: Optional[Any]     		=   num_parallel_samples

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

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

   lowercase__	: str     		=   activation_function
   lowercase__	: Union[str, Any]     		=   init_std

   lowercase__	: Any     		=   use_cache

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

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



 @property
 def   lowercase__  (       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
   )








lowerCamelCase__     :		dict[tuple[int, int, int], int]        = {}

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_ )		->						int:


  '''simple docstring'''






  if late == 3 or absent == 2:
    return 0

  # if we have no days left, and have not failed any other rules,
  # we have a prize string
  if days == 0:
    return 1

  # No easy solution, so now we need to do the recursive calculation

  # First, check if the combination is already in the cache, and
  # if yes, return the stored value from there since we already
  # know the number of possible prize strings from this point on
  lowercase__	: Tuple     		=   (days, absent, late)
  if key in cache:
    return cache[key]

  # now we calculate the three possible ways that can unfold from
  # this point on, depending on our attendance today

  # 1) if we are late (but not absent), the "absent" counter stays as
  # it is, but the "late" counter increases by one
  lowercase__	: Union[str, Any]     		=   _calculate(days - 1			,			lowercase_			,			late + 1 )

  # 2) if we are absent, the "absent" counter increases by 1, and the
  # "late" counter resets to 0
  lowercase__	: List[str]     		=   _calculate(days - 1			,			absent + 1			,			0 )

  # 3) if we are on time, this resets the "late" counter and keeps the
  # absent counter
  lowercase__	: Dict     		=   _calculate(days - 1			,			lowercase_			,			0 )

  lowercase__	: List[str]     		=   state_late + state_absent + state_ontime

  lowercase__	: List[Any]     		=   prizestrings
  return prizestrings

def 		UpperCamelCase						(   lowercase_ = 30 )		->						int:


  '''simple docstring'''






  return _calculate(lowercase_			,			absent=0			,			late=0 )


if __name__ == "__main__":
     print(solution())






