o }oi @sbddlmZmZddlmZddlmZddlmZddl m Z m Z m Z dgZ GdddeZdS) )DictOptional)MultiLayerPerceptron) Classifier) typecheck) LogitsType LogprobsType NeuralTypeSequenceClassifiercsxeZdZedeeeeffddZ      dd e d e d e d ed e de de de ffdd Z e ddZZS)r returncCs&|js dtdtiSdtdtiS)Nlogits)BD log_probs) log_softmaxr rr)selfrk/home/ubuntu/.local/lib/python3.10/site-packages/nemo/collections/nlp/modules/common/sequence_classifier.py output_typesszSequenceClassifier.output_typesreluTr hidden_size num_classes num_layers activationrdropoutuse_transformer_initidx_conditioned_onc s@tj||d||_||_t|||||d|_|j|ddS)a Initializes the SequenceClassifier module. Args: hidden_size: the hidden size of the mlp head on the top of the encoder num_classes: number of the classes to predict num_layers: number of the linear layers of the mlp head on the top of the encoder activation: type of activations between layers of the mlp head log_softmax: applies the log softmax on the output dropout: the dropout used for the mlp head use_transformer_init: initializes the weights with the same approach used in Transformer idx_conditioned_on: index of the token to use as the sequence representation for the classification task, default is the first token )rr)rrrrr)rN)super__init__r_idx_conditioned_onrmlp post_init) rrrrrrrrr __class__rrr !szSequenceClassifier.__init__cCs&||}||dd|jf}|S)N)rr"r!)r hidden_statesr rrrforwardDs zSequenceClassifier.forward)rrTrTr)__name__ __module__ __qualname__propertyrrstrr rintboolfloatr rr' __classcell__rrr$rr s8  #N)typingrrnemo.collections.common.partsr.nemo.collections.nlp.modules.common.classifierrnemo.core.classesrnemo.core.neural_typesrrr __all__r rrrrs