import math

from argparse import ArgumentParser

from copy import deepcopy

from typing import Any

import pytorch_lightning as pl

import torch

import torch.nn as nn

import torch.nn.functional as F

from pytorch_lightning import seed_everything

from torch.optim import Adam

from pl_bolts.models.self_supervised import BYOL

# from pl_bolts.callbacks.self_supervised import BYOLMAWeightUpdate

from pl_bolts.optimizers.lars_scheduling import LARSWrapper

from pl_bolts.optimizers.lr_scheduler import LinearWarmupCosineAnnealingLR

from models.resnet_simclr import ResNetSimCLR

import re

import time

import yaml

import logging

import os

from clinical_ts.simclr_dataset_wrapper import SimCLRDataSetWrapper

from clinical_ts.create_logger import create_logger

import pickle

from pytorch_lightning import Trainer, seed_everything

from torch import nn

from torch.nn import functional as F

from online_evaluator import SSLOnlineEvaluator

from ecg_datamodule import ECGDataModule

from pytorch_lightning.loggers import TensorBoardLogger

import pdb

logger = create_logger(__name__)

method="byol"

def mean(res, key1, key2=None):

    if key2 is not None:

        return torch.stack([x[key1][key2] for x in res]).mean()

    return torch.stack([x[key1] for x in res if type(x) == dict and key1 in x.keys()]).mean()

class MLP(nn.Module):

    def __init__(self, input_dim=512, hidden_size=4096, output_dim=256):

        super().__init__()

        self.output_dim = output_dim

        self.input_dim = input_dim

        self.model = nn.Sequential(

            nn.Linear(input_dim, hidden_size, bias=False),

            nn.BatchNorm1d(hidden_size),

            nn.ReLU(inplace=True),

            nn.Linear(hidden_size, output_dim, bias=True))

    def forward(self, x):

        x = self.model(x)

        return x

class SiameseArm(nn.Module):

    def __init__(self, encoder=None, out_dim=128, hidden_size=512, projector_dim=512):

        super().__init__()

        if encoder is None:

            encoder = torchvision_ssl_encoder('resnet50')

        # Encoder

        self.encoder = encoder

        # Pooler

        self.pooler = nn.AdaptiveAvgPool2d((1, 1))

        # Projector

        projector_dim = encoder.l1.in_features

        self.projector = MLP(

            input_dim=projector_dim, hidden_size=hidden_size, output_dim=out_dim)

        # Predictor

        self.predictor = MLP(

            input_dim=out_dim, hidden_size=hidden_size, output_dim=out_dim)

    def forward(self, x):

        y = self.encoder(x)[0]

        y = y.view(y.size(0), -1)

        z = self.projector(y)

        h = self.predictor(z)

        return y, z, h

class BYOLMAWeightUpdate(pl.Callback):

    def __init__(self, initial_tau=0.996):

        """

        Args:

            initial_tau: starting tau. Auto-updates with every training step

        """

        super().__init__()

        self.initial_tau = initial_tau

        self.current_tau = initial_tau

    def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):

        # get networks

        online_net = pl_module.online_network

        target_net = pl_module.target_network

        # update weights

        self.update_weights(online_net, target_net)

        # update tau after

        self.current_tau = self.update_tau(pl_module, trainer)

    def update_tau(self, pl_module, trainer):

        max_steps = len(trainer.train_dataloader) * trainer.max_epochs

        tau = 1 - (1 - self.initial_tau) * (math.cos(math.pi *

                                                     pl_module.global_step / max_steps) + 1) / 2

        return tau

    def update_weights(self, online_net, target_net):

        # apply MA weight update

        for (name, online_p), (_, target_p) in zip(online_net.named_parameters(), target_net.named_parameters()):

            if 'weight' in name:

                target_p.data = self.current_tau * target_p.data + \

                    (1 - self.current_tau) * online_p.data

class CustomBYOL(pl.LightningModule):

    def __init__(self,

                 num_classes=5,

                 learning_rate: float = 0.2,

                 weight_decay: float = 1.5e-6,

                 input_height: int = 32,

                 batch_size: int = 32,

                 num_workers: int = 0,

                 warmup_epochs: int = 10,

                 max_epochs: int = 1000,

                 config=None,

                 transformations=None,

                 **kwargs):

        """

