
from typing import List, Optional, Union

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


lowercase__		:Tuple   =    logging.get_logger(__name__)

lowercase__		:int   =    {
    "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 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Any       					='''time_series_transformer'''
							lowercase_       :       str       					={
							    '''hidden_size''': '''d_model''',
							    '''num_attention_heads''': '''encoder_attention_heads''',
							    '''num_hidden_layers''': '''encoder_layers''',
							}




							def __init__(				self   ,A__ = None   ,A__ = None   ,A__ = "student_t"   ,A__ = "nll"   ,A__ = 1   ,A__ = [1, 2, 3, 4, 5, 6, 7]   ,A__ = "mean"   ,A__ = 0   ,A__ = 0   ,A__ = 0   ,A__ = 0   ,A__ = None   ,A__ = None   ,A__ = 3_2   ,A__ = 3_2   ,A__ = 2   ,A__ = 2   ,A__ = 2   ,A__ = 2   ,A__ = True   ,A__ = "gelu"   ,A__ = 6_4   ,A__ = 0.1   ,A__ = 0.1   ,A__ = 0.1   ,A__ = 0.1   ,A__ = 0.1   ,A__ = 1_0_0   ,A__ = 0.02   ,A__=True   ,**A__   ,):
												# time series specific configuration
												lowercase							=						prediction_length
												lowercase							=						context_length or 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 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 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(5_0   ,(cat + 1) // 2) for cat in self.cardinality]
												lowercase							=						num_parallel_samples

												# Transformer architecture configuration
												lowercase							=						input_size * len(A__) + 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

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





							@property
							def  A__      (				self):
												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
												)












from math import factorial

UpperCAmelCase__				   =		{str(digit): factorial(digit) for digit in range(10)}
def 						_a    (      a :int )							->			int:
 if not isinstance(a	,							a ):
  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(a ) )
def 						_a    (      a :int = 60	,							a :int = 1_000_000 )							->			int:

 if not isinstance(a	,							a ) or not isinstance(a	,							a ):
  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
 a		  =      0
 # the cached sizes of the previous chains
 a		  =      {}

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

  # Stop computing the chain when you find a cached size, a repeating item or the
  # length is greater then the desired one.
  a		  =      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(a )
   chain_set_length += 1
   a		  =      digit_factorial_sum(a )

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

  a		  =      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()}""")


