# ===============================
# IMPORTS
# ===============================
import os
import json
import math
import numpy as np
import pandas as pd
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 StandardScaler
from multiprocessing import Pool, cpu_count
from functools import partial
from tqdm import tqdm

# ===============================
# DEVICE
# ===============================
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")
if device.type == "cuda":
    print("GPU:", torch.cuda.get_device_name(0))
    torch.backends.cudnn.benchmark = True

# ===============================
# DATA LOADING
# ===============================
def load_kaggle_asl_data(base_path):
    train_df = pd.read_csv(os.path.join(base_path, "train.csv"))
    with open(os.path.join(base_path, "sign_to_prediction_index_map.json")) as f:
        sign_to_idx = json.load(f)
    return train_df, sign_to_idx

def extract_hand_landmarks_from_parquet(path):
    try:
        df = pd.read_parquet(path)
        hand = df[df["type"].isin(["left_hand", "right_hand"])]
        if len(hand) == 0:
            return None

        frames = sorted(hand['frame'].unique())
        landmarks_seq = []

        for frame in frames:
            lm_frame = hand[hand['frame'] == frame]
            lm_list = []
            for i in range(21):
                lm = lm_frame[lm_frame['landmark_index'] == i]
                if len(lm) == 0:
                    lm_list.append([0.0, 0.0, 0.0])
                else:
                    lm_list.append([lm['x'].values[0], lm['y'].values[0], lm['z'].values[0]])
            landmarks_seq.append(lm_list)

        return np.array(landmarks_seq, dtype=np.float32)  # (T, 21, 3)
    except:
        return None

def get_features_sequence(landmarks_seq, max_frames=96):
    if landmarks_seq is None or len(landmarks_seq) == 0:
        return None

    # Center on wrist (landmark 0)
    landmarks_seq = landmarks_seq - landmarks_seq[:, 0:1, :]

    # Rough scale normalization (using index finger length as reference)
    scale = np.linalg.norm(landmarks_seq[:, 8] - landmarks_seq[:, 5], axis=1, keepdims=True)
    scale = np.maximum(scale, 1e-6)
    landmarks_seq /= scale

    # Flatten → (T, 63)
    seq = landmarks_seq.reshape(landmarks_seq.shape[0], -1)

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

    return seq.astype(np.float32)

def process_row(row, base_path, max_frames=96):
    path = os.path.join(base_path, row['path'])
    if not os.path.exists(path):
        return None, None
    lm = extract_hand_landmarks_from_parquet(path)
    feat = get_features_sequence(lm, max_frames)
    if feat is None:
        return None, None
    return feat, row['sign']

# ===============================
# LOAD & PROCESS (with progress)
# ===============================
base_path = "asl_kaggle"  # ← change if needed
train_df, sign_to_idx = load_kaggle_asl_data(base_path)

print("Processing videos...")
rows = [row for _, row in train_df.iterrows()]

with Pool(cpu_count()) as pool:
    results = list(tqdm(pool.imap(
        partial(process_row, base_path=base_path, max_frames=96),
        rows
    ), total=len(rows)))

X, y = [], []
for feat, sign in results:
    if feat is not None:
        X.append(feat)
        y.append(sign)

X = np.stack(X)  # (N, T, 63)
print(f"Loaded {len(X)} valid samples  |  shape: {X.shape}")

# Global normalization (very important!)
scaler = StandardScaler()
X_reshaped = X.reshape(-1, X.shape[-1])
X_reshaped = scaler.fit_transform(X_reshaped)
X = X_reshaped.reshape(X.shape)

# ===============================
# LABELS
# ===============================
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y = le.fit_transform(y)
num_classes = len(le.classes_)
print(f"Classes: {num_classes}")

# ===============================
# SPLIT
# ===============================
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.15, stratify=y, random_state=42
)

# ===============================
# DATASET + DATALOADER
# ===============================
class ASLSequenceDataset(Dataset):
    def __init__(self, X, y):
        self.X = torch.from_numpy(X).float()
        self.y = torch.from_numpy(y).long()

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

    def __getitem__(self, idx):
        return self.X[idx], self.y[idx]

train_loader = DataLoader(ASLSequenceDataset(X_train, y_train),
                          batch_size=64, shuffle=True, num_workers=4, pin_memory=True)
test_loader  = DataLoader(ASLSequenceDataset(X_test, y_test),
                          batch_size=96, shuffle=False, num_workers=4, pin_memory=True)

