






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A					=	logging.get_logger(__name__)

__A					=	{
    "andreasmadsen/efficient_mlm_m0.40": (
        "https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"
    ),
}



class 				_A			(     UpperCamelCase				):
    """simple docstring"""

    lowerCamelCase	:							Dict				= 'roberta-prelayernorm'



    def __init__(				self      :     int ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=50265 ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=768 ,     __SCREAMING_SNAKE_CASE      :     Any=12 ,     __SCREAMING_SNAKE_CASE      :     List[str]=12 ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=3072 ,     __SCREAMING_SNAKE_CASE      :     List[Any]="gelu" ,     __SCREAMING_SNAKE_CASE      :     List[str]=0.1 ,     __SCREAMING_SNAKE_CASE      :     Optional[Any]=0.1 ,     __SCREAMING_SNAKE_CASE      :     Tuple=512 ,     __SCREAMING_SNAKE_CASE      :     Any=2 ,     __SCREAMING_SNAKE_CASE      :     Dict=0.02 ,     __SCREAMING_SNAKE_CASE      :     List[Any]=1e-12 ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=1 ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]=0 ,     __SCREAMING_SNAKE_CASE      :     str=2 ,     __SCREAMING_SNAKE_CASE      :     Union[str, Any]="absolute" ,     __SCREAMING_SNAKE_CASE      :     Optional[int]=True ,     __SCREAMING_SNAKE_CASE      :     Tuple=None ,     **__SCREAMING_SNAKE_CASE      :     str ,     )      ->							Any:
           super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE ,     bos_token_id=__SCREAMING_SNAKE_CASE ,     eos_token_id=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=vocab_size
           __UpperCAmelCase     				=hidden_size
           __UpperCAmelCase     				=num_hidden_layers
           __UpperCAmelCase     				=num_attention_heads
           __UpperCAmelCase     				=hidden_act
           __UpperCAmelCase     				=intermediate_size
           __UpperCAmelCase     				=hidden_dropout_prob
           __UpperCAmelCase     				=attention_probs_dropout_prob
           __UpperCAmelCase     				=max_position_embeddings
           __UpperCAmelCase     				=type_vocab_size
           __UpperCAmelCase     				=initializer_range
           __UpperCAmelCase     				=layer_norm_eps
           __UpperCAmelCase     				=position_embedding_type
           __UpperCAmelCase     				=use_cache
           __UpperCAmelCase     				=classifier_dropout




class 				_A			(     UpperCamelCase				):
    """simple docstring"""

    @property
    def _a							(				self      :     List[str]      )      ->							Mapping[str, Mapping[int, str]]:
           if self.task == "multiple-choice":
                  __UpperCAmelCase     				={0: """batch""", 1: """choice""", 2: """sequence"""}
           else:
                  __UpperCAmelCase     				={0: """batch""", 1: """sequence"""}
           return OrderedDict(
               [
                   ("""input_ids""", dynamic_axis),
                   ("""attention_mask""", dynamic_axis),
               ]      )








from typing import List, Optional

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


lowerCAmelCase       		=    logging.get_logger(__name__)

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




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Optional[int]										=						'''autoformer'''
	_lowerCAmelCase     :		Any										=						{
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	    '''num_hidden_layers''': '''encoder_layers''',
	}


	def __init__(    self				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = "student_t"				,     lowercase__ = "nll"				,     lowercase__ = 1				,     lowercase__ = [1, 2, 3, 4, 5, 6, 7]				,     lowercase__ = True				,     lowercase__ = 0				,     lowercase__ = 0				,     lowercase__ = 0				,     lowercase__ = 0				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = 6_4				,     lowercase__ = 2				,     lowercase__ = 2				,     lowercase__ = 2				,     lowercase__ = 2				,     lowercase__ = 3_2				,     lowercase__ = 3_2				,     lowercase__ = "gelu"				,     lowercase__ = 0.1				,     lowercase__ = 0.1				,     lowercase__ = 0.1				,     lowercase__ = 0.1				,     lowercase__ = 0.1				,     lowercase__ = 1_0_0				,     lowercase__ = 0.0_2				,     lowercase__ = True				,     lowercase__=True				,     lowercase__ = 1_0				,     lowercase__ = 2_5				,     lowercase__ = 3				,     **lowercase__				,     ):
							# time series specific configuration
							__UpperCAmelCase			:							Optional[int]	    =	prediction_length
							__UpperCAmelCase			:							Tuple	    =	context_length if context_length is not None else prediction_length
							__UpperCAmelCase			:							Tuple	    =	distribution_output
							__UpperCAmelCase			:							Tuple	    =	loss
							__UpperCAmelCase			:							Dict	    =	input_size
							__UpperCAmelCase			:							str	    =	num_time_features
							__UpperCAmelCase			:							str	    =	lags_sequence
							__UpperCAmelCase			:							int	    =	scaling
							__UpperCAmelCase			:							int	    =	num_dynamic_real_features
							__UpperCAmelCase			:							List[str]	    =	num_static_real_features
							__UpperCAmelCase			:							List[str]	    =	num_static_categorical_features
							if cardinality is not None and num_static_categorical_features > 0:
													if len(lowercase__) != num_static_categorical_features:
																			raise ValueError(
																			    '''The cardinality should be a list of the same length as `num_static_categorical_features`''')
													__UpperCAmelCase			:							Union[str, Any]	    =	cardinality
							else:
													__UpperCAmelCase			:							List[Any]	    =	[0]
							if embedding_dimension is not None and num_static_categorical_features > 0:
													if len(lowercase__) != num_static_categorical_features:
																			raise ValueError(
																			    '''The embedding dimension should be a list of the same length as `num_static_categorical_features`''')
													__UpperCAmelCase			:							Union[str, Any]	    =	embedding_dimension
							else:
													__UpperCAmelCase			:							int	    =	[min(5_0				,     (cat + 1) // 2) for cat in self.cardinality]
							__UpperCAmelCase			:							Tuple	    =	num_parallel_samples

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

							__UpperCAmelCase			:							Union[str, Any]	    =	dropout
							__UpperCAmelCase			:							int	    =	attention_dropout
							__UpperCAmelCase			:							int	    =	activation_dropout
							__UpperCAmelCase			:							str	    =	encoder_layerdrop
							__UpperCAmelCase			:							Dict	    =	decoder_layerdrop

							__UpperCAmelCase			:							int	    =	activation_function
							__UpperCAmelCase			:							Optional[int]	    =	init_std

							__UpperCAmelCase			:							Optional[int]	    =	use_cache

							# Autoformer
							__UpperCAmelCase			:							str	    =	label_length
							__UpperCAmelCase			:							int	    =	moving_average
							__UpperCAmelCase			:							Optional[int]	    =	autocorrelation_factor

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


	@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
							)

