o i@sPdZddlmZddlZddlmZddlmZddlm Z GdddeZ dS) )Embedding Frontend for text based inputs.)TupleN)check_argument_types) AbsFrontend)PositionalEncodingcsleZdZdZddedfdededeffdd Zd ej d ej d e ej ej ffd d Z d efddZ Z S) Embeddingrig? input_size embed_dimpositional_dropout_ratecs>tsJt||_tjtj||||||_dS)aInitialize. Args: input_size: Number of input tokens. embed_dim: Embedding Size. pos_enc_class: PositionalEncoding or ScaledPositionalEncoding positional_dropout_rate: dropout rate after adding positional encoding N) rsuper__init__r torchnn Sequentialrembed)selfrr pos_enc_classr  __class__Q/home/ubuntu/.local/lib/python3.10/site-packages/espnet2/mt/frontend/embedding.pyr s    zEmbedding.__init__input input_lengthsreturncCs||}||fS)a!Apply a sliding window on the input. Args: input: Input (B, T) or (B, T,D), with D. input_lengths: Input lengths within batch. Returns: Tensor: Output with dimensions (B, T, D). Tensor: Output lengths within batch. )r)rrrxrrrforward+s zEmbedding.forwardcCs|jS)zDReturn output length of feature dimension D, i.e. the embedding dim.)r )rrrr output_size<szEmbedding.output_size)__name__ __module__ __qualname____doc__rintfloatr r Tensorrrr __classcell__rrrrrs* r) r typingrr typeguardr!espnet2.asr.frontend.abs_frontendr1espnet.nets.pytorch_backend.transformer.embeddingrrrrrrs