


from typing import List, Optional

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


snake_case							:	Optional[Any]					      =				logging.get_logger(__name__)

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







class 	snake_case_       (SCREAMING_SNAKE_CASE__				):
					UpperCAmelCase__ :   List[Any]   				=      'autoformer'
					UpperCAmelCase__ :   Dict   				=      {
					    'hidden_size': 'd_model',
					    'num_attention_heads': 'encoder_attention_heads',
					    'num_hidden_layers': 'encoder_layers',
					}
					def __init__(				self       :Dict      ,__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 = 1_00      ,__snake_case       :float = 0.02      ,__snake_case       :bool = True      ,__snake_case       :List[Any]=True      ,__snake_case       :int = 10      ,__snake_case       :int = 25      ,__snake_case       :int = 3      ,**__snake_case       :Optional[int]      ,) ->   int:
												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(a_				) != 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(a_				) != 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=a_      ,**a_				)



					@property
					def 			lowerCamelCase__(				self       :Any				) ->   Union[str, Any]:
												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 .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