# ===============================
# MODEL
# ===============================
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 TransformerASL(nn.Module):
    def __init__(self, input_dim=63, num_classes=250, d_model=192, nhead=6, num_layers=4):
        super().__init__()
        self.proj = nn.Linear(input_dim, d_model)
        self.norm_in = nn.LayerNorm(d_model)

        self.pos = PositionalEncoding(d_model)

        encoder_layer = nn.TransformerEncoderLayer(
            d_model=d_model,
            nhead=nhead,
            dim_feedforward=d_model*4,
            dropout=0.15,
            activation='gelu',
            batch_first=True,
            norm_first=True
        )
        self.encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)

        self.head = nn.Sequential(
            nn.LayerNorm(d_model),
            nn.Dropout(0.25),
            nn.Linear(d_model, num_classes)
        )

    def forward(self, x, key_padding_mask=None):
        x = self.proj(x)
        x = self.norm_in(x)
        x = self.pos(x)

        x = self.encoder(x, src_key_padding_mask=key_padding_mask)
        x = x.mean(dim=1)           # global average pooling
        x = self.head(x)
        return x

model = TransformerASL(input_dim=63, num_classes=num_classes).to(device)
print(f"Model parameters: {sum(p.numel() for p in model.parameters()):,}")

# ===============================
# TRAINING SETUP
# ===============================
criterion = nn.CrossEntropyLoss(label_smoothing=0.05)
optimizer = optim.AdamW(model.parameters(), lr=8e-4, weight_decay=1e-4, betas=(0.9, 0.98))
scheduler = optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=15, T_mult=2)

# ===============================
# TRAIN / EVAL
# ===============================
def create_padding_mask(seq_len, max_len):
    # True = ignore this position
    return torch.arange(max_len, device=device)[None, :] >= seq_len[:, None]

def train_epoch():
    model.train()
    total_loss = 0
    correct = 0
    total = 0

    for x, y in tqdm(train_loader, desc="Train"):
        x, y = x.to(device), y.to(device)

        # Very simple length heuristic (can be improved later)
        real_lengths = (x.abs().sum(dim=2) > 1e-6).sum(dim=1)
        mask = create_padding_mask(real_lengths, x.size(1))

        optimizer.zero_grad(set_to_none=True)
        logits = model(x, key_padding_mask=mask)

        loss = criterion(logits, y)
        loss.backward()

        # STRONG clipping — very important for landmarks
        grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=0.8)

        optimizer.step()

        total_loss += loss.item()
        correct += (logits.argmax(dim=-1) == y).sum().item()
        total += y.size(0)

        # Debug exploding gradients
        if torch.isnan(loss) or grad_norm > 50:
            print(f"WARNING - NaN or huge grad! norm={grad_norm:.2f}")

    return total_loss / len(train_loader), correct / total * 100

@torch.no_grad()
def evaluate():
    model.eval()
    correct = 0
    total = 0
    for x, y in test_loader:
        x, y = x.to(device), y.to(device)
        real_lengths = (x.abs().sum(dim=2) > 1e-6).sum(dim=1)
        mask = create_padding_mask(real_lengths, x.size(1))

        logits = model(x, key_padding_mask=mask)
        correct += (logits.argmax(dim=-1) == y).sum().item()
        total += y.size(0)
    return correct / total * 100

# ===============================
# TRAINING LOOP
# ===============================
best_acc = 0
patience = 18
wait = 0
epochs = 80

for epoch in range(epochs):
    loss, train_acc = train_epoch()
    test_acc = evaluate()

    print(f"[{epoch+1:2d}/{epochs}]  loss: {loss:.4f}  |  train: {train_acc:.2f}%  |  test: {test_acc:.2f}%")

    scheduler.step()

    if test_acc > best_acc:
        best_acc = test_acc
        wait = 0
        torch.save({
            'model': model.state_dict(),
            'optimizer': optimizer.state_dict(),
            'scaler': scaler,
            'label_encoder_classes': le.classes_
        }, "best_asl_transformer.pth")
        print("→ Saved new best model")
    else:
        wait += 1
        if wait >= patience:
            print("Early stopping triggered")
            break

print(f"\nBest test accuracy achieved: {best_acc:.2f}%")