AVhdZddlmZddlmZddlmZddlmZddlmZddlmZddl m Z dd l m Z dd l mZd Zd Ze j"Ze j$Ze j&Ze j(Ze j*Ze j,Ze j.Ze j0Ze j2Ze j4Ze j6Zd ZddZdZdZy)avModule implementing RNN Cells. This module provides a number of basic commonly used RNN cells, such as LSTM (Long Short Term Memory) or GRU (Gated Recurrent Unit), and a number of operators that allow adding dropouts, projections, or embeddings for inputs. Constructing multi-layer cells is supported by the class `MultiRNNCell`, or by calling the `rnn` ops several times. )context) constant_op)dtypes)tensor) tensor_shape) tensor_util) rnn_cell_impl) array_ops)nestbiaskernelc>fd}tj||S)zCCreate tensors of zeros based on state_size, batch_size, and dtype.ct|}tj|}tjst|d}|j ||S)z7Combine s with batch_size to get a proper tensor shape.dtypeT)static)_concatr zerosrexecuting_eagerly set_shape)scsizec_static batch_sizers S/home/dcms/DCMS/lib/python3.12/site-packages/tensorflow/python/ops/rnn_cell_impl.pyget_state_shapez,_zero_state_tensors..get_state_shape5sJ AA ??1E *D  $ $ &Qt4h nnX K)r map_structure) state_sizerrrs `` r_zero_state_tensorsr!2s   OZ 88rct|tjrn|}tj|}|j j dk(rtj|d}n|j j dk7rtd|ztj|}|j |jnd}|jr3tj|jt j"nd}t|tjrn|}tj|}|j j dk(rtj|d}n|j j dk7rtd|ztj|}|j |jnd}|jr3tj|jt j"nd}|rFtj|j%|}|j |j}|Sd}|S||td|d|tj&||fd}|S) aConcat that enables int, Tensor, or TensorShape values. This function takes a size specification, which can be an integer, a TensorShape, or a Tensor, and converts it into a concatenated Tensor (if static = False) or a list of integers (if static = True). Args: prefix: The prefix; usually the batch size (and/or time step size). (TensorShape, int, or Tensor.) suffix: TensorShape, int, or Tensor. static: If `True`, return a python list with possibly unknown dimensions. Otherwise return a `Tensor`. Returns: shape: the concatenation of prefix and suffix. Raises: ValueError: if `suffix` is not a scalar or vector (or TensorShape). ValueError: if prefix or suffix was `None` and asked for dynamic Tensors out. rzCprefix tensor must be either a scalar or vector, but saw tensor: %sNrzCsuffix tensor must be either a scalar or vector, but saw tensor: %sz%Provided a prefix or suffix of None: z and ) isinstancerTensorrconstant_valueshapendimsr expand_dims ValueErrorr TensorShapeas_listis_fully_definedrconstantrint32 concatenateconcat)prefixsuffixrpp_staticrs_staticr's rrrAs, &A))&1Hww}}   1 %a !   O      (Agg1qyy{tH    QYY[ =   &A))&1Hww}}   1 %a !   O      (Agg1qyy{tH    QYY[ =    $ $X . : :8 DE${{6EMMOE ,=AE ,  yAI >Df M    aVQ 'E ,rc< t||y#t$rYywxYw)NTF)getattrAttributeError)obj attr_names r_hasattrr<s* C   s  c <t|dt|dt|dxs t|dt|g}gd}t|sLt||Dcgc] \}}|r | }}}t dj ||dj |ycc}}w) aRaises a TypeError if cell is not like an RNNCell. NOTE: Do not rely on the error message (in particular in tests) which can be subject to change to increase readability. Use ASSERT_LIKE_RNNCELL_ERROR_REGEXP. Args: cell_name: A string to give a meaningful error referencing to the name of the functionargument. cell: The object which should behave like an RNNCell. Raises: TypeError: A human-friendly exception. output_sizer get_initial_state zero_state)z!'output_size' property is missingz 'state_size' property is missingz=either 'zero_state' or 'get_initial_state' method is requiredzis not callablez-The argument {!r} ({}) is not an RNNCell: {}.z, N)r<callableallzip TypeErrorformatjoin) cell_namecell conditionserrorserrorconds rassert_like_rnncellrMs t]#t\"t()IXdL-Itn *  & Z'*6:'> K tde KF K 7>> tTYYv.    Ks  B%BN)F) __doc__tensorflow.python.eagerrtensorflow.python.frameworkrrrrr)tensorflow.python.keras.layers.legacy_rnnr tensorflow.python.opsr tensorflow.python.utilr _BIAS_VARIABLE_NAME_WEIGHTS_VARIABLE_NAME BasicLSTMCell BasicRNNCell DeviceWrapperDropoutWrapperGRUCell LayerRNNCellLSTMCellLSTMStateTuple MultiRNNCellResidualWrapperRNNCellr!rr<rMrrrbs,3..43C+'!++ )) ++ --   ))  ! !--)) //    9DN"r