from __future__ import print_function, division, absolute_import

from .serialize import dask_serialize, dask_deserialize, serialize, deserialize

import keras


@dask_serialize.register(keras.Model)
def serialize_keras_model(model):
    import keras
    if keras.__version__ < '1.2.0':
        raise ImportError("Need Keras >= 1.2.0. "
                          "Try pip install keras --upgrade --no-deps")

    header = model._updated_config()
    weights = model.get_weights()
    headers, frames = list(zip(*map(serialize, weights)))
    header['headers'] = headers
    header['nframes'] = [len(L) for L in frames]
    frames = [frame for L in frames for frame in L]
    return header, frames


@dask_deserialize.register(keras.Model)
def deserialize_keras_model(header, frames):
    from keras.models import model_from_config
    n = 0
    weights = []
    for head, length in zip(header['headers'], header['nframes']):
        x = deserialize(head, frames[n: n + length])
        weights.append(x)
        n += length
    model = model_from_config(header)
    model.set_weights(weights)
    return model
