# coding=utf-8

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import os
import time

from tqdm import tqdm
from functools import partial

import torch
import torch.nn as nn
from torch.utils.data import DataLoader
from torch.cuda import amp

import numpy as np
import pandas as pd

import matplotlib.pyplot as plt

from torch_harmonics.examples.sfno import PdeDataset
from torch_harmonics.examples.sfno import SphericalFourierNeuralOperatorNet as SFNO

# wandb logging
import wandb
wandb.login()

def l2loss_sphere(solver, prd, tar, relative=False, squared=False):
    loss = solver.integrate_grid((prd - tar)**2, dimensionless=True).sum(dim=-1)
    if relative:
        loss = loss / solver.integrate_grid(tar**2, dimensionless=True).sum(dim=-1)
    
    if not squared:
        loss = torch.sqrt(loss)
    loss = loss.mean()

    return loss

def spectral_l2loss_sphere(solver, prd, tar, relative=False, squared=False):
    # compute coefficients
    coeffs = torch.view_as_real(solver.sht(prd - tar))
    coeffs = coeffs[..., 0]**2 + coeffs[..., 1]**2
    norm2 = coeffs[..., :, 0] + 2 * torch.sum(coeffs[..., :, 1:], dim=-1)
    loss = torch.sum(norm2, dim=(-1,-2))

    if relative:
        tar_coeffs = torch.view_as_real(solver.sht(tar))
        tar_coeffs = tar_coeffs[..., 0]**2 + tar_coeffs[..., 1]**2
        tar_norm2 = tar_coeffs[..., :, 0] + 2 * torch.sum(tar_coeffs[..., :, 1:], dim=-1)
        tar_norm2 = torch.sum(tar_norm2, dim=(-1,-2))
        loss = loss / tar_norm2

    if not squared:
        loss = torch.sqrt(loss)
    loss = loss.mean()

    return loss

def spectral_loss_sphere(solver, prd, tar, relative=False, squared=False):
    # gradient weighting factors
    lmax = solver.sht.lmax
    ls = torch.arange(lmax).float()
    spectral_weights = (ls*(ls + 1)).reshape(1, 1, -1, 1).to(prd.device)

    # compute coefficients
    coeffs = torch.view_as_real(solver.sht(prd - tar))
    coeffs = coeffs[..., 0]**2 + coeffs[..., 1]**2
    coeffs = spectral_weights * coeffs
    norm2 = coeffs[..., :, 0] + 2 * torch.sum(coeffs[..., :, 1:], dim=-1)
    loss = torch.sum(norm2, dim=(-1,-2))

    if relative:
        tar_coeffs = torch.view_as_real(solver.sht(tar))
        tar_coeffs = tar_coeffs[..., 0]**2 + tar_coeffs[..., 1]**2
        tar_coeffs = spectral_weights * tar_coeffs
        tar_norm2 = tar_coeffs[..., :, 0] + 2 * torch.sum(tar_coeffs[..., :, 1:], dim=-1)
        tar_norm2 = torch.sum(tar_norm2, dim=(-1,-2))
        loss = loss / tar_norm2

    if not squared:
        loss = torch.sqrt(loss)
    loss = loss.mean()

    return loss

def h1loss_sphere(solver, prd, tar, relative=False, squared=False):
    # gradient weighting factors
    lmax = solver.sht.lmax
    ls = torch.arange(lmax).float()
    spectral_weights = (ls*(ls + 1)).reshape(1, 1, -1, 1).to(prd.device)

    # compute coefficients
    coeffs = torch.view_as_real(solver.sht(prd - tar))
    coeffs = coeffs[..., 0]**2 + coeffs[..., 1]**2
    h1_coeffs = spectral_weights * coeffs
    h1_norm2 = h1_coeffs[..., :, 0] + 2 * torch.sum(h1_coeffs[..., :, 1:], dim=-1)
    l2_norm2 = coeffs[..., :, 0] + 2 * torch.sum(coeffs[..., :, 1:], dim=-1)
    h1_loss = torch.sum(h1_norm2, dim=(-1,-2))
    l2_loss = torch.sum(l2_norm2, dim=(-1,-2))

     # strictly speaking this is not exactly h1 loss 
    if not squared:
        loss = torch.sqrt(h1_loss) + torch.sqrt(l2_loss)
    else:
        loss = h1_loss + l2_loss

    if relative:
        raise NotImplementedError("Relative H1 loss not implemented")

    loss = loss.mean()


    return loss


def fluct_l2loss_sphere(solver, prd, tar, inp, relative=False, polar_opt=0):
    # compute the weighting factor first
    fluct = solver.integrate_grid((tar - inp)**2, dimensionless=True, polar_opt=polar_opt)
    weight = fluct / torch.sum(fluct, dim=-1, keepdim=True)
    # weight = weight.reshape(*weight.shape, 1, 1)
    
    loss = weight * solver.integrate_grid((prd - tar)**2, dimensionless=True, polar_opt=polar_opt)
    if relative:
        loss = loss / (weight * solver.integrate_grid(tar**2, dimensionless=True, polar_opt=polar_opt))
    loss = torch.mean(loss)
    return loss


def main(train=True, load_checkpoint=False, enable_amp=False):