        Args:

            datamodule: The datamodule

            learning_rate: the learning rate

            weight_decay: optimizer weight decay

            input_height: image input height

            batch_size: the batch size

            num_workers: number of workers

            warmup_epochs: num of epochs for scheduler warm up

            max_epochs: max epochs for scheduler

        """

        super().__init__()

        self.save_hyperparameters()

        self.config = config

        self.transformations = transformations

        self.online_network = SiameseArm(

            encoder=self.init_model(), out_dim=config["model"]["out_dim"])

        self.target_network = deepcopy(self.online_network)

        self.weight_callback = BYOLMAWeightUpdate()

        self.log_dict = {}

        self.epoch = 0

        # self.model_device = self.online_network.encoder.features[0][0].weight.device

    def init_model(self):

        model = ResNetSimCLR(**self.config["model"])

        # return model.features

        return model

    # def on_train_batch_end(self, outputs, batch: Any, batch_idx: int, dataloader_idx: int) -> None:

    def on_train_batch_end(self, outputs, batch: Any, batch_idx: int, dataloader_idx: int) -> None:

        # Add callback for user automatically since it's key to BYOL weight update

        self.weight_callback.on_train_batch_end(

            self.trainer, self, outputs, batch, batch_idx, 0)

    def forward(self, x):

        y, _, _ = self.online_network(x)

        return y

    def cosine_similarity(self, a, b):

        a = F.normalize(a, dim=-1)

        b = F.normalize(b, dim=-1)

        sim = (a * b).sum(-1).mean()

        return sim

    def shared_step(self, batch, batch_idx):

        # (img_1, img_2), y = batch

        (img_1, y1), (img_2, y2) = batch

        img_1 = self.to_device(img_1)

        img_2 = self.to_device(img_2)

        # Image 1 to image 2 loss

        y1, z1, h1 = self.online_network(img_1)

        with torch.no_grad():

            y2, z2, h2 = self.target_network(img_2)

        loss_a = - 2 * self.cosine_similarity(h1, z2)

        # Image 2 to image 1 loss

        y1, z1, h1 = self.online_network(img_2)

        with torch.no_grad():

            y2, z2, h2 = self.target_network(img_1)

        # L2 normalize

        loss_b = - 2 * self.cosine_similarity(h1, z2)

        # Final loss

        total_loss = loss_a + loss_b

        return loss_a, loss_b, total_loss

    def training_step(self, batch, batch_idx):

        loss_a, loss_b, total_loss = self.shared_step(batch, batch_idx)

        # log results

        # result = pl.TrainResult(minimize=total_loss)

        # result.log('train_loss/1_2_loss', loss_a, on_epoch=True)

        # result.log('train_loss/2_1_loss', loss_b, on_epoch=True)

        # result.log('train_loss/total_loss', total_loss, on_epoch=True)

        # # log results

        # self.log_dict({'1_2_loss': loss_a, '2_1_loss': loss_b,

        #               'train_loss': total_loss})

        return total_loss

    def validation_step(self, batch, batch_idx, dataloader_idx):

        if dataloader_idx != 0:

            return {}

        loss_a, loss_b, total_loss = self.shared_step(batch, batch_idx)

        # # log results

        # result = pl.EvalResult()

        # result.log('val_loss/1_2_loss', loss_a, on_epoch=True)

        # result.log('val_loss/2_1_loss', loss_b, on_epoch=True)

        # result.log('val_loss/total_loss', total_loss, on_epoch=True)

        # self.log_dict({'1_2_loss': loss_a, '2_1_loss': loss_b,

        #               'train_loss': total_loss})

        results = {

            'val_loss': total_loss,

            'val_1_2_loss' : loss_a,

            'val_2_1_loss': loss_b

        }

        return results

    def validation_epoch_end(self, outputs):

