import os
import json
import math
import numpy as np
import polars as pl
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from multiprocessing import Pool, cpu_count
from functools import partial
from tqdm import tqdm
from collections import Counter

# ===============================
# GPU CONFIGURATION
# ===============================
print("=" * 60)
print("GPU CONFIGURATION")
print("=" * 60)

if torch.cuda.is_available():
    print(f"✓ CUDA available!")
    print(f"✓ GPU: {torch.cuda.get_device_name(0)}")
    device = torch.device('cuda:0')
    torch.backends.cudnn.benchmark = True
    torch.backends.cudnn.enabled = True
else:
    print("✗ CUDA not available, using CPU")
    device = torch.device('cpu')

print("=" * 60)

# ===============================
# SELECTED LANDMARK INDICES
# ===============================
IMPORTANT_FACE_INDICES = sorted(list(set([
    0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
    55, 65, 66, 105, 107, 336, 296, 334,
    33, 133, 160, 159, 158, 144, 145, 153,
    362, 263, 387, 386, 385, 373, 374, 380,
    1, 2, 98, 327,
    61, 185, 40, 39, 37, 0, 267, 269, 270, 409,
    291, 146, 91, 181, 84, 17, 314, 405, 321, 375,
    78, 191, 80, 81, 82, 13, 312, 311, 310, 415,
    308, 324, 318, 402, 317, 14, 87, 178, 88, 95
])))

NUM_FACE_POINTS = len(IMPORTANT_FACE_INDICES)
NUM_HAND_POINTS = 21 * 2
TOTAL_POINTS_PER_FRAME = NUM_HAND_POINTS + NUM_FACE_POINTS


# ===============================
# DATA AUGMENTATION
# ===============================
def augment_sequence(x, modality_mask):
    """Apply random augmentations to training data"""
    x = x.copy()

    # Random temporal cropping (simulate different signing speeds)
    if np.random.rand() < 0.3 and len(x) > 20:
        start = np.random.randint(0, max(1, len(x) // 4))
        x = x[start:]
        modality_mask = modality_mask[start:]

    # Random spatial scaling
    if np.random.rand() < 0.5:
        scale = np.random.uniform(0.85, 1.15)
        x = x * scale

    # Random rotation (around z-axis for x,y coordinates)
    if np.random.rand() < 0.5:
        angle = np.random.uniform(-0.3, 0.3)
        cos_a, sin_a = np.cos(angle), np.sin(angle)

        # Reshape to get xyz coordinates
        x_reshaped = x.reshape(len(x), -1, 3)
        x_rot = x_reshaped.copy()
        x_rot[..., 0] = x_reshaped[..., 0] * cos_a - x_reshaped[..., 1] * sin_a
        x_rot[..., 1] = x_reshaped[..., 0] * sin_a + x_reshaped[..., 1] * cos_a
        x = x_rot.reshape(x.shape)

    # Random masking (simulate occlusion) - only for some frames
    if np.random.rand() < 0.3:
        n_mask = int(len(x) * 0.15)  # mask 15% of frames
        mask_indices = np.random.choice(len(x), n_mask, replace=False)
        x[mask_indices] *= 0.1  # dim but don't completely zero

    # Random noise
    if np.random.rand() < 0.4:
        noise = np.random.normal(0, 0.02, x.shape)
        x = x + noise

    # Random time warping (speed up or slow down)
    if np.random.rand() < 0.3 and len(x) > 20:
        speed = np.random.uniform(0.8, 1.2)
        new_len = int(len(x) * speed)
        new_len = min(new_len, len(x))
        indices = np.linspace(0, len(x) - 1, new_len).astype(int)
        x = x[indices]
        modality_mask = modality_mask[indices]

    return x, modality_mask


# ===============================
# ENHANCED DATA EXTRACTION (POLARS)
# ===============================
def extract_multi_landmarks(path, min_valid_frames=3):
    """
    Extract both hands + selected face landmarks with modality flags
    Returns: dict with 'landmarks', 'left_hand_valid', 'right_hand_valid', 'face_valid'
    """
    try:
        df = pl.read_parquet(path)
        seq = []
        left_valid_frames = []
        right_valid_frames = []
        face_valid_frames = []

        all_types = df.select("type").unique().to_series().to_list()
        has_data = any(t in all_types for t in ["left_hand", "right_hand", "face"])

        if not has_data:
            return None

        frames = sorted(df.select("frame").unique().to_series().to_list())

        if len(frames) < min_valid_frames:
            return None

        for frame in frames:
            frame_df = df.filter(pl.col("frame") == frame)
            frame_points = np.full((TOTAL_POINTS_PER_FRAME, 3), np.nan, dtype=np.float32)

            pos = 0
            left_valid = False
            right_valid = False
            face_valid = False

