import os
import polars as pl
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import TensorDataset, DataLoader
from concurrent.futures import ProcessPoolExecutor
from tqdm import tqdm
from sklearn.model_selection import train_test_split

# --- CONFIGURATION ---
BASE_PATH = "asl_kaggle"
TARGET_FRAMES = 22
# Hand landmarks + Lip landmarks (approximate indices for high-value face points)
LIPS = [61, 146, 91, 181, 84, 17, 314, 405, 321, 375, 291, 308, 324, 318, 402, 317, 14, 87, 178, 88, 95]
HANDS = list(range(468, 543))
SELECTED_INDICES = LIPS + HANDS
NUM_FEATS = len(SELECTED_INDICES) * 3  # X, Y, Z for each selected point
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


# --- DATA PROCESSING ---

def load_kaggle_metadata(base_path):
    return pl.read_csv(os.path.join(base_path, "train.csv"))


def load_and_preprocess(path, base_path=BASE_PATH, target_frames=TARGET_FRAMES):
    parquet_path = os.path.join(base_path, path)
    df = pl.read_parquet(parquet_path)

    # 1. Spatial Normalization (Nose Anchor)
    anchors = (
        df.filter((pl.col("type") == "face") & (pl.col("landmark_index") == 0))
        .select([pl.col("frame"), pl.col("x").alias("nx"), pl.col("y").alias("ny"), pl.col("z").alias("nz")])
    )

    processed = (
        df.join(anchors, on="frame", how="left")
        .with_columns([
            (pl.col("x") - pl.col("nx")).fill_null(0.0),
            (pl.col("y") - pl.col("ny")).fill_null(0.0),
            (pl.col("z") - pl.col("nz")).fill_null(0.0),
        ])
        .sort(["frame", "type", "landmark_index"])
    )

    # 2. Reshape & Feature Selection
    # Get unique frames and total landmarks (543)
    raw_tensor = processed.select(["x", "y", "z"]).to_numpy().reshape(-1, 543, 3)

    # Slice to keep only Hands and Lips
    reduced_tensor = raw_tensor[:, SELECTED_INDICES, :]

    # 3. Temporal Normalization (Resample to fixed frame count)
    curr_len = reduced_tensor.shape[0]
    indices = np.linspace(0, curr_len - 1, num=target_frames).round().astype(int)
    return reduced_tensor[indices]


# --- MODEL ARCHITECTURE ---

class ASLClassifier(nn.Module):
    def __init__(self, num_classes, target_frames=TARGET_FRAMES, num_feats=NUM_FEATS):
        super().__init__()
        self.conv1 = nn.Conv1d(num_feats, 256, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm1d(256)
        self.conv2 = nn.Conv1d(256, 512, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm1d(512)
        self.pool = nn.MaxPool1d(2)
        self.dropout = nn.Dropout(0.5)
        self.fc = nn.Linear(512, num_classes)

    def forward(self, x):
        # x: (Batch, Frames, Selected_Landmarks, 3)
        x = x.view(x.shape[0], x.shape[1], -1)  # Flatten landmarks/coords
        x = x.transpose(1, 2)  # (Batch, Features, Time)

        x = F.relu(self.bn1(self.conv1(x)))
        x = self.pool(x)
        x = F.relu(self.bn2(self.conv2(x)))
        x = self.pool(x)

        x = F.adaptive_avg_pool1d(x, 1).squeeze(-1)
        x = self.dropout(x)
        return self.fc(x)


# --- TRAINING FUNCTION ---

def train_model(model, train_loader, val_loader, epochs=20, lr=0.001):
    criterion = nn.CrossEntropyLoss()
    optimizer = torch.optim.Adam(model.parameters(), lr=lr)
    scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', patience=3, factor=0.5)

    best_val_acc = 0.0

    for epoch in range(epochs):
        # Training phase
        model.train()
        train_loss = 0.0
        train_correct = 0
        train_total = 0

        pbar = tqdm(train_loader, desc=f"Epoch {epoch + 1}/{epochs} [Train]")
        for inputs, labels in pbar:
            inputs, labels = inputs.to(device), labels.to(device)

            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            train_loss += loss.item()
            _, predicted = torch.max(outputs, 1)
            train_total += labels.size(0)
            train_correct += (predicted == labels).sum().item()

            pbar.set_postfix({'loss': f'{loss.item():.4f}', 'acc': f'{100 * train_correct / train_total:.2f}%'})

        train_acc = 100 * train_correct / train_total
        avg_train_loss = train_loss / len(train_loader)

        # Validation phase
        model.eval()
        val_loss = 0.0
        val_correct = 0
        val_total = 0

        with torch.no_grad():
            for inputs, labels in tqdm(val_loader, desc=f"Epoch {epoch + 1}/{epochs} [Val]"):
                inputs, labels = inputs.to(device), labels.to(device)
                outputs = model(inputs)
                loss = criterion(outputs, labels)

                val_loss += loss.item()
                _, predicted = torch.max(outputs, 1)
                val_total += labels.size(0)
                val_correct += (predicted == labels).sum().item()

        val_acc = 100 * val_correct / val_total
        avg_val_loss = val_loss / len(val_loader)

        scheduler.step(avg_val_loss)

        print(f"\nEpoch {epoch + 1}/{epochs}:")
        print(f"  Train Loss: {avg_train_loss:.4f} | Train Acc: {train_acc:.2f}%")
        print(f"  Val Loss: {avg_val_loss:.4f} | Val Acc: {val_acc:.2f}%")

        # Save best model
        if val_acc > best_val_acc:
            best_val_acc = val_acc
            torch.save(model.state_dict(), 'best_asl_model.pth')
            print(f"  ✓ New best model saved! (Val Acc: {val_acc:.2f}%)")

        print()

    print(f"Training complete! Best validation accuracy: {best_val_acc:.2f}%")


# --- EXECUTION ---

if __name__ == "__main__":
    asl_data = load_kaggle_metadata(BASE_PATH)

    # Process all files
    paths = asl_data["path"].to_list()
    labels = asl_data["sign"].to_list()

    # Create label mapping
    unique_signs = sorted(set(labels))
    sign_to_idx = {sign: idx for idx, sign in enumerate(unique_signs)}
    label_indices = [sign_to_idx[sign] for sign in labels]

    print(f"Processing {len(paths)} files in parallel...")
    with ProcessPoolExecutor() as executor:
        results = list(tqdm(executor.map(load_and_preprocess, paths), total=len(paths)))

    # Create tensors
    X = torch.tensor(np.array(results), dtype=torch.float32)
    y = torch.tensor(label_indices, dtype=torch.long)

    print(f"Dataset Tensor Shape: {X.shape}")
    print(f"Labels Tensor Shape: {y.shape}")
    print(f"Number of unique signs: {len(unique_signs)}")

    # Train/Val split
    X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)

    # Create DataLoaders
    train_dataset = TensorDataset(X_train, y_train)
    val_dataset = TensorDataset(X_val, y_val)

    train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
    val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)

    print(f"Train samples: {len(train_dataset)}")
    print(f"Val samples: {len(val_dataset)}")

    # Initialize and train model
    model = ASLClassifier(num_classes=len(unique_signs))
    model.to(device)

    print(f"\nModel initialized with {sum(p.numel() for p in model.parameters()):,} parameters")
    print("Starting training...\n")

    train_model(model, train_loader, val_loader, epochs=20, lr=0.002)