import os

import torch

import torch.optim as optim

from torch.nn import CrossEntropyLoss

from torch.nn import functional as F

from torch.optim import Adam

from torch.utils.data import DataLoader

os.environ["WANDB_API_KEY"] = "KEY"

os.environ["WANDB_MODE"] = 'offline'

from itertools import combinations

import clip

import matplotlib.pyplot as plt

import numpy as np

import torch.nn as nn

import torchvision.transforms as transforms

import tqdm

from BrainAligning_retrieval.eegdatasets_leaveone import EEGDataset

from einops.layers.torch import Rearrange, Reduce

from lavis.models.clip_models.loss import ClipLoss

from sklearn.metrics import confusion_matrix

from torch.utils.data import DataLoader, Dataset

import random

from utils import wandb_logger

import csv

class PositionalEncoding(nn.Module):

    def __init__(self, d_model, max_len=5000):

        super(PositionalEncoding, self).__init__()

        pe = torch.zeros(max_len, d_model)

        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)

        div_term = torch.exp(torch.arange(0, d_model + 1, 2).float() * (-math.log(10000.0) / d_model))

        pe[:, 0::2] = torch.sin(position * div_term[:d_model // 2 + 1])

        pe[:, 1::2] = torch.cos(position * div_term[:d_model // 2])

        self.register_buffer('pe', pe)

    def forward(self, x):

        pe = self.pe[:x.size(0), :].unsqueeze(1).repeat(1, x.size(1), 1)

        x = x + pe

        return x

class EEGAttention(nn.Module):

    def __init__(self, channel, d_model, nhead):

        super(EEGAttention, self).__init__()

        self.pos_encoder = PositionalEncoding(d_model)

        self.encoder_layer = nn.TransformerEncoderLayer(d_model=d_model, nhead=nhead)

        self.transformer_encoder = nn.TransformerEncoder(self.encoder_layer, num_layers=1)

        self.channel = channel

        self.d_model = d_model

    def forward(self, src):

        src = src.permute(2, 0, 1)  # Change shape to [time_length, batch_size, channel]

        src = self.pos_encoder(src)

        output = self.transformer_encoder(src)

        return output.permute(1, 2, 0)  # Change shape back to [batch_size, channel, time_length]

class PatchEmbedding(nn.Module):

    def __init__(self, emb_size=40):

        super().__init__()

        # revised from shallownet

        self.shape = (63, 250)

        self.tsconv = EEGNetv4(

            in_chans=self.shape[0],

            n_classes=1440,   

            input_window_samples=self.shape[1],

            final_conv_length='auto',

            pool_mode='mean',

            F1=8,

            D=20,

            F2=160,

            kernel_length=4,

            third_kernel_size=(4, 2),

            drop_prob=0.25

        )

    def forward(self, x: Tensor) -> Tensor:

        x = x.unsqueeze(3)     

        # print("x", x.shape)   

        x = self.tsconv(x)

        return x

class ResidualAdd(nn.Module):

    def __init__(self, fn):

        super().__init__()

        self.fn = fn

    def forward(self, x, **kwargs):

        res = x

        x = self.fn(x, **kwargs)

        x += res

        return x

class FlattenHead(nn.Sequential):

    def __init__(self):

        super().__init__()

    def forward(self, x):

        x = x.contiguous().view(x.size(0), -1)

        return x

class Enc_eeg(nn.Sequential):

    def __init__(self, emb_size=40, **kwargs):

        super().__init__(

            PatchEmbedding(emb_size),

            FlattenHead()

