
'''simple docstring'''


from typing import List, Optional

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


__lowercase							:   str					=     logging.get_logger(__name__)

__lowercase							:   Optional[Any]					=     {
    '''huggingface/autoformer-tourism-monthly''': '''https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json''',
}

class 	__lowercase				(  _lowercase    ):
			lowerCamelCase			:		List[str]	      =			"autoformer"
			lowerCamelCase			:		Optional[int]	      =			{
			    "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 = True			,   A = 0			,   A = 0			,   A = 0			,   A = 0			,   A = None			,   A = None			,   A = 6_4			,   A = 2			,   A = 2			,   A = 2			,   A = 2			,   A = 3_2			,   A = 3_2			,   A = "gelu"			,   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=True			,   A = 1_0			,   A = 2_5			,   A = 3			,   **A			,   ):
								# time series specific configuration
								lowerCamelCase_					:      Dict     =		prediction_length
								lowerCamelCase_					:      str     =		context_length if context_length is not None else prediction_length
								lowerCamelCase_					:      Optional[int]     =		distribution_output
								lowerCamelCase_					:      List[str]     =		loss
								lowerCamelCase_					:      Optional[Any]     =		input_size
								lowerCamelCase_					:      Tuple     =		num_time_features
								lowerCamelCase_					:      int     =		lags_sequence
								lowerCamelCase_					:      List[str]     =		scaling
								lowerCamelCase_					:      Tuple     =		num_dynamic_real_features
								lowerCamelCase_					:      List[str]     =		num_static_real_features
								lowerCamelCase_					:      Tuple     =		num_static_categorical_features
								if cardinality is not None 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_					:      str     =		cardinality
								else:
													lowerCamelCase_					:      int     =		[0]
								if embedding_dimension is not None 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[str]     =		[min(5_0			,   (cat + 1) // 2      ) for cat in self.cardinality]
								lowerCamelCase_					:      Any     =		num_parallel_samples

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

								lowerCamelCase_					:      Union[str, Any]     =		dropout
								lowerCamelCase_					:      int     =		attention_dropout
								lowerCamelCase_					:      int     =		activation_dropout
								lowerCamelCase_					:      Optional[int]     =		encoder_layerdrop
								lowerCamelCase_					:      Union[str, Any]     =		decoder_layerdrop

								lowerCamelCase_					:      str     =		activation_function
								lowerCamelCase_					:      Optional[int]     =		init_std

								lowerCamelCase_					:      Any     =		use_cache

								# Autoformer
								lowerCamelCase_					:      List[Any]     =		label_length
								lowerCamelCase_					:      Union[str, Any]     =		moving_average
								lowerCamelCase_					:      str     =		autocorrelation_factor

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






			@property
			def        UpperCAmelCase__ (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
								)




'''simple docstring'''


from __future__ import annotations




def lowercase_	(   _lowercase			,       _lowercase			,       _lowercase				)     ->							dict[str, float]:
					'''simple docstring'''

					if (voltage, current, resistance).count(0				) != 1:
										raise ValueError('''One and only one argument must be 0'''				)
					if resistance < 0:
										raise ValueError('''Resistance cannot be negative'''				)
					if voltage == 0:
										return {"voltage": float(current * resistance				)}
					elif current == 0:
										return {"current": voltage / resistance}
					elif resistance == 0:
										return {"resistance": voltage / current}
					else:
										raise ValueError('''Exactly one argument must be 0'''				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


