# Path: util.py
# def numpy_floatX(data):
#     """
#         Returns the source as an appropriate float type.
#     """
#     return numpy.asarray(data, dtype=config.floatX)


import theano
from theano import tensor
from util import numpy_floatX

def adadelta(lr, tparams, grads, x_c, mask, y_mask, y, cost):
    """
    An adaptive learning rate optimizer

    Parameters
    ----------
    lr : Theano SharedVariable
        Initial learning rate
    tpramas: Theano SharedVariable
        Model parameters
    grads: Theano variable
        Gradients of cost w.r.t to parameres
    x: Theano variable
        Model inputs
    mask: Theano variable
        Sequence mask
    y: Theano variable
        Targets
    cost: Theano variable
        Objective fucntion to minimize

    Notes
    -----
    For more information, see [ADADELTA]_.

    .. [ADADELTA] Matthew D. Zeiler, *ADADELTA: An Adaptive Learning
       Rate Method*, arXiv:1212.5701.
    """


# Path: util.py
# def numpy_floatX(data):
#     """
#         Returns the source as an appropriate float type.
#     """
#     return numpy.asarray(data, dtype=config.floatX)


# Path: nn_optimizers.py
import theano
from theano import tensor
from util import numpy_floatX


def adadelta(lr, tparams, grads, x_c, mask, y_mask, y, cost):
    """
    An adaptive learning rate optimizer

    Parameters
    ----------
    lr : Theano SharedVariable
        Initial learning rate
    tpramas: Theano SharedVariable
        Model parameters
    grads: Theano variable
        Gradients of cost w.r.t to parameres
    x: Theano variable
        Model inputs
    mask: Theano variable
        Sequence mask
    y: Theano variable
        Targets
    cost: Theano variable
        Objective fucntion to minimize

    Notes
    -----
    For more information, see [ADADELTA]_.

    .. [ADADELTA] Matthew D. Zeiler, *ADADELTA: An Adaptive Learning
       Rate Method*, arXiv:1212.5701.
    """


    zipped_grads = [theano.shared(p.get_value() * numpy_floatX(0.),