o Gip% @sddlmZddlmZddlmZddlZddlZddlZddlmZGdddej j Z dd ej d ej d e d ej fd dZd ej dej dej d dfddZd edej d efddZdeed efddZdej dej d ej fddZdS))List)Tuple)UnionN)Fsac @sLeZdZedejdejdedejfddZedeejdffdd Z dS) _IndexSelectFunctionsrcindex default_valuereturncCs|||t|||S)aReturns a new tensor which indexes the input tensor along dimension 0 using the entries in `index`. If the entry in `index` is -1, then the corresponding entry in the returned tensor is 0. Caution: `index.dtype == torch.int32` and `index.ndim == 1`. Args: src: The input tensor. Either 1-D or 2-D with dtype torch.int32 or torch.float32. index: 1-D tensor of dtype torch.int32 containing the indexes. If an entry is -1, the corresponding entry in the returned value is 0. The elements of `index` should be in the range `[-1..src.shape[0]-1]`. default_value: Used only when `src` is a 1-D tensor. It sets ans[i] to default_value if index[i] is -1. Returns: A tensor with shape (index.numel(), *src.shape[1:]) and dtype the same as `src`, e.g. if `src.ndim == 1`, ans.shape would be (index.shape[0],); if `src.ndim == 2`, ans.shape would be (index.shape[0], src.shape[1]). Will satisfy `ans[i] == src[index[i]]` if `src.ndim == 1`, or `ans[i,j] == src[index[i],j]` if `src.ndim == 2`, except for entries where `index[i] == -1` which will be zero. )save_for_backward_k2 index_select)ctxrr r r:/home/ubuntu/.local/lib/python3.10/site-packages/k2/ops.pyforwards "z_IndexSelectFunction.forwardNcCs:|j\}}tj|j|j|jdd}t||||ddfS)NF)dtypedevice requires_grad) saved_tensorstorchzerosshaperrr index_add)rout_gradrr ansrrrbackwardBs z_IndexSelectFunction.backward) __name__ __module__ __qualname__ staticmethodrTensorfloatrrrrrrrrs $rrr r r cCst|||}|S)aReturns a new tensor which indexes the input tensor along dimension 0 using the entries in `index`. If the entry in `index` is -1, then the corresponding entry in the returned tensor is 0. Caution: `index.dtype == torch.int32` and `index.ndim == 1`. Args: src: The input tensor. Either 1-D or 2-D with dtype `torch.int32`, `torch.int64`, `torch.float32`, or `torch.float64`. index: 1-D tensor of dtype `torch.int32` containing the indexes. If an entry is -1, the corresponding entry in the returned value is 0. The elements of `index` should be in the range `[-1..src.shape[0]-1]`. default_value: Used only when `src` is a 1-D tensor. It sets ans[i] to default_value if index[i] is -1. Returns: A tensor with shape ``(index.numel(), *src.shape[1:])`` and dtype the same as `src`, e.g. if `src.ndim == 1`, `ans.shape` would be `(index.shape[0],)`; if `src.ndim == 2`, `ans.shape` would be `(index.shape[0], src.shape[1])`. Will satisfy `ans[i] == src[index[i]]` if `src.ndim == 1`, or `ans[i, j] == src[index[i], j]` if `src.ndim == 2`, except for entries where `index[i] == -1` which will be zero. )rapply)rr r rrrrrSs"rvaluein_outcCst|||dS)aIt implements in_out[index[i]] += value[i]. Caution: It has similar semantics with `torch.Tensor.index_add_` except that: - `index.dtype == torch.int32` - `-1 <= index[i] < in_out.shape[0]` - `index[i] == -1` is ignored. - `index` has to be a 1-D **contiguous** tensor. Caution: `in_out` is modified **in-place**. Caution: This functions does NOT support autograd. Args: index: A 1-D **contiguous** tensor with dtype `torch.int32`. Must satisfy `-1 <= index[i] < in_out.shape[0]` value: A 1-D or 2-D tensor with dtype `torch.int32`, `torch.float32`, or `torch.float64`. Must satisfy `index.shape[0] == value.shape[0]` in_out: A 1-D or 2-D tensor with the same dtype as `value`. It satisfies `in_out.shape[1] == value.shape[1]` if it is a 2-D tensor. Returns: Return None. N)r r)r r%r&rrrrys#rindexesc Cstj|jd|dd\}}t|}|D]6\}}t|tjr*t||t j ||qt|t j s2J|j tjks:J|j|ddd\}}t|||q|D] \}}t|||qO|S)a*Select a list of FSAs from `src` with a 1-D tensor. Args: src: An FsaVec. indexes: A 1-D `torch.Tensor` of dtype `torch.int32` containing the ids of FSAs to select. Returns: Return an FsaVec containing only those FSAs specified by `indexes`. rT)axisr'need_value_indexesF)r(r))r r arcsrnamed_tensor_attr isinstancerr"setattrk2opsr RaggedTensorrint32named_non_tensor_attr) rr' ragged_arc value_indexesout_fsanamer% ragged_value_rrr index_fsas&   r9srcscs|D]}t|jdksJd|jqdd|D}tj|dd}t|}dd|D}tjt|}|D]1fd d|D}t|dt j rOt |}nt|dt j sYJt j j|dd}t||q6|D]}|D]\}t|st||qpqj|S) aConcatenate a list of FsaVec into a single FsaVec. Caution: Only common tensor attributes are kept in the output FsaVec. For non-tensor attributes, only one copy is kept in the output FsaVec. We choose the first copy of the FsaVec that has the lowest index in `srcs`. Args: srcs: A list of FsaVec. Each element MUST be an FsaVec. Returns: Return a single FsaVec concatenated from the input FsaVecs. zExpect an FsaVec. Given: cSsg|]}|jqSr)r*).0fsarrr szcat..r)r(css$|] }tt|VqdS)N)setdictr+keysr<rrrr s zcat..csg|]}t|qSr)getattrrBr6rrr>s)lenrr catrr? intersectionlistr,rr"r.r0raggedr-r2hasattr)r:rsrc_ragged_arcsans_ragged_arcsr5common_tensor_attributesvaluesr%rrErrGs2    rG step1_arc_map step2_arc_mapcCsL|jdksJ|jtjksJ|jdksJ|jtjksJtj||ddS)aCompose arc maps from two Fsa operations. It implements: - ans_arc_map[i] = step1_arc_map[step2_arc_map[i]] if step2_arc_map[i] is not -1 - ans_arc_map[i] = -1 if step2_arc_map[i] is -1 for i in 0 to `step2_arc_map.numel() - 1`. Args: step1_arc_map: A 1-D tensor with dtype torch.int32 from the first Fsa operation. step2_arc_map: A 1-D tensor with dtype torch.int32 from the second Fsa operation. Returns: Return a 1-D tensor with dtype torch.int32. It has the same number of elements as step2_arc_map. That is, ans_arc_map.shape == step2_arc_map.shape. r)r )ndimrrr1r r)rPrQrrrcompose_arc_mapss rT)r)typingrrrrr.r r=rautogradFunctionrr"r#rrr9rGrTrrrrs8    :  & &'2