# Copyright 2022 The Authors
# 
# 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
# 
#     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.

from __future__ import print_function
import keras
from keras.layers import Dense, Conv2D, BatchNormalization, Activation
from keras.layers import AveragePooling2D, Input, Flatten, Lambda
from keras.optimizers import Adam, SGD
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, ReduceLROnPlateau
from keras.callbacks import ReduceLROnPlateau
from keras.preprocessing.image import ImageDataGenerator
from keras.regularizers import l2
from keras import backend as K
from keras.models import Model
from keras.datasets import mnist, cifar10, cifar100
import tensorflow as tf
import numpy as np
import os
from scipy.io import loadmat
import math
from utils.model import resnet_v1, resnet_v2
import cleverhans.attacks as attacks
from cleverhans.utils_tf import model_eval
from utils.keras_wraper_ensemble import KerasModelWrapper
from utils.utils_model_eval import model_eval_targetacc, model_eval_for_SPSA, model_eval_for_SPSA_targetacc

FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_integer('batch_size', 50, 'batch_size for attack')
tf.app.flags.DEFINE_string('optimizer', 'mom', '')
tf.app.flags.DEFINE_float('mean_var', 10, 'parameter in MMLDA')
tf.app.flags.DEFINE_string('attack_method', 'FastGradientMethod', '')
tf.app.flags.DEFINE_string('attack_method_for_advtrain', 'FastGradientMethod', '')
tf.app.flags.DEFINE_integer('version', 2, '')
tf.app.flags.DEFINE_float('lr', 0.01, 'initial lr')
tf.app.flags.DEFINE_bool('target', True, 'is target attack or not')
tf.app.flags.DEFINE_bool('use_target', False, 'whether use target attack or untarget attack for adversarial training')
tf.app.flags.DEFINE_bool('use_ball', True, 'whether use ball loss or softmax')
tf.app.flags.DEFINE_bool('use_MMLDA', True, 'whether use MMLDA or softmax')
tf.app.flags.DEFINE_bool('use_advtrain', True, 'whether use advtraining or normal training')
tf.app.flags.DEFINE_float('adv_ratio', 1.0, 'the ratio of adversarial examples in each mini-batch')
tf.app.flags.DEFINE_integer('epoch', 1, 'the epoch of model to load')
tf.app.flags.DEFINE_bool('use_BN', True, 'whether use batch normalization in the network')
tf.app.flags.DEFINE_string('dataset', 'mnist', '')
tf.app.flags.DEFINE_bool('normalize_output_for_ball', True, 'whether apply softmax in the inference phase')
tf.app.flags.DEFINE_bool('use_random', False, 'whether use random center or MMLDA center in the network')
tf.app.flags.DEFINE_bool('use_dense', True, 'whether use extra dense layer in the network')
tf.app.flags.DEFINE_bool('use_leaky', False, 'whether use leaky relu in the network')

tf.app.flags.DEFINE_float('CW_confidence', 1.0, 'the confidence for CW-L2 attacks')
tf.app.flags.DEFINE_float('SPSA_epsilon', 8, 'the eps for SPSA attacks in 256 pixel values')

# Load the dataset
if FLAGS.dataset=='mnist':
    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train = np.expand_dims(x_train, axis=3)
    x_test = np.expand_dims(x_test, axis=3)
    epochs = 50
    num_class = 10
    epochs_inter = [30,40]
    x_place = tf.placeholder(tf.float32, shape=(None, 28, 28, 3))

elif FLAGS.dataset=='cifar10':
    (x_train, y_train), (x_test, y_test) = cifar10.load_data()
    epochs = 200
    num_class = 10
    epochs_inter = [100,150]
    x_place = tf.placeholder(tf.float32, shape=(None, 32, 32, 3))

elif FLAGS.dataset=='cifar100':
    (x_train, y_train), (x_test, y_test) = cifar100.load_data()
    epochs = 200
    num_class = 100
    epochs_inter = [100,150]
    x_place = tf.placeholder(tf.float32, shape=(None, 32, 32, 3))

else:
    print('Unknown dataset')


# These parameters are usually fixed
subtract_pixel_mean = True
version = FLAGS.version # Model version
n = 5 # n=5 for resnet-32 v1

