# PFLlib: Personalized Federated Learning Algorithm Library
# Copyright (C) 2021  Jianqing Zhang

# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.

# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.

# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.

import os
import random
import sys

import numpy as np
import torch
import torchvision
import torchvision.transforms as transforms
from utils.dataset_utils import check, save_file, separate_data, split_data

random.seed(1)
np.random.seed(1)
num_clients = 20
dir_path = "Cifar10_niid/"


# Allocate data to users
def generate_dataset(dir_path, num_clients, niid, balance, partition):
    if not os.path.exists(dir_path):
        os.makedirs(dir_path)

    # Setup directory for train/test data
    config_path = dir_path + "config.json"
    train_path = dir_path + "train/"
    test_path = dir_path + "test/"

    if check(
        config_path,
        train_path,
        test_path,
        num_clients,
        niid,
        balance,
        partition,
    ):
        return

    # Get Cifar10 data
    transform = transforms.Compose(
        [
            transforms.ToTensor(),
            # transforms.RandomResizedCrop(size=(24, 24)),
            # transforms.RandomHorizontalFlip(p=0.1),
            transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
        ]
    )

    trainset = torchvision.datasets.CIFAR10(
        root="Cifar10_rawdata",
        train=True,
        download=True,
        transform=transform,
    )
    testset = torchvision.datasets.CIFAR10(
        root="Cifar10_rawdata",
        train=False,
        download=True,
        transform=transform,
    )
    trainloader = torch.utils.data.DataLoader(
        trainset, batch_size=len(trainset.data), shuffle=False
    )
    testloader = torch.utils.data.DataLoader(
        testset, batch_size=len(testset.data), shuffle=False
    )

    for _, train_data in enumerate(trainloader, 0):
        trainset.data, trainset.targets = train_data
    for _, test_data in enumerate(testloader, 0):
        testset.data, testset.targets = test_data

    dataset_image = []
    dataset_label = []

    dataset_image.extend(trainset.data.cpu().detach().numpy())
    dataset_image.extend(testset.data.cpu().detach().numpy())
    dataset_label.extend(trainset.targets.cpu().detach().numpy())
    dataset_label.extend(testset.targets.cpu().detach().numpy())
    dataset_image = np.array(dataset_image)
    dataset_label = np.array(dataset_label)

    num_classes = len(set(dataset_label))
    print(f"Number of classes: {num_classes}")

    # dataset = []
    # for i in range(num_classes):
    #     idx = dataset_label == i
    #     dataset.append(dataset_image[idx])

    X, y, statistic = separate_data(
        (dataset_image, dataset_label),
        num_clients,
        num_classes,
        niid,
        balance,
        partition,
        class_per_client=2,
    )
    train_data, test_data = split_data(X, y)
    save_file(
        config_path,
        train_path,
        test_path,
        train_data,
        test_data,
        num_clients,
        num_classes,
        statistic,
        niid,
        balance,
        partition,
    )


if __name__ == "__main__":
    niid = True if sys.argv[1] == "noniid" else False
    balance = True if sys.argv[2] == "balance" else False
    partition = sys.argv[3] if sys.argv[3] != "-" else None
    num_clients = int(sys.argv[4]) if sys.argv[4] != "-" else num_clients

    generate_dataset(dir_path, num_clients, niid, balance, partition)