#!/usr/bin/env python3
"""

Supports three training modes:
- none (legacy): Periodic checkpoint saves + vLLM restarts
- shared_vllm: Single-copy mode with CUDA IPC weight sharing
- lora_only: LoRA adapter training

Usage:
    # Legacy mode (manages vLLM internally)
    python -m example_trainer.grpo --model-name Qwen/Qwen2.5-3B-Instruct
    
    # Shared vLLM mode (requires external vLLM with VLLM_ENABLE_SHARED_WEIGHTS=1)
    python -m example_trainer.grpo --model-name Qwen/Qwen2.5-3B-Instruct \\
        --weight-bridge-mode shared_vllm

    # LoRA mode (requires external vLLM with --enable-lora)
    python -m example_trainer.grpo --model-name Qwen/Qwen2.5-3B-Instruct \\
        --weight-bridge-mode lora_only --lora-r 16 --lora-alpha 32
"""

from .cli import parse_args, config_from_args
from .trainers import train_legacy, train_shared_vllm, train_lora


def main():
    """Main entry point for GRPO trainer."""
    args = parse_args()
    config = config_from_args(args)

    print("\n" + "="*60)
    print("GRPO TRAINER")
    print("="*60)
    print(f"Model: {config.model_name}")
    print(f"Mode: {config.weight_bridge_mode}")
    print(f"Training steps: {config.training_steps}")
    print(f"GRPO: kl_coef={config.kl_coef}, clip_eps={config.clip_eps}")
    print(f"{'='*60}\n")

    if config.weight_bridge_mode == "shared_vllm":
        # Single-copy mode: attach to vLLM's weights, update in-place
        # Note: For easier usage, consider using run.py which starts vLLM for you
        train_shared_vllm(config)

    elif config.weight_bridge_mode == "lora_only":
        # LoRA mode: freeze base model, train adapters only
        train_lora(config)

    else:
        # Legacy mode: periodic checkpoint saves + vLLM restarts
        train_legacy(config)


if __name__ == "__main__":
    main()