o i@sldZddlZddlmZmZmZddlZGdddZGdddeZGdd d eZ Gd d d ee Z dS) zScorer interface module.N)AnyListTuplec @szeZdZdZdejdefddZddeded edefd d Z d ejdedejde ejeffd dZ dede fddZ dS)ScorerInterfaceaScorer interface for beam search. The scorer performs scoring of the all tokens in vocabulary. Examples: * Search heuristics * :class:`espnet.nets.scorers.length_bonus.LengthBonus` * Decoder networks of the sequence-to-sequence models * :class:`espnet.nets.pytorch_backend.nets.transformer.decoder.Decoder` * :class:`espnet.nets.pytorch_backend.nets.rnn.decoders.Decoder` * Neural language models * :class:`espnet.nets.pytorch_backend.lm.transformer.TransformerLM` * :class:`espnet.nets.pytorch_backend.lm.default.DefaultRNNLM` * :class:`espnet.nets.pytorch_backend.lm.seq_rnn.SequentialRNNLM` xreturncCdS)Get an initial state for decoding (optional). Args: x (torch.Tensor): The encoded feature tensor Returns: initial state Nselfrr r P/home/ubuntu/.local/lib/python3.10/site-packages/espnet/nets/scorer_interface.py init_states zScorerInterface.init_stateNstateinew_idcCs|durdS||S)aCSelect state with relative ids in the main beam search. Args: state: Decoder state for prefix tokens i (int): Index to select a state in the main beam search new_id (int): New label index to select a state if necessary Returns: state: pruned state Nr )r rrrr r r select_state&s zScorerInterface.select_stateycCt)aScore new token (required). Args: y (torch.Tensor): 1D torch.int64 prefix tokens. state: Scorer state for prefix tokens x (torch.Tensor): The encoder feature that generates ys. Returns: tuple[torch.Tensor, Any]: Tuple of scores for next token that has a shape of `(n_vocab)` and next state for ys NotImplementedError)r rrrr r r score4szScorerInterface.scorecCr)zScore eos (optional). Args: state: Scorer state for prefix tokens Returns: float: final score gr )r rr r r final_scoreFs zScorerInterface.final_score)N)__name__ __module__ __qualname____doc__torchTensorrrintrrrfloatrr r r r r s   rc @sTeZdZdZdejdefddZdejdeedejde ejeeffd d Z d S) BatchScorerInterfacezBatch scorer interface.rrcCs ||S)r )rr r r r batch_init_stateVs z%BatchScorerInterface.batch_init_stateysstatesxsc Cstd|jjt}t}tt|||D]\}\}}} |||| \} } | | | | qt |d |j dd}||fS)aScore new token batch (required). Args: ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen). states (List[Any]): Scorer states for prefix tokens. xs (torch.Tensor): The encoder feature that generates ys (n_batch, xlen, n_feat). Returns: tuple[torch.Tensor, List[Any]]: Tuple of batchfied scores for next token with shape of `(n_batch, n_vocab)` and next state list for ys. z?{} batch score is implemented through for loop not parallelizedr)warningswarnformat __class__rlist enumerateziprappendrcatviewshape) r r#r$r%scores outstatesrrrrroutstater r r batch_scoreas  z BatchScorerInterface.batch_scoreN) rrrrrrrr"rrr5r r r r r!Ss r!c @s>eZdZdZdejdejdedejdeejeff ddZd S) PartialScorerInterfaceaPartial scorer interface for beam search. The partial scorer performs scoring when non-partial scorer finished scoring, and receives pre-pruned next tokens to score because it is too heavy to score all the tokens. Examples: * Prefix search for connectionist-temporal-classification models * :class:`espnet.nets.scorers.ctc.CTCPrefixScorer` r next_tokensrrrcCr)aScore new token (required). Args: y (torch.Tensor): 1D prefix token next_tokens (torch.Tensor): torch.int64 next token to score state: decoder state for prefix tokens x (torch.Tensor): The encoder feature that generates ys Returns: tuple[torch.Tensor, Any]: Tuple of a score tensor for y that has a shape `(len(next_tokens),)` and next state for ys r)r rr7rrr r r score_partialsz$PartialScorerInterface.score_partialN) rrrrrrrrr8r r r r r6s  r6c @sBeZdZdZdejdejdeedejdeejeff ddZ d S) BatchPartialScorerInterfacez/Batch partial scorer interface for beam search.r#r7r$r%rcCr)a8Score new token (required). Args: ys (torch.Tensor): torch.int64 prefix tokens (n_batch, ylen). next_tokens (torch.Tensor): torch.int64 tokens to score (n_batch, n_token). states (List[Any]): Scorer states for prefix tokens. xs (torch.Tensor): The encoder feature that generates ys (n_batch, xlen, n_feat). Returns: tuple[torch.Tensor, Any]: Tuple of a score tensor for ys that has a shape `(n_batch, n_vocab)` and next states for ys r)r r#r7r$r%r r r batch_score_partialsz/BatchPartialScorerInterface.batch_score_partialN) rrrrrrrrrr:r r r r r9s r9) rr'typingrrrrrr!r6r9r r r r sJ.!