Datasets:

Urdatorn
/

sphragis

from collections import Counter, defaultdict
import json
from pathlib import Path
import re

import pyarrow.parquet as pq
import pytest

from scripts.build_dataset import HypotacticParser
from scripts.conllu_units import load_conllu_units
from scripts.metre_units import (
    line_features,
    load_metre_units,
    load_syllable_units,
    syllable_line,
)
from scripts.dataset_variants import (
    BOTTLENECK_TARGET,
    ELIGIBILITY_MINIMUM,
    METRE_BASE_CONFIGS,
    PUBLIC_CONFIG_PREFIX,
    SENTENCE_BASE_CONFIGS,
)
from scripts.metrical_lines import PUBLIC_METRICAL_LINE_FIELD_SET, sanitize_metrical_lines
from scripts.text_units import load_text_units


REPO_ROOT = Path(__file__).resolve().parents[1]
METRE_ROOT = REPO_ROOT.parent / "sphragis-metre"
SPLITS = ("train", "validation", "validation2", "test")
METRE_TRACKS = tuple(PUBLIC_CONFIG_PREFIX[base] for base in METRE_BASE_CONFIGS)
PUBLICATIONS = (
    *((REPO_ROOT, base, (1, 5, 10)) for base in SENTENCE_BASE_CONFIGS),
    *((METRE_ROOT, track, (1, 5, 10)) for track in METRE_TRACKS),
)
# The per-author unit table in each README documents the main track only; the
# other tracks' per-author counts live in build_report.json, because several
# tables of the same shape in one README cannot be told apart by pattern.
README_AUTHOR_TABLES = tuple(
    publication for publication in PUBLICATIONS if publication[1] in {"sentence", "verse"}
)
TREEBANK_SOURCES = {
    "agdt", "ud_perseus", "ud_proiel", "ud_ptnk", "gorman", "pedalion",
    "harrington",
}


def config_path(root: Path, base: str, suffix: int, split: str) -> Path:
    return root / "data" / f"{base}_{suffix}" / f"{split}-00000-of-00001.parquet"


def test_sentence_build_uses_every_treebank_and_no_external_metrical_source() -> None:
    revisions = json.loads((REPO_ROOT / "metadata/source_revisions.json").read_text())
    report = json.loads((REPO_ROOT / "metadata/build_report.json").read_text())
    inventory = report["authorship_curation"]["treebank_source_inventory"]
    assert set(revisions) == TREEBANK_SOURCES
    assert set(inventory["rows_by_source"]) == TREEBANK_SOURCES
    assert all(count > 0 for count in inventory["rows_by_source"].values())
    assert sum(inventory["rows_by_source"].values()) == inventory["total_rows"]


def test_readme_configuration_counts_match_parquet_rows() -> None:
    for root, base, suffixes in PUBLICATIONS:
        readme = (root / "README.md").read_text(encoding="utf-8")
        table_rows = {
            match.group("config"): {
                "authors": int(match.group("authors").replace(",", "")),
                "train": int(match.group("train").replace(",", "")),
                "validation": int(match.group("validation").replace(",", "")),
                "validation2": int(match.group("validation2").replace(",", "")),
                "test": int(match.group("test").replace(",", "")),
            }
            for match in re.finditer(
                r"^\| `(?P<config>[^`]+)` \| (?P<authors>[\d,]+) \| [^|]+ "
                r"\| (?P<train>[\d,]+) \| (?P<validation>[\d,]+) "
                r"\| (?P<validation2>[\d,]+) \| (?P<test>[\d,]+) \|$",
                readme,
                flags=re.MULTILINE,
            )
        }
        # One README documents both sentence tracks, so match on this base.
        table_rows = {
            config: counts for config, counts in table_rows.items()
            if config.rsplit("_", 1)[0] == base
        }
        expected_configs = {f"{base}_{suffix}" for suffix in suffixes}
        assert set(table_rows) == expected_configs
        for config in sorted(expected_configs):
            expected = {}
            authors = set()
            for split in SPLITS:
                path = root / "data" / config / f"{split}-00000-of-00001.parquet"
                table = pq.read_table(path, columns=["author"])
                expected[split] = table.num_rows
                authors.update(table["author"].to_pylist())
            assert table_rows[config] == {"authors": len(authors), **expected}


