# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Multi-GPU settings
"""
import argparse
import time

import numpy as np
import scipy.sparse as sp
from sklearn.preprocessing import StandardScaler

import pgl
from pgl.utils.logger import log
from pgl.utils import paddle_helper
import paddle
import paddle.fluid as fluid
import reader
from model import graphsage_mean, graphsage_meanpool,\
        graphsage_maxpool, graphsage_lstm


def fixed_offset(data, num_nodes, scale):
    """Test
    """
    len_data = len(data)
    len_per_part = int(len_data / scale)
    offset = np.arange(0, scale, dtype="int64")
    offset = offset * num_nodes
    offset = np.repeat(offset, len_per_part)
    if len(data.shape) > 1:
        data += offset.reshape([-1, 1])
    else:
        data += offset


def load_data(normalize=True, symmetry=True, scale=1):
    """
        data from https://github.com/matenure/FastGCN/issues/8
        reddit_adj.npz: https://drive.google.com/open?id=174vb0Ws7Vxk_QTUtxqTgDHSQ4El4qDHt
        reddit.npz: https://drive.google.com/open?id=19SphVl_Oe8SJ1r87Hr5a6znx3nJu1F2J
    """
    data = np.load("data/reddit.npz")
    adj = sp.load_npz("data/reddit_adj.npz")
    if symmetry:
        adj = adj + adj.T
    adj = adj.tocoo()
    src = adj.row.reshape([-1, 1])
    dst = adj.col.reshape([-1, 1])
    edges = np.hstack([src, dst])

    num_class = 41

    train_label = data['y_train']
    val_label = data['y_val']
    test_label = data['y_test']

    train_index = data['train_index']
    val_index = data['val_index']
    test_index = data['test_index']

    feature = data["feats"].astype("float32")

    if normalize:
        scaler = StandardScaler()
        scaler.fit(feature[train_index])
        feature = scaler.transform(feature)

    if scale > 1:
        num_nodes = feature.shape[0]
        feature = np.tile(feature, [scale, 1])
        train_label = np.tile(train_label, [scale])
        val_label = np.tile(val_label, [scale])
        test_label = np.tile(test_label, [scale])
        edges = np.tile(edges, [scale, 1])
        fixed_offset(edges, num_nodes, scale)
        train_index = np.tile(train_index, [scale])
        fixed_offset(train_index, num_nodes, scale)
        val_index = np.tile(val_index, [scale])
        fixed_offset(val_index, num_nodes, scale)
        test_index = np.tile(test_index, [scale])
        fixed_offset(test_index, num_nodes, scale)

    log.info("Feature shape %s" % (repr(feature.shape)))

    graph = pgl.graph.Graph(
        num_nodes=feature.shape[0],
        edges=edges,
        node_feat={"feature": feature})

    return {
        "graph": graph,
        "train_index": train_index,
        "train_label": train_label,
        "val_label": val_label,
        "val_index": val_index,
        "test_index": test_index,
        "test_label": test_label,
        "feature": feature,
        "num_class": 41
    }


def build_graph_model(graph_wrapper, num_class, k_hop, graphsage_type,
                      hidden_size, feature):
    """Test"""
    node_index = fluid.layers.data(
        "node_index", shape=[None], dtype="int64", append_batch_size=False)

    node_label = fluid.layers.data(
        "node_label", shape=[None, 1], dtype="int64", append_batch_size=False)

    for i in range(k_hop):
        if graphsage_type == 'graphsage_mean':
            feature = graphsage_mean(
                graph_wrapper,
                feature,
                hidden_size,
                act="relu",
                name="graphsage_mean_%s % i")
        elif graphsage_type == 'graphsage_meanpool':
            feature = graphsage_meanpool(
                graph_wrapper,
                feature,
                hidden_size,
                act="relu",
                name="graphsage_meanpool_%s % i")
        elif graphsage_type == 'graphsage_maxpool':
            feature = graphsage_maxpool(
                graph_wrapper,
                feature,
                hidden_size,
                act="relu",
                name="graphsage_maxpool_%s % i")
        elif graphsage_type == 'graphsage_lstm':
            feature = graphsage_lstm(
                graph_wrapper,
                feature,
                hidden_size,
                act="relu",
                name="graphsage_maxpool_%s % i")
        else:
            raise ValueError("graphsage type %s is not"
                             " implemented" % graphsage_type)

    feature = fluid.layers.gather(feature, node_index)
    logits = fluid.layers.fc(feature,
                             num_class,
                             act=None,
                             name='classification_layer')
    proba = fluid.layers.softmax(logits)

    loss = fluid.layers.softmax_with_cross_entropy(
        logits=logits, label=node_label)
    loss = fluid.layers.mean(loss)
    acc = fluid.layers.accuracy(input=proba, label=node_label, k=1)
    return loss, acc


