#!/usr/bin/env python3
"""
MemPal × ConvoMem Benchmark
==============================

Evaluates MemPal's retrieval against the ConvoMem benchmark.
75,336 QA pairs across 6 evidence categories.

For each evidence item:
1. Ingest all conversations into a fresh MemPal palace (one drawer per message)
2. Query with the question
3. Check if any retrieved message matches the evidence messages

Since ConvoMem has 75K items across many files, we sample a subset for benchmarking.
Downloads evidence files from HuggingFace on first run.

Usage:
    python benchmarks/convomem_bench.py                          # sample 100 items
    python benchmarks/convomem_bench.py --limit 500              # sample 500 items
    python benchmarks/convomem_bench.py --category user_evidence  # one category only
    python benchmarks/convomem_bench.py --mode aaak              # test AAAK compression
"""

import os
import sys
import json
import shutil
import ssl
import tempfile
import argparse
import urllib.request
from pathlib import Path
from collections import defaultdict
from datetime import datetime

import chromadb

# Bypass SSL for restricted environments
ssl._create_default_https_context = ssl._create_unverified_context

sys.path.insert(0, str(Path(__file__).parent.parent))

HF_BASE = "https://huggingface.co/datasets/Salesforce/ConvoMem/resolve/main/core_benchmark/evidence_questions"

CATEGORIES = {
    "user_evidence": "User Facts",
    "assistant_facts_evidence": "Assistant Facts",
    "changing_evidence": "Changing Facts",
    "abstention_evidence": "Abstention",
    "preference_evidence": "Preferences",
    "implicit_connection_evidence": "Implicit Connections",
}

# Sample files per category (1_evidence = single-message evidence, simplest)
SAMPLE_FILES = {
    "user_evidence": "1_evidence/0050e213-5032-42a0-8041-b5eef2f8ab91_Telemarketer.json",
    "assistant_facts_evidence": None,  # will discover
    "changing_evidence": None,
    "abstention_evidence": None,
    "preference_evidence": None,
    "implicit_connection_evidence": None,
}


# =============================================================================
# DATA LOADING
# =============================================================================


def download_evidence_file(category, subpath, cache_dir):
    """Download a single evidence file from HuggingFace."""
    url = f"{HF_BASE}/{category}/{subpath}"
    cache_path = os.path.join(cache_dir, category, subpath.replace("/", "_"))
    os.makedirs(os.path.dirname(cache_path), exist_ok=True)

    if os.path.exists(cache_path):
        with open(cache_path) as f:
            return json.load(f)

    print(f"    Downloading: {category}/{subpath}...")
    try:
        urllib.request.urlretrieve(url, cache_path)
        with open(cache_path) as f:
            return json.load(f)
    except Exception as e:
        print(f"    Failed to download {url}: {e}")
        return None


def discover_files(category, cache_dir):
    """Discover available files for a category via HuggingFace API."""
    api_url = f"https://huggingface.co/api/datasets/Salesforce/ConvoMem/tree/main/core_benchmark/evidence_questions/{category}/1_evidence"
    cache_path = os.path.join(cache_dir, f"{category}_filelist.json")

    if os.path.exists(cache_path):
        with open(cache_path) as f:
            return json.load(f)

    try:
        req = urllib.request.Request(api_url)
        with urllib.request.urlopen(req, timeout=15) as resp:
            files = json.loads(resp.read())
            paths = [
                f["path"].split(f"{category}/")[1] for f in files if f["path"].endswith(".json")
            ]
            os.makedirs(os.path.dirname(cache_path), exist_ok=True)
            with open(cache_path, "w") as f:
                json.dump(paths, f)
            return paths
    except Exception as e:
        print(f"    Failed to list files for {category}: {e}")
        return []


def load_evidence_items(categories, limit, cache_dir):
    """Load evidence items from specified categories."""
    all_items = []

    for category in categories:
        # Discover files
        files = discover_files(category, cache_dir)
        if not files:
            # Fallback to known file
            known = SAMPLE_FILES.get(category)
            if known:
                files = [known]
            else:
                print(f"  Skipping {category} — no files found")
                continue

        # Download files until we have enough items
        items_for_cat = []
        for fpath in files:
            if len(items_for_cat) >= limit:
                break
            data = download_evidence_file(category, fpath, cache_dir)
            if data and "evidence_items" in data:
                for item in data["evidence_items"]:
                    item["_category_key"] = category
                    items_for_cat.append(item)

        all_items.extend(items_for_cat[:limit])
        print(f"  {CATEGORIES.get(category, category)}: {len(items_for_cat[:limit])} items loaded")

    return all_items


# =============================================================================
# RETRIEVAL
# =============================================================================


def retrieve_for_item(item, top_k=10, mode="raw"):
    """
    Ingest conversations, query, check if evidence was retrieved.

    Returns:
        recall: float (fraction of evidence messages found in top-k)
        details: dict with retrieved texts and match info
    """
    conversations = item.get("conversations", [])
    question = item["question"]
    evidence_messages = item.get("message_evidences", [])
    evidence_texts = set(e["text"].strip().lower() for e in evidence_messages)

    # Build corpus: one doc per message
    corpus = []
    corpus_speakers = []
    for conv in conversations:
        for msg in conv.get("messages", []):
            corpus.append(msg["text"])
            corpus_speakers.append(msg["speaker"])

    if not corpus:
        return 0.0, {"error": "empty corpus"}

    tmpdir = tempfile.mkdtemp(prefix="mempal_convomem_")
    palace_path = os.path.join(tmpdir, "palace")

    try:
        client = chromadb.PersistentClient(path=palace_path)
        collection = client.create_collection("mempal_drawers")

