# Copyright 2022 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""train resnet."""

import os
import numpy as np
import mindspore as ms
import mindspore.nn as nn
import mindspore.train.callback as callback
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
from mindspore.communication.management import init, get_rank
from mindspore.train.loss_scale_manager import FixedLossScaleManager
from slb import create_slb
from src.lr_generator import get_lr
from src.CrossEntropySmooth import CrossEntropySmooth
from src.metric import DistAccuracy
from src.resnet import conv_variance_scaling_initializer
from src.resnet import resnet18 as resnet
from src.model_utils.config import config

if config.dataset == "cifar10":
    from src.dataset import create_dataset1 as create_dataset
else:
    if config.mode_name == "GRAPH":
        from src.dataset import create_dataset2 as create_dataset
    else:
        from src.dataset import create_dataset_pynative as create_dataset

ms.set_seed(1)


class LossCallBack(LossMonitor):
    """
    Monitor the loss in training.
    If the loss in NAN or INF terminating training.
    """

    def __init__(self, has_trained_epoch=0):
        super(LossCallBack, self).__init__()
        self.has_trained_epoch = has_trained_epoch

    def step_end(self, run_context):
        cb_params = run_context.original_args()
        loss = cb_params.net_outputs

        if isinstance(loss, (tuple, list)):
            if isinstance(loss[0], ms.Tensor) and isinstance(loss[0].asnumpy(), np.ndarray):
                loss = loss[0]

        if isinstance(loss, ms.Tensor) and isinstance(loss.asnumpy(), np.ndarray):
            loss = np.mean(loss.asnumpy())

        cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num + 1

        if isinstance(loss, float) and (np.isnan(loss) or np.isinf(loss)):
            raise ValueError("epoch: {} step: {}. Invalid loss, terminating training.".format(
                cb_params.cur_epoch_num, cur_step_in_epoch))
        if self._per_print_times != 0 and cb_params.cur_step_num % self._per_print_times == 0:
            print("epoch: %s step: %s, loss is %s" % (cb_params.cur_epoch_num + int(self.has_trained_epoch),
                                                      cur_step_in_epoch, loss), flush=True)


def filter_checkpoint_parameter_by_list(origin_dict, param_filter):
    """remove useless parameters according to filter_list"""
    for key in list(origin_dict.keys()):
        for name in param_filter:
            if name in key:
                print("Delete parameter from checkpoint: ", key)
                del origin_dict[key]
                break


def set_parameter():
    """set_parameter"""
    target = config.device_target

    # init context
    if config.mode_name == "GRAPH":
        ms.set_context(mode=ms.GRAPH_MODE, device_target=target, save_graphs=False)
    else:
        ms.set_context(mode=ms.PYNATIVE_MODE, device_target=target, save_graphs=False)

    if config.run_distribute:
        # GPU target
        init()
        ms.set_auto_parallel_context(device_num=config.device_num,
                                     parallel_mode=ms.ParallelMode.DATA_PARALLEL,
                                     gradients_mean=True)


def init_weight(net):
    """init_weight"""
    for _, cell in net.cells_and_names():
        if isinstance(cell, nn.Conv2d):
            if config.conv_init == "XavierUniform":
                cell.weight.set_data(ms.common.initializer.initializer(ms.common.initializer.XavierUniform(),
                                                                       cell.weight.shape,
                                                                       cell.weight.dtype))
            elif config.conv_init == "TruncatedNormal":
                weight = conv_variance_scaling_initializer(cell.in_channels,
                                                           cell.out_channels,
                                                           cell.kernel_size[0])
                cell.weight.set_data(weight)
        if isinstance(cell, nn.Dense):
            if config.dense_init == "TruncatedNormal":
                cell.weight.set_data(ms.common.initializer.initializer(ms.common.initializer.TruncatedNormal(),
                                                                       cell.weight.shape,
                                                                       cell.weight.dtype))
            elif config.dense_init == "RandomNormal":
                in_channel = cell.in_channels
                out_channel = cell.out_channels
                weight = np.random.normal(loc=0, scale=0.01, size=out_channel * in_channel)
                weight = ms.Tensor(np.reshape(weight, (out_channel, in_channel)), dtype=cell.weight.dtype)
                cell.weight.set_data(weight)


def load_pretrained_ckpt(net):
    if config.pre_trained:
        if os.path.isfile(config.pre_trained):
            ckpt = ms.load_checkpoint(config.pre_trained)
            if ckpt.get("epoch_num") and ckpt.get("step_num"):
                config.has_trained_epoch = int(ckpt["epoch_num"].data.asnumpy())
                config.has_trained_step = int(ckpt["step_num"].data.asnumpy())
            else:
                config.has_trained_epoch = 0
                config.has_trained_step = 0

            if config.has_trained_epoch > config.epoch_size:
                raise RuntimeError("If retrain, epoch_size should be bigger than has_trained_epoch after "
                                   "loading pretrained weight, but got epoch_size {}, has_trained_epoch {}"
                                   "".format(config.epoch_size, config.has_trained_epoch))

            if config.filter_weight:
                filter_list = [x.name for x in net.end_point.get_parameters()]
                filter_checkpoint_parameter_by_list(ckpt, filter_list)
            ms.load_param_into_net(net, ckpt)
        else:
            print(f"Invalid pre_trained {config.pre_trained} parameter.")


