[content] crowd-pilot/serialize_dataset_parquet.py (python)
def _fenced_block(path: str, language: Optional[str], content: str) -> str:
    lang = (language or "").lower()
    return f"```{lang}
{content}
```
"


def _apply_change(content: str, offset: int, length: int, new_text: str) -> str:
    # Mirrors crowd_code_player.replay_file.apply_change
    base = str(content)
    text = str(new_text) if pd.notna(new_text) else ""
    text = text.replace("\
", "
").replace("\
", "
")
    if offset > len(base):
        base = base + (" " * (offset - len(base)))
    return base[:offset] + text + base[offset + length:]


def _session_to_transcript(
    df: pd.DataFrame,
    long_pause_threshold_ms: int,
) -> str:

    file_states: Dict[str, str] = {}
    terminal_state: str = ""
    per_file_event_counts: Dict[str, int] = {}
    per_file_cursor_positions: Dict[str, Tuple[int, int]] = {}  # (offset, length) for each file
    last_time_ms: Optional[int] = None

    parts: List[str] = []

    for i in range(len(df)):
        row = df.iloc[i]
        file_path: str = row["File"]
        event_time: int = row["Time"]
        language: Optional[str] = row["Language"]

        # Long pause detection
        if last_time_ms is not None:
            delta = event_time - last_time_ms
            if delta > long_pause_threshold_ms:
                # TODO (f.srambical): think about whether we want to emit this as an observation or not
                parts.append(f"<obs long_pause ms=\"{delta}\" />")
        last_time_ms = event_time

        event_type = row["Type"]

        match event_type:
            case "tab":
                # File switch event
                parts.append(f"<act focus file=\"{file_path}\" />")
                
                # If Text is present, this is the first time opening the file
                # and the entire file content is captured
                text = row["Text"]
                if pd.notna(text):
                    file_content = str(text).replace("\
", "
").replace("\
", "
")
                    file_states[file_path] = file_content
                    parts.append(f"// observation: file={file_path}")
                    parts.append(_fenced_block(file_path, language, _clean_text(file_content)))

            case "terminal_command":
                # Terminal command execution
                command = row["Text"]
                command_str = str(command).replace("\
", "
").replace("\
", "
")
                parts.append(f"<act terminal_command />")
                parts.append(_fenced_block(file_path, "bash", _clean_text(command_str)))

            case "terminal_output":
                # Terminal output capture
                output = row["Text"]
                output_str = str(output).replace("\
", "
").replace("\
", "
")
                parts.append(f"<obs terminal_output />")
                parts.append(_fenced_block(file_path, None, _clean_text(output_str)))

            case "terminal_focus":
                # Terminal focus event
                parts.append(f"<act focus target=\"terminal\" />")

            case "git_branch_checkout":
                # Git branch checkout event
                branch_info = row["Text"]
                branch_str = str(branch_info).replace("\
", "
").replace("\
", "
")
                parts.append(f"<act git_branch_checkout />")
                parts.append(f"// git: {_clean_text(branch_str)}")

            case "selection_command" | "selection_mouse" | "selection_keyboard":
                # Handle cursor movement
                offset = row["RangeOffset"]
                length = row["RangeLength"]
                old_cursor = per_file_cursor_positions.get(file_path, (0, 0))
                new_cursor = (offset, length)
                per_file_cursor_positions[file_path] = new_cursor
                
                # Emit cursor movement observation if position changed
                if old_cursor != new_cursor:
                    parts.append(f"<act cursor file=\"{file_path}\" offset=\"{offset}\" len=\"{length}\" />")

            case "content":
                # Handle file edit events
                offset = row["RangeOffset"]
                length = row["RangeLength"]
                new_text = row["Text"]
                new_text_str = str(new_text) if pd.notna(new_text) else ""

                operation = "noop"
                if length == 0 and new_text_str:
                    operation = "insert"
                elif length > 0 and not new_text_str:
                    operation = "delete"
                elif length > 0 and new_text_str:
                    operation = "replace"

                parts.append(f"<act {operation} file=\"{file_path}\" offset=\"{offset}\" len=\"{length}\" />")

                if new_text_str and (operation == "insert" or operation == "replace"):
                    parts.append(_fenced_block(file_path, language, _clean_text(new_text_str)))

                before = file_states.get(file_path, "")
                after = _apply_change(before, offset, length, new_text)
                file_states[file_path] = after
                per_file_event_counts[file_path] = per_file_event_counts.get(file_path, 0) + 1

                # Update cursor position after edit (cursor moves to end of inserted/replaced text)
                per_file_cursor_positions[file_path] = (offset + len(new_text_str), 0)

            case _:
                raise ValueError(f"Unknown event type: {event_type}")

    return "
".join(parts).strip()


 


def load_hf_csv(hf_path: str, split: str) -> Dataset:
    loaded = load_dataset(hf_path, split=split)

    assert isinstance(loaded, Dataset), "Expected a Dataset from load_dataset"
    return loaded


def _discover_local_sessions(root: Path) -> List[Path]:
    # Recursively find all CSV files
    paths: List[Path] = []
    for p in root.rglob("*.csv"):
        if p.is_file():
            paths.append(p)
    paths.sort()
    return paths


def _chunk_text(text: str, target_chars: int, overlap_chars: int) -> List[str]:
    """Split a long text into overlapping chunks near target length."""
    if target_chars <= 0:
        return [text]
    n = len(text)
    if n <= target_chars:
        return [text]

    chunks: List[str] = []
    start = 0
    # Ensure sane overlap
    overlap = max(0, min(overlap_chars, target_chars // 2))
    while start < n:
        end_target = min(start + target_chars, n)
        if end_target < n:
            end = end_target
        else:
            end = n
        chunk = text[start:end].strip()
        chunks.append(chunk)
        if end == n:
            break
        # advance with overlap
        start = max(0, end - overlap)
        if start >= n:
            break
    return chunks