from __future__ import annotations

import argparse
import json
import logging
import sys
import time

from .config import load_settings
from .generator import QuestionGenerator
from .retriever import WikipediaRetriever

logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")


def index_cmd(args):
    """Seed Wikipedia articles + build FAISS index."""
    s = load_settings()
    r = WikipediaRetriever(s)

    titles = getattr(args, "titles", None) or (CORE_TOPICS if getattr(args, "auto_seed", False) else [])
    if titles:
        added = r.preload_pages(titles, lang="en")
        logging.info("Seeded %d new articles", added)

    n = r.build_index()
    print(f"Indexed {n} documents into {s.index_path}")


CORE_TOPICS = [
    "Photosynthesis", "Cell_(biology)", "DNA", "Gravity", "Earth_(planet)",
    "Water_cycle", "Atom", "Chemical_reaction", "Evolution", "Solar_System",
    "Electricity", "Light", "Human_anatomy", "Plant", "Ecosystem",
]


def seed_wikipedia_cmd(args):
    """Standalone: fetch & cache Wikipedia summaries via `wikipedia` lib."""
    import wikipedia
    wikipedia.wikipedia.USER_AGENT = "MultiQGenBot/0.1 (contact@multiqgen.github)"
    lang = getattr(args, "lang", "en")
    titles = args.titles or CORE_TOPICS
    out_path = getattr(args, "out", None)

    wikipedia.set_lang(lang)
    s = load_settings()
    out_path = out_path or s.sources_path
    out_path.parent.mkdir(parents=True, exist_ok=True)

    existing_urls = set()
    if out_path.exists():
        with open(out_path, encoding="utf-8") as fh:
            for line in fh:
                obj = json.loads(line)
                existing_urls.add(obj.get("url"))

    added = 0
    with open(out_path, "a", encoding="utf-8") as fh:
        for title in titles:
            try:
                page = wikipedia.page(title)
                content_url = page.url
                if content_url in existing_urls:
                    logging.info("Skip cached: %s", title)
                    continue
                summary = wikipedia.summary(title, sentences=8)
                if not summary:
                    continue
                obj = {
                    "title": page.title,
                    "lang": lang,
                    "text": summary,
                    "url": content_url,
                }
                fh.write(json.dumps(obj, ensure_ascii=False) + "\n")
                added += 1
                logging.info("Cached: %s (%d chars)", title, len(summary))
                time.sleep(1)  # be polite
            except Exception as e:
                logging.warning("Failed '%s': %s", title, type(e).__name__)
    print(f"Seeded {added} new articles into {out_path}")


def serve_cmd(args):
    from .api import create_app
    app = create_app()
    app.run(host=getattr(args, "host", "0.0.0.0"), port=args.port, debug=False)


def finetune_cmd(args):
    """
    Minimal LoRA finetuning script:
    - uses datasets.load_dataset('cais/mmlu') + ('openai/gsm8k') to
      produce a merged Q/A training set in Arabic & English
    - writes LoRA weights to models/
    """
    from datasets import concatenate_datasets, load_dataset
    from peft import LoraConfig, get_peft_model
    from transformers import (
        AutoModelForCausalLM,
        AutoTokenizer,
        Trainer,
        TrainingArguments,
    )

    out_dir = getattr(args, "out", "models/qwen-lora")
    epochs = getattr(args, "epochs", 2)

    s = load_settings()
    en_mmlu = load_dataset("cais/mmlu", "all", split="train").select(range(5000))
    en_gsm = load_dataset("openai/gsm8k", "main", split="train").select(range(3000))

    def to_qa(ex):
        return {"text": f"Q: {ex['question']}\nA: {ex['answer'].split('####')[-1].strip()}"}

    mmlu_qa = en_mmlu.map(lambda x: {"question": x["question"], "answer": f"{x['choices'][x['answer']]}"})
    gsm_qa = en_gsm.map(to_qa)

    # NOTE: Arabic translation via NLLB not implemented in PoC; using English subset
    combined = concatenate_datasets([mmlu_qa, gsm_qa])
    tokenizer = AutoTokenizer.from_pretrained(s.llm_name)
    tokenizer.pad_token = tokenizer.eos_token

    model = AutoModelForCausalLM.from_pretrained(s.llm_name, torch_dtype="auto", device_map="auto")
    lora = LoraConfig(
        r=16,
        lora_alpha=32,
        target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
        lora_dropout=0.05,
        bias="none",
        task_type="CAUSAL_LM",
    )
    model = get_peft_model(model, lora)

    def tokenize_fn(x):
        return tokenizer(x["text"], truncation=True, padding="max_length", max_length=512)

    tokenized = combined.map(tokenize_fn, remove_columns=["text", "question", "answer", "choices", "subject", "extract"])

    args_ = TrainingArguments(
        output_dir=out_dir,
        per_device_train_batch_size=4,
        gradient_accumulation_steps=4,
        num_train_epochs=epochs,
        fp16=True,
        logging_steps=100,
        save_steps=500,
        save_total_limit=2,
        report_to="none",
    )