        )

class Proj_eeg(nn.Sequential):

    def __init__(self, embedding_dim=1440, proj_dim=1024, drop_proj=0.5):

        super().__init__(

            nn.Linear(embedding_dim, proj_dim),

            ResidualAdd(nn.Sequential(

                nn.GELU(),

                nn.Linear(proj_dim, proj_dim),

                nn.Dropout(drop_proj),

            )),

            nn.LayerNorm(proj_dim),

        )

class ATM_E(nn.Module):    

    def __init__(self, num_channels=63, sequence_length=250, num_subjects=1, num_features=64, num_latents=1024, num_blocks=1):

        super(ATM_E, self).__init__()

        self.attention_model = EEGAttention(num_channels, num_channels, nhead=1)   

        self.subject_wise_linear = nn.ModuleList([nn.Linear(sequence_length, sequence_length) for _ in range(num_subjects)])

        self.enc_eeg = Enc_eeg()

        self.proj_eeg = Proj_eeg()        

        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))

        self.loss_func = ClipLoss()       

    def forward(self, x):

        x = self.attention_model(x)

        # print(f'After attention shape: {x.shape}')

        x = self.subject_wise_linear[0](x)

        # print(f'After subject-specific linear transformation shape: {x.shape}')

        eeg_embedding = self.enc_eeg(x)

        # print(f'After enc_eeg shape: {eeg_embedding.shape}')

        out = self.proj_eeg(eeg_embedding)

        return out  

def train_model(model, dataloader, optimizer, device, text_features_all, img_features_all):

    model.train()

    text_features_all = text_features_all.to(device).float() # (n_cls, d)

    img_features_all = (img_features_all[::10]).to(device).float()

    total_loss = 0

    correct = 0

    total = 0

    alpha=0.99

    features_list = []  # List to store features

    save_features= True

    for batch_idx, (eeg_data, labels, text, text_features, img, img_features) in enumerate(dataloader):

        eeg_data = eeg_data.to(device)

        text_features = text_features.to(device).float()

        img_features = img_features.to(device).float()

        labels = labels.to(device)

        optimizer.zero_grad()

        eeg_features = model(eeg_data).float()

        features_list.append(eeg_features)

        logit_scale = model.logit_scale

        img_loss = model.loss_func(eeg_features, img_features, logit_scale)

        text_loss = model.loss_func(eeg_features, text_features, logit_scale)

        # loss = img_loss + text_loss

        # print("text_loss", text_loss)

        # print("img_loss", img_loss)

        loss = alpha * img_loss + (1 - alpha) * text_loss

        loss.backward()

        optimizer.step()

        total_loss += loss.item()

        # logits = logit_scale * eeg_features @ text_features_all.T # (n_batch, n_cls)

        logits_img = logit_scale * eeg_features @ img_features_all.T

        # logits_text = logit_scale * eeg_features @ text_features_all.T

        # logits_single = (logits_text + logits_img) / 2.0        

        # logits_text = logit_scale * eeg_features @ text_features_all.T

        logits_single = logits_img

        predicted = torch.argmax(logits_single, dim=1) # (n_batch, ) \in {0, 1, ..., n_cls-1}

        batch_size = predicted.shape[0]

        total += batch_size

        correct += (predicted == labels).sum().item()

    average_loss = total_loss / (batch_idx+1)

    accuracy = correct / total

    return average_loss, accuracy

def evaluate_model(model, dataloader, device, text_features_all, img_features_all, k):

    model.eval()

    text_features_all = text_features_all.to(device).float()

    img_features_all = img_features_all.to(device).float()

    total_loss = 0

    correct = 0

    total = 0

    alpha = 0.99

    top5_correct = 0

    top5_correct_count = 0

    all_labels = set(range(text_features_all.size(0)))

    top5_acc = 0

    with torch.no_grad():

        for batch_idx, (eeg_data, labels, text, text_features, img, img_features) in enumerate(dataloader):

            eeg_data = eeg_data.to(device)

            text_features = text_features.to(device).float()

            labels = labels.to(device)

            img_features = img_features.to(device).float()

            eeg_features = model(eeg_data).float()

            logit_scale = model.logit_scale 

            # print(eeg_features.type, text_features.type, img_features.type)

            img_loss = model.loss_func(eeg_features, img_features, logit_scale)

            text_loss = model.loss_func(eeg_features, text_features, logit_scale)

            loss = img_loss*alpha + text_loss*(1-alpha)

            total_loss += loss.item()

            for idx, label in enumerate(labels):

                possible_classes = list(all_labels - {label.item()})

                selected_classes = random.sample(possible_classes, k-1) + [label.item()]

