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 eegdatasets_leaveone import EEGDataset

from einops.layers.torch import Rearrange, Reduce

from sklearn.metrics import confusion_matrix

from torch.utils.data import DataLoader, Dataset

import random

from util import wandb_logger

from braindecode.models import EEGNetv4, ATCNet, EEGConformer, EEGITNet, ShallowFBCSPNet

import csv

from torch import Tensor

import itertools

import math

import re

from subject_layers.Transformer_EncDec import Encoder, EncoderLayer

from subject_layers.SelfAttention_Family import FullAttention, AttentionLayer

from subject_layers.Embed import DataEmbedding

import numpy as np

from loss import ClipLoss

import argparse

from torch import nn

from torch.optim import AdamW

class Config:

    def __init__(self):

        self.task_name = 'classification'  # Example task name

        self.seq_len = 250                 # Sequence length

        self.pred_len = 250                # Prediction length

        self.output_attention = False      # Whether to output attention weights

        self.d_model = 250                 # Model dimension

        self.embed = 'timeF'               # Time encoding method

        self.freq = 'h'                    # Time frequency

        self.dropout = 0.25                # Dropout rate

        self.factor = 1                    # Attention scaling factor

        self.n_heads = 4                   # Number of attention heads

        self.e_layers = 1                  # Number of encoder layers

        self.d_ff = 256                    # Feedforward network dimension

        self.activation = 'gelu'           # Activation function

        self.enc_in = 63                   # Encoder input dimension (example value)

class iTransformer(nn.Module):

    def __init__(self, configs, joint_train=False,  num_subjects=10):

        super(iTransformer, self).__init__()

        self.task_name = configs.task_name

        self.seq_len = configs.seq_len

        self.pred_len = configs.pred_len

        self.output_attention = configs.output_attention

        # Embedding

        self.enc_embedding = DataEmbedding(configs.seq_len, configs.d_model, configs.embed, configs.freq, configs.dropout, joint_train=False, num_subjects=num_subjects)

        # Encoder

        self.encoder = Encoder(

            [

                EncoderLayer(

                    AttentionLayer(

                        FullAttention(False, configs.factor, attention_dropout=configs.dropout, output_attention=configs.output_attention),

                        configs.d_model, configs.n_heads

                    ),

                    configs.d_model,

                    configs.d_ff,

                    dropout=configs.dropout,

                    activation=configs.activation

                ) for l in range(configs.e_layers)

            ],

            norm_layer=torch.nn.LayerNorm(configs.d_model)

        )

    def forward(self, x_enc, x_mark_enc, subject_ids=None):

        # Embedding

        enc_out = self.enc_embedding(x_enc, x_mark_enc, subject_ids)

        enc_out, attns = self.encoder(enc_out, attn_mask=None)

        enc_out = enc_out[:, :63, :]      

        # print("enc_out", enc_out.shape)

        return enc_out

class PatchEmbedding(nn.Module):

    def __init__(self, emb_size=40):

        super().__init__()

        # Revised from ShallowNet

        self.tsconv = nn.Sequential(

            nn.Conv2d(1, 40, (1, 25), stride=(1, 1)),

            nn.AvgPool2d((1, 51), (1, 5)),

            nn.BatchNorm2d(40),

            nn.ELU(),

            nn.Conv2d(40, 40, (63, 1), stride=(1, 1)),

            nn.BatchNorm2d(40),

            nn.ELU(),

            nn.Dropout(0.5),

        )

        self.projection = nn.Sequential(

            nn.Conv2d(40, emb_size, (1, 1), stride=(1, 1)),  

            Rearrange('b e (h) (w) -> b (h w) e'),

        )

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

        # b, _, _, _ = x.shape

        x = x.unsqueeze(1)     

        # print("x", x.shape)   

        x = self.tsconv(x)

        # print("tsconv", x.shape)   

        x = self.projection(x)

        # print("projection", x.shape)  

        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 ATMS(nn.Module):    

    def __init__(self, num_channels=63, sequence_length=250, num_subjects=2, num_features=64, num_latents=1024, num_blocks=1):

        super(ATMS, self).__init__()

        default_config = Config()

        self.encoder = iTransformer(default_config)   

        self.subject_wise_linear = nn.ModuleList([nn.Linear(default_config.d_model, 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, subject_ids):

        x = self.encoder(x, None, subject_ids)