def test_readme_author_unit_counts_and_shares_match_atomic_parquets() -> None:
    for root, base, _suffixes in README_AUTHOR_TABLES:
        readme = (root / "README.md").read_text(encoding="utf-8")
        documented = {
            match.group("author"): {
                **{
                    split: int(match.group(split).replace(",", ""))
                    for split in ("train", "validation", "validation2", "test", "total")
                },
                "share": match.group("share"),
            }
            for match in re.finditer(
                r"^\| (?P<author>[^|*]+?) \| (?P<train>[\d,]+) "
                r"\| (?P<validation>[\d,]+) \| (?P<validation2>[\d,]+) "
                r"\| (?P<test>[\d,]+) "
                r"\| (?P<total>[\d,]+) \((?P<share>\d+\.\d%)\) \|$",
                readme,
                flags=re.MULTILINE,
            )
        }
        expected = defaultdict(Counter)
        for split in SPLITS:
            authors = pq.read_table(
                config_path(root, base, 1, split), columns=["author"],
            )["author"].to_pylist()
            for author in authors:
                expected[author][split] += 1
                expected[author]["total"] += 1
        grand_total = sum(counts["total"] for counts in expected.values())
        assert documented == {
            author: {
                **dict(counts),
                "share": f"{100 * counts['total'] / grand_total:.1f}%",
            }
            for author, counts in expected.items()
        }


def test_all_atomic_splits_are_balanced_and_chunkable() -> None:
    for root, base, _suffixes in PUBLICATIONS:
        counts = defaultdict(Counter)
        for split in SPLITS:
            table = pq.read_table(
                config_path(root, base, 1, split), columns=["author", "split"],
            )
            for row in table.to_pylist():
                counts[row["author"]][row["split"]] += 1
        assert counts
        for author, author_counts in counts.items():
            for split in SPLITS:
                assert author_counts[split] >= ELIGIBILITY_MINIMUM[split], (base, author, split)
                assert author_counts[split] % BOTTLENECK_TARGET == 0, (
                    base, author, split,
                )


def test_chunk_sizes_and_atomic_coverage_are_exact_in_every_split() -> None:
    for root, base, suffixes in PUBLICATIONS:
        atomic = {}
        for split in SPLITS:
            atomic[split] = {
                row["id"]: load_text_units(row["text"])
                for row in pq.read_table(
                    config_path(root, base, 1, split), columns=["id", "text"],
                ).to_pylist()
            }
        for suffix in suffixes[1:]:
            for split in SPLITS:
                # The metre publication names the column "conllu" in every one of
                # its tracks; the sentence publication carries two parse layers.
                conllu_column = "conllu" if root == METRE_ROOT else "conllu_human"
                rows = pq.read_table(
                    config_path(root, base, suffix, split),
                    columns=["text", conllu_column, "chunk_size", "constituent_ids"],
                ).to_pylist()
                represented = [
                    row_id for row in rows for row_id in row["constituent_ids"]
                ]
                assert len(represented) == len(set(represented))
                assert set(represented) == set(atomic[split])
                for row in rows:
                    assert row["chunk_size"] == suffix
                    assert len(row["constituent_ids"]) == suffix
                    assert len(load_text_units(row["text"])) == suffix
                    assert len(load_conllu_units(row[conllu_column])) == suffix
                    assert load_text_units(row["text"]) == [
                        unit
                        for row_id in row["constituent_ids"]
                        for unit in atomic[split][row_id]
                    ]