# Computed depth from supplied model parameter n
if version == 1:
    depth = n * 6 + 2
    feature_dim = 64
elif version == 2:
    depth = n * 9 + 2
    feature_dim = 256


if FLAGS.use_random==True:
    name_random = '_random'
else:
    name_random = ''

if FLAGS.use_leaky==True:
    name_leaky = '_withleaky'
else:
    name_leaky = ''

if FLAGS.use_dense==True:
    name_dense = ''
else:
    name_dense = '_nodense'

    
#Load means in MMLDA
kernel_dict = loadmat('kernel_paras/meanvar1_featuredim'+str(feature_dim)+'_class'+str(num_class)+name_random+'.mat')
mean_logits = kernel_dict['mean_logits'] #num_class X num_dense
mean_logits = FLAGS.mean_var * tf.constant(mean_logits,dtype=tf.float32)


#MMLDA prediction function
def MMLDA_layer(x, means=mean_logits, num_class=num_class, use_ball=FLAGS.use_ball):
    #x_shape = batch_size X num_dense
    x_expand = tf.tile(tf.expand_dims(x,axis=1),[1,num_class,1]) #batch_size X num_class X num_dense
    mean_expand = tf.expand_dims(means,axis=0) #1 X num_class X num_dense
    logits = -tf.reduce_sum(tf.square(x_expand - mean_expand), axis=-1) #batch_size X num_class
    if use_ball==True:
        if FLAGS.normalize_output_for_ball==False:
            return logits
        else:
            return tf.nn.softmax(logits, axis=-1)
    else:
        return tf.nn.softmax(logits, axis=-1)


# Load the data.
y_test_target = np.zeros_like(y_test)
for i in range(y_test.shape[0]):
    l = np.random.randint(num_class)
    while l == y_test[i][0]:
        l = np.random.randint(num_class)
    y_test_target[i][0] = l
print('Finish crafting y_test_target!!!!!!!!!!!')

# Input image dimensions.
input_shape = x_train.shape[1:]

# Normalize data.
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255

clip_min = 0.0
clip_max = 1.0
# If subtract pixel mean is enabled
if subtract_pixel_mean:
    x_train_mean = np.mean(x_train, axis=0)
    x_train -= x_train_mean
    x_test -= x_train_mean
    clip_min -= x_train_mean
    clip_max -= x_train_mean
print (np.min(x_train_mean))
print (np.max(x_train_mean))

# Convert class vectors to binary class matrices.
y_train = keras.utils.to_categorical(y_train, num_class)
y_test_index = np.squeeze(np.copy(y_test).astype('int32'))
y_test = keras.utils.to_categorical(y_test, num_class)
y_test_target_index = np.squeeze(np.copy(y_test_target).astype('int32'))
y_test_target = keras.utils.to_categorical(y_test_target, num_class)


# Define input TF placeholder
y_place = tf.placeholder(tf.float32, shape=(None, num_class))
y_target = tf.placeholder(tf.float32, shape=(None, num_class))
sess = tf.Session()
keras.backend.set_session(sess)


model_input = Input(shape=input_shape)

#dim of logtis is batchsize x dim_means
if version == 2:
    original_model,_,_,_,final_features = resnet_v2(input=model_input, depth=depth, num_classes=num_class, \
                                                    use_BN=FLAGS.use_BN, use_dense=FLAGS.use_dense, use_leaky=FLAGS.use_leaky)
else:
    original_model,_,_,_,final_features = resnet_v1(input=model_input, depth=depth, num_classes=num_class, \
                                                    use_BN=FLAGS.use_BN, use_dense=FLAGS.use_dense, use_leaky=FLAGS.use_leaky)

if FLAGS.use_BN==True:
    BN_name = '_withBN'
    print('Use BN in the model')
else:
    BN_name = '_noBN'
    print('Do not use BN in the model')


