#########################################################################
##   This file is part of the α,β-CROWN (alpha-beta-CROWN) verifier    ##
##                                                                     ##
## Copyright (C) 2021-2022, Huan Zhang <huan@huan-zhang.com>           ##
##                     Kaidi Xu, Zhouxing Shi, Shiqi Wang              ##
##                     Linyi Li, Jinqi (Kathryn) Chen                  ##
##                     Zhuolin Yang, Yihan Wang                        ##
##                                                                     ##
##      See CONTRIBUTORS for author contacts and affiliations.         ##
##                                                                     ##
##     This program is licenced under the BSD 3-Clause License,        ##
##        contained in the LICENCE file in this directory.             ##
##                                                                     ##
#########################################################################
"""
This file shows how to use customized models and customized dataloaders.

An example config file, `exp_configs/custom_model.py` has been provided.

python abcrown.py --config exp_configs/custom_model_data_example.yaml
"""

import os
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import arguments
import numpy as np


def simple_conv_model(in_channel, out_dim):
    """Simple Convolutional model."""
    model = nn.Sequential(
        nn.Conv2d(in_channel, 16, 4, stride=2, padding=0),
        nn.ReLU(),
        nn.Conv2d(16, 32, 4, stride=2, padding=0),
        nn.ReLU(),
        nn.Flatten(),
        nn.Linear(32*6*6,100),
        nn.ReLU(),
        nn.Linear(100, out_dim)
    )
    return model


def two_relu_toy_model(in_dim=2, out_dim=2):
    """A very simple model, 2 inputs, 2 ReLUs, 2 outputs"""
    model = nn.Sequential(
        nn.Linear(in_dim, 2),
        nn.ReLU(),
        nn.Linear(2, out_dim)
    )
    """[relu(x+2y)-relu(2x+y)+2, 0*relu(2x-y)+0*relu(-x+y)]"""
    model[0].weight.data = torch.tensor([[1., 2.], [2., 1.]])
    model[0].bias.data = torch.tensor([0., 0.])
    model[2].weight.data = torch.tensor([[1., -1.], [0., 0.]])
    model[2].bias.data = torch.tensor([2., 0.])
    return model


def simple_box_data(eps=2.):
    """a customized box data: x=[-1, 1], y=[-1, 1]"""
    X = torch.tensor([[0., 0.]]).float()
    labels = torch.tensor([0]).long()
    eps_temp = torch.tensor(eps).reshape(1, -1)
    data_max = torch.tensor(10.).reshape(1, -1)
    data_min = torch.tensor(-10.).reshape(1, -1)
    return X, labels, data_max, data_min, eps_temp


def box_data(dim, low=0., high=1., segments=10, num_classes=10, eps=None):
    """Generate fake datapoints."""
    step = (high - low) / segments
    data_min = torch.linspace(low, high - step, segments).unsqueeze(1).expand(segments, dim)  # Per element lower bounds.
    data_max = torch.linspace(low + step, high, segments).unsqueeze(1).expand(segments, dim)  # Per element upper bounds.
    X = (data_min + data_max) / 2.  # Fake data.
    labels = torch.remainder(torch.arange(0, segments, dtype=torch.int64), num_classes)  # Fake label.
    eps = None  # Lp norm perturbation epsilon. Not used, since we will return per-element min and max.
    return X, labels, data_max, data_min, eps


def cifar10(eps, use_bounds=False):
    """Example dataloader. For MNIST and CIFAR you can actually use existing ones in utils.py."""
    assert eps is not None
    database_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'datasets')
    # You can access the mean and std stored in config file.
    mean = torch.tensor(arguments.Config["data"]["mean"])
    std = torch.tensor(arguments.Config["data"]["std"])
    normalize = transforms.Normalize(mean=mean, std=std)
    test_data = datasets.CIFAR10(database_path, train=False, download=True, transform=transforms.Compose([transforms.ToTensor(), normalize]))
    # Load entire dataset.
    testloader = torch.utils.data.DataLoader(test_data, batch_size=10000, shuffle=False, num_workers=4)
    X, labels = next(iter(testloader))
    if use_bounds:
        # Option 1: for each example, we return its element-wise lower and upper bounds.
        # If you use this option, set --spec_type ("specifications"->"type" in config) to 'bound'.
        absolute_max = torch.reshape((1. - mean) / std, (1, -1, 1, 1))
        absolute_min = torch.reshape((0. - mean) / std, (1, -1, 1, 1))
        # Be careful with normalization.
        new_eps = torch.reshape(eps / std, (1, -1, 1, 1))
        data_max = torch.min(X + new_eps, absolute_max)
        data_min = torch.max(X - new_eps, absolute_min)
        # In this case, the epsilon does not matter here.
        ret_eps = None
    else:
        # Option 2: return a single epsilon for all data examples, as well as clipping lower and upper bounds.
        # Set data_max and data_min to be None if no clip. For CIFAR-10 we clip to [0,1].
        data_max = torch.reshape((1. - mean) / std, (1, -1, 1, 1))
        data_min = torch.reshape((0. - mean) / std, (1, -1, 1, 1))
        if eps is None:
            raise ValueError('You must specify an epsilon')
        # Rescale epsilon.
        ret_eps = torch.reshape(eps / std, (1, -1, 1, 1))
    return X, labels, data_max, data_min, ret_eps


def simple_cifar10(eps):
    """Example dataloader. For MNIST and CIFAR you can actually use existing ones in utils.py."""
    assert eps is not None
    database_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'datasets')
    # You can access the mean and std stored in config file.
    mean = torch.tensor(arguments.Config["data"]["mean"])
    std = torch.tensor(arguments.Config["data"]["std"])
    normalize = transforms.Normalize(mean=mean, std=std)
    test_data = datasets.CIFAR10(database_path, train=False, download=True,\
            transform=transforms.Compose([transforms.ToTensor(), normalize]))
    # Load entire dataset.
    testloader = torch.utils.data.DataLoader(test_data,\
            batch_size=10000, shuffle=False, num_workers=4)
    X, labels = next(iter(testloader))
    # Set data_max and data_min to be None if no clip. For CIFAR-10 we clip to [0,1].
    data_max = torch.reshape((1. - mean) / std, (1, -1, 1, 1))
    data_min = torch.reshape((0. - mean) / std, (1, -1, 1, 1))
    if eps is None:
        raise ValueError('You must specify an epsilon')
    # Rescale epsilon.
    ret_eps = torch.reshape(eps / std, (1, -1, 1, 1))
    return X, labels, data_max, data_min, ret_eps
