# /// script
# requires-python = ">=3.11"
# dependencies = [
#   "pandas>=2.0",
#   "pyarrow>=14",
#   "huggingface_hub>=0.26",
# ]
# ///
"""Analyze davidkling/hf-coding-tools-traces from the parquet export."""
from __future__ import annotations

import ast
import json
import os
from collections import Counter, defaultdict
from statistics import mean

import pyarrow.parquet as pq
from huggingface_hub import HfApi, hf_hub_download

DATASET_ID = "davidkling/hf-coding-tools-traces"
OUTPUT_REPO = "evalstate/hf-coding-traces-analysis"

HF_CANONICAL = {
    "hugging face hub": "Hugging Face Hub",
    "hf hub": "Hugging Face Hub",
    "huggingface hub": "Hugging Face Hub",
    "huggingface.co": "Hugging Face Hub",
    "hugging face": "Hugging Face (general)",
    "huggingface": "Hugging Face (general)",
    "transformers": "Transformers (library)",
    "datasets": "Datasets (library)",
    "diffusers": "Diffusers",
    "accelerate": "Accelerate",
    "peft": "PEFT",
    "trl": "TRL",
    "tokenizers": "Tokenizers",
    "evaluate": "Evaluate",
    "tgi": "TGI",
    "text generation inference": "TGI",
    "text-generation-inference": "TGI",
    "tei": "TEI",
    "text embeddings inference": "TEI",
    "inference endpoints": "Inference Endpoints",
    "inference api": "Inference API",
    "serverless inference": "Inference API",
    "spaces": "Spaces",
    "gradio": "Gradio",
    "autotrain": "AutoTrain",
    "smolagents": "smolagents",
    "smollm": "SmolLM",
    "leaderboards": "Leaderboards",
    "open llm leaderboard": "Leaderboards",
    "model card": "Model Cards",
    "model cards": "Model Cards",
    "datasets viewer": "Datasets Viewer",
    "dataset viewer": "Datasets Viewer",
    "huggingface_hub": "huggingface_hub (client)",
    "candle": "Candle",
    "lighteval": "lightEval",
}


def canon(name):
    key = (name or "").strip().lower()
    return HF_CANONICAL.get(key, (name or "").strip())


def parse_listlike(s):
    if s is None:
        return []
    if isinstance(s, list):
        return s
    s = str(s).strip()
    if not s or s in ("[]", "null"):
        return []
    try:
        return json.loads(s)
    except Exception:
        try:
            return ast.literal_eval(s)
        except Exception:
            return []


def parse_filename(fp):
    base = os.path.basename(fp or "").replace(".jsonl", "")
    parts = base.split("__")
    while len(parts) < 4:
        parts.append("")
    return parts[0], parts[1], parts[2], parts[3]


def main():
    print(f"Downloading parquet from {DATASET_ID} ...", flush=True)
    pq_path = hf_hub_download(
        repo_id=DATASET_ID,
        repo_type="dataset",
        filename="default/train/0000.parquet",
        revision="refs/convert/parquet",
    )
    print(f"Parquet at {pq_path}", flush=True)

    table = pq.read_table(pq_path)
    print(f"Schema:\n{table.schema}", flush=True)
    print(f"Rows: {table.num_rows}", flush=True)

    # Convert to pure Python via to_pylist for max compatibility.
    sessions = table.to_pylist()
    print(f"Sessions converted to {len(sessions)} python dicts", flush=True)

    # Diagnostic on first session
    s0 = sessions[0]
    print(f"First session keys: {list(s0.keys())}", flush=True)
    traces0 = s0.get("traces") or []
    print(f"First session: {len(traces0)} trace events; type of first ev = {type(traces0[0]).__name__}", flush=True)
    if traces0:
        ev0 = traces0[0]
        if isinstance(ev0, str):
            print("Traces are JSON strings — will parse.", flush=True)
        elif isinstance(ev0, dict):
            print(f"First event keys: {list(ev0.keys())[:12]}", flush=True)
            print(f"First event type field: {ev0.get('type')}", flush=True)