#Whether use target attack for adversarial training
if FLAGS.use_target==False:
    is_target = ''
else:
    is_target = 'target'


if FLAGS.use_advtrain==True:
    dirr = 'advtrained_models/'+FLAGS.dataset+'/'
    attack_method_for_advtrain = '_'+is_target+FLAGS.attack_method_for_advtrain
    adv_ratio_name = '_advratio'+str(FLAGS.adv_ratio)
    mean_var = int(FLAGS.mean_var)
else:
    dirr = 'trained_models/'+FLAGS.dataset+'/'
    attack_method_for_advtrain = ''
    adv_ratio_name = ''
    mean_var = FLAGS.mean_var


if FLAGS.use_MMLDA==True:
    print('Using MMLDA')
    new_layer = Lambda(MMLDA_layer)
    predictions = new_layer(final_features)
    model = Model(input=model_input, output=predictions)
    use_ball_=''
    if FLAGS.use_ball==False:
        print('Using softmax function')
        use_ball_='_softmax'
    filepath_dir = dirr+'resnet32v'+str(version)+'_meanvar'+str(mean_var) \
                                                                +'_'+FLAGS.optimizer \
                                                                +'_lr'+str(FLAGS.lr) \
                                                                +'_batchsize'+str(FLAGS.batch_size) \
                                                                +attack_method_for_advtrain+adv_ratio_name+BN_name+name_leaky+name_dense+name_random+use_ball_+'/' \
                                                                +'model.'+str(FLAGS.epoch).zfill(3)+'.h5'
else:
    print('Using softmax loss')
    model = original_model
    filepath_dir = dirr+'resnet32v'+str(version)+'_'+FLAGS.optimizer \
                                                            +'_lr'+str(FLAGS.lr) \
                                                            +'_batchsize'+str(FLAGS.batch_size)+attack_method_for_advtrain+adv_ratio_name+BN_name+name_leaky+'/' \
                                                                +'model.'+str(FLAGS.epoch).zfill(3)+'.h5'


wrap_ensemble = KerasModelWrapper(model, num_class=num_class)


model.load_weights(filepath_dir)

# Initialize the attack method
if FLAGS.attack_method == 'SaliencyMapMethod':
    num_samples = 100
    eval_par = {'batch_size': 1}
    att = attacks.SaliencyMapMethod(wrap_ensemble, sess=sess)
    att_params = {
        'theta': 1.,
        'gamma': 0.1,
        'clip_min': clip_min,
        'clip_max': clip_max,
    }
    adv_x = att.generate(x_place, **att_params)
elif FLAGS.attack_method == 'CarliniWagnerL2': #Update on 2019.3.29
    num_samples = 500
    eval_par = {'batch_size': 10}
    att = attacks.CarliniWagnerL2(wrap_ensemble, sess=sess)
    if FLAGS.target==False:
        att_params = {
            'batch_size': 10,
            'confidence': FLAGS.CW_confidence,
            'learning_rate': 5e-3,
            'binary_search_steps': 9,
            'max_iterations': 1000,
            'initial_const': 0.01,
            'abort_early': True,
            'clip_min': clip_min,
            'clip_max': clip_max
        }
    else:
        att_params = {
            'batch_size': 10,
            'confidence': FLAGS.CW_confidence,
            'y_target': y_target,
            'learning_rate': 5e-3,
            'binary_search_steps': 9,
            'max_iterations': 1000,
            'initial_const': 0.01,
            'abort_early': True,
            'clip_min': clip_min,
            'clip_max': clip_max
        }
    if FLAGS.use_MMLDA == True and FLAGS.use_ball == True:
        is_MMC = True
    else:
        is_MMC = False
    adv_x = att.generate(x_place, is_MMC=is_MMC, **att_params)
elif FLAGS.attack_method == 'ElasticNetMethod':
    num_samples = 1000
    eval_par = {'batch_size': 100}
    att = attacks.ElasticNetMethod(wrap_ensemble, sess=sess)
    att_params = {
        'batch_size': 100,
        'confidence': 0.1,
        'learning_rate': 0.01,
        'binary_search_steps': 1,
        'max_iterations': 1000,
        'initial_const': 1.0,
        'beta': 1e-2,
        'fista': True,
        'decision_rule': 'EN',
        'clip_min': clip_min,
        'clip_max': clip_max
    }
    adv_x = att.generate(x_place, **att_params)
elif FLAGS.attack_method == 'DeepFool':
    num_samples = 1000
    eval_par = {'batch_size': 1}
    att = attacks.DeepFool(wrap_ensemble, sess=sess)
    att_params = {
        'max_iter': 10, 
        'clip_min': clip_min, 
        'clip_max': clip_max,
        'nb_candidate': 1
    }
    adv_x = att.generate(x_place, **att_params)
elif FLAGS.attack_method == 'LBFGS':
    num_samples = 1000
    eval_par = {'batch_size': 1}
    att = attacks.LBFGS(wrap_ensemble, sess=sess)
    clip_min = np.mean(clip_min)
    clip_max = np.mean(clip_max)
    att_params = {
        'y_target': y_target,
        'batch_size': 1,
        'binary_search_steps': 1,
        'max_iterations': 100,
        'initial_const': 1.0,
        'clip_min': clip_min,
        'clip_max': clip_max
    }
    adv_x = att.generate(x_place, **att_params)
elif FLAGS.attack_method == 'SPSA': #Update on 2019.3.29
    num_samples = 1000
    eval_par = {'batch_size': 1}
    x = tf.placeholder(tf.float32, shape=(1, 32, 32, 3))
    y_index = tf.placeholder(tf.uint8, shape=())
    if FLAGS.target:
        y_target_index = tf.placeholder(tf.uint8, shape=())
    else:
        y_target_index = None
    att = attacks.SPSA(wrap_ensemble, sess=sess)
    if FLAGS.use_MMLDA == True and FLAGS.use_ball == True:
        is_MMC = True
    else:
        is_MMC = False
    adv_x = att.generate(x, y_index, y_target=y_target_index, epsilon=FLAGS.SPSA_epsilon / 256., num_steps=10,
                 is_targeted=FLAGS.target, early_stop_loss_threshold=None,
                 learning_rate=0.01, delta=0.01, batch_size=128, spsa_iters=1,
                 is_debug=False, is_MMC=is_MMC)