def test_sentence_publication_contains_no_hypotactic_fields_or_provenance() -> None:
    forbidden = {
        "metre", "metrical_lines", "syllables", "scansion",
        "alignment_component_id", "component_sentence_index",
        "component_line_index", "parent_sentence_ids", "hypotactic_file",
        "book", "poem_sequence", "line_number",
    }
    for suffix in (1, 5, 10):
        for split in SPLITS:
            path = config_path(REPO_ROOT, "sentence", suffix, split)
            schema = pq.ParquetFile(path).schema_arrow
            assert not (forbidden & set(schema.names))
            rows = pq.read_table(path, columns=["genre", "source_records"]).to_pylist()
            assert {row["genre"] for row in rows} <= {"prose", "verse"}
            for row in rows:
                assert all(
                    record["source"] != "hypotactic"
                    for record in row["source_records"]
                )


def test_metre_publication_omits_redundant_genre_column() -> None:
    for suffix in (1, 5, 10):
        for split in SPLITS:
            path = config_path(METRE_ROOT, "verse", suffix, split)
            assert "genre" not in pq.ParquetFile(path).schema_arrow.names


def test_metre_chunks_preserve_aligned_metre_and_syllable_line_units() -> None:
    atomic_syllables = {}
    atomic_metres = {}
    for split in SPLITS:
        rows = pq.read_table(
            config_path(METRE_ROOT, "verse", 1, split),
            columns=["id", "metre", "syllables"],
        ).to_pylist()
        atomic_syllables[split] = {
            row["id"]: load_syllable_units(row["syllables"])
            for row in rows
        }
        atomic_metres[split] = {
            row["id"]: load_metre_units(row["metre"])
            for row in rows
        }
        assert all(len(units) == 1 for units in atomic_syllables[split].values())
        assert all(len(units) == 1 for units in atomic_metres[split].values())
    for suffix in (5, 10):
        for split in SPLITS:
            rows = pq.read_table(
                config_path(METRE_ROOT, "verse", suffix, split),
                columns=["constituent_ids", "metre", "syllables"],
            ).to_pylist()
            for row in rows:
                assert load_metre_units(row["metre"]) == [
                    metre
                    for row_id in row["constituent_ids"]
                    for metre in atomic_metres[split][row_id]
                ]
                assert load_syllable_units(row["syllables"]) == [
                    syllables
                    for row_id in row["constituent_ids"]
                    for syllables in atomic_syllables[split][row_id]
                ]


def parse_boundary_fixture(separator: str) -> list[dict]:
    parser = HypotacticParser("test")
    parser.feed(
        '<div class="poem" data-author="Test" data-work="Test" data-number="1">'
        '<div class="line hexameter" data-number="1" data-metre="hexameter">'
        '<span class="word"><span class="syll long">μῆ</span></span>'
        f'{separator}<span class="word"><span class="syll long">ἄει</span></span>'
        f'{separator}<span class="word"><span class="syll short hiatus">θεά</span></span>'
        '</div></div>'
    )
    return parser.lines[0]["syllables"]


def test_hypotactic_word_boundaries_are_serialization_invariant() -> None:
    minified = parse_boundary_fixture("")
    spaced = parse_boundary_fixture(" \n  ")
    assert minified == spaced
    assert [syllable["features"] for syllable in minified] == [
        ["caesura", "hiatus"], ["caesura"], [],
    ]


def test_hypotactic_upstream_hiatus_is_replaced_by_derived_hiatus() -> None:
    syllables = parse_boundary_fixture("")
    assert "hiatus" not in syllables[-1]["features"]


def test_bare_hypotactic_diastole_is_long() -> None:
    parser = HypotacticParser("test")
    parser.feed(
        '<div class="poem" data-author="Test" data-work="Test" data-number="1">'
        '<div class="line hexameter" data-number="1" data-metre="hexameter">'
        '<span class="word"><span class="syll diastole">ας</span></span>'
        '</div></div>'
    )
    assert parser.lines[0]["syllables"][0]["quantity"] == "long"