            # Left hand
            left = frame_df.filter(pl.col("type") == "left_hand")
            if left.height > 0:
                valid_count = 0
                for i in range(21):
                    row = left.filter(pl.col("landmark_index") == i)
                    if row.height > 0:
                        coords = row.select(["x", "y", "z"]).row(0)
                        if all(c is not None for c in coords):
                            frame_points[pos] = coords
                            valid_count += 1
                    pos += 1
                left_valid = (valid_count >= 10)
            else:
                pos += 21

            # Right hand
            right = frame_df.filter(pl.col("type") == "right_hand")
            if right.height > 0:
                valid_count = 0
                for i in range(21):
                    row = right.filter(pl.col("landmark_index") == i)
                    if row.height > 0:
                        coords = row.select(["x", "y", "z"]).row(0)
                        if all(c is not None for c in coords):
                            frame_points[pos] = coords
                            valid_count += 1
                    pos += 1
                right_valid = (valid_count >= 10)
            else:
                pos += 21

            # Face
            face = frame_df.filter(pl.col("type") == "face")
            if face.height > 0:
                valid_count = 0
                for idx in IMPORTANT_FACE_INDICES:
                    row = face.filter(pl.col("landmark_index") == idx)
                    if row.height > 0:
                        coords = row.select(["x", "y", "z"]).row(0)
                        if all(c is not None for c in coords):
                            frame_points[pos] = coords
                            valid_count += 1
                    pos += 1
                face_valid = (valid_count >= len(IMPORTANT_FACE_INDICES) * 0.3)

            valid_ratio = 1 - np.isnan(frame_points).mean()
            if valid_ratio >= 0.20:
                frame_points = np.nan_to_num(frame_points, nan=0.0)
                seq.append(frame_points)
                left_valid_frames.append(left_valid)
                right_valid_frames.append(right_valid)
                face_valid_frames.append(face_valid)

        if len(seq) < min_valid_frames:
            return None

        return {
            'landmarks': np.stack(seq),
            'left_hand_valid': np.array(left_valid_frames),
            'right_hand_valid': np.array(right_valid_frames),
            'face_valid': np.array(face_valid_frames)
        }

    except Exception as e:
        return None


def get_features_sequence(landmarks_data, max_frames=100):
    """Enhanced feature extraction with separate modality processing"""
    if landmarks_data is None:
        return None, None, None

    landmarks_3d = landmarks_data['landmarks']
    if len(landmarks_3d) == 0:
        return None, None, None

    T, N, _ = landmarks_3d.shape

    # Separate modalities for independent normalization
    left_hand = landmarks_3d[:, :21, :]
    right_hand = landmarks_3d[:, 21:42, :]
    face = landmarks_3d[:, 42:, :]

    features_list = []

    for modality, valid_mask in [
        (left_hand, landmarks_data['left_hand_valid']),
        (right_hand, landmarks_data['right_hand_valid']),
        (face, landmarks_data['face_valid'])
    ]:
        valid_frames = modality[valid_mask] if valid_mask.any() else modality
        if len(valid_frames) > 0:
            center = np.mean(valid_frames, axis=(0, 1), keepdims=True)
            spread = np.std(valid_frames, axis=(0, 1), keepdims=True).max()
        else:
            center = 0
            spread = 1

        modality_norm = (modality - center) / max(spread, 1e-6)
        flat = modality_norm.reshape(T, -1)