                # selected_text_features = text_features_all[selected_classes]

                selected_img_features = img_features_all[selected_classes]

                if k==200:

                    logits_img = logit_scale * eeg_features[idx] @ selected_img_features.T

                    # logits_text = logit_scale * eeg_features[idx] @ selected_text_features.T

                    # logits_single = (logits_text + logits_img) / 2.0

                    logits_single = logits_img

                    # print("logits_single", logits_single.shape)

                    # predicted_label = selected_classes[torch.argmax(logits_single).item()]

                    predicted_label = selected_classes[torch.argmax(logits_single).item()] # (n_batch, ) \in {0, 1, ..., n_cls-1}

                    if predicted_label == label.item():

                        # print("predicted_label", predicted_label)

                        correct += 1

                    # print("logits_single", logits_single)

                    _, top5_indices = torch.topk(logits_single, 5, largest =True)

                    if label.item() in [selected_classes[i] for i in top5_indices.tolist()]:     

                        # print("top5_indices", top5_indices)

                        # print("Yes")               

                        top5_correct_count+=1     

                    # print("*"*50)                               

                    total += 1

                elif k==2 or k==4 or k==10:

                    logits_img = logit_scale * eeg_features[idx] @ selected_img_features.T

                    # logits_text = logit_scale * eeg_features[idx] @ selected_text_features.T

                    # logits_single = (logits_text + logits_img) / 2.0

                    logits_single = logits_img

                    # print("logits_single", logits_single.shape)

                    # predicted_label = selected_classes[torch.argmax(logits_single).item()]

                    predicted_label = selected_classes[torch.argmax(logits_single).item()] # (n_batch, ) \in {0, 1, ..., n_cls-1}

                    if predicted_label == label.item():

                        correct += 1

                    total += 1

                else:

                    print("Error.")

    average_loss = total_loss / (batch_idx+1)

    accuracy = correct / total

    top5_acc = top5_correct_count / total

    return average_loss, accuracy, top5_acc

def main_train_loop(sub, model, train_dataloader, test_dataloader, optimizer, device, 

                    text_features_train_all, text_features_test_all, img_features_train_all, img_features_test_all, config, logger=None):

    logger = wandb_logger(config) if logger else None

    logger.watch(model,logger) 

    train_losses, train_accuracies = [], []

    test_losses, test_accuracies = [], []

    v2_accs = []

    v4_accs = []

    v10_accs = []

    best_accuracy = 0.0

    best_model_weights = None

    best_epoch_info = {}

    results = []

    for epoch in range(config['epochs']):

        train_loss, train_accuracy = train_model(model, train_dataloader, optimizer, device, text_features_train_all, img_features_train_all)

        if epoch%5 == 0:                        

            if config['insubject']==True:                

                torch.save(model.state_dict(), f"./models/{sub}_{epoch}.pth")

            else:

                torch.save(model.state_dict(), f"./models/across_{epoch}.pth")

        train_losses.append(train_loss)

        train_accuracies.append(train_accuracy)

        test_loss, test_accuracy, top5_acc = evaluate_model(model, test_dataloader, device, text_features_test_all, img_features_test_all,k=200)

        _, v2_acc, _ = evaluate_model(model, test_dataloader, device, text_features_test_all, img_features_test_all, k = 2)

        _, v4_acc, _ = evaluate_model(model, test_dataloader, device, text_features_test_all, img_features_test_all, k = 4)