def test_line_features_use_half_morae_and_listed_caesura_positions() -> None:
    syllables = [
        {"text": "μῆ", "quantity": "long", "features": []},
        {"text": "νι", "quantity": "short", "features": []},
        {
            "text": "ν",
            "quantity": "short",
            "features": ["caesura", "hiatus"],
        },
        {"text": "ἄει", "quantity": "long", "features": []},
    ]
    assert syllable_line(syllables) == {
        "features": {
            "hiatus": 1,
            "longa": 2,
            "brevia": 2,
            "morae": 3,
            "caesurae": [{"isochronic": 2, "isosyllabic": 3}],
        },
        "syllables": syllables,
    }


def test_unquantified_syllable_rejects_line_features() -> None:
    with pytest.raises(ValueError, match="cannot derive line time"):
        line_features([
            {"text": "?", "quantity": "unknown", "features": []},
        ])


def test_upstream_hypotactic_syllable_classes_are_not_published() -> None:
    parser = HypotacticParser("test")
    parser.feed(
        '<div class="poem" data-author="Test" data-work="Test" data-number="1">'
        '<div class="line hexameter" data-number="1" data-metre="hexameter">'
        '<span class="word"><span class="syll long lbn correption">μῆ</span></span>'
        '<span class="word"><span class="syll short fixme context-menu-active">νι</span>'
        '<span class="syll long synizesis">ν</span></span>'
        '</div></div>'
    )
    assert [syllable["features"] for syllable in parser.lines[0]["syllables"]] == [
        ["caesura"], [], [],
    ]


def test_published_boundary_features_cover_every_author_but_never_line_final() -> None:
    feature_authors = defaultdict(set)
    authors = set()
    for split in SPLITS:
        for row in pq.read_table(
            config_path(METRE_ROOT, "verse", 1, split),
            columns=["author", "syllables"],
        ).to_pylist():
            authors.add(row["author"])
            for line in load_syllable_units(row["syllables"]):
                syllables = line["syllables"]
                assert not ({"caesura", "hiatus"} & set(syllables[-1]["features"]))
                for syllable in syllables[:-1]:
                    for feature in {"caesura", "hiatus"} & set(syllable["features"]):
                        feature_authors[feature].add(row["author"])
    assert feature_authors["caesura"] == authors
    assert feature_authors["hiatus"] == authors


def test_metre_syntax_origin_is_explicit_and_provenance_matches() -> None:
    atomic_origins = set()
    for split in SPLITS:
        rows = pq.read_table(
            config_path(METRE_ROOT, "verse", 1, split),
            columns=["syntax_annotation", "treebank_source", "source_records"],
        ).to_pylist()
        for row in rows:
            origin = row["syntax_annotation"]
            atomic_origins.add(origin)
            sources = {record["source"] for record in row["source_records"]}
            assert "hypotactic" in sources
            assert origin == "predicted"
            assert row["treebank_source"] == "stoicheia_tagger_parser"
            assert "stoicheia_tagger_parser" in sources
    assert atomic_origins == {"predicted"}

    for suffix in (5, 10):
        for split in SPLITS:
            origins = set(pq.read_table(
                config_path(METRE_ROOT, "verse", suffix, split),
                columns=["syntax_annotation"],
            )["syntax_annotation"].to_pylist())
            assert origins == {"predicted"}


def test_predicted_metre_trees_use_reconstructed_sentence_context() -> None:
    report = json.loads((METRE_ROOT / "metadata/build_report.json").read_text())
    syntax = report["automatic_syntax"]
    assert syntax["inference_unit"] == (
        "punctuation-delimited context with punctuation removed before parsing"
    )
    assert syntax["publication_unit"] == (
        "metrical line cropped from its inferred sentence tree"
    )
    assert 0 < syntax["context_sentences"] < syntax["predictions_in_cache"]
    assert 0 < syntax["context_sentences_spanning_multiple_lines"]

    found_prediction = False
    for split in SPLITS:
        rows = pq.read_table(
            config_path(METRE_ROOT, "verse", 1, split),
            columns=["syntax_annotation", "source_records"],
        ).to_pylist()
        for row in rows:
            if row["syntax_annotation"] != "predicted":
                continue
            found_prediction = True
            records = row["source_records"]
            model_record = next(
                record for record in records
                if record["source"] == "stoicheia_tagger_parser"
            )
            assert "removing all editorial punctuation" in (
                model_record["annotation_provenance"]
            )
            break
        if found_prediction:
            break
    assert found_prediction