def init_group_params(net):
    decayed_params = []
    no_decayed_params = []
    for param in net.trainable_params():
        if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
            decayed_params.append(param)
        else:
            no_decayed_params.append(param)

    group_params = [{'params': decayed_params, 'weight_decay': config.weight_decay},
                    {'params': no_decayed_params},
                    {'order_params': net.trainable_params()}]
    return group_params


def set_save_ckpt_dir():
    """set save ckpt dir"""
    ckpt_save_dir = os.path.join(config.output_path, config.checkpoint_path)
    if config.run_distribute:
        ckpt_save_dir = ckpt_save_dir + "ckpt_" + str(get_rank()) + "/"
    return ckpt_save_dir


def init_loss_scale():
    if config.dataset == "imagenet2012" or config.dataset == "cifar10":
        if not config.use_label_smooth:
            config.label_smooth_factor = 0.0
        loss = CrossEntropySmooth(sparse=True, reduction="mean",
                                  smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
    else:
        loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
    return loss


class TemperatureScheduler(callback.Callback):
    """
    TemperatureScheduler for SLB.
    """
    def __init__(self, model, epoch_size=100, has_trained_epoch=0,
                 t_start_val=1.0, t_start_time=0.2, t_end_time=0.6, t_factor=1.2):
        super().__init__()
        self.epochs = epoch_size
        self.has_trained_epoch = has_trained_epoch
        self.t_start_val = t_start_val
        self.t_start_time = t_start_time
        self.t_end_time = t_end_time
        self.t_factor = t_factor
        self.model = model

    def epoch_begin(self, run_context):
        """
        Epoch_begin.
        """
        cb_params = run_context.original_args()
        epoch = cb_params.cur_epoch_num + self.has_trained_epoch
        # Compute temperature value
        t = self.t_start_val
        t_start_epoch = int(self.epochs*self.t_start_time)
        t_end_epoch = int(self.epochs*self.t_end_time)
        if epoch > t_start_epoch:
            t *= self.t_factor**(min(epoch, t_end_epoch) - t_start_epoch)
        # Assign new value to temperature parameter
        for _, cell in self.model.train_network.cells_and_names():
            if cell.cls_name == 'SlbFakeQuantizerPerLayer': # for SLB
                cell.set_temperature(t)
                if epoch >= t_end_epoch:
                    cell.set_temperature_end_flag()


def train_net():
    """train net"""
    print("Train configure: {}".format(config))
    target = config.device_target
    if target != "GPU":
        raise NotImplementedError("SLB only support running on GPU now!")
    set_parameter()
    dataset = create_dataset(dataset_path=config.data_path, do_train=True,
                             batch_size=config.batch_size, train_image_size=config.train_image_size,
                             eval_image_size=config.eval_image_size, target=target,
                             distribute=config.run_distribute)
    step_size = dataset.get_dataset_size()
    net = resnet(class_num=config.class_num)

    init_weight(net)
    algo = create_slb(config.quant_type)
    net = algo.apply(net)
    load_pretrained_ckpt(net)

    lr = get_lr(lr_init=config.lr_init,
                lr_end=config.lr_end,
                lr_max=config.lr_max,
                warmup_epochs=config.warmup_epochs,
                total_epochs=config.epoch_size,
                steps_per_epoch=step_size,
                lr_decay_mode=config.lr_decay_mode)
    if config.pre_trained:
        lr = lr[config.has_trained_epoch * step_size:]
    lr = ms.Tensor(lr)
    # define optimizer
    group_params = init_group_params(net)
    if config.optimizer == 'Momentum':
        opt = nn.Momentum(group_params, lr, config.momentum, weight_decay=config.weight_decay,
                          loss_scale=config.loss_scale)

    loss = init_loss_scale()
    loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)

    metrics = {"acc"}
    if config.run_distribute:
        metrics = {'acc': DistAccuracy(batch_size=config.batch_size, device_num=config.device_num)}
    model = ms.Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics=metrics,
                     amp_level="O0", boost_level=config.boost_mode, keep_batchnorm_fp32=False,
                     boost_config_dict={"grad_freeze": {"total_steps": config.epoch_size * step_size}})

    # define callbacks
    time_cb = TimeMonitor(data_size=step_size)
    loss_cb = LossCallBack(config.has_trained_epoch)

    cb = [time_cb, loss_cb]
    if algo:
        algo_cb = algo.callback()
        cb.append(algo_cb)
        cb.append(TemperatureScheduler(model, config.epoch_size, config.has_trained_epoch, config.t_start_val,
                                       config.t_start_time, config.t_end_time, config.t_factor))
    ckpt_save_dir = set_save_ckpt_dir()
    if config.save_checkpoint:
        ckpt_append_info = [{"epoch_num": config.has_trained_epoch, "step_num": config.has_trained_step}]
        config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
                                     keep_checkpoint_max=config.keep_checkpoint_max,
                                     append_info=ckpt_append_info)
        ckpt_cb = ModelCheckpoint(prefix="resnet", directory=ckpt_save_dir, config=config_ck)
        cb += [ckpt_cb]
    # train model
    dataset_sink_mode = target != "CPU"
    model.train(config.epoch_size - config.has_trained_epoch, dataset, callbacks=cb,
                sink_size=dataset.get_dataset_size(), dataset_sink_mode=dataset_sink_mode)


if __name__ == '__main__':
    train_net()