"""Run vanilla RAG baseline with FAISS retrieval.

Usage:
    python scripts/run_baseline.py --config configs/default.yaml --memory-bank data/memory_bank.pt --question "Who wrote Hamlet?"
"""

import argparse
import logging

import torch
import yaml

from hag.config import EncoderConfig, GeneratorConfig, HopfieldConfig, PipelineConfig
from hag.encoder import Encoder
from hag.generator import Generator
from hag.memory_bank import MemoryBank
from hag.retriever_faiss import FAISSRetriever
from hag.pipeline import RAGPipeline

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)


def main() -> None:
    parser = argparse.ArgumentParser(description="Run vanilla RAG baseline")
    parser.add_argument("--config", type=str, default="configs/default.yaml")
    parser.add_argument("--memory-bank", type=str, required=True)
    parser.add_argument("--question", type=str, required=True)
    parser.add_argument("--top-k", type=int, default=5)
    args = parser.parse_args()

    with open(args.config) as f:
        cfg = yaml.safe_load(f)

    # Override retriever type to faiss
    pipeline_config = PipelineConfig(
        hopfield=HopfieldConfig(**{**cfg.get("hopfield", {}), "top_k": args.top_k}),
        encoder=EncoderConfig(**cfg.get("encoder", {})),
        generator=GeneratorConfig(**cfg.get("generator", {})),
        retriever_type="faiss",
    )

    # Load memory bank to get embeddings for FAISS
    from hag.config import MemoryBankConfig

    mb = MemoryBank(MemoryBankConfig(**cfg.get("memory", {})))
    mb.load(args.memory_bank)

    # Build FAISS index from memory bank embeddings
    import numpy as np

    embeddings_np = mb.embeddings.T.numpy().astype(np.float32)  # (N, d)
    faiss_ret = FAISSRetriever(top_k=args.top_k)
    faiss_ret.build_index(embeddings_np, mb.passages)

    encoder = Encoder(pipeline_config.encoder)
    generator = Generator(pipeline_config.generator)

    pipeline = RAGPipeline(
        config=pipeline_config,
        encoder=encoder,
        generator=generator,
        faiss_retriever=faiss_ret,
    )

    result = pipeline.run(args.question)
    print(f"\nQuestion: {result.question}")
    print(f"Answer: {result.answer}")
    print(f"\nRetrieved passages:")
    for i, p in enumerate(result.retrieved_passages):
        print(f"  [{i+1}] {p[:200]}...")


if __name__ == "__main__":
    main()