    trainer = Trainer(
        model=model,
        args=args_,
        train_dataset=tokenized,
    )
    trainer.train()
    model.save_pretrained(out_dir)
    print("Fine-tuning complete")


def eval_cmd(args):
    """Evaluate accuracy + difficulty calibration."""
    import os
    s = load_settings()
    # use smoke model for quick eval unless overridden
    smoke = os.getenv("MQG_SMOKE", "1") in ("1", "true", "True")
    gen = QuestionGenerator(s, smoke=smoke)
    sample_topics = {
        "easy": ["Photosynthesis", "Water cycle", "Gravity basics"],
        "medium": ["Newton's laws", "Chemical bonding", "Egyptian civilization"],
        "hard": ["Quantum tunneling", "Gödel's theorems", "Mitochondrial DNA"],
    }
    stats = {"easy": [], "medium": [], "hard": []}
    for diff, topics in sample_topics.items():
        for t in topics:
            try:
                q = gen.generate(topic=t, lang="en", difficulty=diff)
                stats[diff].append(q.to_dict())
                print(json.dumps(q.to_dict(), indent=2, ensure_ascii=False))
            except Exception as e:
                print({"error": str(e)}, file=sys.stderr)
    print("Evaluation samples:", len(stats["hard"]), "hard", len(stats["medium"]), "medium", len(stats["easy"]), "easy")


def prefill_cmd(args):
    """Pre-generate a question bank (offline): topics x langs x difficulties x types.

    Generates questions ahead of time and stores them in data/questions.jsonl so
    the game can draw instantly with zero LLM latency / rate limits at runtime.
    """
    from .bank import QuestionBank

    s = load_settings()
    bank = QuestionBank(s.bank_path)
    gen = QuestionGenerator(s, smoke=args.smoke)
    topics = args.topics or CORE_TOPICS
    langs = args.langs
    difficulties = args.difficulties
    types = args.types

    print(f"Bank file: {s.bank_path} (existing: {bank.stats()['total']})")
    generated = 0
    failed = 0
    for topic in topics:
        for lang in langs:
            for diff in difficulties:
                for qtype in types:
                    made = 0
                    attempts = 0
                    while made < args.per_cell and attempts < args.per_cell * 4:
                        attempts += 1
                        try:
                            q = gen.generate(topic=topic, lang=lang, difficulty=diff, qtype=qtype)
                        except Exception as e:
                            logging.error("Failed %s/%s/%s: %s", topic, lang, diff, str(e)[:200])
                            failed += 1
                            break
                        if bank.add(q.to_dict()):
                            made += 1
                            generated += 1
                        else:
                            attempts -= 1  # duplicate id → retry without burning an attempt
                    if made < args.per_cell:
                        logging.warning("Short on %s/%s/%s: %d/%d", topic, lang, diff, made, args.per_cell)
    print(f"Done. Generated {generated} new questions. Bank total: {bank.stats()['total']}. Failures: {failed}")


if __name__ == "__main__":
    parser = argparse.ArgumentParser(prog="mqg")
    subparser = parser.add_subparsers(dest="command")

    p_index = subparser.add_parser("index", help="Build FAISS index from cached summaries")
    p_index.add_argument("--langs", nargs="*")
    p_index.set_defaults(func=index_cmd)

    p_seed = subparser.add_parser("seed-wikipedia", help="Fetch Wikipedia summaries")
    p_seed.add_argument("--lang", default="en")
    p_seed.add_argument("--titles", nargs="*", default=None)
    p_seed.add_argument("--out", default=None)
    p_seed.set_defaults(func=seed_wikipedia_cmd)

    p_serve = subparser.add_parser("serve")
    p_serve.add_argument("--host", default="0.0.0.0")
    p_serve.add_argument("--port", type=int, default=5000)
    p_serve.set_defaults(func=serve_cmd)

    p_ft = subparser.add_parser("finetune")
    p_ft.add_argument("--out", default="models/qwen-lora")
    p_ft.add_argument("--epochs", type=int, default=2)
    p_ft.set_defaults(func=finetune_cmd)

    subparser.add_parser("eval").set_defaults(func=eval_cmd)

    p_prefill = subparser.add_parser("prefill", help="Pre-generate question bank")
    p_prefill.add_argument("--topics", nargs="*", default=None)
    p_prefill.add_argument("--langs", nargs="*", default=["en", "ar"])
    p_prefill.add_argument("--difficulties", nargs="*", default=["easy", "medium", "hard"])
    p_prefill.add_argument("--types", nargs="*", default=["multiple_choice"])
    p_prefill.add_argument("--per-cell", type=int, default=5)
    p_prefill.add_argument("--smoke", action="store_true", help="use the small local model")
    p_prefill.set_defaults(func=prefill_cmd)

    args = parser.parse_args()
    if not hasattr(args, "func"):
        parser.print_help()
        sys.exit(0)
    args.func(args)