""" helper functions for dumping some Keras layers to C files """

import numpy as np


def printVector(f, vector, name, dtype='float', dotp=False):
    """ prints vector as one-dimensional C array """
    if dotp:
        vector = vector.reshape((vector.shape[0]//4, 4, vector.shape[1]//8, 8))
        vector = vector.transpose((2, 0, 3, 1))
    v = np.reshape(vector, (-1))
    f.write('static const {} {}[{}] = {{\n   '.format(dtype, name, len(v)))
    for i in range(0, len(v)):
        f.write('{}'.format(v[i]))
        if (i!=len(v)-1):
            f.write(',')
        else:
            break;
        if (i%8==7):
            f.write("\n   ")
        else:
            f.write(" ")
    f.write('\n};\n\n')
    return vector

def printSparseVector(f, A, name, have_diag=True):
    N = A.shape[0]
    M = A.shape[1]
    W = np.zeros((0,), dtype='int')
    W0 = np.zeros((0,))
    if have_diag:
        diag = np.concatenate([np.diag(A[:,:N]), np.diag(A[:,N:2*N]), np.diag(A[:,2*N:])])
        A[:,:N] = A[:,:N] - np.diag(np.diag(A[:,:N]))
        A[:,N:2*N] = A[:,N:2*N] - np.diag(np.diag(A[:,N:2*N]))
        A[:,2*N:] = A[:,2*N:] - np.diag(np.diag(A[:,2*N:]))
        printVector(f, diag, name + '_diag')
    AQ = np.minimum(127, np.maximum(-128, np.round(A*128))).astype('int')
    idx = np.zeros((0,), dtype='int')
    for i in range(M//8):
        pos = idx.shape[0]
        idx = np.append(idx, -1)
        nb_nonzero = 0
        for j in range(N//4):
            block = A[j*4:(j+1)*4, i*8:(i+1)*8]
            qblock = AQ[j*4:(j+1)*4, i*8:(i+1)*8]
            if np.sum(np.abs(block)) > 1e-10:
                nb_nonzero = nb_nonzero + 1
                idx = np.append(idx, j*4)
                vblock = qblock.transpose((1,0)).reshape((-1,))
                W0 = np.concatenate([W0, block.reshape((-1,))])
                W = np.concatenate([W, vblock])
        idx[pos] = nb_nonzero
    f.write('#ifdef DOT_PROD\n')
    printVector(f, W, name, dtype='qweight')
    f.write('#else /*DOT_PROD*/\n')
    printVector(f, W0, name, dtype='qweight')
    f.write('#endif /*DOT_PROD*/\n')
    printVector(f, idx, name + '_idx', dtype='int')
    return AQ

def dump_sparse_gru(self, f, hf):
    name = 'sparse_' + self.name
    print("printing layer " + name + " of type sparse " + self.__class__.__name__)
    weights = self.get_weights()
    qweights = printSparseVector(f, weights[1], name + '_recurrent_weights')
    printVector(f, weights[-1], name + '_bias')
    subias = weights[-1].copy()
    subias[1,:] = subias[1,:] - np.sum(qweights*(1./128),axis=0)
    printVector(f, subias, name + '_subias')
    if hasattr(self, 'activation'):
        activation = self.activation.__name__.upper()
    else:
        activation = 'TANH'
    if hasattr(self, 'reset_after') and not self.reset_after:
        reset_after = 0
    else:
        reset_after = 1
    neurons = weights[0].shape[1]//3
    max_rnn_neurons = neurons
    f.write('const SparseGRULayer {} = {{\n   {}_bias,\n   {}_subias,\n   {}_recurrent_weights_diag,\n   {}_recurrent_weights,\n   {}_recurrent_weights_idx,\n   {}, ACTIVATION_{}, {}\n}};\n\n'
            .format(name, name, name, name, name, name, weights[0].shape[1]//3, activation, reset_after))
    hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
    hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
    hf.write('extern const SparseGRULayer {};\n\n'.format(name));
    return max_rnn_neurons