    # set seed
    torch.manual_seed(333)
    torch.cuda.manual_seed(333)

    # set device
    device = torch.device('cuda:1' if torch.cuda.is_available() else 'cpu')
    if torch.cuda.is_available():
        torch.cuda.set_device(device.index)

    # 1 hour prediction steps
    dt = 1*3600
    dt_solver = 150
    nsteps = dt//dt_solver
    dataset = PdeDataset(dt=dt, nsteps=nsteps, dims=(256, 512), device=device, normalize=True)
    # There is still an issue with parallel dataloading. Do NOT use it at the moment     
    # dataloader = DataLoader(dataset, batch_size=4, shuffle=True, num_workers=4, persistent_workers=True)
    dataloader = DataLoader(dataset, batch_size=4, shuffle=True, num_workers=0, persistent_workers=False)
    solver = dataset.solver.to(device)

    nlat = dataset.nlat
    nlon = dataset.nlon

    # training function
    def train_model(model, dataloader, optimizer, gscaler, scheduler=None, nepochs=20, nfuture=0, num_examples=256, num_valid=8, loss_fn='l2'):

        train_start = time.time()

        for epoch in range(nepochs):

            # time each epoch
            epoch_start = time.time()

            dataloader.dataset.set_initial_condition('random')
            dataloader.dataset.set_num_examples(num_examples)

            # do the training
            acc_loss = 0
            model.train()

            for inp, tar in dataloader:
                
                with amp.autocast(enabled=enable_amp):

                    prd = model(inp)
                    for _ in range(nfuture):
                        prd = model(prd)

                    if loss_fn == 'l2':
                        loss = l2loss_sphere(solver, prd, tar, relative=False)
                    elif loss_fn == 'h1':
                        loss = h1loss_sphere(solver, prd, tar, relative=False)
                    elif loss_fn == 'spectral':
                        loss = spectral_loss_sphere(solver, prd, tar, relative=False)
                    elif loss_fn == 'fluct':
                        loss = fluct_l2loss_sphere(solver, prd, tar, inp, relative=True)
                    else:
                        raise NotImplementedError(f'Unknown loss function {loss_fn}')

                acc_loss += loss.item() * inp.size(0)

                optimizer.zero_grad(set_to_none=True)
                # gscaler.scale(loss).backward()
                gscaler.scale(loss).backward()
                gscaler.step(optimizer)
                gscaler.update()

            acc_loss = acc_loss / len(dataloader.dataset)

            dataloader.dataset.set_initial_condition('random')
            dataloader.dataset.set_num_examples(num_valid)

            # perform validation
            valid_loss = 0
            model.eval()
            with torch.no_grad():
                for inp, tar in dataloader:
                    prd = model(inp)
                    for _ in range(nfuture):
                        prd = model(prd)
                    loss = l2loss_sphere(solver, prd, tar, relative=True)

                    valid_loss += loss.item() * inp.size(0)

            valid_loss = valid_loss / len(dataloader.dataset)

            if scheduler is not None:
                scheduler.step(valid_loss)

            epoch_time = time.time() - epoch_start

            print(f'--------------------------------------------------------------------------------')
            print(f'Epoch {epoch} summary:')
            print(f'time taken: {epoch_time}')
            print(f'accumulated training loss: {acc_loss}')
            print(f'relative validation loss: {valid_loss}')

            if wandb.run is not None:
                current_lr = optimizer.param_groups[0]['lr']
                wandb.log({"loss": acc_loss, "validation loss": valid_loss, "learning rate": current_lr})


        train_time = time.time() - train_start

        print(f'--------------------------------------------------------------------------------')
        print(f'done. Training took {train_time}.')
        return valid_loss

    # rolls out the FNO and compares to the classical solver
    def autoregressive_inference(model, dataset, path_root, nsteps, autoreg_steps=10, nskip=1, plot_channel=0, nics=20):

        model.eval()

        losses = np.zeros(nics)
        fno_times = np.zeros(nics)
        nwp_times = np.zeros(nics)

        for iic in range(nics):
            ic = dataset.solver.random_initial_condition(mach=0.2)
            inp_mean = dataset.inp_mean
            inp_var = dataset.inp_var

            prd = (dataset.solver.spec2grid(ic) - inp_mean) / torch.sqrt(inp_var)
            prd = prd.unsqueeze(0)
            uspec = ic.clone()

            # ML model
            start_time = time.time()
            for i in range(1, autoreg_steps+1):
                # evaluate the ML model
                prd = model(prd)

                if iic == nics-1 and nskip > 0 and i % nskip == 0:

                    # do plotting
                    fig = plt.figure(figsize=(7.5, 6))
                    dataset.solver.plot_griddata(prd[0, plot_channel], fig, vmax=4, vmin=-4)
                    plt.savefig(path_root+'_pred_'+str(i//nskip)+'.png')
                    plt.clf()

            fno_times[iic] = time.time() - start_time

            # classical model
            start_time = time.time()
            for i in range(1, autoreg_steps+1):
                
                # advance classical model
                uspec = dataset.solver.timestep(uspec, nsteps)

                if iic == nics-1 and i % nskip == 0 and nskip > 0:
                    ref = (dataset.solver.spec2grid(uspec) - inp_mean) / torch.sqrt(inp_var)

                    fig = plt.figure(figsize=(7.5, 6))
                    dataset.solver.plot_griddata(ref[plot_channel], fig, vmax=4, vmin=-4)
                    plt.savefig(path_root+'_truth_'+str(i//nskip)+'.png')
                    plt.clf()

            nwp_times[iic] = time.time() - start_time

            # ref = (dataset.solver.spec2grid(uspec) - inp_mean) / torch.sqrt(inp_var)
            ref = dataset.solver.spec2grid(uspec)
            prd = prd * torch.sqrt(inp_var) + inp_mean
            losses[iic] = l2loss_sphere(solver, prd, ref, relative=True).item()
            

        return losses, fno_times, nwp_times

    def count_parameters(model):
        return sum(p.numel() for p in model.parameters() if p.requires_grad)

    # prepare dicts containing models and corresponding metrics
    models = {}
    metrics = {}

    # # U-Net if installed
    # from models.unet import UNet
    # models['unet_baseline'] = partial(UNet)

    # SFNO models
    models['sfno_sc3_layer4_edim256_linear']    = partial(SFNO, spectral_transform='sht', filter_type='linear', img_size=(nlat, nlon),
                                                     num_layers=4, scale_factor=3, embed_dim=256, operator_type='driscoll-healy')
    models['sfno_sc3_layer4_edim256_real']      = partial(SFNO, spectral_transform='sht', filter_type='non-linear', img_size=(nlat, nlon),
                                                     num_layers=4, scale_factor=3, embed_dim=256, complex_activation = 'real', operator_type='diagonal')
    # FNO models
    models['fno_sc3_layer4_edim256_linear']     = partial(SFNO, spectral_transform='fft', filter_type='linear', img_size=(nlat, nlon),
                                                     num_layers=4, scale_factor=3, embed_dim=256, operator_type='diagonal')
    models['fno_sc3_layer4_edim256_real']       = partial(SFNO, spectral_transform='fft', filter_type='non-linear', img_size=(nlat, nlon),
                                                     num_layers=4, scale_factor=3, embed_dim=256, complex_activation='real')

    # iterate over models and train each model
    root_path = os.path.dirname(__file__)
    for model_name, model_handle in models.items():

        model = model_handle().to(device)

        metrics[model_name] = {}

        num_params = count_parameters(model)
        print(f'number of trainable params: {num_params}')
        metrics[model_name]['num_params'] = num_params

        if load_checkpoint:
            model.load_state_dict(torch.load(os.path.join(root_path, 'checkpoints/'+model_name)))

        # run the training
        if train:
            run = wandb.init(project="sfno spherical swe", group=model_name, name=model_name + '_' + str(time.time()), config=model_handle.keywords)

            # optimizer:
            optimizer = torch.optim.Adam(model.parameters(), lr=1E-3)
            scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min')
            gscaler = amp.GradScaler(enabled=enable_amp)

            start_time = time.time()

            print(f'Training {model_name}, single step')
            train_model(model, dataloader, optimizer, gscaler, scheduler, nepochs=200, loss_fn='l2')

            # multistep training
            print(f'Training {model_name}, two step')
            optimizer = torch.optim.Adam(model.parameters(), lr=5E-5)
            scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min')
            gscaler = amp.GradScaler(enabled=enable_amp)
            dataloader.dataset.nsteps = 2 * dt//dt_solver
            train_model(model, dataloader, optimizer, gscaler, scheduler, nepochs=20, nfuture=1)
            dataloader.dataset.nsteps = 1 * dt//dt_solver

            training_time = time.time() - start_time

            run.finish()

            torch.save(model.state_dict(), os.path.join(root_path, 'checkpoints/'+model_name))

        # set seed
        torch.manual_seed(333)
        torch.cuda.manual_seed(333)

        with torch.inference_mode():
            losses, fno_times, nwp_times = autoregressive_inference(model, dataset, os.path.join(root_path,'paper_figures/'+model_name), nsteps=nsteps, autoreg_steps=10)
            metrics[model_name]['loss_mean'] = np.mean(losses)
            metrics[model_name]['loss_std'] = np.std(losses)
            metrics[model_name]['fno_time_mean'] = np.mean(fno_times)
            metrics[model_name]['fno_time_std'] = np.std(fno_times)
            metrics[model_name]['nwp_time_mean'] = np.mean(nwp_times)
            metrics[model_name]['nwp_time_std'] = np.std(nwp_times)
            if train:
                metrics[model_name]['training_time'] = training_time

    df = pd.DataFrame(metrics)
    df.to_pickle(os.path.join(root_path, 'output_data/metrics.pkl'))

if __name__ == "__main__":
    import torch.multiprocessing as mp
    mp.set_start_method('forkserver', force=True)

    main(train=True, load_checkpoint=False, enable_amp=False)