        # outputs[0] because we are using multiple datasets!

        val_loss = mean(outputs[0], 'val_loss')

        loss_a = mean(outputs[0], 'val_1_2_loss')

        loss_b = mean(outputs[0], 'val_2_1_loss')

        log = {

            'val_loss': val_loss,

            'val_1_2_loss' : loss_a,

            'val_2_1_loss': loss_b

        }

        return {'val_loss': val_loss, 'log': log, 'progress_bar': log}

    def configure_optimizers(self):

        optimizer = Adam(self.parameters(), lr=self.hparams.learning_rate,

                         weight_decay=self.hparams.weight_decay)

        # optimizer = LARSWrapper(optimizer)

        optimizer = optimizer

        scheduler = LinearWarmupCosineAnnealingLR(

            optimizer,

            warmup_epochs=self.hparams.warmup_epochs,

            max_epochs=self.hparams.max_epochs

        )

        return [optimizer], [scheduler]

    def on_train_start(self):

        # log configuration

        config_str = re.sub(r"[,\}\{]", "<br/>", str(self.config))

        config_str = re.sub(r"[\[\]\']", "", config_str)

        transformation_str = re.sub(r"[\}]", "<br/>", str(["<br>" + str(

            t) + ":<br/>" + str(t.get_params()) for t in self.transformations]))

        transformation_str = re.sub(r"[,\"\{\'\[\]]", "", transformation_str)

        self.logger.experiment.add_text(

            "configuration", str(config_str), global_step=0)

        self.logger.experiment.add_text("transformations", str(

            transformation_str), global_step=0)

        self.epoch = 0

    def on_epoch_end(self):

        self.epoch += 1

    def get_representations(self, x):

        return self.online_network(x)[0]

    def get_model(self):

        return self.online_network.encoder

    def get_device(self):

        return self.online_network.encoder.features[0][0].weight.device

    def to_device(self, x):

        return x.type(self.type()).to(self.get_device())

    def type(self):

        return self.online_network.encoder.features[0][0].weight.type()

def parse_args(parent_parser):

    parser = ArgumentParser(parents=[parent_parser], add_help=False)

    parser.add_argument('-t', '--trafos', nargs='+', help='add transformation to data augmentation pipeline',

                        default=["GaussianNoise", "ChannelResize", "RandomResizedCrop"])

    # GaussianNoise

    parser.add_argument(

            '--gaussian_scale', help='std param for gaussian noise transformation', default=0.005, type=float)

    # RandomResizedCrop

    parser.add_argument('--rr_crop_ratio_range',

                            help='ratio range for random resized crop transformation', default=[0.5, 1.0], type=float)

    parser.add_argument(

            '--output_size', help='output size for random resized crop transformation', default=250, type=int)

    # DynamicTimeWarp

    parser.add_argument(

            '--warps', help='number of warps for dynamic time warp transformation', default=3, type=int)

    parser.add_argument(

            '--radius', help='radius of warps of dynamic time warp transformation', default=10, type=int)

    # TimeWarp

    parser.add_argument(

            '--epsilon', help='epsilon param for time warp', default=10, type=float)

    # ChannelResize

    parser.add_argument('--magnitude_range', nargs='+',

                            help='range for scale param for ChannelResize transformation', default=[0.5, 2], type=float)

    # Downsample

    parser.add_argument(

            '--downsample_ratio', help='downsample ratio for Downsample transformation', default=0.2, type=float)

    # TimeOut

    parser.add_argument('--to_crop_ratio_range', nargs='+',

                            help='ratio range for timeout transformation', default=[0.2, 0.4], type=float)

