src/ls_export_ws.py

"""Convert Label Studio JSON export to BIO format.

Reads a Label Studio JSON export (with annotations), converts span
annotations back to syllable-level BIO tags, and writes BIO format
compatible with tree-1's load_data_vlsp2013().

Usage:
    uv run src/ls_export_ws.py --input ls_export_cycle1.json --output gold_ws_cycle1.txt
"""

import argparse
import json
import sys
import unicodedata
from pathlib import Path


def nfc(text):
    """Normalize text to NFC for consistent Unicode handling."""
    return unicodedata.normalize("NFC", text)


def text_to_syllable_offsets(text):
    """Compute (start, end) character offsets for each space-delimited syllable.

    Returns list of (start, end) tuples.
    """
    offsets = []
    pos = 0
    for syl in text.split(" "):
        start = pos
        end = pos + len(syl)
        offsets.append((start, end))
        pos = end + 1  # skip space
    return offsets


def spans_to_bio(spans, text):
    """Convert Label Studio span annotations to syllable-level BIO tags.

    Args:
        spans: list of span annotation dicts (from Label Studio result)
        text: the original text string

    Returns:
        (syllables, tags) tuple
    """
    syllables = text.split(" ")
    syl_offsets = text_to_syllable_offsets(text)
    n = len(syllables)
    tags = [None] * n

    # Sort spans by start offset
    sorted_spans = sorted(spans, key=lambda s: s["value"]["start"])

    for span in sorted_spans:
        span_start = span["value"]["start"]
        span_end = span["value"]["end"]

        # Find syllables covered by this span
        first_syl = None
        for i, (s, e) in enumerate(syl_offsets):
            if s >= span_start and e <= span_end:
                if first_syl is None:
                    first_syl = i
                    tags[i] = "B-W"
                else:
                    tags[i] = "I-W"

    return syllables, tags


def validate_bio(syllables, tags, sent_id):
    """Validate BIO tags: all syllables tagged, no gaps."""
    errors = []
    for i, (syl, tag) in enumerate(zip(syllables, tags)):
        if tag is None:
            errors.append(f"Syllable {i} ({syl!r}) has no tag")
        elif tag not in ("B-W", "I-W"):
            errors.append(f"Syllable {i} ({syl!r}) has invalid tag {tag!r}")

    if tags and tags[0] == "I-W":
        errors.append("First syllable has I-W tag (should be B-W)")

    for i in range(1, len(tags)):
        if tags[i] == "I-W" and tags[i - 1] is None:
            errors.append(f"I-W at position {i} follows untagged syllable")

    if errors:
        return [f"[{sent_id}] {e}" for e in errors]
    return []


def get_annotations(task):
    """Extract the best annotation result from a task.

    Prefers completed annotations over predictions.
    For annotations, takes the most recent one.
    """
    # Check for human annotations first
    annotations = task.get("annotations", [])
    if annotations:
        # Filter to completed annotations
        completed = [a for a in annotations if not a.get("was_cancelled", False)]
        if completed:
            # Take the most recent
            best = max(completed, key=lambda a: a.get("updated_at", ""))
            return best.get("result", [])

    # Fall back to predictions
    predictions = task.get("predictions", [])
    if predictions:
        return predictions[0].get("result", [])

    return None


def main():
    parser = argparse.ArgumentParser(
        description="Convert Label Studio JSON export to BIO format"
    )
    parser.add_argument(
        "--input",
        required=True,
        help="Path to Label Studio JSON export",
    )
    parser.add_argument(
        "--output",
        required=True,
        help="Output BIO file path",
    )
    parser.add_argument(
        "--require-annotation",
        action="store_true",
        help="Only include tasks with human annotations (skip prediction-only)",
    )
    args = parser.parse_args()

    # Read export
    with open(args.input, "r", encoding="utf-8") as f:
        tasks = json.load(f)
    print(f"Loaded {len(tasks)} tasks from {args.input}")

    sentences = []
    errors = []
    n_annotated = 0
    n_prediction_only = 0

    for task in tasks:
        data = task.get("data", {})
        text = nfc(data.get("text", ""))
        sent_id = data.get("sent_id", "unknown")

        if not text:
            errors.append(f"[{sent_id}] Empty text")
            continue

        # Get annotations
        annotations = task.get("annotations", [])
        completed = [a for a in annotations if not a.get("was_cancelled", False)]
        has_human = len(completed) > 0

        if args.require_annotation and not has_human:
            n_prediction_only += 1
            continue

        result = get_annotations(task)
        if result is None:
            errors.append(f"[{sent_id}] No annotations or predictions")
            continue

        if has_human:
            n_annotated += 1
        else:
            n_prediction_only += 1

        # Filter to label spans only
        spans = [r for r in result if r.get("type") == "labels"]

        # Convert to BIO
        syllables, tags = spans_to_bio(spans, text)

        # Validate
        bio_errors = validate_bio(syllables, tags, sent_id)
        if bio_errors:
            errors.extend(bio_errors)
            continue

        sentences.append({
            "sent_id": sent_id,
            "text": text,
            "syllables": syllables,
            "tags": tags,
        })

    # Report
    print(f"Human-annotated: {n_annotated}")
    print(f"Prediction-only: {n_prediction_only}")

    if errors:
        print(f"\n{len(errors)} errors:")
        for e in errors:
            print(f"  - {e}")

    print(f"\nConverted {len(sentences)} sentences")

    # Sort by sent_id for deterministic output
    sentences.sort(key=lambda s: s["sent_id"])

    # Write BIO
    output_path = Path(args.output)
    with open(output_path, "w", encoding="utf-8") as f:
        for sent in sentences:
            f.write(f"# sent_id = {sent['sent_id']}\n")
            f.write(f"# text = {sent['text']}\n")
            for syl, tag in zip(sent["syllables"], sent["tags"]):
                f.write(f"{syl}\t{tag}\n")
            f.write("\n")

    print(f"Written to {output_path}")

    if errors:
        sys.exit(1)


if __name__ == "__main__":
    main()