"""
Bloom's Level: Remember
Simple factual recall from long context.
"""
import logging
import os
import random
import time
from typing import List, Dict, Any

from tqdm import tqdm

from src.generator import generate_text
from src.metrics import exact_match_score, compute_accuracy
from src.utils import ensure_dir, save_jsonl, save_json

logger = logging.getLogger(__name__)

FILLERS = [
    "The museum houses artifacts from the ancient world.",
    "Coral reefs support diverse marine ecosystems.",
    "Railway gauges vary between countries.",
    "The periodic table organizes elements systematically.",
    "Cloud formation depends on atmospheric pressure.",
]


def run_remember(
    model_name: str,
    num_sentences: int,
    num_examples: int,
    out_dir: str,
    depths: List[float] = None,
) -> Dict[str, Any]:
    ensure_dir(out_dir)
    if depths is None:
        depths = [0.0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0]

    results = {}
    start = time.time()

    for depth in depths:
        logger.info(f"[REMEMBER] Depth {depth:.1%}")
        preds = []
        for _ in tqdm(range(num_examples), desc=f"Remember {depth:.1%}", leave=False):
            sents = [random.choice(FILLERS) for _ in range(num_sentences)]
            secret = f"FACT-{random.randint(1000, 9999)}"
            fact = f"The critical fact is: {secret}."
            idx = int(depth * len(sents))
            sents.insert(idx, fact)
            doc = " ".join(sents)
            prompt = f"Read the text and extract the critical fact.\n\n{doc}\n\nCritical fact:"
            ans = generate_text(
                [{"role": "user", "content": prompt}],
                model_name=model_name,
                max_new_tokens=20,
            )
            correct = exact_match_score(ans, secret)
            preds.append({
                "model_answer": ans,
                "correct": correct,
                "secret": secret,
                "depth": depth,
            })

        save_jsonl(os.path.join(out_dir, f"remember_depth_{depth}.jsonl"), preds)
        acc = compute_accuracy(preds)
        results[depth] = {"accuracy": acc, "predictions": preds}
        logger.info(f"[REMEMBER] Depth {depth:.1%}: acc={acc:.3f}")

    summary = {
        "experiment": "remember",
        "cognitive_level": "remember",
        "num_sentences": num_sentences,
        "num_examples": num_examples,
        "depths": {str(d): results[d]["accuracy"] for d in depths},
        "time_minutes": (time.time() - start) / 60,
    }
    save_json(os.path.join(out_dir, "remember_summary.json"), summary)
    logger.info(f"[REMEMBER] Time={(time.time()-start)/60:.1f} min")
    return summary