


import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize






def   _lowercase(					__a				:						ndarray     ):
							return np.dot(__a    ,     __a     )




class       lowercase_					:





				def __init__(				self       ,  *,
				    lowercase_ = np.inf       ,  lowercase_ = "linear"       ,  lowercase_ = 0.0       ,  )  -> None:
											a__      =regularization
											a__      =gamma
											if kernel == "linear":
																		a__      =self.__linear
											elif kernel == "rbf":
																		if self.gamma == 0:
																									raise ValueError('rbf kernel requires gamma')
																		if not isinstance(self.gamma       ,  (float, int)):
																									raise ValueError('gamma must be float or int')
																		if not self.gamma > 0:
																									raise ValueError('gamma must be > 0')
																		a__      =self.__rbf
																		# in the future, there could be a default value like in sklearn
																		# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
																		# previously it was 1/(n_features)
											else:
																		a__      =F"""Unknown kernel: {kernel}"""
																		raise ValueError(lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> float:
											return np.dot(lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> float:
											return np.exp(-(self.gamma * norm_squared(vectora - vectora)))





				def 						__UpperCamelCase    (				self       ,  lowercase_       ,  lowercase_)  -> None:
											a__      =observations
											a__      =classes

											# using Wolfe's Dual to calculate w.
											# Primal problem: minimize 1/2*norm_squared(w)
											#   constraint: yn(w . xn + b) >= 1
											#
											# With l a vector
											# Dual problem: maximize sum_n(ln) -
											#       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
											#   constraint: self.C >= ln >= 0
											#           and sum_n(ln*yn) = 0
											# Then we get w using w = sum_n(ln*yn*xn)
											# At the end we can get b ~= mean(yn - w . xn)
											#
											# Since we use kernels, we only need l_star to calculate b
											# and to classify observations

											((a__)    ,     )      =np.shape(lowercase_)

											def to_minimize(lowercase_) -> float:
																		a__      =0
																		((a__)    ,     )      =np.shape(lowercase_)
																		for i in range(lowercase_):
																									for j in range(lowercase_):
																																s += (
																																    candidate[i]
																																    * candidate[j]
																																    * classes[i]
																																    * classes[j]
																																    * self.kernel(observations[i]       ,  observations[j])
																																)
																		return 1 / 2 * s - sum(lowercase_)

											a__      =LinearConstraint(lowercase_       ,  0       ,  0)
											a__      =Bounds(0       ,  self.regularization)

											a__      =minimize(
											    lowercase_       ,  np.ones(lowercase_)       ,  bounds=lowercase_       ,  constraints=[ly_contraint]).x
											a__      =l_star

											# calculating mean offset of separation plane to points
											a__      =0
											for i in range(lowercase_):
																		for j in range(lowercase_):
																									s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
																									    observations[i]       ,  observations[j])
											a__      =s / n





				def 						__UpperCamelCase    (				self       ,  lowercase_)  -> int:
											a__      =sum(
											    self.optimum[n]
											    * self.classes[n]
											    * self.kernel(self.observations[n]       ,  lowercase_)
											    for n in range(len(self.classes)))
											return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
	import doctest

	doctest.testmod()





from typing import List, Optional

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


_lowerCAmelCase:						List[Any]   				=       logging.get_logger(__name__)

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




class       lowercase_					(lowercase__   ):
				snake_case	    ='autoformer'
				snake_case	    ={
				    '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_ = 64       ,  lowercase_ = 2       ,  lowercase_ = 2       ,  lowercase_ = 2       ,  lowercase_ = 2       ,  lowercase_ = 32       ,  lowercase_ = 32       ,  lowercase_ = "gelu"       ,  lowercase_ = 0.1       ,  lowercase_ = 0.1       ,  lowercase_ = 0.1       ,  lowercase_ = 0.1       ,  lowercase_ = 0.1       ,  lowercase_ = 100       ,  lowercase_ = 0.02       ,  lowercase_ = True       ,  lowercase_=True       ,  lowercase_ = 10       ,  lowercase_ = 25       ,  lowercase_ = 3       ,  **lowercase_       ,  )  -> Union[str, Any]:
											# time series specific configuration
											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(lowercase_) != 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(lowercase_) != 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=lowercase_       ,  **lowercase_)





				@property
				def 						__UpperCamelCase    (				self)  -> int:
											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
											)

