
'''simple docstring'''

from typing import List, Optional, Union

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


snake_case									=    logging.get_logger(__name__)

snake_case									=    {
    """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    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				Optional[Any]	=			"""time_series_transformer"""
 A_				:				List[Any]	=			{
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
     """num_hidden_layers""": """encoder_layers""",
 }


 def __init__(    self  : Any			,							a__  : Optional[int] = None			,							a__  : Optional[int] = None			,							a__  : str = "student_t"			,							a__  : str = "nll"			,							a__  : int = 1			,							a__  : List[int] = [1, 2, 3, 4, 5, 6, 7]			,							a__  : Optional[Union[str, bool]] = "mean"			,							a__  : int = 0			,							a__  : int = 0			,							a__  : int = 0			,							a__  : int = 0			,							a__  : Optional[List[int]] = None			,							a__  : Optional[List[int]] = None			,							a__  : int = 32			,							a__  : int = 32			,							a__  : int = 2			,							a__  : int = 2			,							a__  : int = 2			,							a__  : int = 2			,							a__  : bool = True			,							a__  : str = "gelu"			,							a__  : int = 64			,							a__  : float = 0.1			,							a__  : float = 0.1			,							a__  : float = 0.1			,							a__  : float = 0.1			,							a__  : float = 0.1			,							a__  : int = 100			,							a__  : float = 0.02			,							a__  : str=True			,							**a__  : Dict			,							):
  '''simple docstring'''

  lowerCAmelCase__     :   Tuple     		=	prediction_length
  lowerCAmelCase__     :   Optional[Any]     		=	context_length or prediction_length
  lowerCAmelCase__     :   int     		=	distribution_output
  lowerCAmelCase__     :   Optional[int]     		=	loss
  lowerCAmelCase__     :   Optional[Any]     		=	input_size
  lowerCAmelCase__     :   Union[str, Any]     		=	num_time_features
  lowerCAmelCase__     :   int     		=	lags_sequence
  lowerCAmelCase__     :   int     		=	scaling
  lowerCAmelCase__     :   int     		=	num_dynamic_real_features
  lowerCAmelCase__     :   Optional[Any]     		=	num_static_real_features
  lowerCAmelCase__     :   str     		=	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__     :   List[Any]     		=	cardinality
  else:
   lowerCAmelCase__     :   Any     		=	[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__     :   Dict     		=	embedding_dimension
  else:
   lowerCAmelCase__     :   List[Any]     		=	[min(50			,							(cat + 1) // 2    ) for cat in self.cardinality]
  lowerCAmelCase__     :   Union[str, Any]     		=	num_parallel_samples

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

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

  lowerCAmelCase__     :   str     		=	activation_function
  lowerCAmelCase__     :   int     		=	init_std

  lowerCAmelCase__     :   Tuple     		=	use_cache

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


 @property
 def    _A	(    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
  )







'''simple docstring'''

from math import factorial

snake_case									=    {str(digit): factorial(digit) for digit in range(10)}


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 if not isinstance(lowerCamelCase_      ,			lowerCamelCase_			):
  raise TypeError("Parameter number must be int"			)

 if number < 0:
  raise ValueError("Parameter number must be greater than or equal to 0"			)

 # Converts number in string to iterate on its digits and adds its factorial.
 return sum(DIGIT_FACTORIAL[digit] for digit in str(lowerCamelCase_			)			)


def        UpperCAmelCase_ (  lowerCamelCase_ = 6_0      ,			lowerCamelCase_ = 1_0_0_0_0_0_0			):







 """simple docstring"""


 if not isinstance(lowerCamelCase_      ,			lowerCamelCase_			) or not isinstance(lowerCamelCase_      ,			lowerCamelCase_			):
  raise TypeError("Parameters chain_length and number_limit must be int"			)

 if chain_length <= 0 or number_limit <= 0:
  raise ValueError(
      "Parameters chain_length and number_limit must be greater than 0"			)

 # the counter for the chains with the exact desired length
 lowerCAmelCase__     :   str     		=	0
 # the cached sizes of the previous chains
 lowerCAmelCase__     :   dict[int, int]     		=	{}

 for start_chain_element in range(1      ,			lowerCamelCase_			):
  # The temporary set will contain the elements of the chain
  lowerCAmelCase__     :   Any     		=	set()
  lowerCAmelCase__     :   int     		=	0

  # Stop computing the chain when you find a cached size, a repeating item or the
  # length is greater then the desired one.
  lowerCAmelCase__     :   Dict     		=	start_chain_element
  while (
      chain_element not in chain_sets_lengths
      and chain_element not in chain_set
      and chain_set_length <= chain_length
  ):
   chain_set.add(lowerCamelCase_			)
   chain_set_length += 1
   lowerCAmelCase__     :   Dict     		=	digit_factorial_sum(lowerCamelCase_			)

  if chain_element in chain_sets_lengths:
   chain_set_length += chain_sets_lengths[chain_element]

  lowerCAmelCase__     :   Optional[Any]     		=	chain_set_length

  # If chain contains the exact amount of elements increase the counter
  if chain_set_length == chain_length:
   chains_counter += 1

 return chains_counter


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(f'{solution()}')