    # resume training

    parser.add_argument('--resume', action='store_true')

    parser.add_argument(

            '--gpus', help='number of gpus to use; use cpu if gpu=0', type=int, default=1)

    parser.add_argument(

            '--num_nodes', default=1, help='number of cluster nodes', type=int)

    parser.add_argument(

            '--distributed_backend', help='sets backend type')

    parser.add_argument('--batch_size', type=int)

    parser.add_argument('--epochs', type=int)

    parser.add_argument('--debug', action='store_true')

    parser.add_argument('--warm_up', default=1, type=int)

    parser.add_argument('--precision', type=int)

    parser.add_argument('--datasets', dest="target_folders",

                            nargs='+', help='used datasets for pretraining')

    parser.add_argument('--log_dir', default="./experiment_logs")

    parser.add_argument(

            '--percentage', help='determines how much of the dataset shall be used during the pretraining', type=float, default=1.0)

    parser.add_argument('--lr', type=float, help="learning rate")

    parser.add_argument('--out_dim', type=int, help="output dimension of model")

    parser.add_argument('--filter_cinc', default=False, action="store_true", help="only valid if cinc is selected: filter out the ptb data")

    parser.add_argument('--base_model')

    parser.add_argument('--widen',type=int, help="use wide xresnet1d50")

    parser.add_argument('--run_callbacks', default=False, action="store_true", help="run callbacks which asses linear evaluaton and finetuning metrics during pretraining")

    parser.add_argument('--checkpoint_path', default="")

    return parser

def init_logger(config):

    level = logging.INFO

    if config['debug']:

        level = logging.DEBUG

    # remove all handlers to change basic configuration

    for handler in logging.root.handlers[:]:

        logging.root.removeHandler(handler)

    if not os.path.isdir(config['log_dir']):

        os.mkdir(config['log_dir'])

    logging.basicConfig(filename=os.path.join(config['log_dir'], 'info.log'), level=level,

                        format='%(asctime)s %(name)s:%(lineno)s %(levelname)s:  %(message)s  ')

    return logging.getLogger(__name__)

def pretrain_routine(args):

    t_params = {"gaussian_scale": args.gaussian_scale, "rr_crop_ratio_range": args.rr_crop_ratio_range, "output_size": args.output_size, "warps": args.warps, "radius": args.radius,

                "epsilon": args.epsilon, "magnitude_range": args.magnitude_range, "downsample_ratio": args.downsample_ratio, "to_crop_ratio_range": args.to_crop_ratio_range,

                "bw_cmax":0.1, "em_cmax":0.5, "pl_cmax":0.2, "bs_cmax":1}

    transformations = args.trafos

    checkpoint_config = os.path.join("checkpoints", "bolts_config.yaml")

    config_file = checkpoint_config if args.resume and os.path.isfile(

        checkpoint_config) else "bolts_config.yaml"

    config = yaml.load(open(config_file, "r"), Loader=yaml.FullLoader)

    args_dict = vars(args)

    for key in set(config.keys()).union(set(args_dict.keys())):

        config[key] = config[key] if (key not in args_dict.keys() or key in args_dict.keys(

        ) and key in config.keys() and args_dict[key] is None) else args_dict[key]

    if args.target_folders is not None:

        config["dataset"]["target_folders"] = args.target_folders

    config["dataset"]["percentage"] = args.percentage if args.percentage is not None else config["dataset"]["percentage"]

    config["dataset"]["filter_cinc"] = args.filter_cinc if args.filter_cinc is not None else config["dataset"]["filter_cinc"]

    config["model"]["base_model"] = args.base_model if args.base_model is not None else config["model"]["base_model"]

    config["model"]["widen"] = args.widen if args.widen is not None else config["model"]["widen"]

    if args.out_dim is not None:

        config["model"]["out_dim"] = args.out_dim

    init_logger(config)

    dataset = SimCLRDataSetWrapper(

        config['batch_size'], **config['dataset'], transformations=transformations, t_params=t_params)

    for i, t in enumerate(dataset.transformations):

        logger.info(str(i) + ". Transformation: " +

                    str(t) + ": " + str(t.get_params()))

    date = time.asctime()

    label_to_num_classes = {"label_all": 71, "label_diag": 44, "label_form": 19,

                            "label_rhythm": 12, "label_diag_subclass": 23, "label_diag_superclass": 5}

    ptb_num_classes = label_to_num_classes[config["eval_dataset"]

                                           ["ptb_xl_label"]]

    abr = {"Transpose": "Tr", "TimeOut": "TO", "DynamicTimeWarp": "DTW", "RandomResizedCrop": "RRC", "ChannelResize": "ChR", "GaussianNoise": "GN",