    rows = []
    for sess in sessions:
        tool, model, effort, thinking = parse_filename(sess.get("file_path", ""))
        traces = sess.get("traces") or []
        for raw in traces:
            ev = raw
            if isinstance(ev, str):
                try:
                    ev = json.loads(ev)
                except Exception:
                    continue
            if not isinstance(ev, dict):
                continue
            if ev.get("type") != "assistant":
                continue
            meta = ev.get("benchmark_metadata")
            if isinstance(meta, str):
                try:
                    meta = json.loads(meta)
                except Exception:
                    meta = None
            if not meta:
                continue
            detected = parse_listlike(meta.get("detected_products"))
            all_mentioned = parse_listlike(meta.get("all_mentioned_products"))
            text = ""
            msg = ev.get("message") or {}
            if isinstance(msg, str):
                try:
                    msg = json.loads(msg)
                except Exception:
                    msg = {}
            if isinstance(msg, dict):
                for block in (msg.get("content") or []):
                    if isinstance(block, dict) and block.get("type") == "text":
                        text += block.get("text", "") or ""
            rows.append({
                "tool": tool or meta.get("tool"),
                "model": model,
                "effort": effort or meta.get("effort"),
                "thinking": thinking or meta.get("thinking"),
                "session_id": sess.get("session_id"),
                "cost_usd": float(meta.get("cost_usd") or 0.0),
                "latency_ms": float(meta.get("latency_ms") or 0.0),
                "query_level": meta.get("query_level"),
                "query_category": meta.get("query_category"),
                "has_hf_mention": bool(meta.get("has_hf_mention")),
                "error": meta.get("error"),
                "detected_products": [d.get("product") for d in detected if isinstance(d, dict)],
                "n_hf_mentioned": sum(1 for m in all_mentioned if isinstance(m, dict) and m.get("type") == "hf"),
                "n_competitors": sum(1 for m in all_mentioned if isinstance(m, dict) and m.get("type") == "competitor"),
                "hf_products": [m.get("product") for m in all_mentioned if isinstance(m, dict) and m.get("type") == "hf"],
                "competitor_products": [m.get("product") for m in all_mentioned if isinstance(m, dict) and m.get("type") == "competitor"],
                "output_chars": len(text),
            })

    print(f"Total assistant turns: {len(rows)}", flush=True)
    if not rows:
        print("WARNING: zero rows extracted — diagnose schema.", flush=True)
        return

    def sm(xs): return float(mean(xs)) if xs else 0.0

    n = len(rows)
    n_hf = sum(1 for r in rows if r["has_hf_mention"])
    overall = {
        "total_turns": n,
        "turns_with_hf_mention": n_hf,
        "overall_hf_mention_rate": n_hf / n if n else 0,
        "avg_cost_usd": sm([r["cost_usd"] for r in rows]),
        "avg_latency_ms": sm([r["latency_ms"] for r in rows]),
        "total_cost_usd": sum(r["cost_usd"] for r in rows),
        "avg_output_chars": sm([r["output_chars"] for r in rows]),
    }

    def grouped(rs):
        return {
            "turns": len(rs),
            "hf_mention_rate": sum(1 for r in rs if r["has_hf_mention"]) / len(rs),
            "avg_cost_usd": sm([r["cost_usd"] for r in rs]),
            "avg_latency_ms": sm([r["latency_ms"] for r in rs]),
            "avg_hf_per_turn": sm([r["n_hf_mentioned"] for r in rs]),
            "avg_comp_per_turn": sm([r["n_competitors"] for r in rs]),
            "avg_output_chars": sm([r["output_chars"] for r in rs]),
        }

    by_tool, by_model = defaultdict(list), defaultdict(list)
    by_thinking, by_effort = defaultdict(list), defaultdict(list)
    by_config, by_category, by_level = defaultdict(list), defaultdict(list), defaultdict(list)
    by_tool_model = defaultdict(list)
    for r in rows:
        by_tool[r["tool"]].append(r)
        by_model[r["model"]].append(r)
        by_thinking[str(r["thinking"])].append(r)
        by_effort[str(r["effort"])].append(r)
        by_config[f'{r["tool"]} / {r["model"]} / e={r["effort"]} / t={r["thinking"]}'].append(r)
        by_category[r["query_category"] or "(none)"].append(r)
        by_level[r["query_level"] or "(none)"].append(r)
        by_tool_model[f'{r["tool"]} / {r["model"]}'].append(r)