def test_gold_is_diagnostic_only_and_quality_metrics_are_reported() -> None:
    report = json.loads((METRE_ROOT / "metadata/build_report.json").read_text())
    syntax = report["automatic_syntax"]
    quality = syntax["gold_reference_quality"]
    assert syntax["machine_predictions_replacing_prior_gold"] > 0
    assert quality["comparison_lines"] == syntax["gold_reference_lines"]
    assert quality["matched_character_span_tokens"] > 0
    for name in (
        "token_boundary_precision",
        "token_boundary_recall",
        "exact_tokenization_line_rate",
        "lemma_exact_nfc_accuracy_on_matched_tokens",
        "upos_accuracy_on_matched_tokens",
        "deprel_accuracy_on_matched_tokens",
        "uas_on_matched_tokens",
        "las_on_matched_tokens",
    ):
        assert 0 <= quality[name] <= 1


def test_scansion_is_not_published() -> None:
    for suffix in (1, 5, 10):
        for split in SPLITS:
            path = config_path(METRE_ROOT, "verse", suffix, split)
            assert "scansion" not in pq.ParquetFile(path).schema_arrow.names


def test_metrical_line_sanitizer_drops_all_provenance_and_identifiers() -> None:
    unsafe = json.dumps([{
        "text": "μῆνιν ἄειδε",
        "metre": "hexameter",
        "syllables": [{"text": "μῆ", "quantity": "long", "features": []}],
        "hypotactic_file": "iliad1.html",
        "hypotactic_author": "Homer",
        "hypotactic_work": "Iliad",
        "book": "1",
        "poem_sequence": "1",
        "number": "1",
        "speaker": "Achilles",
        "id": "vm-secret",
        "coverage_in_sentence": "full",
        "scansion": "–",
    }], ensure_ascii=False)
    lines = sanitize_metrical_lines(unsafe)
    assert len(lines) == 1
    assert set(lines[0]) == PUBLIC_METRICAL_LINE_FIELD_SET

	from collections import Counter, defaultdict
	import json
	from pathlib import Path
	import re

	import pyarrow.parquet as pq
	import pytest

	from scripts.build_dataset import HypotacticParser
	from scripts.conllu_units import load_conllu_units
	from scripts.metre_units import (
	line_features,
	load_metre_units,
	load_syllable_units,
	syllable_line,
	)
	from scripts.dataset_variants import (
	BOTTLENECK_TARGET,
	ELIGIBILITY_MINIMUM,
	METRE_BASE_CONFIGS,
	PUBLIC_CONFIG_PREFIX,
	SENTENCE_BASE_CONFIGS,
	)
	from scripts.metrical_lines import PUBLIC_METRICAL_LINE_FIELD_SET, sanitize_metrical_lines
	from scripts.text_units import load_text_units


	REPO_ROOT = Path(__file__).resolve().parents[1]
	METRE_ROOT = REPO_ROOT.parent / "sphragis-metre"
	SPLITS = ("train", "validation", "validation2", "test")
	METRE_TRACKS = tuple(PUBLIC_CONFIG_PREFIX[base] for base in METRE_BASE_CONFIGS)
	PUBLICATIONS = (
	*((REPO_ROOT, base, (1, 5, 10)) for base in SENTENCE_BASE_CONFIGS),
	*((METRE_ROOT, track, (1, 5, 10)) for track in METRE_TRACKS),
	)
	# The per-author unit table in each README documents the main track only; the
	# other tracks' per-author counts live in build_report.json, because several
	# tables of the same shape in one README cannot be told apart by pattern.
	README_AUTHOR_TABLES = tuple(
	publication for publication in PUBLICATIONS if publication[1] in {"sentence", "verse"}
	)
	TREEBANK_SOURCES = {
	"agdt", "ud_perseus", "ud_proiel", "ud_ptnk", "gorman", "pedalion",
	"harrington",
	}