        # Deltas
        deltas = np.zeros_like(flat)
        if T > 1:
            deltas[1:] = flat[1:] - flat[:-1]

        features_list.append(flat)
        features_list.append(deltas)

    features = np.concatenate(features_list, axis=1)

    modality_mask = np.stack([
        landmarks_data['left_hand_valid'],
        landmarks_data['right_hand_valid'],
        landmarks_data['face_valid']
    ], axis=1).astype(np.float32)

    # Pad/truncate
    if T < max_frames:
        pad = np.zeros((max_frames - T, features.shape[1]), dtype=np.float32)
        features = np.concatenate([features, pad], axis=0)

        mask_pad = np.zeros((max_frames - T, 3), dtype=np.float32)
        modality_mask = np.concatenate([modality_mask, mask_pad], axis=0)

        frame_mask = np.zeros(max_frames, dtype=bool)
        frame_mask[:T] = True
    else:
        features = features[:max_frames]
        modality_mask = modality_mask[:max_frames]
        frame_mask = np.ones(max_frames, dtype=bool)

    features = np.nan_to_num(features, nan=0.0, posinf=0.0, neginf=0.0)
    features = np.clip(features, -30, 30)

    return features.astype(np.float32), frame_mask, modality_mask


def process_row(row_data, base_path, max_frames=100):
    """Process a single row"""
    path_rel, sign = row_data
    path = os.path.join(base_path, path_rel)
    if not os.path.exists(path):
        return None, None, None, None

    try:
        lm_data = extract_multi_landmarks(path)
        if lm_data is None:
            return None, None, None, None

        feat, frame_mask, modality_mask = get_features_sequence(lm_data, max_frames)
        if feat is None:
            return None, None, None, None

        return feat, frame_mask, modality_mask, sign

    except Exception:
        return None, None, None, None


# ===============================
# MIXUP AUGMENTATION
# ===============================
def mixup_data(x, frame_mask, modality_mask, y, alpha=0.2):
    """Mixup augmentation"""
    if alpha > 0:
        lam = np.random.beta(alpha, alpha)
    else:
        lam = 1

    batch_size = x.size(0)
    index = torch.randperm(batch_size).to(x.device)

    mixed_x = lam * x + (1 - lam) * x[index]
    mixed_frame_mask = frame_mask | frame_mask[index]  # Union of valid frames
    mixed_modality_mask = torch.max(modality_mask, modality_mask[index])

    y_a, y_b = y, y[index]
    return mixed_x, mixed_frame_mask, mixed_modality_mask, y_a, y_b, lam


# ===============================
# ENHANCED MODEL WITH ATTENTION POOLING
# ===============================
class PositionalEncoding(nn.Module):
    def __init__(self, d_model, max_len=128):
        super().__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, 2).float() * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        self.register_buffer('pe', pe.unsqueeze(0))

    def forward(self, x):
        return x + self.pe[:, :x.size(1)]


class ModalityAwareTransformer(nn.Module):
    def __init__(self, input_dim, num_classes, d_model=512, nhead=8, num_layers=6, dropout=0.15):
        super().__init__()

        # Main projection
        self.proj = nn.Linear(input_dim, d_model)

        # Modality embedding (3 modalities: left_hand, right_hand, face)
        self.modality_embed = nn.Linear(3, d_model)

        self.norm_in = nn.LayerNorm(d_model)
        self.pos = PositionalEncoding(d_model)

        enc_layer = nn.TransformerEncoderLayer(
            d_model=d_model,
            nhead=nhead,
            dim_feedforward=d_model * 4,
            dropout=dropout,
            activation='gelu',
            batch_first=True,
            norm_first=True
        )
        self.encoder = nn.TransformerEncoder(enc_layer, num_layers=num_layers)

        # Attention pooling
        self.attention_pool = nn.Linear(d_model, 1)

        self.head = nn.Sequential(
            nn.LayerNorm(d_model),
            nn.Dropout(0.3),
            nn.Linear(d_model, d_model // 2),
            nn.GELU(),
            nn.Dropout(0.2),
            nn.Linear(d_model // 2, num_classes)
        )

    def forward(self, x, modality_mask=None, key_padding_mask=None):
        # Project features
        x = self.proj(x)

