testsAndMisc/python_pkg/token_audit/parse.py

"""Read Claude Code transcripts into :class:`Session` objects.

Transcripts are JSONL under ``~/.claude/projects/<slug>/<session-id>.jsonl``,
one JSON object per line, appended live. Three properties of that format drive
the design here:

1. **Lines can be malformed.** A transcript being written while it is read can
   end in a partial line, and older sessions contain records this package does
   not model. Every line is therefore parsed defensively and skipped on error;
   losing one line is always better than aborting a weekly report.
2. **A tool call and its result are in different messages.** ``tool_use`` blocks
   carry the name and input, ``tool_result`` blocks carry the payload and only a
   ``tool_use_id`` back-reference. Attributing result size to the right tool
   requires holding an id map for the length of the file.
3. **Order matters.** The image-cost model needs to know how many turns followed
   each image, so :func:`iter_events` preserves the interleaving rather than
   returning separate lists.
"""

from __future__ import annotations

import json
import re
from typing import TYPE_CHECKING, Any

from python_pkg.token_audit import discover, records, turns
from python_pkg.token_audit.model import IMAGE_SUFFIXES, Session, ToolCall, Turn

if TYPE_CHECKING:
    from collections.abc import Iterator
    from pathlib import Path

# Divisor turning rendered characters into an approximate token count. Four is
# the usual English-text rule of thumb and is only ever used for ranking tools,
# never for the reconciled totals.
CHARS_PER_TOKEN = 4

# Median billed size of one screenshot, measured from real transcripts by
# isolating turns whose only new content was a single image (n=484, median
# 2,566, p90 2,692). Anthropic bills images by pixel area, so a constant is a
# far better estimate than the base64 payload length, which is ~20x too high.
# Halving a screenshot's dimensions cuts this roughly fourfold.
IMAGE_TOKENS = 2566

# Commands that land work: a plain commit or the scripted wrap-up.
COMMIT = re.compile(r"\bgit(\s+-C\s+\S+)?\s+commit\b|finish_auto\.sh")


def _load_line(line: str) -> dict[str, Any] | None:
    """Parse one JSONL line, returning ``None`` if it is not a usable record."""
    try:
        record = json.loads(line)
    except ValueError, UnicodeDecodeError:
        return None
    return record if isinstance(record, dict) else None


def _blocks(record: dict[str, Any]) -> list[dict[str, Any]]:
    """Return the content blocks of a record's message, if it has any."""
    message = record.get("message")
    if not isinstance(message, dict):
        return []
    content = message.get("content")
    if not isinstance(content, list):
        return []
    return [block for block in content if isinstance(block, dict)]


def _result_tokens(block: dict[str, Any], *, is_image: bool = False) -> int:
    """Estimate how many tokens a tool result occupied in context.

    Text is charged by length, but an **image is not**. An image arrives as a
    base64 blob whose character count has nothing to do with its billed size —
    Anthropic charges by pixel area. Measuring images by payload length
    overstates them by roughly 20x (a 240KB PNG looks like ~82k tokens against
    a real cost near 2.5k), which is enough to make screenshots look like the
    dominant cost driver when they are not. Validated against real transcripts
    by isolating turns whose only new content was one image: the context grew
    ~1.6-3k tokens, never the tens of thousands the payload length implied.
    """
    content = block.get("content")
    if content is None:
        return 0
    if is_image:
        return IMAGE_TOKENS
    return len(json.dumps(content)) // CHARS_PER_TOKEN


def iter_events(
    path: Path,
    window: records.Window | None = None,
) -> Iterator[tuple[str, object]]:
    """Yield ``("turn", Turn)`` and ``("tool", ToolCall)`` in transcript order.

    Emitting a single ordered stream — rather than two lists — is what lets
    :mod:`imagecost` count how many turns each image survived for. Turns are
    one per API message (see :mod:`records`), and only records inside
    ``window`` are emitted.
    """
    pending: dict[str, dict[str, Any]] = {}
    merger = records.TurnMerger()
    with path.open(encoding="utf-8", errors="replace") as handle:
        for line in handle:
            record = _load_line(line)
            if record is None or not records.in_window(record, window):
                continue
            pairs: list[tuple[str, object]] = []
            for block in _blocks(record):
                pairs += _block_events(block, pending, merger)
            yield from pairs
            found = turns.usage_of(record)
            if found is not None:
                done = merger.push(turns.build_turn(record, *found))
                if done is not None:
                    yield "turn", done
    held = merger.flush()
    if held is not None:
        yield "turn", held


def _block_events(
    block: dict[str, Any],
    pending: dict[str, dict[str, Any]],
    merger: records.TurnMerger,
) -> list[tuple[str, object]]:
    """Events one content block emits: remember a ``tool_use``, pair a result.

    A ``tool_result`` ends the assistant message before it, so the held turn is
    released first to keep the stream in transcript order.
    """
    kind = block.get("type")
    if kind == "tool_use":
        call_id = block.get("id")
        if isinstance(call_id, str):
            pending[call_id] = block
        return []
    if kind != "tool_result":
        return []
    out: list[tuple[str, object]] = []
    held = merger.flush()
    if held is not None:
        out.append(("turn", held))
    call = _pair_result(block, pending)
    if call is not None:
        out.append(("tool", call))
    return out


def _pair_result(
    block: dict[str, Any],
    pending: dict[str, dict[str, Any]],
) -> ToolCall | None:
    """Join a ``tool_result`` back to the ``tool_use`` that produced it."""
    use = pending.pop(block.get("tool_use_id", ""), None)
    if use is None:
        return None
    tool_input = use.get("input")
    tool_input = tool_input if isinstance(tool_input, dict) else {}
    path = tool_input.get("file_path")
    skill = tool_input.get("skill")
    command = tool_input.get("command")
    call_path = path if isinstance(path, str) else None
    return ToolCall(
        name=str(use.get("name") or "unknown"),
        result_tokens=_result_tokens(block, is_image=_looks_like_image(call_path)),
        path=call_path,
        skill=skill if isinstance(skill, str) else None,
        commit=isinstance(command, str) and COMMIT.search(command) is not None,
    )


def _looks_like_image(path: str | None) -> bool:
    """Whether a tool-result path refers to an image, by extension."""
    if path is None:
        return False
    dot = path.rfind(".")
    return dot != -1 and path[dot:].lower() in IMAGE_SUFFIXES


def load_session(path: Path, window: records.Window | None = None) -> Session:
    """Read one transcript file into a :class:`Session`.

    A subagent transcript (``<session>/subagents/agent-*.jsonl``) is tagged
    with its parent session id and the ``agentType``/``description`` the
    spawning ``Agent`` call recorded in the sibling ``.meta.json``. With no
    description anywhere, the first prompt line stands in, then the agent id.
    """
    session = Session(
        session_id=path.stem, path=str(path), cwd=discover.first_cwd(path)
    )
    if path.parent.name == "subagents":
        meta = discover.agent_meta(path)
        session.parent_id = path.parent.parent.name
        session.agent_type = str(meta.get("agentType") or "unknown")
        session.description = str(
            meta.get("description")
            or discover.first_prompt(path)
            or f"(no label) {path.stem}"
        )
    for kind, event in iter_events(path, window):
        if kind == "turn" and isinstance(event, Turn):
            session.turns.append(event)
        elif isinstance(event, ToolCall):
            session.tools.append(event)
    return session


# Public names for the record primitives, shared with token_dashboard's walker
# (which needs prompt boundaries and timestamps that iter_events drops).
load_line = _load_line
content_blocks = _blocks
pair_result = _pair_result