traj_normalize/core.py

"""Core normalization logic for Nebius SWE-rebench OpenHands trajectories.

The upstream parquet stores ``tool_call.function.arguments`` as a *serialized
JSON string*. Chat templates (e.g. Qwen3-Coder) iterate ``arguments|items`` and
therefore require a mapping -- feeding the raw string produces empty or broken
tool calls at train/infer time. This module deserializes arguments, canonicalizes
message fields per role, and optionally validates tool calls against the
upstream ``tools.json``.
"""

from __future__ import annotations

import ast
import json
from dataclasses import dataclass, field
from typing import Any, Iterable, Optional

# Canonical per-role fields, mirroring the upstream README's role2field_names.
ROLE_FIELDS = {
    "system": ("role", "content"),
    "user": ("role", "content"),
    "assistant": ("role", "content", "tool_calls"),
    "tool": ("role", "content", "name", "tool_call_id"),
}
KNOWN_ROLES = frozenset(ROLE_FIELDS)


@dataclass
class Stats:
    """Accumulates fix/drop counters; serialized into meta.json."""

    input_rows: int = 0
    resolved_rows: int = 0
    written_success: int = 0
    written_fail: int = 0
    skipped_unresolved: int = 0
    drop_reasons: dict = field(default_factory=dict)
    fixes: dict = field(default_factory=dict)
    unknown_tools: dict = field(default_factory=dict)
    missing_required_params: dict = field(default_factory=dict)
    unparseable_examples: list = field(default_factory=list)

    def bump(self, bucket: dict, key: str, n: int = 1) -> None:
        bucket[key] = bucket.get(key, 0) + n

    def drop(self, reason: str) -> None:
        self.bump(self.drop_reasons, reason)

    def fix(self, kind: str, n: int = 1) -> None:
        self.bump(self.fixes, kind, n)


def load_tools_schema(path: Optional[str]) -> Optional[dict]:
    """Load an OpenAI-style tools.json -> {name: {required: set, properties: set}}."""
    if not path:
        return None
    with open(path) as f:
        tools = json.load(f)
    schema = {}
    for entry in tools:
        fn = entry.get("function", entry)
        name = fn.get("name")
        if not name:
            continue
        params = fn.get("parameters") or {}
        schema[name] = {
            "required": set(params.get("required") or []),
            "properties": set((params.get("properties") or {}).keys()),
        }
    return schema


def deserialize_arguments(raw: Any, stats: Stats) -> tuple[Optional[dict], Optional[str]]:
    """Return (arguments_dict, drop_reason). arguments_dict is None on failure."""
    if raw is None:
        stats.fix("missing_arguments")
        return {}, None
    if isinstance(raw, dict):
        return raw, None
    if not isinstance(raw, str):
        return None, "arguments_not_object"

    value: Any = raw
    rounds = 0
    while isinstance(value, str):
        rounds += 1
        if rounds > 3:
            return None, "arguments_unparseable"
        try:
            value = json.loads(value)
        except (json.JSONDecodeError, ValueError):
            try:
                value = ast.literal_eval(value)
                stats.fix("literal_eval_fallback")
            except (ValueError, SyntaxError, MemoryError):
                snippet = value[:200] if isinstance(value, str) else repr(value)[:200]
                if len(stats.unparseable_examples) < 5:
                    stats.unparseable_examples.append(snippet)
                return None, "arguments_unparseable"
    if rounds > 1:
        stats.fix("double_encoded_arguments")
    stats.fix("arguments_deserialized")
    if not isinstance(value, dict):
        return None, "arguments_not_object"
    return value, None


def normalize_tool_call(tc: Any, stats: Stats) -> tuple[Optional[dict], Optional[str]]:
    """Normalize one tool call to {"id"?, "type"?, "function": {"name", "arguments": dict}}."""
    if not isinstance(tc, dict):
        return None, "tool_call_not_object"

    # Some producers put name/arguments at the top level instead of under "function".
    fn = tc.get("function") if isinstance(tc.get("function"), dict) else None
    if fn is None:
        if "name" in tc or "arguments" in tc:
            fn = {"name": tc.get("name"), "arguments": tc.get("arguments")}
            stats.fix("flattened_tool_call")
        else:
            return None, "tool_call_missing_function"

    args, err = deserialize_arguments(fn.get("arguments"), stats)
    if err:
        return None, err

    out = {"type": tc.get("type", "function"),
           "function": {"name": fn.get("name"), "arguments": args}}
    if tc.get("id") is not None:
        out["id"] = tc["id"]
    return out, None


def normalize_message(msg: Any, stats: Stats) -> tuple[Optional[dict], Optional[str]]:
    if not isinstance(msg, dict):
        return None, "message_not_object"
    role = msg.get("role")
    if role not in KNOWN_ROLES:
        return None, f"unknown_role:{role}"

    out = {k: msg.get(k) for k in ROLE_FIELDS[role] if msg.get(k) is not None or k in ("role", "content")}

    if role == "assistant":
        tcs = msg.get("tool_calls")
        if tcs:
            norm = []
            for tc in tcs:
                ntc, err = normalize_tool_call(tc, stats)
                if err:
                    return None, err
                norm.append(ntc)
            out["tool_calls"] = norm
        else:
            out.pop("tool_calls", None)
    return out, None


def validate_against_tools(messages: Iterable[dict], schema: dict, stats: Stats) -> int:
    """Soft validation: count tool calls whose name or required params are off.

    Returns the number of calls in *these* messages using an unknown tool name.
    """
    unknown = 0
    for msg in messages:
        if msg.get("role") != "assistant":
            continue
        for tc in msg.get("tool_calls") or []:
            fn = tc.get("function", {})
            name = fn.get("name")
            spec = schema.get(name)
            if spec is None:
                stats.bump(stats.unknown_tools, str(name))
                unknown += 1
                continue
            missing = spec["required"] - set((fn.get("arguments") or {}).keys())
            for p in missing:
                stats.bump(stats.missing_required_params, f"{name}:{p}")
    return unknown


def normalize_row(row: dict, stats: Stats, tools_schema: Optional[dict] = None,
                  strict_tools: bool = False) -> tuple[Optional[list], Optional[str]]:
    """Normalize one parquet row -> messages list, or (None, drop_reason)."""
    traj = row.get("trajectory")
    if not traj:
        return None, "empty_trajectory"

    messages = []
    for msg in traj:
        nm, err = normalize_message(msg, stats)
        if err:
            return None, err
        messages.append(nm)

    if not any(m["role"] == "assistant" for m in messages):
        return None, "no_assistant_messages"

    if tools_schema:
        n_unknown = validate_against_tools(messages, tools_schema, stats)
        if strict_tools and n_unknown:
            return None, "unknown_tool"

    return messages, None


def row_to_record(row: dict, messages: list, emit_tools: bool = True) -> dict:
    rec = {
        "messages": messages,
        "trajectory_id": row.get("trajectory_id"),
        "instance_id": row.get("instance_id"),
        "repo": row.get("repo"),
        "resolved": int(row.get("resolved") or 0),
        "exit_status": row.get("exit_status"),
        "model_patch": row.get("model_patch"),
        "gen_tests_correct": row.get("gen_tests_correct"),
        "pred_passes_gen_tests": row.get("pred_passes_gen_tests"),
    }
    if emit_tools and row.get("tools"):
        rec["tools"] = row["tools"]
    return rec