preds = wrap_ensemble.get_probs(adv_x)
if FLAGS.attack_method == 'LBFGS':
    print(model_eval_targetacc(
        sess,
        x_place,
        y_place,
        y_target,
        preds,
        x_test[:num_samples],
        y_test[:num_samples],
        y_test_target[:num_samples],
        args=eval_par))
elif FLAGS.attack_method == 'SPSA':
    if FLAGS.target==False:
        acc = model_eval_for_SPSA(
        sess, 
        x, 
        y_place, 
        y_index, 
        preds, 
        x_test[:num_samples], 
        y_test_index[:num_samples], 
        y_test[:num_samples],
        args=eval_par)
        print('adv_acc: %.3f' %acc)
    else:
        acc = model_eval_for_SPSA_targetacc(
        sess, 
        x, 
        y_place, 
        y_index, 
        y_target_index,
        preds, 
        x_test[:num_samples], 
        y_test_index[:num_samples], 
        y_test[:num_samples],
        y_test_target_index[:num_samples],
        args=eval_par)
        print('adv_acc_target: %.3f' %acc)
elif FLAGS.attack_method == 'CarliniWagnerL2':
    l2dis_test = np.zeros((num_samples,))
    reshape_dis = tf.reshape(x_place - adv_x, shape = [-1, 3072])
    if FLAGS.target==False:
        for i in range(int(num_samples/10)):
            l2dis_test[i*10:(i+1)*10]=sess.run(tf.norm(reshape_dis, ord=2, axis=-1), feed_dict={x_place: x_test[i*10:(i+1)*10], \
                y_place: y_test[i*10:(i+1)*10]})
            print('Predict batch for test ', i, ', l2dis_mean is ', np.mean(l2dis_test[i*10:(i+1)*10]))
        print('Total l2dismean is ',np.mean(l2dis_test))
        acc = model_eval(sess, x_place, y_place, preds, x_test[:num_samples], y_test[:num_samples], args=eval_par)
        print('adv_acc: %.3f' %acc)
    else:
        for i in range(int(num_samples/10)):
            l2dis_test[i*10:(i+1)*10]=sess.run(tf.norm(reshape_dis, ord=2, axis=-1), feed_dict={x_place: x_test[i*10:(i+1)*10], \
                y_place: y_test[i*10:(i+1)*10], y_target: y_test_target[i*10:(i+1)*10]})
            print('Predict batch for test ', i, ', l2dis_mean is ', np.mean(l2dis_test[i*10:(i+1)*10]))
        print('Total l2dismean is ',np.mean(l2dis_test))
        acc = model_eval_targetacc(sess, x_place, y_place, y_target, preds, x_test[:num_samples], y_test[:num_samples], y_test_target[:num_samples], args=eval_par)
        print('adv_acc_target: %.3f' %acc)

