"""
Copyright 2023 LINE Corporation
LINE Corporation licenses this file to you 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:
    https://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.
"""

import numpy as np
import dutils
import pandas as pd
from collections import Counter
from tqdm import tqdm
import os
from pandas.core.common import flatten
import argparse

MAX_LEN = 1000
N_CLASS = 4

parser = argparse.ArgumentParser(
        description="Spatial Temporal Graph Convolution Network"
    )
parser.add_argument(
    "--data-root",
    default="dataset/babel_v1.0_sequence/",
    help="the root path of the dataset",
    type=str
)
parser.add_argument(
    "--split",
    default=1,
    help="the split of the dataset",
    type=int
)
parser.add_argument(
    "--output-folder",
    default="dataset/processed_data",
    help="the output folder of the generated data",
    type=str
)
args = parser.parse_args()

os.makedirs(args.output_folder, exist_ok=True)

def main(data_root):
    train_data = dutils.read_pkl(os.path.join(data_root, "babel_v1.0_train_ntu_sk_ntu-style_preprocessed.pkl"))
    test_data = dutils.read_pkl(os.path.join(data_root, "babel_v1.0_val_ntu_sk_ntu-style_preprocessed.pkl"))

    act2idx = dutils.read_json(f"./prepare/configs/action_label_split{args.split}.json")

    label_train_data(data_root, train_data, act2idx)
    label_val_data(data_root, test_data, act2idx)


def label_train_data(data_root, train_data, act2idx):
    sid = []
    x = []
    y = []
    loc = []

    for i, seq_labels in enumerate(tqdm(train_data["Y"])):
        if len(seq_labels) > MAX_LEN:
            continue
        
        y_ = []
        loc_ = []
        flag = False

        for frame, labels in seq_labels.items():
            label_set = set(labels) & set(act2idx.keys())
            label_list = list(label_set)
            if len(label_list) > 0:
                flag = True
                loc_.append(act2idx[label_list[0]])
                y_.append(act2idx[label_list[0]])
            else:
                loc_.append(N_CLASS)

        max_t = len(loc_)
        loc_ = np.array(loc_)
        y_ = list(set(y_))

        if flag:

            # print (train_data["X"][i].shape, len(loc_))
            loc.append(loc_)
            sid.append(train_data["sid"][i])
            x.append(train_data["X"][i][:,:max_t,...])
            y.append(y_)
            
    data = {"sid": sid, "X": x, "Y": y, "L":loc}

    dutils.write_pkl(data, os.path.join(args.output_folder, f"train_split{args.split}.pkl"))
    print (f"#Train sequence: {len(x)}")


def label_val_data(data_root, test_data, act2idx):
    sid = []
    x = []
    y = []
    loc = []
    for i, seq_labels in enumerate(tqdm(test_data["Y"])):
        if len(seq_labels) > MAX_LEN:
            continue

        y_ = []
        loc_ = []
        flag = False

        for frame, labels in seq_labels.items():
            label_set = set(labels) & set(act2idx.keys())
            label_list = list(label_set)
            if len(label_list) > 0:
                flag = True
                loc_.append(act2idx[label_list[0]])
                y_.append(act2idx[label_list[0]])
            else:
                loc_.append(N_CLASS)

        max_t = len(loc_)
        loc_ = np.array(loc_)
        y_ = list(set(y_))

        if flag:
            loc.append(loc_)
            sid.append(test_data["sid"][i])
            x.append(test_data["X"][i][:,:max_t,...])
            y.append(y_)
            
    data = {"sid": sid, "X": x, "Y": y, "L":loc}

    dutils.write_pkl(data, os.path.join(args.output_folder, f"val_split{args.split}.pkl"))
    print (f"#Test sequence: {len(x)}")


if __name__ == "__main__":
    main(args.data_root)