a Sic@sbdZddlmmZddlmZddlmZ ddZ ddZ d d Z d d Z d dZddZdS)z#Utilities for RNN cells and layers.N)control_flow_util) tf_loggingcCst|tr|dur|dusJ|r>|| d}|d| }t|dkrb|dd}|dd}t|dkrxt|}n|d}dd}||}||}|||fS)aStandardizes `__call__` to a single list of tensor inputs. When running a model loaded from a file, the input tensors `initial_state` and `constants` can be passed to `RNN.__call__()` as part of `inputs` instead of by the dedicated keyword arguments. This method makes sure the arguments are separated and that `initial_state` and `constants` are lists of tensors (or None). Args: inputs: Tensor or list/tuple of tensors. which may include constants and initial states. In that case `num_constant` must be specified. initial_state: Tensor or list of tensors or None, initial states. constants: Tensor or list of tensors or None, constant tensors. num_constants: Expected number of constants (if constants are passed as part of the `inputs` list. Returns: inputs: Single tensor or tuple of tensors. initial_state: List of tensors or None. constants: List of tensors or None. NrcSs.|dust|tr|St|tr(t|S|gSN) isinstancelisttuple)xr V/var/www/html/django/DPS/env/lib/python3.9/site-packages/keras/layers/rnn/rnn_utils.pyto_list_or_noneLs  z)standardize_args..to_list_or_none)rrlenr)inputs initial_state constants num_constantsr r r r standardize_argss      rcCst|dot|tj S)z6Check whether the state_size contains multiple states.__len__)hasattrrtf TensorShape) state_sizer r r is_multiple_stateYs rcCs*|durt|d}|j}t||j|S)Nr)rshapedtypegenerate_zero_filled_stater)cellr batch_sizerr r r #generate_zero_filled_state_for_cell`srcsXdusdur$tddfdd}tj|rLtj||S||SdS)zBGenerate a zero filled tensor with shape [batch_size, state_size].Nz[batch_size and dtype cannot be None while constructing initial state. Received: batch_size=z, dtype=cs&t|}g|}tj|dS)N)r)rras_listzeros)unnested_state_size flat_dimsinit_state_sizebatch_size_tensorrr r create_zerosos z0generate_zero_filled_state..create_zeros) ValueErrorrnest is_nested map_structure)r%rrr&r r$r rgs rcCsbtr dSt|ddsdSttjjrz caching_device..) rexecuting_eagerlygetattrr IsInWhileLoopcompatv1get_default_graphloggingwarning _dtype_policy compute_dtypevariable_dtype)rnn_cellr r r caching_devicezs$  r<cCs$tjj}|j|kr d|jiSiS)awReturn the dict config for RNN cell wrt to enable_caching_device field. Since enable_caching_device is a internal implementation detail for speed up the RNN variable read when running on the multi remote worker setting, we don't want this config to be serialized constantly in the JSON. We will only serialize this field when a none default value is used to create the cell. Args: rnn_cell: the RNN cell for serialize. Returns: A dict which contains the JSON config for enable_caching_device value or empty dict if the enable_caching_device value is same as the default value. enable_caching_device)rr3r4#executing_eagerly_outside_functionsr+)r;default_enable_caching_devicer r r config_for_enable_caching_devices    r@)__doc__tensorflow.compat.v2r3v2r keras.utilsrtensorflow.python.platformrr6rrrrr<r@r r r r s  ?5