def update(self, doc, process=None):
        if process is None:
            pk = 'id'
            id = doc.id
            doc.synced = False
            set = doc
        elif process == UPDATE_PROCESS:
            if self.db.docs.find({'sid': doc.sid}).count() == 0:
                return self.insert(doc, synced=True)
            pk = 'sid'
            id = doc.sid
            doc.synced = not doc.merged() # must be commited if was merged
            doc.merged(False)
            set = {'tid': doc.tid, 'data': doc.data, 'user': doc.user,
                   'public': doc.public, 'synced': doc.synced}
        elif process == COMMIT_PROCESS:
            pk = 'id'
            id = doc.id
            doc.synced = True
            set = {'sid': doc.sid, 'tid': doc.tid, 'synced': doc.synced}
        else:
            raise DatabaseException('Incorrect update process')

        try:
            rs = self.db.docs.update({pk: id}, {'$set': set}, safe=True)
            assert rs['n'] == 1
        except Exception as e:
            raise DatabaseException(e)
        return doc
def testWrapperWeights(self, wrapper):
    """Tests that wrapper weights contain wrapped cells weights."""
    base_cell = layers.SimpleRNNCell(1, name="basic_rnn_cell")
    rnn_cell = wrapper(base_cell)
    rnn_layer = layers.RNN(rnn_cell)
    inputs = ops.convert_to_tensor_v2_with_dispatch([[[1]]],
                                                    dtype=dtypes.float32)
    rnn_layer(inputs)

    wrapper_name = generic_utils.to_snake_case(wrapper.__name__)
    expected_weights = ["rnn/" + wrapper_name + "/" + var for var in
                        ("kernel:0", "recurrent_kernel:0", "bias:0")]
    self.assertLen(rnn_cell.weights, 3)
    self.assertCountEqual([v.name for v in rnn_cell.weights], expected_weights)
    self.assertCountEqual([v.name for v in rnn_cell.trainable_variables],
                          expected_weights)
    self.assertCountEqual([v.name for v in rnn_cell.non_trainable_variables],
                          [])
    self.assertCountEqual([v.name for v in rnn_cell.cell.weights],
                          expected_weights)