        _, v10_acc, _ = evaluate_model(model, test_dataloader, device, text_features_test_all, img_features_test_all, k = 10)

        test_losses.append(test_loss)

        test_accuracies.append(test_accuracy)

        v2_accs.append(v2_acc)

        v4_accs.append(v4_acc)

        v10_accs.append(v10_acc)

        # Append results for this epoch

        epoch_results = {

        "epoch": epoch + 1,

        "train_loss": train_loss,

        "train_accuracy": train_accuracy,

        "test_loss": test_loss,

        "test_accuracy": test_accuracy,

        "v2_acc": v2_acc,

        "v4_acc": v4_acc,

        "v10_acc": v10_acc,

        "top5_acc":top5_acc

        }

        results.append(epoch_results)

        if test_accuracy > best_accuracy:

            best_accuracy = test_accuracy

            best_model_weights = model.state_dict().copy()

            best_epoch_info = {

                "epoch": epoch + 1,

                "train_loss": train_loss,

                "train_accuracy": train_accuracy,

                "test_loss": test_loss,

                "test_accuracy": test_accuracy,

                "v2_acc":v2_acc,

                "v4_acc":v4_acc,

                "v10_acc":v10_acc

            }

        logger.log({

            "Train Loss": train_loss,

            "Train Accuracy": train_accuracy,

            "Test Loss": test_loss,

            "Test Accuracy": test_accuracy,

            "v2 Accuracy": v2_acc,

            "v4 Accuracy": v4_acc,

            "v10 Accuracy": v10_acc,

            "Epoch": epoch

        })

        print(f"Epoch {epoch + 1}/{config['epochs']} - Train Loss: {train_loss:.4f}, Train Accuracy: {train_accuracy:.4f}, Test Loss: {test_loss:.4f}, Test Accuracy: {test_accuracy:.4f}, Top5 Accuracy: {top5_acc:.4f}")

        print(f"Epoch {epoch + 1}/{config['epochs']} - v2 Accuracy:{v2_acc} - v4 Accuracy:{v4_acc} - v10 Accuracy:{v10_acc}")

    # model.load_state_dict(best_model_weights)

    # torch.save(model.state_dict(), '{train_pos_img_text}.pth')

    fig, axs = plt.subplots(3, 2, figsize=(10, 15))

    axs[0, 0].plot(train_losses, label='Train Loss')

    axs[0, 0].plot(test_losses, label='Test Loss')

    axs[0, 0].legend()

    axs[0, 0].set_title("Loss Curve")

    axs[0, 1].plot(train_accuracies, label='Train Accuracy')

    axs[0, 1].plot(test_accuracies, label='Test Accuracy')

    axs[0, 1].legend()

    axs[0, 1].set_title("Accuracy Curve")

    axs[1, 0].plot(v2_accs, label='2-class Accuracy')

    axs[1, 0].legend()

    axs[1, 0].set_title("2-Class Accuracy Curve")

    axs[1, 1].plot(v4_accs, label='4-class Accuracy')

    axs[1, 1].legend()

    axs[1, 1].set_title("4-Class Accuracy Curve")

    axs[2, 0].plot(v10_accs, label='10-class Accuracy')

    axs[2, 0].legend()

    axs[2, 0].set_title("10-Class Accuracy Curve")

    info_text = (f"Best Model Info (from Epoch {best_epoch_info['epoch']}):\n"

                f"Train Loss: {best_epoch_info['train_loss']:.4f}\n"

                f"Train Accuracy: {best_epoch_info['train_accuracy']:.4f}\n"

                f"Test Loss: {best_epoch_info['test_loss']:.4f}\n"

                f"Test Accuracy: {best_epoch_info['test_accuracy']:.4f}\n"

                f"v2_acc:{best_epoch_info['v2_acc']:.4f}\n"

                f"v4_acc:{best_epoch_info['v4_acc']:.4f}\n"

                f"v10_acc:{best_epoch_info['v10_acc']:.4f}")

