import json
import os
import sys
from pathlib import Path
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

# Ensure repo root is on path when running directly
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
    sys.path.insert(0, str(ROOT))

from shared.metrics import compute_rouge, compute_bleu, factuality_score  # noqa: E402
from shared.utils import print_banner, load_yaml_config  # noqa: E402
from legaldoc_summarizer.dataset_loader import load_legal_dataset  # noqa: E402


def _resolve_model_id(cfg):
    finetuned = cfg.get("finetuned_model") or os.getenv("LEGALDOC_MODEL_ID")
    local_dir = Path(cfg.get("finetuned_local_dir", "models/legaldoc_summarizer"))
    if finetuned:
        return finetuned
    if local_dir.exists():
        return str(local_dir)
    return cfg["base_model"]


def _build_hf_kwargs(token: str | None) -> dict:
    if not token:
        return {}
    return {"token": token}


def _fallback_hf_kwargs(token: str | None) -> dict:
    if not token:
        return {}
    return {"use_auth_token": token}


def evaluate_model():
    print_banner("Evaluating LegalDoc Summarizer")

    cfg = load_yaml_config(Path(__file__).resolve().parent / "config.yaml")
    model_id = _resolve_model_id(cfg)
    auth_token = os.getenv("HUGGINGFACEHUB_API_TOKEN") or os.getenv("HF_TOKEN")

    kwargs = _build_hf_kwargs(auth_token)
    try:
        tokenizer = AutoTokenizer.from_pretrained(model_id, **kwargs)
        model = AutoModelForSeq2SeqLM.from_pretrained(model_id, **kwargs)
    except TypeError:
        fallback_kwargs = _fallback_hf_kwargs(auth_token)
        tokenizer = AutoTokenizer.from_pretrained(model_id, **fallback_kwargs)
        model = AutoModelForSeq2SeqLM.from_pretrained(model_id, **fallback_kwargs)

    dataset_path = ROOT / "datasets/legal_sample.jsonl"
    if not dataset_path.exists():
        print_banner("Dataset not found. Creating sample dataset...")
        load_legal_dataset()
    dataset = load_dataset("json", data_files=str(dataset_path), split="train[:100]")

    preds, refs = [], []
    for row in dataset:
        inputs = tokenizer(row["question"], return_tensors="pt", truncation=True)
        output = model.generate(**inputs, max_new_tokens=256)
        preds.append(tokenizer.decode(output[0], skip_special_tokens=True))
        refs.append(row["answer"])

    results = {}
    results.update(compute_rouge(preds, refs))
    results.update(compute_bleu(preds, refs))
    results.update(factuality_score(preds, refs))

    results_dir = ROOT / "models/legaldoc_summarizer"
    results_dir.mkdir(parents=True, exist_ok=True)
    with open(results_dir / "eval_results.json", "w") as f:
        json.dump(results, f, indent=2)
    print("✅ Evaluation complete:", results)


if __name__ == "__main__":
    evaluate_model()