



"""simple docstring"""







from typing import List, Optional

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


A    =			logging.get_logger(__name__)

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






class      UpperCAmelCase__		(      UpperCamelCase     ):
		lowerCAmelCase_       :	Union[str, Any] 							=     """autoformer"""
		lowerCAmelCase_       :	int 							=     {
		    """hidden_size""": """d_model""",
		    """num_attention_heads""": """encoder_attention_heads""",
		    """num_hidden_layers""": """encoder_layers""",
		}



		def __init__( self  :    Optional[Any]      ,							snake_case  :    Optional[int] = None      ,							snake_case  :    Optional[int] = None      ,							snake_case  :    str = "student_t"      ,							snake_case  :    str = "nll"      ,							snake_case  :    int = 1      ,							snake_case  :    List[int] = [1, 2, 3, 4, 5, 6, 7]      ,							snake_case  :    bool = True      ,							snake_case  :    int = 0      ,							snake_case  :    int = 0      ,							snake_case  :    int = 0      ,							snake_case  :    int = 0      ,							snake_case  :    Optional[List[int]] = None      ,							snake_case  :    Optional[List[int]] = None      ,							snake_case  :    int = 64      ,							snake_case  :    int = 2      ,							snake_case  :    int = 2      ,							snake_case  :    int = 2      ,							snake_case  :    int = 2      ,							snake_case  :    int = 32      ,							snake_case  :    int = 32      ,							snake_case  :    str = "gelu"      ,							snake_case  :    float = 0.1      ,							snake_case  :    float = 0.1      ,							snake_case  :    float = 0.1      ,							snake_case  :    float = 0.1      ,							snake_case  :    float = 0.1      ,							snake_case  :    int = 100      ,							snake_case  :    float = 0.02      ,							snake_case  :    bool = True      ,							snake_case  :    Union[str, Any]=True      ,							snake_case  :    int = 10      ,							snake_case  :    int = 25      ,							snake_case  :    int = 3      ,							**snake_case  :    Dict      ,							)			->						Union[str, Any]:






									'''simple docstring'''

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

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

									A         =					dropout
									A         =					attention_dropout
									A         =					activation_dropout
									A         =					encoder_layerdrop
									A         =					decoder_layerdrop

									A         =					activation_function
									A         =					init_std

									A         =					use_cache

									# Autoformer
									A         =					label_length
									A         =					moving_average
									A         =					autocorrelation_factor

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



		@property
		def 					A_		( self  :    List[str]				)			->						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"""







def   lowerCAmelCase__			(     lowerCamelCase__			) ->							int:

							if not isinstance(lowerCamelCase__				,      lowerCamelCase__			):
														raise ValueError('multiplicative_persistence() only accepts integral values'			)
							if num < 0:
														raise ValueError('multiplicative_persistence() does not accept negative values'			)

							A         =					0
							A         =					str(lowerCamelCase__			)

							while len(lowerCamelCase__			) != 1:
														A         =					[int(lowerCamelCase__			) for i in num_string]

														A         =					1
														for i in range(0				,      len(lowerCamelCase__			)			):
																					total *= numbers[i]

														A         =					str(lowerCamelCase__			)

														steps += 1
							return steps

def   lowerCAmelCase__			(     lowerCamelCase__			) ->							int:

							if not isinstance(lowerCamelCase__				,      lowerCamelCase__			):
														raise ValueError('additive_persistence() only accepts integral values'			)
							if num < 0:
														raise ValueError('additive_persistence() does not accept negative values'			)

							A         =					0
							A         =					str(lowerCamelCase__			)

							while len(lowerCamelCase__			) != 1:
														A         =					[int(lowerCamelCase__			) for i in num_string]

														A         =					0
														for i in range(0				,      len(lowerCamelCase__			)			):
																					total += numbers[i]

														A         =					str(lowerCamelCase__			)

														steps += 1
							return steps


if __name__ == "__main__":
							import doctest

							doctest.testmod()