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

        # print("x", 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)

        out = self.proj_eeg(eeg_embedding)

        return out  

def extract_id_from_string(s):

    match = re.search(r'\d+$', s)

    if match:

        return int(match.group())

    return None

def train_model(sub, eeg_model, dataloader, optimizer, device, text_features_all, img_features_all, config):

    eeg_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()

        batch_size = eeg_data.size(0)  

        subject_id = extract_id_from_string(sub)

        # eeg_data = eeg_data.permute(0, 2, 1)

        subject_ids = torch.full((batch_size,), subject_id, dtype=torch.long).to(device)

        # if not config.insubject:

        #     subject_ids = torch.full((batch_size,), -1, dtype=torch.long).to(device)     

        eeg_features = eeg_model(eeg_data, subject_ids).float()

        features_list.append(eeg_features)

        logit_scale = eeg_model.logit_scale

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

        text_loss = eeg_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()

        # Compute the corresponding logits

        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()

        del eeg_data, eeg_features, img_features

    average_loss = total_loss / (batch_idx+1)

    accuracy = correct / total

    return average_loss, accuracy, torch.cat(features_list, dim=0)

def evaluate_model(sub, eeg_model, dataloader, device, text_features_all, img_features_all, k, config):

    eeg_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

    # Get all unique classes

    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()

            batch_size = eeg_data.size(0) 

            subject_id = extract_id_from_string(sub)

            # eeg_data = eeg_data.permute(0, 2, 1)

            subject_ids = torch.full((batch_size,), subject_id, dtype=torch.long).to(device)

            # if not config.insubject:

            #     subject_ids = torch.full((batch_size,), -1, dtype=torch.long).to(device)          

            eeg_features = eeg_model(eeg_data, subject_ids)

            logit_scale = eeg_model.logit_scale 

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

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

            text_loss = eeg_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):

                # First, select k-1 classes excluding the correct class

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

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

                selected_img_features = img_features_all[selected_classes]

                selected_text_features = text_features_all[selected_classes]

                if k==200:

                    # Compute the corresponding logits

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

                    logits_single = logits_img

                    # print("logits_single", logits_single.shape)

                    # Get the predicted class

                    # 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

                    # logits_single is the model's output, shape (n_batch, n_classes)

                    # label is the true label, shape (n_batch,)

                    # Get the indices of the top-5 predictions

                    # print("logits_single", logits_single)

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

                    # Check if the true label is in the top-5 predictions

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

                        top5_correct_count+=1                                

                    total += 1

                elif k == 50 or k == 100:

                    # For k=50 or 100, select k classes for evaluation

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

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

                    logits_single = logits_img

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

                    if predicted_label == label.item():

                        correct += 1

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

                    # Check if the true label is in the top-5 predictions

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

                        top5_correct_count+=1                                

                    total += 1

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

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

                    # Compute the corresponding logits

                    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)

                    # Get the predicted class

                    # 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.")

            del eeg_data, eeg_features, img_features

    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, current_time, eeg_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(eeg_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 = []  # List to store results for each epoch

    for epoch in range(config.epochs):

        # Train the model

        train_loss, train_accuracy, features_tensor = train_model(sub, eeg_model, train_dataloader, optimizer, device, text_features_train_all, img_features_train_all, config=config)

        if (epoch +1) % 5 == 0:                    

            # Get the current time and format it as a string (e.g., '2024-01-17_15-30-00')                  

            if config.insubject==True:       

                os.makedirs(f"./models/contrast/{config.encoder_type}/{sub}/{current_time}", exist_ok=True)             

                file_path = f"./models/contrast/{config.encoder_type}/{sub}/{current_time}/{epoch+1}.pth"

                torch.save(eeg_model.state_dict(), file_path)            

            else:                

                os.makedirs(f"./models/contrast/across/{config.encoder_type}/{current_time}", exist_ok=True)             

                file_path = f"./models/contrast/across/{config.encoder_type}/{current_time}/{epoch+1}.pth"

                torch.save(eeg_model.state_dict(), file_path)

            print(f"model saved in {file_path}!")

        train_losses.append(train_loss)

        train_accuracies.append(train_accuracy)

        # Evaluate the model

        test_loss, test_accuracy, top5_acc = evaluate_model(sub, eeg_model, test_dataloader, device, text_features_test_all, img_features_test_all,k=200, config=config)

        _, v2_acc, _ = evaluate_model(sub, eeg_model, test_dataloader, device, text_features_test_all, img_features_test_all, k = 2, config=config)