def dump_gru_layer(self, f, hf, dotp=False, sparse=False):
    name = self.name
    print("printing layer " + name + " of type " + self.__class__.__name__)
    weights = self.get_weights()
    if sparse:
        qweight = printSparseVector(f, weights[0], name + '_weights', have_diag=False)
    else:
        qweight = printVector(f, weights[0], name + '_weights')

    if dotp:
        f.write('#ifdef DOT_PROD\n')
        qweight2 = np.clip(np.round(128.*weights[1]).astype('int'), -128, 127)
        printVector(f, qweight2, name + '_recurrent_weights', dotp=True, dtype='qweight')
        f.write('#else /*DOT_PROD*/\n')
    else:
        qweight2 = weights[1]

    printVector(f, weights[1], name + '_recurrent_weights')
    if dotp:
        f.write('#endif /*DOT_PROD*/\n')

    printVector(f, weights[-1], name + '_bias')
    subias = weights[-1].copy()
    subias[0,:] = subias[0,:] - np.sum(qweight*(1./128.),axis=0)
    subias[1,:] = subias[1,:] - np.sum(qweight2*(1./128.),axis=0)
    printVector(f, subias, name + '_subias')
    if hasattr(self, 'activation'):
        activation = self.activation.__name__.upper()
    else:
        activation = 'TANH'
    if hasattr(self, 'reset_after') and not self.reset_after:
        reset_after = 0
    else:
        reset_after = 1
    neurons = weights[0].shape[1]//3
    max_rnn_neurons = neurons
    f.write('const GRULayer {} = {{\n   {}_bias,\n   {}_subias,\n   {}_weights,\n   NULL,\n   {}_recurrent_weights,\n   {}, {}, ACTIVATION_{}, {}\n}};\n\n'
            .format(name, name, name, name, name, weights[0].shape[0], weights[0].shape[1]//3, activation, reset_after))
    hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
    hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
    hf.write('extern const GRULayer {};\n\n'.format(name));
    return max_rnn_neurons

def dump_dense_layer_impl(name, weights, bias, activation, f, hf):
    printVector(f, weights, name + '_weights')
    printVector(f, bias, name + '_bias')
    f.write('const DenseLayer {} = {{\n   {}_bias,\n   {}_weights,\n   {}, {}, ACTIVATION_{}\n}};\n\n'
            .format(name, name, name, weights.shape[0], weights.shape[1], activation))
    hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights.shape[1]))
    hf.write('extern const DenseLayer {};\n\n'.format(name));

def dump_dense_layer(self, f, hf):
    name = self.name
    print("printing layer " + name + " of type " + self.__class__.__name__)
    weights = self.get_weights()
    activation = self.activation.__name__.upper()
    dump_dense_layer_impl(name, weights[0], weights[1], activation, f, hf)
    return False

def dump_conv1d_layer(self, f, hf):
    name = self.name
    print("printing layer " + name + " of type " + self.__class__.__name__)
    weights = self.get_weights()
    printVector(f, weights[0], name + '_weights')
    printVector(f, weights[-1], name + '_bias')
    activation = self.activation.__name__.upper()
    max_conv_inputs = weights[0].shape[1]*weights[0].shape[0]
    f.write('const Conv1DLayer {} = {{\n   {}_bias,\n   {}_weights,\n   {}, {}, {}, ACTIVATION_{}\n}};\n\n'
            .format(name, name, name, weights[0].shape[1], weights[0].shape[0], weights[0].shape[2], activation))
    hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[2]))
    hf.write('#define {}_STATE_SIZE ({}*{})\n'.format(name.upper(), weights[0].shape[1], (weights[0].shape[0]-1)))
    hf.write('#define {}_DELAY {}\n'.format(name.upper(), (weights[0].shape[0]-1)//2))
    hf.write('extern const Conv1DLayer {};\n\n'.format(name));
    return max_conv_inputs