#!/usr/bin/env python3
"""Quick launcher for Pear1.5 hardware-agnostic training.

Usage:
    python launch.py --platform colab
    python launch.py --platform kaggle-t4
    python launch.py --platform kaggle-tpu
    python launch.py --platform local
    python launch.py --platform all
"""
import argparse
import asyncio
import os
import sys
import subprocess
from pathlib import Path

# Add scripts to path
sys.path.insert(0, str(Path(__file__).parent))

from hardware import get_hardware_info, get_training_config, print_hardware_summary


def launch_colab(notebook_path: str = None):
    """Launch on Colab via browser."""
    url = "https://colab.research.google.com/drive/1BCLulJMl1xEErqRPbzn9caxH4wk56wBB"
    print(f"Opening Colab: {url}")
    print("1. Connect to runtime (Runtime -> Connect)")
    print("2. Run the first cell to install dependencies")
    print("3. Run the training cell")
    
    if sys.platform == "win32":
        os.system(f'start "" "{url}"')
    elif sys.platform == "darwin":
        os.system(f'open "{url}"')
    else:
        os.system(f'xdg-open "{url}"')


def launch_kaggle(gpu_type: str = "t4"):
    """Launch on Kaggle."""
    url = "https://www.kaggle.com/code/new"
    print(f"Opening Kaggle: {url}")
    print(f"1. Set accelerator to: {'TPU v5e-8' if gpu_type == 'tpu' else 'GPU T4 x2'}")
    print("2. Add dataset: pear15-expanded-data")
    print("3. Add HF_TOKEN secret")
    print("4. Paste train_agnostic.py content into a cell and run")
    
    if sys.platform == "win32":
        os.system(f'start "" "{url}"')
    elif sys.platform == "darwin":
        os.system(f'open "{url}"')
    else:
        os.system(f'xdg-open "{url}"')


def launch_lightning():
    """Launch on Lightning AI."""
    url = "https://lightning.ai/tk8679a/studios"
    print(f"Opening Lightning AI: {url}")
    print("1. Create new Studio with A100")
    print("2. Open terminal")
    print("3. Run: pip install -r requirements.txt")
    print("4. Run: python train_agnostic.py --data-path /data --output-dir /checkpoints")
    
    if sys.platform == "win32":
        os.system(f'start "" "{url}"')
    elif sys.platform == "darwin":
        os.system(f'open "{url}"')
    else:
        os.system(f'xdg-open "{url}"')


def launch_local(args):
    """Launch training locally."""
    print("Launching local training...")
    
    cmd = [
        sys.executable, "scripts/train_agnostic.py",
        "--data-path", args.data_path,
        "--output-dir", args.output_dir,
        "--resume-from", args.resume_from,
    ]
    
    if args.max_steps:
        cmd.extend(["--max-steps", str(args.max_steps)])
    if args.hf_model_path:
        cmd.extend(["--hf-model-path", args.hf_model_path])
    if args.seed:
        cmd.extend(["--seed", str(args.seed)])
        
    print(f"Running: {' '.join(cmd)}")
    subprocess.run(cmd, cwd=Path(__file__).parent.parent)


def launch_all():
    """Launch on all available platforms."""
    print("=" * 60)
    print("LAUNCHING ON ALL PLATFORMS")
    print("=" * 60)
    
    # Detect local hardware
    hw = get_hardware_info()
    config = get_training_config(hw)
    print_hardware_summary(hw, config)
    
    # Launch local in background
    print("\n1. Starting local training...")
    import subprocess
    local_proc = subprocess.Popen([
        sys.executable, "scripts/train_agnostic.py",
        "--data-path", "/content/pear15-expanded-data",
        "--output-dir", "/content/checkpoints/local",
        "--resume-from", "latest",
    ], cwd=Path(__file__).parent.parent)
    
    # Open Colab
    print("\n2. Opening Colab A100...")
    launch_colab()
    
    # Open Kaggle T4
    print("\n3. Opening Kaggle T4...")
    launch_kaggle("t4")
    
    # Open Kaggle TPU
    print("\n4. Opening Kaggle TPU v5e-8...")
    launch_kaggle("tpu")
    
    # Open Lightning
    print("\n5. Opening Lightning AI...")
    launch_lightning()
    
    print("\n" + "=" * 60)
    print("ALL PLATFORMS LAUNCHED")
    print("=" * 60)
    print("Monitor progress:")
    print("  - Colab: Check notebook output")
    print("  - Kaggle: Check notebook output")  
    print("  - Local: Check terminal output")
    print("  - Checkpoints saved to respective output dirs")
    print("\nPress Ctrl+C to stop local training")
    
    try:
        local_proc.wait()
    except KeyboardInterrupt:
        local_proc.terminate()
        print("Stopped local training")


def main():
    parser = argparse.ArgumentParser(description="Pear1.5 Hardware-Agnostic Training Launcher")
    parser.add_argument("--platform", choices=["colab", "kaggle-t4", "kaggle-tpu", "lightning", "local", "all"],
                        default="local", help="Platform to launch on")
    parser.add_argument("--data-path", default="/content/pear15-expanded-data",
                        help="Path to expanded dataset")
    parser.add_argument("--output-dir", default="/content/checkpoints",
                        help="Output directory for checkpoints")
    parser.add_argument("--resume-from", default="latest",
                        help="Resume from checkpoint (step, 'latest', or path)")
    parser.add_argument("--max-steps", type=int, default=None,
                        help="Maximum steps to run")
    parser.add_argument("--hf-model-path", default=None,
                        help="HF model path for fresh init")
    parser.add_argument("--seed", type=int, default=0)
    args = parser.parse_args()
    
    if args.platform == "colab":
        launch_colab()
    elif args.platform == "kaggle-t4":
        launch_kaggle("t4")
    elif args.platform == "kaggle-tpu":
        launch_kaggle("tpu")
    elif args.platform == "lightning":
        launch_lightning()
    elif args.platform == "local":
        launch_local(args)
    elif args.platform == "all":
        launch_all()


if __name__ == "__main__":
    main()