o ih!@sddlZddlZddlZddlmZmZmZmZddlmZddl m Z  Gdddej Z Gdddej Z d d d ZGd d d ejZdS)N)cuda function_nodelinkvariable)normal) type_checkc@s.eZdZd ddZddZddZdd ZdS) EmbedIDFunctionNcCs||_d|_dSN) ignore_label_w_shape)selfr r f/home/ubuntu/.local/lib/python3.10/site-packages/espnet/nets/chainer_backend/deterministic_embed_id.py__init__s zEmbedIDFunction.__init__cCsRt|dk|\}}t|jjdk|jdkt|jtjk|jdkdS)Ni)rexpectsizedtypekindndimnumpyfloat32)r in_typesx_typew_typer r rcheck_type_forwards z"EmbedIDFunction.check_type_forwardc Cs|d|\}}|j|_tj|stdt|t|tj |}t rI| d|k|t |k}|jdurA||||jk}|sItd|jdurd||jk}||dd|||d|fS||fS)NrzCnumpy and cupy must not be used together type(W): {0}, type(x): {1}rz;Each not ignored `x` value need to satisfy`0 <= x < len(W)`.N) retain_inputsshaper r same_types ValueErrorformattyperget_array_modulechaineris_debug logical_andlenr logical_orallwhere)r inputsxWxpvalid_xmaskr r rforward s*      " zEmbedIDFunction.forwardcCs,|}t|j|j||d}d|fS)Nr)get_retained_inputs EmbedIDGradr r apply)r indexes grad_outputsr.gWr r rbackward;s zEmbedIDFunction.backwardr )__name__ __module__ __qualname__rrr4r;r r r rrs   rc@s&eZdZdddZddZddZdS) r6NcCs||_||_d|_dSr )w_shaper _gy_shape)r r?r r r rrDs zEmbedIDGrad.__init__c Cs|dtj|}|\}}|j|_|j|j|jd}|turCt j | | |jdD]\}}||jkr7q-|||7<q-|fS |j|jt|ftjd}|j|jtjdt|| } d| | <|jdurvd|dd|jf<|j| |jdj|jdd}|fS)Nr)r?gF)copy)r rr&r!r@zerosr?rrsixmoveszipravelreshaperr r*rarangeint32Tdotastype) r r.r1r/gyr:ixigyxiidxr r rr4Is&  $ !"  "zEmbedIDGrad.forwardc Cstj|}|dj}|d}|jdur0||jk}d|jkr(|jdks0n||d|}||}|jdurR||d|dd|j\}}} t j |||}d|fS)Nrrrr f) rr&r5datar r?r-broadcast_arraysrDr' functions) r r8gradsr1r/ggWr3ggyzero_r r rr;ys     zEmbedIDGrad.backwardr )r<r=r>rr4r;r r r rr6Cs  0r6cCst|d||fdS)aEfficient linear function for one-hot input. This function implements so called *word embeddings*. It takes two arguments: a set of IDs (words) ``x`` in :math:`B` dimensional integer vector, and a set of all ID (word) embeddings ``W`` in :math:`V \\times d` float32 matrix. It outputs :math:`B \\times d` matrix whose ``i``-th column is the ``x[i]``-th column of ``W``. This function is only differentiable on the input ``W``. Args: x (chainer.Variable | np.ndarray): Batch vectors of IDs. Each element must be signed integer. W (chainer.Variable | np.ndarray): Distributed representation of each ID (a.k.a. word embeddings). ignore_label (int): If ignore_label is an int value, i-th column of return value is filled with 0. Returns: chainer.Variable: Embedded variable. .. rubric:: :class:`~chainer.links.EmbedID` Examples: >>> x = np.array([2, 1]).astype('i') >>> x array([2, 1], dtype=int32) >>> W = np.array([[0, 0, 0], ... [1, 1, 1], ... [2, 2, 2]]).astype('f') >>> W array([[ 0., 0., 0.], [ 1., 1., 1.], [ 2., 2., 2.]], dtype=float32) >>> F.embed_id(x, W).data array([[ 2., 2., 2.], [ 1., 1., 1.]], dtype=float32) >>> F.embed_id(x, W, ignore_label=1).data array([[ 2., 2., 2.], [ 0., 0., 0.]], dtype=float32) r r)rr7)r/r0r r r rembed_ids,r^cs.eZdZdZdZdfdd ZddZZS)EmbedIDaEfficient linear layer for one-hot input. This is a link that wraps the :func:`~chainer.functions.embed_id` function. This link holds the ID (word) embedding matrix ``W`` as a parameter. Args: in_size (int): Number of different identifiers (a.k.a. vocabulary size). out_size (int): Output dimension. initialW (Initializer): Initializer to initialize the weight. ignore_label (int): If `ignore_label` is an int value, i-th column of return value is filled with 0. .. rubric:: :func:`~chainer.functions.embed_id` Attributes: W (~chainer.Variable): Embedding parameter matrix. Examples: >>> W = np.array([[0, 0, 0], ... [1, 1, 1], ... [2, 2, 2]]).astype('f') >>> W array([[ 0., 0., 0.], [ 1., 1., 1.], [ 2., 2., 2.]], dtype=float32) >>> l = L.EmbedID(W.shape[0], W.shape[1], initialW=W) >>> x = np.array([2, 1]).astype('i') >>> x array([2, 1], dtype=int32) >>> y = l(x) >>> y.data array([[ 2., 2., 2.], [ 1., 1., 1.]], dtype=float32) Ncsftt|||_||durtd}t|||f|_ WddS1s,wYdS)NrB) superr_rr init_scoperNormalr Parameterr0)r in_sizeout_sizeinitialWr  __class__r rrs  "zEmbedID.__init__cCst||j|jdS)zExtracts the word embedding of given IDs. Args: x (chainer.Variable): Batch vectors of IDs. Returns: chainer.Variable: Batch of corresponding embeddings. r])r^r0r )r r/r r r__call__s zEmbedID.__call__)NN)r<r=r>__doc__r rri __classcell__r r rgrr_s % r_r )r'rrErrrrchainer.initializersr chainer.utilsr FunctionNoderr6r^Linkr_r r r rs  1 L/