def run_epoch(batch_iter,
              exe,
              program,
              prefix,
              model_loss,
              model_acc,
              epoch,
              log_per_step=100):
    """Test"""
    batch = 0
    total_loss = 0.
    total_acc = 0.
    total_sample = 0
    start = time.time()
    for batch_feed_dict in batch_iter():
        batch += 1
        batch_loss, batch_acc = exe.run(program,
                                        fetch_list=[model_loss, model_acc],
                                        feed=batch_feed_dict)

        if batch % log_per_step == 0:
            log.info("Batch %s %s-Loss %s %s-Acc %s" %
                     (batch, prefix, batch_loss, prefix, batch_acc))

        num_samples = len(batch_feed_dict["node_index"])
        total_loss += batch_loss * num_samples
        total_acc += batch_acc * num_samples
        total_sample += num_samples
    end = time.time()

    log.info("%s Epoch %s Loss %.5lf Acc %.5lf Speed(per batch) %.5lf sec" %
             (prefix, epoch, total_loss / total_sample,
              total_acc / total_sample, (end - start) / batch))


def main(args):
    """Test """
    data = load_data(args.normalize, args.symmetry, args.scale)
    log.info("preprocess finish")
    log.info("Train Examples: %s" % len(data["train_index"]))
    log.info("Val Examples: %s" % len(data["val_index"]))
    log.info("Test Examples: %s" % len(data["test_index"]))
    log.info("Num nodes %s" % data["graph"].num_nodes)
    log.info("Num edges %s" % data["graph"].num_edges)
    log.info("Average Degree %s" % np.mean(data["graph"].indegree()))

    place = fluid.CUDAPlace(0) if args.use_cuda else fluid.CPUPlace()
    train_program = fluid.Program()
    startup_program = fluid.Program()

    samples = []
    if args.samples_1 > 0:
        samples.append(args.samples_1)
    if args.samples_2 > 0:
        samples.append(args.samples_2)

    with fluid.program_guard(train_program, startup_program):
        graph_wrapper = pgl.graph_wrapper.GraphWrapper(
            "sub_graph",
            fluid.CPUPlace(),
            node_feat=data['graph'].node_feat_info())

        model_loss, model_acc = build_graph_model(
            graph_wrapper,
            num_class=data["num_class"],
            feature=graph_wrapper.node_feat["feature"],
            hidden_size=args.hidden_size,
            graphsage_type=args.graphsage_type,
            k_hop=len(samples))

    test_program = train_program.clone(for_test=True)

    train_iter = reader.multiprocess_graph_reader(
        data['graph'],
        graph_wrapper,
        samples=samples,
        num_workers=args.sample_workers,
        batch_size=args.batch_size,
        node_index=data['train_index'],
        node_label=data["train_label"])

    val_iter = reader.multiprocess_graph_reader(
        data['graph'],
        graph_wrapper,
        samples=samples,
        num_workers=args.sample_workers,
        batch_size=args.batch_size,
        node_index=data['val_index'],
        node_label=data["val_label"])

    test_iter = reader.multiprocess_graph_reader(
        data['graph'],
        graph_wrapper,
        samples=samples,
        num_workers=args.sample_workers,
        batch_size=args.batch_size,
        node_index=data['test_index'],
        node_label=data["test_label"])

    with fluid.program_guard(train_program, startup_program):
        adam = fluid.optimizer.Adam(learning_rate=args.lr)
        adam.minimize(model_loss)

    exe = fluid.Executor(place)
    exe.run(startup_program)

    for epoch in range(args.epoch):
        run_epoch(
            train_iter,
            program=train_program,
            exe=exe,
            prefix="train",
            model_loss=model_loss,
            model_acc=model_acc,
            epoch=epoch)

        run_epoch(
            val_iter,
            program=test_program,
            exe=exe,
            prefix="val",
            model_loss=model_loss,
            model_acc=model_acc,
            log_per_step=10000,
            epoch=epoch)

    run_epoch(
        test_iter,
        program=test_program,
        prefix="test",
        exe=exe,
        model_loss=model_loss,
        model_acc=model_acc,
        log_per_step=10000,
        epoch=epoch)


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='graphsage')
    parser.add_argument("--use_cuda", action='store_true', help="use_cuda")
    parser.add_argument(
        "--normalize", action='store_true', help="normalize features")
    parser.add_argument(
        "--symmetry", action='store_true', help="undirect graph")
    parser.add_argument("--graphsage_type", type=str, default="graphsage_mean")
    parser.add_argument("--sample_workers", type=int, default=5)
    parser.add_argument("--epoch", type=int, default=10)
    parser.add_argument("--hidden_size", type=int, default=128)
    parser.add_argument("--batch_size", type=int, default=128)
    parser.add_argument("--lr", type=float, default=0.01)
    parser.add_argument("--samples_1", type=int, default=25)
    parser.add_argument("--samples_2", type=int, default=10)
    parser.add_argument("--scale", type=int, default=1)
    args = parser.parse_args()
    log.info(args)
    main(args)