        # Add modality information
        if modality_mask is not None:
            mod_embed = self.modality_embed(modality_mask)
            x = x + mod_embed

        x = self.norm_in(x)
        x = self.pos(x)
        x = self.encoder(x, src_key_padding_mask=key_padding_mask)

        # Attention-based pooling
        attn_weights = self.attention_pool(x)  # (B, T, 1)
        if key_padding_mask is not None:
            attn_weights = attn_weights.masked_fill(key_padding_mask.unsqueeze(-1), -1e9)
        attn_weights = F.softmax(attn_weights, dim=1)
        x = (x * attn_weights).sum(dim=1)

        return self.head(x)


def load_kaggle_asl_data(base_path):
    """Load training metadata using Polars"""
    train_path = os.path.join(base_path, "train.csv")
    train_df = pl.read_csv(train_path)
    return train_df, None


# ===============================
# DATASET WITH AUGMENTATION
# ===============================
class ASLMultiDataset(Dataset):
    def __init__(self, X, frame_masks, modality_masks, y, training=False, max_frames=100):
        self.X = X
        self.frame_masks = frame_masks
        self.modality_masks = modality_masks
        self.y = y
        self.training = training
        self.max_frames = max_frames

    def __len__(self):
        return len(self.X)

    def __getitem__(self, idx):
        x = self.X[idx].copy()
        frame_mask = self.frame_masks[idx].copy()
        modality_mask = self.modality_masks[idx].copy()
        y = self.y[idx]

        if self.training:
            # Apply augmentation
            x, modality_mask = augment_sequence(x, modality_mask)

            # Re-pad if needed after augmentation
            if len(x) < self.max_frames:
                pad = np.zeros((self.max_frames - len(x), x.shape[1]), dtype=np.float32)
                x = np.concatenate([x, pad], axis=0)

                mask_pad = np.zeros((self.max_frames - len(x), 3), dtype=np.float32)
                modality_mask = np.concatenate([modality_mask, mask_pad], axis=0)

                frame_mask = np.zeros(self.max_frames, dtype=bool)
                frame_mask[:len(x)] = True
            else:
                x = x[:self.max_frames]
                modality_mask = modality_mask[:self.max_frames]
                frame_mask = np.ones(self.max_frames, dtype=bool)

        return (
            torch.from_numpy(x).float(),
            torch.from_numpy(frame_mask).bool(),
            torch.from_numpy(modality_mask).float(),
            torch.tensor(y, dtype=torch.long)
        )


# ===============================
# TRAINING SINGLE MODEL
# ===============================
def train_model(X_tr, fm_tr, mm_tr, y_tr, X_te, fm_te, mm_te, y_te,
                num_classes, input_dim, model_idx=0, epochs=80):
    """Train a single model"""

    # Set different seed for each model
    torch.manual_seed(42 + model_idx)
    np.random.seed(42 + model_idx)

    batch_size = 64 if device.type == 'cuda' else 32

    train_dataset = ASLMultiDataset(X_tr, fm_tr, mm_tr, y_tr, training=True, max_frames=100)
    test_dataset = ASLMultiDataset(X_te, fm_te, mm_te, y_te, training=False, max_frames=100)

    train_loader = DataLoader(
        train_dataset,
        batch_size=batch_size, shuffle=True,
        num_workers=4, pin_memory=device.type == 'cuda'
    )

    test_loader = DataLoader(
        test_dataset,
        batch_size=batch_size * 2, shuffle=False,
        num_workers=4, pin_memory=device.type == 'cuda'
    )

    # Enhanced model
    model = ModalityAwareTransformer(
        input_dim=input_dim,
        num_classes=num_classes,
        d_model=512,
        nhead=8,
        num_layers=6,
        dropout=0.15
    ).to(device)

    print(f"\n[Model {model_idx + 1}] Parameters: {sum(p.numel() for p in model.parameters()):,}")

    criterion = nn.CrossEntropyLoss(label_smoothing=0.1)
    optimizer = optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-4)