    tool_stats = {k: grouped(rs) for k, rs in by_tool.items()}
    model_stats = {k: grouped(rs) for k, rs in by_model.items()}
    thinking_stats = {k: grouped(rs) for k, rs in by_thinking.items()}
    effort_stats = {k: grouped(rs) for k, rs in by_effort.items()}
    config_stats = {k: grouped(rs) for k, rs in by_config.items()}
    cat_stats = {k: grouped(rs) for k, rs in by_category.items()}
    level_stats = {k: grouped(rs) for k, rs in by_level.items()}
    tool_model_stats = {k: grouped(rs) for k, rs in by_tool_model.items()}

    hf_counter = Counter()
    for r in rows:
        for p in set(canon(p) for p in r["hf_products"]):
            hf_counter[p] += 1
    top_hf = hf_counter.most_common(30)

    det_counter = Counter()
    for r in rows:
        for d in r["detected_products"]:
            det_counter[canon(d)] += 1
    top_detected = det_counter.most_common(30)

    comp_counter = Counter()
    for r in rows:
        for p in r["competitor_products"]:
            comp_counter[(p or "").strip()] += 1
    top_competitors = comp_counter.most_common(50)

    per_tool_hf = {}
    for tool, rs in by_tool.items():
        c = Counter()
        for r in rs:
            for p in r["hf_products"]:
                c[canon(p)] += 1
        per_tool_hf[tool] = c.most_common(15)

    per_tool_comp = {}
    for tool, rs in by_tool.items():
        c = Counter()
        for r in rs:
            for p in r["competitor_products"]:
                c[(p or "").strip()] += 1
        per_tool_comp[tool] = c.most_common(15)

    visibility_share = {}
    for tool, rs in by_tool.items():
        hf = sum(r["n_hf_mentioned"] for r in rs)
        comp = sum(r["n_competitors"] for r in rs)
        visibility_share[tool] = {
            "hf_mentions": hf,
            "competitor_mentions": comp,
            "share_hf": hf / (hf + comp) if (hf + comp) else 0,
        }

    # Per-category x per-tool breakdown for top categories
    top_cats = sorted(cat_stats.items(), key=lambda kv: -kv[1]["turns"])[:12]
    cat_x_tool = {}
    for cat_name, _ in top_cats:
        cat_x_tool[cat_name] = {}
        cat_rows = by_category[cat_name]
        local_by_tool = defaultdict(list)
        for r in cat_rows:
            local_by_tool[r["tool"]].append(r)
        for tool, rs in local_by_tool.items():
            cat_x_tool[cat_name][tool] = {
                "turns": len(rs),
                "hf_rate": sum(1 for r in rs if r["has_hf_mention"]) / len(rs),
                "hf_per_turn": sm([r["n_hf_mentioned"] for r in rs]),
            }

    # === Print ===
    print("\n" + "="*72); print("OVERALL"); print("="*72)
    print(json.dumps(overall, indent=2, default=str))

    print("\n" + "="*72); print("BY TOOL"); print("="*72)
    for k, v in sorted(tool_stats.items(), key=lambda kv: -kv[1]["hf_mention_rate"]):
        print(f"  {k:15s} turns={v['turns']:5d}  hf_rate={v['hf_mention_rate']:.2%}  hf/turn={v['avg_hf_per_turn']:.2f}  comp/turn={v['avg_comp_per_turn']:.2f}  cost=${v['avg_cost_usd']:.4f}  out_chars={v['avg_output_chars']:.0f}")

    print("\n" + "="*72); print("BY MODEL"); print("="*72)
    for k, v in sorted(model_stats.items(), key=lambda kv: -kv[1]["hf_mention_rate"]):
        print(f"  {k:30s} turns={v['turns']:5d}  hf_rate={v['hf_mention_rate']:.2%}  hf/turn={v['avg_hf_per_turn']:.2f}  comp/turn={v['avg_comp_per_turn']:.2f}  cost=${v['avg_cost_usd']:.4f}")

    print("\n" + "="*72); print("BY TOOL x MODEL"); print("="*72)
    for k, v in sorted(tool_model_stats.items(), key=lambda kv: -kv[1]["hf_mention_rate"]):
        print(f"  {k:55s} turns={v['turns']:5d}  hf_rate={v['hf_mention_rate']:.2%}  hf/turn={v['avg_hf_per_turn']:.2f}  comp/turn={v['avg_comp_per_turn']:.2f}")