           "TimeWarp": "TW", "ToTensor": "TT", "GaussianBlur": "GB", "BaselineWander": "BlW", "PowerlineNoise": "PlN", "EMNoise": "EM", "BaselineShift": "BlS"}

    trs = re.sub(r"[,'\]\[]", "", str([abr[str(tr)] if abr[str(tr)] not in [

                 "TT", "Tr"] else '' for tr in dataset.transformations]))

    name = str(date) + "_" + method + "_" + str(

        time.time_ns())[-3:] + "_" + trs[1:]

    tb_logger = TensorBoardLogger(args.log_dir, name=name, version='')

    config["log_dir"] = os.path.join(args.log_dir, name)

    print(config)

    return config, dataset, date, transformations, t_params, ptb_num_classes, tb_logger

def aftertrain_routine(config, args, trainer, pl_model, datamodule, callbacks):

    scores = {}

    for ca in callbacks:

        if isinstance(ca, SSLOnlineEvaluator):

            scores[str(ca)] = {"macro": ca.best_macro}

    results = {"config": config, "trafos": args.trafos, "scores": scores}

    with open(os.path.join(config["log_dir"], "results.pkl"), 'wb') as handle:

        pickle.dump(results, handle)

    trainer.save_checkpoint(os.path.join(config["log_dir"], "checkpoints", "model.ckpt"))

    with open(os.path.join(config["log_dir"], "config.txt"), "w") as text_file:

        print(config, file=text_file)

def cli_main():

    from pytorch_lightning import Trainer

    from online_evaluator import SSLOnlineEvaluator

    from ecg_datamodule import ECGDataModule

    from clinical_ts.create_logger import create_logger

    from os.path import exists

    parser = ArgumentParser()

    parser = parse_args(parser)

    logger.info("parse arguments")

    args = parser.parse_args()

    config, dataset, date, transformations, t_params, ptb_num_classes, tb_logger = pretrain_routine(args)

    # data

    ecg_datamodule = ECGDataModule(config, transformations, t_params)

    callbacks = []

    if args.run_callbacks:

            # callback for online linear evaluation/fine-tuning

        linear_evaluator = SSLOnlineEvaluator(drop_p=0,

                                          z_dim=512, num_classes=ptb_num_classes, hidden_dim=None, lin_eval_epochs=config["eval_epochs"], eval_every=config["eval_every"], mode="linear_evaluation", verbose=False)

        fine_tuner = SSLOnlineEvaluator(drop_p=0,

                                          z_dim=512, num_classes=ptb_num_classes, hidden_dim=None, lin_eval_epochs=config["eval_epochs"], eval_every=config["eval_every"], mode="fine_tuning", verbose=False)

        callbacks.append(linear_evaluator)

        callbacks.append(fine_tuner)

    # configure trainer

    trainer = Trainer(logger=tb_logger, max_epochs=config["epochs"], gpus=args.gpus,

                      distributed_backend=args.distributed_backend, auto_lr_find=False, num_nodes=args.num_nodes, precision=config["precision"], callbacks=callbacks)

    # pytorch lightning module

    pl_model = CustomBYOL(5, learning_rate=config["lr"], weight_decay=eval(config["weight_decay"]),

                              warm_up_epochs=config["warm_up"], max_epochs=config[

                                  "epochs"], num_workers=config["dataset"]["num_workers"],

                              batch_size=config["batch_size"], config=config, transformations=ecg_datamodule.transformations)

    # load checkpoint

    if args.checkpoint_path != "":

        if exists(args.checkpoint_path):

            logger.info("Retrieve checkpoint from " + args.checkpoint_path)

            pl_model.load_from_checkpoint(args.checkpoint_path)

        else:

            raise("checkpoint does not exist")

    # start training

    trainer.fit(pl_model, ecg_datamodule)

    aftertrain_routine(config, args, trainer, pl_model, ecg_datamodule, callbacks)

if __name__ == "__main__":

    cli_main()