    # OneCycleLR scheduler
    scheduler = optim.lr_scheduler.OneCycleLR(
        optimizer,
        max_lr=1e-3,
        steps_per_epoch=len(train_loader),
        epochs=epochs,
        pct_start=0.1,
        anneal_strategy='cos'
    )

    best_acc = 0.0
    save_path = f"best_asl_model_{model_idx}.pth"

    print(f"\n{'=' * 60}")
    print(f"TRAINING MODEL {model_idx + 1}")
    print(f"{'=' * 60}")

    for epoch in range(epochs):
        model.train()
        total_loss = correct = total = 0

        for x, frame_mask, modality_mask, yb in tqdm(train_loader, desc=f"Epoch {epoch + 1}", leave=False):
            x = x.to(device)
            frame_mask = frame_mask.to(device)
            modality_mask = modality_mask.to(device)
            yb = yb.to(device)

            # Apply mixup
            if np.random.rand() < 0.5:
                x, frame_mask, modality_mask, y_a, y_b, lam = mixup_data(
                    x, frame_mask, modality_mask, yb, alpha=0.2
                )

                key_padding_mask = ~frame_mask
                optimizer.zero_grad(set_to_none=True)
                logits = model(x, modality_mask=modality_mask, key_padding_mask=key_padding_mask)
                loss = lam * criterion(logits, y_a) + (1 - lam) * criterion(logits, y_b)

                # Use original labels for accuracy
                correct += (logits.argmax(-1) == yb).sum().item()
            else:
                key_padding_mask = ~frame_mask
                optimizer.zero_grad(set_to_none=True)
                logits = model(x, modality_mask=modality_mask, key_padding_mask=key_padding_mask)
                loss = criterion(logits, yb)
                correct += (logits.argmax(-1) == yb).sum().item()

            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
            optimizer.step()
            scheduler.step()

            total_loss += loss.item()
            total += yb.size(0)

        train_acc = correct / total * 100

        # Eval
        model.eval()
        correct = total = 0
        with torch.no_grad():
            for x, frame_mask, modality_mask, yb in test_loader:
                x = x.to(device)
                frame_mask = frame_mask.to(device)
                modality_mask = modality_mask.to(device)
                yb = yb.to(device)

                key_padding_mask = ~frame_mask
                logits = model(x, modality_mask=modality_mask, key_padding_mask=key_padding_mask)
                correct += (logits.argmax(-1) == yb).sum().item()
                total += yb.size(0)

        test_acc = correct / total * 100

        print(f"[{epoch + 1:2d}/{epochs}] Loss: {total_loss / len(train_loader):.4f} | "
              f"Train: {train_acc:.2f}% | Test: {test_acc:.2f}%", end="")

        if test_acc > best_acc:
            best_acc = test_acc
            torch.save(model.state_dict(), save_path)
            print("  → saved")
        else:
            print()

    print(f"\nModel {model_idx + 1} - Best test accuracy: {best_acc:.2f}%")
    return save_path, best_acc


# ===============================
# ENSEMBLE PREDICTION
# ===============================
def ensemble_predict(model_paths, test_loader, num_classes, input_dim):
    """Make predictions using ensemble of models"""
    all_preds = []

    for model_path in model_paths:
        model = ModalityAwareTransformer(
            input_dim=input_dim,
            num_classes=num_classes,
            d_model=512,
            nhead=8,
            num_layers=6
        ).to(device)

        model.load_state_dict(torch.load(model_path))
        model.eval()

        preds = []
        with torch.no_grad():
            for x, frame_mask, modality_mask, _ in test_loader:
                x = x.to(device)
                frame_mask = frame_mask.to(device)
                modality_mask = modality_mask.to(device)

                key_padding_mask = ~frame_mask
                logits = model(x, modality_mask=modality_mask, key_padding_mask=key_padding_mask)
                preds.append(F.softmax(logits, dim=-1))

        all_preds.append(torch.cat(preds, dim=0))

    # Average predictions
    ensemble_pred = torch.stack(all_preds).mean(0)
    return ensemble_pred.argmax(-1).cpu().numpy()


# ===============================
# MAIN
# ===============================
def main():
    base_path = "asl_kaggle"
    max_frames = 100
    MIN_SAMPLES_PER_CLASS = 3  # Relaxed from 5
    NUM_ENSEMBLE_MODELS = 3
    EPOCHS = 80

    print("\nLoading metadata...")
    train_df, _ = load_kaggle_asl_data(base_path)
    print(f"Total samples in train.csv: {train_df.height}")

    rows = [(row[0], row[1]) for row in train_df.select(["path", "sign"]).iter_rows()]

    print("\nProcessing sequences with BOTH hands + FACE (enhanced)...")
    print("This may take a few minutes...")

