[content] crowd-pilot/serialize_dataset_array_record.py (python)
#!/usr/bin/env python3
"""
CSV sessions -> ArrayRecord shards for MaxText Grain pretraining.

This adapts the Parquet serialization logic to write ArrayRecord files
containing UTF-8 encoded transcript chunks as records.

Docs: https://array-record.readthedocs.io/en/latest/
"""

from __future__ import annotations

import argparse
from dataclasses import dataclass
import os
from pathlib import Path
from typing import List, Optional, Tuple, cast, Dict
import random

import pandas as pd  # type: ignore
try:
    from .serialize_common import (
        SerializeConfig,
        _session_to_transcript,
        _discover_local_sessions,
        _chunk_text,
    )
except Exception:  # pragma: no cover
    from serialize_common import (
        SerializeConfig,
        _session_to_transcript,
        _discover_local_sessions,
        _chunk_text,
    )

# ArrayRecord
from array_record.python import array_record_module as arm  # type: ignore


@dataclass
class ArrayRecordSerializeConfig(SerializeConfig):
    arrayrecord_group_size: int


def _clean_text(text: str) -> str:
    # Normalize line endings and strip trailing spaces; preserve tabs/newlines.
    return text.replace("
", "
").replace("
", "
").rstrip()


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 to_array_record(
    cfg: ArrayRecordSerializeConfig,
) -> None:
    os.makedirs(cfg.output_dir, exist_ok=True)

    required_cols = ["Sequence", "Time", "File", "RangeOffset", "RangeLength", "Text", "Language", "Type"]

    # Build a list of per-session DataFrames with file paths
    session_dataframes: List[Tuple[pd.DataFrame, str]] = []
    root = Path(cast(str, cfg.csv_root)).expanduser().resolve()
    csv_files = _discover_local_sessions(root)
    assert csv_files, f"No CSV files found under {root}"
    for csv_file in csv_files:
        df = pd.read_csv(csv_file)
        missing_local = [c for c in required_cols if c not in df.columns]
        assert not missing_local, f"Missing required CSV columns in {csv_file}: {missing_local}"
        session_dataframes.append((df, str(csv_file)))

    random.seed(42)
    session_dataframes = [(df, path) for df, path in session_dataframes]
    random.shuffle(session_dataframes)
    
    total_sessions = len(session_dataframes)
    val_count = int(total_sessions * cfg.val_ratio)
    train_count = total_sessions - val_count

    train_rows = 0
    val_rows = 0
    train_shard_idx = 0
    val_shard_idx = 0
    docs_written = 0

    def write_shard(chunks: List[str], split: str, shard_idx: int) -> int:
        if not chunks:
            return 0
        out_path = Path(cfg.output_dir) / f"{split}_{shard_idx:05d}.array_record"
        # Configure group_size; other options can be added via config if needed
        options = f"group_size:{cfg.arrayrecord_group_size}"
        writer = arm.ArrayRecordWriter(str(out_path), options)
        try:
            for chunk in chunks:
                writer.write(chunk.encode("utf-8"))
        finally:
            writer.close()
        return len(chunks)

    for i, (session_df, session_path) in enumerate(session_dataframes):
        session_df = pd.DataFrame(session_df.copy())
        transcript = _session_to_transcript(
            session_df,
            long_pause_threshold_ms=cfg.long_pause_threshold_ms,
        )
        # Skip sessions that are too short
        if len(transcript.strip()) < cfg.min_session_chars:
            print(f"Skipping session {session_path} because it's too short ({len(transcript.strip())} chars)")
            continue
        chunks = _chunk_text(transcript, cfg.target_chars, cfg.overlap_chars)
        if not chunks:
            continue
        docs_written += len(chunks)
        
        # Write chunks to appropriate split based on position
        if i < train_count:
            rows_written = write_shard(chunks, "train", train_shard_idx)
            train_rows += rows_written
            train_shard_idx += 1
        else:
            rows_written = write_shard(chunks, "val", val_shard_idx)
            val_rows += rows_written
            val_shard_idx += 1
            
        if cfg.max_docs and docs_written >= cfg.max_docs:
            break

    print(f"Wrote {train_rows} train and {val_rows} val documents to {cfg.output_dir}")


def parse_args() -> ArrayRecordSerializeConfig:
    p = argparse.ArgumentParser(description="Serialize HF CSV sessions to ArrayRecord for MaxText Grain")
    p.add_argument("--csv_root", type=str, required=True, help="Root directory containing per-session CSV files")
    p.add_argument("--output_dir", type=str, required=True, help="Output directory for ArrayRecord shards")
    p.add_argument("--shard_size", type=int, default=20000, help="Rows per shard (currently one session per shard)")
    # FIXME(f.srambical): It is awkward that the target number is in character-space instead of in token-space.
    p.add_argument("--target_chars", type=int, default=8192, help="Target characters per document chunk. This should be ~3-4x the max token length of the model you are using.")
    p.add_argument("--overlap_chars", type=int, default=128, help="Character overlap between chunks")
    p.add_argument("--min_session_chars", type=int, default=1024, help="Minimum characters to keep a session")
    p.add_argument("--max_docs", type=int, default=None, help="Stop after writing this many unique docs")
    p.add_argument("--long_pause_threshold_ms", type=int, default=120000, help="Threshold (ms) to annotate long pauses and emit a keyframe")
    p.add_argument("--val_ratio", type=float, default=0.10, help="Fraction of sessions to route to validation [0,1)")
    p.add_argument("--arrayrecord_group_size", type=int, default=1000, help="ArrayRecord group_size option controlling index granularity and compression grouping")
    args = p.parse_args()
    return ArrayRecordSerializeConfig(
        output_dir=args.output_dir,
        shard_size=args.shard_size,
        target_chars=args.target_chars,
        overlap_chars=args.overlap_chars,
        min_session_chars=args.min_session_cha