import os
import polars as pl
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from concurrent.futures import ProcessPoolExecutor
from tqdm import tqdm

# --- 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")
print(f"Using device: {device}")

# --- 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)


# --- EXECUTION ---

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

    # Optimization: Process 100 samples to get a feel for the shape/speed
    # Using multiprocessing to avoid the slow single-thread loop
    paths = asl_data["path"].to_list()

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

    # Stack into one giant Torch tensor
    dataset_tensor = torch.tensor(np.array(results), dtype=torch.float32)
    print(f"Final Tensor Shape: {dataset_tensor.shape}")
    # Shape: (100, 22, 96, 3) -> (Batch, Time, Landmarks, Coords)

    # Initialize Model
    num_unique_signs = asl_data["sign"].n_unique()
    model = ASLClassifier(num_classes=num_unique_signs)
    model.to(device)
    # Test pass
    output = model(dataset_tensor)
    print(f"Model Output Shape: {output.shape}")  # (100, 250)