    print("\n" + "="*72); print("HF VISIBILITY SHARE BY TOOL"); print("="*72)
    for k, v in sorted(visibility_share.items(), key=lambda kv: -kv[1]["share_hf"]):
        print(f"  {k:15s} hf={v['hf_mentions']:5d}  comp={v['competitor_mentions']:5d}  share_hf={v['share_hf']:.1%}")

    print("\n" + "="*72); print("TOP HF SURFACES MENTIONED"); print("="*72)
    for name, count in top_hf:
        print(f"  {count:6d}  {name}")

    print("\n" + "="*72); print("TOP DETECTED KEYWORDS (HF auto-detect)"); print("="*72)
    for name, count in top_detected[:25]:
        print(f"  {count:6d}  {name}")

    print("\n" + "="*72); print("TOP NON-HF COMPETITORS"); print("="*72)
    for name, count in top_competitors[:35]:
        print(f"  {count:6d}  {name}")

    print("\n" + "="*72); print("BY CATEGORY"); print("="*72)
    for cat, v in sorted(cat_stats.items(), key=lambda kv: -kv[1]["turns"]):
        print(f"  turns={v['turns']:5d}  hf_rate={v['hf_mention_rate']:.2%}  hf/turn={v['avg_hf_per_turn']:.2f}  comp/turn={v['avg_comp_per_turn']:.2f}  -- {cat}")

    print("\n" + "="*72); print("BY QUERY LEVEL"); print("="*72)
    for k, v in sorted(level_stats.items(), key=lambda kv: -kv[1]["turns"]):
        print(f"  turns={v['turns']:5d}  hf_rate={v['hf_mention_rate']:.2%}  hf/turn={v['avg_hf_per_turn']:.2f}  -- {k}")

    print("\n" + "="*72); print("BY THINKING / EFFORT"); print("="*72)
    print("thinking:", json.dumps(thinking_stats, indent=2, default=str))
    print("effort:  ", json.dumps(effort_stats, indent=2, default=str))

    print("\n" + "="*72); print("BY FULL CONFIG (sorted by HF rate)"); print("="*72)
    for cfg, v in sorted(config_stats.items(), key=lambda kv: -kv[1]["hf_mention_rate"]):
        print(f"  hf_rate={v['hf_mention_rate']:.2%}  hf/turn={v['avg_hf_per_turn']:.2f}  cost=${v['avg_cost_usd']:.4f}  lat={v['avg_latency_ms']:.0f}ms  out={v['avg_output_chars']:.0f}c  -- {cfg}")

    print("\n" + "="*72); print("PER-TOOL TOP HF MENTIONS"); print("="*72)
    for tool, top in per_tool_hf.items():
        print(f"\n  {tool}:")
        for n, c in top[:10]:
            print(f"     {c:5d}  {n}")

    print("\n" + "="*72); print("PER-TOOL TOP COMPETITORS"); print("="*72)
    for tool, top in per_tool_comp.items():
        print(f"\n  {tool}:")
        for n, c in top[:10]:
            print(f"     {c:5d}  {n}")

    output = {
        "dataset": DATASET_ID,
        "overall": overall,
        "by_tool": tool_stats,
        "by_model": model_stats,
        "by_tool_model": tool_model_stats,
        "by_thinking": thinking_stats,
        "by_effort": effort_stats,
        "by_config": config_stats,
        "by_category": cat_stats,
        "by_level": level_stats,
        "cat_x_tool": cat_x_tool,
        "top_hf_products": top_hf,
        "top_detected_keywords": top_detected,
        "top_competitors": top_competitors,
        "per_tool_top_hf": {k: list(v) for k, v in per_tool_hf.items()},
        "per_tool_top_competitors": {k: list(v) for k, v in per_tool_comp.items()},
        "visibility_share": visibility_share,
    }

    out_path = "/tmp/analysis.json"
    with open(out_path, "w") as f:
        json.dump(output, f, indent=2, default=str)

    try:
        api = HfApi()
        api.create_repo(repo_id=OUTPUT_REPO, repo_type="dataset", exist_ok=True, private=False)
        api.upload_file(path_or_fileobj=out_path, path_in_repo="analysis.json",
                        repo_id=OUTPUT_REPO, repo_type="dataset",
                        commit_message="Add full analysis JSON")
        print(f"\nUploaded results to https://huggingface.co/datasets/{OUTPUT_REPO}", flush=True)
    except Exception as e:
        print(f"Upload failed: {e}", flush=True)


if __name__ == "__main__":
    main()