        _, v4_acc, _ = evaluate_model(sub, eeg_model, test_dataloader, device, text_features_test_all, img_features_test_all, k = 4, config=config)

        _, v10_acc, _ = evaluate_model(sub, eeg_model, test_dataloader, device, text_features_test_all, img_features_test_all, k = 10, config=config)

        _, v50_acc, v50_top5_acc = evaluate_model(sub, eeg_model, test_dataloader, device, text_features_test_all, img_features_test_all,  k=50, config=config)

        _, v100_acc, v100_top5_acc = evaluate_model(sub, eeg_model, test_dataloader, device, text_features_test_all, img_features_test_all,  k=100, config=config)

        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,

        "v50_acc": v50_acc,

        "v100_acc": v100_acc,

        "v50_top5_acc":v50_top5_acc,

        "v100_top5_acc": v100_top5_acc

        }

        results.append(epoch_results)

        # If the test accuracy of the current epoch is the best, save the model and related information

        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} - v50 Accuracy:{v50_acc} - v100 Accuracy:{v100_acc}")

    # # Load the best model weights

    # model.load_state_dict(best_model_weights)

    # # # Save the best model

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

    # Create 5 subplots

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

    # Loss curve

    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")

    # Overall accuracy 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")

    # The following are the three new plots you added, assuming you've already calculated the corresponding accuracies

    # 2-class accuracy plot

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

    axs[1, 0].legend()

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

    # 4-class accuracy plot

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

    axs[1, 1].legend()

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

    # 10-class accuracy plot

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

    axs[2, 0].legend()

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

    # Construct the string information for annotation

    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()

    # Add main title

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

    plt.savefig('pos_img_text')

    logger.finish()

    return results

import datetime

def main():

    # Use argparse to parse the command-line arguments

    parser = argparse.ArgumentParser(description='EEG Transformer Training Script')

    parser.add_argument('--data_path', type=str, default="/root/autodl-tmp/THINGS/Preprocessed_data_250Hz", help='Path to the EEG dataset')

    parser.add_argument('--output_dir', type=str, default='./outputs/contrast', help='Directory to save output results')    

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

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

    parser.add_argument('--batch_size', type=int, default=64, help='Batch size')

    parser.add_argument('--logger', type=bool, default=True, help='Enable WandB logging')

    parser.add_argument('--gpu', type=str, default='cuda:0', help='GPU device to use')

    parser.add_argument('--device', type=str, choices=['cpu', 'gpu'], default='gpu', help='Device to run on (cpu or gpu)')    

    parser.add_argument('--insubject', type=bool, default=True, help='In-subject mode or cross-subject mode')

    parser.add_argument('--encoder_type', type=str, default='ATMS', help='Encoder type')

    parser.add_argument('--subjects', nargs='+', default=['sub-01', 'sub-02', 'sub-03', 'sub-04', 'sub-05', 'sub-06', 'sub-07', 'sub-08', 'sub-09', 'sub-10'], help='List of subject IDs (default: sub-01 to sub-10)')    

    args = parser.parse_args()

    # Set device based on the argument

    if args.device == 'gpu' and torch.cuda.is_available():

        device = torch.device(args.gpu)

    else:

        device = torch.device('cpu')

    subjects = args.subjects        

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

    for sub in subjects:

        eeg_model = globals()[args.encoder_type]()

        eeg_model.to(device)

        optimizer = AdamW(itertools.chain(eeg_model.parameters()), lr=args.lr)

        if args.insubject:

            train_dataset = EEGDataset(args.data_path, subjects=[sub], train=True)

            test_dataset = EEGDataset(args.data_path, subjects=[sub], train=False)

        else:

            train_dataset = EEGDataset(args.data_path, exclude_subject=sub, subjects=subjects, train=True)

            test_dataset = EEGDataset(args.data_path, exclude_subject=sub, subjects=subjects, 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

        results = main_train_loop(sub, current_time, eeg_model, train_loader, test_loader, optimizer, device, 

                                  text_features_train_all, text_features_test_all, img_features_train_all, img_features_test_all, config=args, logger=args.logger)

        # Save results to a CSV file

        results_dir = os.path.join(args.output_dir, args.encoder_type, sub, current_time)

        os.makedirs(results_dir, exist_ok=True)

        if args.insubject:

            results_file = f"{results_dir}/{args.encoder_type}_{sub}.csv"

        else:

            results_file = f"{results_dir}/{args.encoder_type}_cross_exclude_{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()