import theano
import numpy
from theano import tensor
from blocks.model import Model
from blocks.bricks import Linear, Tanh
from ctc import CTC
from blocks.initialization import IsotropicGaussian, Constant
from fuel.datasets import IterableDataset
from fuel.streams import DataStream
from blocks.algorithms import (GradientDescent, Scale,
                               StepClipping, CompositeRule)
from blocks.extensions.monitoring import TrainingDataMonitoring
from blocks.main_loop import MainLoop
from blocks.extensions import FinishAfter, Printing
from blocks.bricks.recurrent import SimpleRecurrent
from blocks.graph import ComputationGraph
try:
    import cPickle as pickle
except:
    import pickle

floatX = theano.config.floatX


@theano.compile.ops.as_op(itypes=[tensor.lvector],
                          otypes=[tensor.lvector])
def print_pred(y_hat):
    blank_symbol = 4
    res = []
    for i, s in enumerate(y_hat):
        if (s != blank_symbol) and (i == 0 or s != y_hat[i - 1]):
            res += [s]
    return numpy.asarray(res)

n_epochs = 200
x_dim = 4
h_dim = 9
num_classes = 4

with open("ctc_test_data.pkl", "rb") as pkl_file:
    try:
        data = pickle.load(pkl_file)
        inputs = data['inputs']
        labels = data['labels']
        # from S x T x B x D to S x T x B
        inputs_mask = numpy.max(data['mask_inputs'], axis=-1)
        labels_mask = data['mask_labels']
    except:
        data = pickle.load(pkl_file, encoding='bytes')
        inputs = data[b'inputs']
        labels = data[b'labels']
        # from S x T x B x D to S x T x B
        inputs_mask = numpy.max(data[b'mask_inputs'], axis=-1)
        labels_mask = data[b'mask_labels']



print('Building model ...')

# x : T x B x F
x = tensor.tensor3('x', dtype=floatX)
# x_mask : T x B
x_mask = tensor.matrix('x_mask', dtype=floatX)
# y : L x B
y = tensor.lmatrix('y')
# y_mask : L x B
y_mask = tensor.matrix('y_mask', dtype=floatX)

# Linear bricks in
x_to_h = Linear(name='x_to_h',
                input_dim=x_dim,
                output_dim=h_dim)
x_transform = x_to_h.apply(x)

# RNN bricks
rnn = SimpleRecurrent(activation=Tanh(),
                      dim=h_dim, name="rnn")
h = rnn.apply(x_transform)

# Linear bricks out 
h_to_o = Linear(name='h_to_o',
                input_dim=h_dim,
                output_dim=num_classes + 1)
h_transform = h_to_o.apply(h)

# y_hat : T x B x C+1
y_hat = tensor.nnet.softmax(
    h_transform.reshape((-1, num_classes + 1))
).reshape((h.shape[0], h.shape[1], -1))
y_hat.name = 'y_hat'

y_hat_mask = x_mask

# Cost
cost = CTC().apply(y, y_hat, y_mask.sum(axis=1), y_hat_mask).mean()
cost.name = 'CTC'

# Initialization
for brick in (rnn, x_to_h, h_to_o):
    brick.weights_init = IsotropicGaussian(0.01)
    brick.biases_init = Constant(0)
    brick.initialize()

print('Bulding DataStream ...')
dataset = IterableDataset({'x': inputs,
                           'x_mask': inputs_mask,
                           'y': labels,
                           'y_mask': labels_mask})
stream = DataStream(dataset)

print('Bulding training process...')
algorithm = GradientDescent(cost=cost,
                            parameters=ComputationGraph(cost).parameters,
                            step_rule=CompositeRule([StepClipping(10.0),
                                                     Scale(0.02)]))
monitor_cost = TrainingDataMonitoring([cost],
                                      prefix="train",
                                      after_epoch=True)

# sample number to monitor
sample = 8

y_hat_max_path = print_pred(tensor.argmax(y_hat[:, sample, :], axis=1))
y_hat_max_path.name = 'Viterbi'
monitor_output = TrainingDataMonitoring([y_hat_max_path],
                                        prefix="y_hat",
                                        every_n_epochs=1)

length = tensor.sum(y_mask[:, sample]).astype('int32')
tar = y[:length, sample].astype('int32')
tar.name = '_Target_Seq'
monitor_target = TrainingDataMonitoring([tar],
                                        prefix="y",
                                        every_n_epochs=1)

model = Model(cost)
main_loop = MainLoop(data_stream=stream, algorithm=algorithm,
                     extensions=[monitor_cost, monitor_output,
                                 monitor_target,
                                 FinishAfter(after_n_epochs=n_epochs),
                                 Printing()],
                     model=model)

print('Starting training ...')
main_loop.run()