o i @sbdZddlZddlmZddlmZddlmZGdddeZdd d d d d idZ ddZ dS)zLanguage model interface.N)ScorerInterface)dynamic_import)fill_missing_argsc@s6eZdZdZeddZedefddZddZ d S) LMInterfacez-LM Interface for ESPnet model implementation.cCs|S)z.Add arguments to command line argument parser.parserrrL/home/ubuntu/.local/lib/python3.10/site-packages/espnet/nets/lm_interface.py add_arguments szLMInterface.add_argumentsn_vocabc sHddlmfdd}tjdi|}t||}t||j}|||S)zInitialize this class with python-level args. Args: idim (int): The number of vocabulary. Returns: LMinterface: A new instance of LMInterface. r get_parsercs |ddS)NF)requiredrrr rr wrap s zLMInterface.build..wrapNr)espnet.bin.lm_trainr argparse Namespacerr )clsr kwargsrargsrr r builds    zLMInterface.buildcCstd)a)Compute LM loss value from buffer sequences. Args: x (torch.Tensor): Input ids. (batch, len) t (torch.Tensor): Target ids. (batch, len) Returns: tuple[torch.Tensor, torch.Tensor, torch.Tensor]: Tuple of loss to backward (scalar), negative log-likelihood of t: -log p(t) (scalar) and the number of elements in x (scalar) Notes: The last two return values are used in perplexity: p(t)^{-n} = exp(-log p(t) / n) z!forward method is not implemented)NotImplementedError)selfxtrrr forward(szLMInterface.forwardN) __name__ __module__ __qualname____doc__ staticmethodr classmethodintrrrrrr r s  rz3espnet.nets.pytorch_backend.lm.default:DefaultRNNLMz6espnet.nets.pytorch_backend.lm.seq_rnn:SequentialRNNLMz8espnet.nets.pytorch_backend.lm.transformer:TransformerLM)defaultseq_rnn transformerr#z)espnet.lm.chainer_backend.lm:DefaultRNNLM)pytorchchainercCs0t|t|t}t|tsJ|d|S)zImport LM class dynamically. Args: module (str): module_name:class_name or alias in `predefined_lms` backend (str): NN backend. e.g., pytorch, chainer Returns: type: LM class z does not implement LMInterface)rpredefined_lmsgetdict issubclassr)modulebackend model_classrrr dynamic_import_lmGs r/) rrespnet.nets.scorer_interfacerespnet.utils.dynamic_importrespnet.utils.fill_missing_argsrrr(r/rrrr s   5