    with Pool(cpu_count()) as pool:
        results = list(tqdm(
            pool.imap(
                partial(process_row, base_path=base_path, max_frames=max_frames),
                rows,
                chunksize=80
            ),
            total=len(rows),
            desc="Landmarks extraction"
        ))

    X_list, frame_masks_list, modality_masks_list, y_list = [], [], [], []
    failed_count = 0
    for feat, frame_mask, modality_mask, sign in results:
        if feat is not None and frame_mask is not None:
            X_list.append(feat)
            frame_masks_list.append(frame_mask)
            modality_masks_list.append(modality_mask)
            y_list.append(sign)
        else:
            failed_count += 1

    if not X_list:
        print(f"\n❌ No valid sequences extracted!")
        print(f"Failed to process: {failed_count}/{len(results)} files")
        return

    print(f"\n✓ Successfully processed: {len(X_list)}/{len(results)} files")
    print(f"✗ Failed: {failed_count}/{len(results)} files")

    X = np.stack(X_list)
    frame_masks = np.stack(frame_masks_list)
    modality_masks = np.stack(modality_masks_list)

    print(f"\nExtracted {len(X):,} sequences")
    print(f"Feature shape: {X.shape[1:]}   (input_dim = {X.shape[2]})")

    # Global normalization
    X = np.clip(X, -30, 30)
    mean = X.mean(axis=(0, 1), keepdims=True)
    std = X.std(axis=(0, 1), keepdims=True) + 1e-8
    X = (X - mean) / std

    # Labels
    le = LabelEncoder()
    y = le.fit_transform(y_list)

    # Filter rare classes
    counts = Counter(y)
    valid = [k for k, v in counts.items() if v >= MIN_SAMPLES_PER_CLASS]
    mask = np.isin(y, valid)

    X = X[mask]
    frame_masks = frame_masks[mask]
    modality_masks = modality_masks[mask]
    y = y[mask]

    le = LabelEncoder()
    y = le.fit_transform(y)

    print(f"After filtering: {len(X):,} samples | {len(le.classes_)} classes")

    # Split
    X_tr, X_te, fm_tr, fm_te, mm_tr, mm_te, y_tr, y_te = train_test_split(
        X, frame_masks, modality_masks, y, test_size=0.15, stratify=y, random_state=42
    )

    # Train ensemble of models
    model_paths = []
    best_accs = []

    for i in range(NUM_ENSEMBLE_MODELS):
        model_path, best_acc = train_model(
            X_tr, fm_tr, mm_tr, y_tr,
            X_te, fm_te, mm_te, y_te,
            num_classes=len(le.classes_),
            input_dim=X.shape[2],
            model_idx=i,
            epochs=EPOCHS
        )
        model_paths.append(model_path)
        best_accs.append(best_acc)

    # Ensemble evaluation
    print("\n" + "=" * 60)
    print("ENSEMBLE EVALUATION")
    print("=" * 60)

    test_dataset = ASLMultiDataset(X_te, fm_te, mm_te, y_te, training=False)
    test_loader = DataLoader(
        test_dataset,
        batch_size=128,
        shuffle=False,
        num_workers=4,
        pin_memory=device.type == 'cuda'
    )

    ensemble_preds = ensemble_predict(model_paths, test_loader, len(le.classes_), X.shape[2])
    ensemble_acc = (ensemble_preds == y_te).mean() * 100

    print(f"\nIndividual model accuracies:")
    for i, acc in enumerate(best_accs):
        print(f"  Model {i + 1}: {acc:.2f}%")

    print(f"\n🎯 Ensemble accuracy: {ensemble_acc:.2f}%")
    print(f"   Improvement: +{ensemble_acc - max(best_accs):.2f}% over best single model")

    print("\n" + "=" * 60)
    print(f"TRAINING COMPLETE")
    print("=" * 60)


if __name__ == "__main__":
    main()