        # Optionally compress
        if mode == "aaak":
            from mempalace.dialect import Dialect

            dialect = Dialect()
            docs = [dialect.compress(doc) for doc in corpus]
        else:
            docs = corpus

        collection.add(
            documents=docs,
            ids=[f"msg_{i}" for i in range(len(corpus))],
            metadatas=[{"speaker": s, "idx": i} for i, s in enumerate(corpus_speakers)],
        )

        results = collection.query(
            query_texts=[question],
            n_results=min(top_k, len(corpus)),
            include=["documents", "metadatas"],
        )

        # Check if any retrieved message matches evidence
        retrieved_indices = [m["idx"] for m in results["metadatas"][0]]
        retrieved_texts = [corpus[i].strip().lower() for i in retrieved_indices]

        found = 0
        for ev_text in evidence_texts:
            for ret_text in retrieved_texts:
                if ev_text in ret_text or ret_text in ev_text:
                    found += 1
                    break

        recall = found / len(evidence_texts) if evidence_texts else 1.0

        return recall, {
            "retrieved_count": len(retrieved_indices),
            "evidence_count": len(evidence_texts),
            "found": found,
        }

    finally:
        shutil.rmtree(tmpdir, ignore_errors=True)


# =============================================================================
# BENCHMARK RUNNER
# =============================================================================


def run_benchmark(categories, limit_per_cat, top_k, mode, cache_dir, out_file):
    """Run the ConvoMem retrieval benchmark."""

    print(f"\n{'=' * 60}")
    print("  MemPal × ConvoMem Benchmark")
    print(f"{'=' * 60}")
    print(f"  Categories:  {len(categories)}")
    print(f"  Limit/cat:   {limit_per_cat}")
    print(f"  Top-k:       {top_k}")
    print(f"  Mode:        {mode}")
    print(f"{'─' * 60}")
    print("\n  Loading data from HuggingFace...\n")

    items = load_evidence_items(categories, limit_per_cat, cache_dir)

    print(f"\n  Total items: {len(items)}")
    print(f"{'─' * 60}\n")

    all_recall = []
    per_category = defaultdict(list)
    results_log = []
    start_time = datetime.now()

    for i, item in enumerate(items):
        question = item["question"]
        answer = item.get("answer", "")
        cat_key = item.get("_category_key", "unknown")
        CATEGORIES.get(cat_key, cat_key)

        recall, details = retrieve_for_item(item, top_k=top_k, mode=mode)
        all_recall.append(recall)
        per_category[cat_key].append(recall)

        results_log.append(
            {
                "question": question,
                "answer": answer,
                "category": cat_key,
                "recall": recall,
                "details": details,
            }
        )

        status = "HIT" if recall >= 1.0 else ("part" if recall > 0 else "miss")
        if (i + 1) % 20 == 0 or i == len(items) - 1:
            print(
                f"  [{i + 1:4}/{len(items)}] avg_recall={sum(all_recall) / len(all_recall):.3f}  last={status}"
            )

    elapsed = (datetime.now() - start_time).total_seconds()
    avg_recall = sum(all_recall) / len(all_recall) if all_recall else 0

    print(f"\n{'=' * 60}")
    print(f"  RESULTS — MemPal ({mode} mode, top-{top_k})")
    print(f"{'=' * 60}")
    print(f"  Time:        {elapsed:.1f}s ({elapsed / max(len(items), 1):.2f}s per item)")
    print(f"  Items:       {len(items)}")
    print(f"  Avg Recall:  {avg_recall:.3f}")

    print("\n  PER-CATEGORY RECALL:")
    for cat_key in sorted(per_category.keys()):
        vals = per_category[cat_key]
        avg = sum(vals) / len(vals)
        name = CATEGORIES.get(cat_key, cat_key)
        perfect = sum(1 for v in vals if v >= 1.0)
        print(f"    {name:25} R={avg:.3f}  perfect={perfect}/{len(vals)}")

    perfect_total = sum(1 for r in all_recall if r >= 1.0)
    zero_total = sum(1 for r in all_recall if r == 0)
    print("\n  DISTRIBUTION:")
    print(f"    Perfect (1.0):  {perfect_total:4} ({perfect_total / len(all_recall) * 100:.1f}%)")
    print(f"    Zero (0.0):     {zero_total:4} ({zero_total / len(all_recall) * 100:.1f}%)")

    print(f"\n{'=' * 60}\n")

    if out_file:
        with open(out_file, "w") as f:
            json.dump(results_log, f, indent=2)
        print(f"  Results saved to: {out_file}")


# =============================================================================
# CLI
# =============================================================================

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="MemPal × ConvoMem Benchmark")
    parser.add_argument("--limit", type=int, default=100, help="Items per category (default: 100)")
    parser.add_argument("--top-k", type=int, default=10, help="Top-k retrieval (default: 10)")
    parser.add_argument(
        "--category",
        choices=list(CATEGORIES.keys()) + ["all"],
        default="all",
        help="Category to test (default: all)",
    )
    parser.add_argument(
        "--mode",
        choices=["raw", "aaak"],
        default="raw",
        help="Retrieval mode",
    )
    parser.add_argument("--cache-dir", default="/tmp/convomem_cache", help="Cache directory")
    parser.add_argument("--out", default=None, help="Output JSON file")
    args = parser.parse_args()

    if args.category == "all":
        categories = list(CATEGORIES.keys())
    else:
        categories = [args.category]

    if not args.out:
        args.out = f"benchmarks/results_convomem_{args.mode}_top{args.top_k}_{datetime.now().strftime('%Y%m%d_%H%M')}.json"

    run_benchmark(categories, args.limit, args.top_k, args.mode, args.cache_dir, args.out)