    axs[2, 1].axis('off')  

    axs[2, 1].text(0.5, 0.5, info_text, fontsize=10, ha='center', va='center', transform=axs[2, 1].transAxes)

    plt.tight_layout()

    plt.suptitle('pos_img_text', fontsize=16, y=1.05)

    plt.savefig('pos_img_text')

    logger.finish()

    return results

def main():

    parser = argparse.ArgumentParser(description='Train EEG-Image/Text Model')

    parser.add_argument('--data_path', type=str, default="/home/ldy/Workspace/THINGS/Preprocessed_data_250Hz", help='Path to the preprocessed data')

    parser.add_argument('--project', type=str, default="train_pos_img_text_rep", help='Project name')

    parser.add_argument('--entity', type=str, default="sustech_rethinkingbci", help='Entity name')

    parser.add_argument('--name', type=str, default="lr=3e-4_img_pos_pro_eeg", help='Experiment name')

    parser.add_argument('--lr', type=float, default=3e-4, help='Learning rate')

    parser.add_argument('--epochs', type=int, default=40, help='Number of training epochs')

    parser.add_argument('--batch_size', type=int, default=1024, help='Batch size')

    parser.add_argument('--logger', action='store_true', help='Enable logging')

    parser.add_argument('--insubject', action='store_true', help='Train within subject')

    parser.add_argument('--encoder_type', type=str, default='ATM_E', help='EEG encoder model type')

    parser.add_argument('--device', type=str, default='cuda:0', help='Device to use for training (e.g., "cuda:0" or "cpu")')

    args = parser.parse_args()

    device = torch.device(args.device if torch.cuda.is_available() else 'cpu')

    data_path = args.data_path

    subjects = ['sub-01', 'sub-02', 'sub-03', 'sub-04', 'sub-05', 'sub-06', 'sub-07', 'sub-08', 'sub-09', 'sub-10']

    for sub in subjects:

        # Re-initialize the model for each subject

        model = globals()[args.encoder_type]((63, 250))

        model.to(device)

        optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr)

        print(f'Processing {sub}: number of parameters:', sum(p.numel() for p in model.parameters()))

        train_dataset = EEGDataset(

            data_path,

            subjects=[sub] if args.insubject else [],

            exclude_subject=sub if not args.insubject else None,

            train=True

        )

        test_dataset = EEGDataset(

            data_path,

            subjects=[sub] if args.insubject else [],

            exclude_subject=sub if not args.insubject else None,

            train=False

        )

        train_loader = DataLoader(

            train_dataset,

            batch_size=args.batch_size,

            shuffle=True,

            num_workers=0,

            drop_last=True

        )

        test_loader = DataLoader(

            test_dataset,

            batch_size=1,

            shuffle=True,

            num_workers=0,

            drop_last=True

        )

        text_features_train_all = train_dataset.text_features

        text_features_test_all = test_dataset.text_features

        img_features_train_all = train_dataset.img_features

        img_features_test_all = test_dataset.img_features

        config = vars(args)

        results = main_train_loop(

            sub,

            model,

            train_loader,

            test_loader,

            optimizer,

            device,

            text_features_train_all,

            text_features_test_all,

            img_features_train_all,

            img_features_test_all,

            config,

            logger=args.logger

        )

        # Save results to a CSV file

        current_time = datetime.datetime.now().strftime("%m-%d_%H-%M")

        results_dir = f"./outputs/{args.encoder_type}/{sub}/{current_time}"

        os.makedirs(results_dir, exist_ok=True)

        results_file = f"{results_dir}/{args.encoder_type}_{'cross_exclude_' if not args.insubject else ''}{sub}.csv"

        with open(results_file, 'w', newline='') as file:

            writer = csv.DictWriter(file, fieldnames=results[0].keys())

            writer.writeheader()

            writer.writerows(results)

        print(f'Results saved to {results_file}')

if __name__ == '__main__':

    main()