import numpy as np

import data_transforms

import data_iterators

import pathfinder

import lasagne as nn

from collections import namedtuple

from functools import partial

import lasagne.layers.dnn as dnn

import lasagne

import theano.tensor as T

import utils

restart_from_save = False

rng = np.random.RandomState(33)

# transformations

p_transform = {'patch_size': (48, 48, 48),

               'mm_patch_size': (48, 48, 48),

               'pixel_spacing': (1., 1., 1.)

               }

p_transform_augment = {

    'translation_range_z': [-3, 3],

    'translation_range_y': [-3, 3],

    'translation_range_x': [-3, 3],

    'rotation_range_z': [-180, 180],

    'rotation_range_y': [-180, 180],

    'rotation_range_x': [-180, 180]

}

# data preparation function

def data_prep_function(data, patch_center, pixel_spacing, luna_origin, p_transform,

                       p_transform_augment, **kwargs):

    x, patch_annotation_tf = data_transforms.transform_patch3d(data=data,

                                                               luna_annotations=None,

                                                               patch_center=patch_center,

                                                               p_transform=p_transform,

                                                               p_transform_augment=p_transform_augment,

                                                               pixel_spacing=pixel_spacing,

                                                               luna_origin=luna_origin)

    x = data_transforms.hu2normHU(x)

    return x

data_prep_function_train = partial(data_prep_function, p_transform_augment=p_transform_augment,

                                   p_transform=p_transform)

data_prep_function_valid = partial(data_prep_function, p_transform_augment=None,

                                   p_transform=p_transform)

# data iterators

batch_size = 16

nbatches_chunk = 1

chunk_size = batch_size * nbatches_chunk

train_valid_ids = utils.load_pkl(pathfinder.LUNA_VALIDATION_SPLIT_PATH)

train_pids, valid_pids = train_valid_ids['train'], train_valid_ids['valid']

train_data_iterator = data_iterators.CandidatesLunaDataGenerator(data_path=pathfinder.LUNA_DATA_PATH,

                                                                 batch_size=chunk_size,

                                                                 transform_params=p_transform,

                                                                 data_prep_fun=data_prep_function_train,

                                                                 rng=rng,

                                                                 patient_ids=train_valid_ids['train'],

                                                                 full_batch=True, random=True, infinite=True,

                                                                 positive_proportion=0.5)

valid_data_iterator = data_iterators.CandidatesLunaValidDataGenerator(data_path=pathfinder.LUNA_DATA_PATH,

                                                                      transform_params=p_transform,

                                                                      data_prep_fun=data_prep_function_valid,

                                                                      patient_ids=train_valid_ids['valid'])

nchunks_per_epoch = train_data_iterator.nsamples / chunk_size

max_nchunks = nchunks_per_epoch * 100

validate_every = int(5. * nchunks_per_epoch)

save_every = int(5. * nchunks_per_epoch)

learning_rate_schedule = {

    0: 1e-4,

    int(max_nchunks * 0.5): 5e-5,

    int(max_nchunks * 0.6): 2.5e-5,

    int(max_nchunks * 0.7): 1.25e-5,

    int(max_nchunks * 0.8): 0.625e-6,

    int(max_nchunks * 0.9): 0.3125e-6

}

# model

conv3d = partial(dnn.Conv3DDNNLayer,

                 filter_size=3,

                 pad='same',

                 W=nn.init.Orthogonal(),

                 b=nn.init.Constant(0.01),

                 nonlinearity=nn.nonlinearities.very_leaky_rectify)

max_pool3d = partial(dnn.MaxPool3DDNNLayer,

                     pool_size=2)

drop = lasagne.layers.DropoutLayer

bn = lasagne.layers.batch_norm

dense = partial(lasagne.layers.DenseLayer,

                W=lasagne.init.Orthogonal('relu'),

                b=lasagne.init.Constant(0.0),

                nonlinearity=lasagne.nonlinearities.rectify)

def inrn_v2(lin):

    n_base_filter = 32

    l1 = conv3d(lin, n_base_filter, filter_size=1)

    l2 = conv3d(lin, n_base_filter, filter_size=1)

    l2 = conv3d(l2, n_base_filter, filter_size=3)

    l3 = conv3d(lin, n_base_filter, filter_size=1)

    l3 = conv3d(l3, n_base_filter, filter_size=3)

    l3 = conv3d(l3, n_base_filter, filter_size=3)

    l = lasagne.layers.ConcatLayer([l1, l2, l3])

    l = conv3d(l, lin.output_shape[1], filter_size=1)

    l = lasagne.layers.ElemwiseSumLayer([l, lin])

    l = lasagne.layers.NonlinearityLayer(l, nonlinearity=lasagne.nonlinearities.rectify)

    return l

def inrn_v2_red(lin):

    # We want to reduce our total volume /4

    den = 16

    nom2 = 4

    nom3 = 5

    nom4 = 7

    ins = lin.output_shape[1]

    l1 = max_pool3d(lin)

    l2 = conv3d(lin, ins // den * nom2, filter_size=3, stride=2)

    l3 = conv3d(lin, ins // den * nom2, filter_size=1)

    l3 = conv3d(l3, ins // den * nom3, filter_size=3, stride=2)

    l4 = conv3d(lin, ins // den * nom2, filter_size=1)

    l4 = conv3d(l4, ins // den * nom3, filter_size=3)

    l4 = conv3d(l4, ins // den * nom4, filter_size=3, stride=2)

    l = lasagne.layers.ConcatLayer([l1, l2, l3, l4])

    return l

def build_model():

    l_in = nn.layers.InputLayer((None, 1,) + p_transform['patch_size'])

    l_target = nn.layers.InputLayer((None, 1))

    l = conv3d(l_in, 64)

    l = inrn_v2_red(l)

    l = inrn_v2(l)

    l = inrn_v2_red(l)

    l = inrn_v2(l)

    l = inrn_v2_red(l)

    l = inrn_v2_red(l)

    l = dense(drop(l), 128)

    l_out = nn.layers.DenseLayer(l, num_units=2,

                                 W=lasagne.init.Orthogonal('relu'),

                                 b=lasagne.init.Constant(0.5),

                                 nonlinearity=nn.nonlinearities.softmax)

    return namedtuple('Model', ['l_in', 'l_out', 'l_target'])(l_in, l_out, l_target)

def build_objective(model, deterministic=False, epsilon=1e-12):

    predictions = nn.layers.get_output(model.l_out)

    targets = T.cast(T.flatten(nn.layers.get_output(model.l_target)), 'int32')

    p = predictions[T.arange(predictions.shape[0]), targets]

    p = T.clip(p, epsilon, 1.)

    loss = T.mean(T.log(p))

    return -loss

def build_updates(train_loss, model, learning_rate):

    updates = nn.updates.adam(train_loss, nn.layers.get_all_params(model.l_out, trainable=True), learning_rate)

    return updates