	def config_path(root: Path, base: str, suffix: int, split: str) -> Path:
	return root / "data" / f"{base}_{suffix}" / f"{split}-00000-of-00001.parquet"


	def test_sentence_build_uses_every_treebank_and_no_external_metrical_source() -> None:
	revisions = json.loads((REPO_ROOT / "metadata/source_revisions.json").read_text())
	report = json.loads((REPO_ROOT / "metadata/build_report.json").read_text())
	inventory = report["authorship_curation"]["treebank_source_inventory"]
	assert set(revisions) == TREEBANK_SOURCES
	assert set(inventory["rows_by_source"]) == TREEBANK_SOURCES
	assert all(count > 0 for count in inventory["rows_by_source"].values())
	assert sum(inventory["rows_by_source"].values()) == inventory["total_rows"]


	def test_readme_configuration_counts_match_parquet_rows() -> None:
	for root, base, suffixes in PUBLICATIONS:
	readme = (root / "README.md").read_text(encoding="utf-8")
	table_rows = {
	match.group("config"): {
	"authors": int(match.group("authors").replace(",", "")),
	"train": int(match.group("train").replace(",", "")),
	"validation": int(match.group("validation").replace(",", "")),
	"validation2": int(match.group("validation2").replace(",", "")),
	"test": int(match.group("test").replace(",", "")),
	}
	for match in re.finditer(
	r"^\\| `(?P<config>[^`]+)` \\| (?P<authors>[\d,]+) \\| [^\|]+ "
	r"\\| (?P<train>[\d,]+) \\| (?P<validation>[\d,]+) "
	r"\\| (?P<validation2>[\d,]+) \\| (?P<test>[\d,]+) \\|$",
	readme,
	flags=re.MULTILINE,
	)
	}
	# One README documents both sentence tracks, so match on this base.
	table_rows = {
	config: counts for config, counts in table_rows.items()
	if config.rsplit("_", 1)[0] == base
	}
	expected_configs = {f"{base}_{suffix}" for suffix in suffixes}
	assert set(table_rows) == expected_configs
	for config in sorted(expected_configs):
	expected = {}
	authors = set()
	for split in SPLITS:
	path = root / "data" / config / f"{split}-00000-of-00001.parquet"
	table = pq.read_table(path, columns=["author"])
	expected[split] = table.num_rows
	authors.update(table["author"].to_pylist())
	assert table_rows[config] == {"authors": len(authors), **expected}


	def test_readme_author_unit_counts_and_shares_match_atomic_parquets() -> None:
	for root, base, _suffixes in README_AUTHOR_TABLES:
	readme = (root / "README.md").read_text(encoding="utf-8")
	documented = {
	match.group("author"): {
	**{
	split: int(match.group(split).replace(",", ""))
	for split in ("train", "validation", "validation2", "test", "total")
	},
	"share": match.group("share"),
	}
	for match in re.finditer(
	r"^\\| (?P<author>[^\|*]+?) \\| (?P<train>[\d,]+) "
	r"\\| (?P<validation>[\d,]+) \\| (?P<validation2>[\d,]+) "
	r"\\| (?P<test>[\d,]+) "
	r"\\| (?P<total>[\d,]+) \((?P<share>\d+\.\d%)\) \\|$",
	readme,
	flags=re.MULTILINE,
	)
	}
	expected = defaultdict(Counter)
	for split in SPLITS:
	authors = pq.read_table(
	config_path(root, base, 1, split), columns=["author"],
	)["author"].to_pylist()
	for author in authors:
	expected[author][split] += 1
	expected[author]["total"] += 1
	grand_total = sum(counts["total"] for counts in expected.values())
	assert documented == {
	author: {
	**dict(counts),
	"share": f"{100 * counts['total'] / grand_total:.1f}%",
	}
	for author, counts in expected.items()
	}


	def test_all_atomic_splits_are_balanced_and_chunkable() -> None:
	for root, base, _suffixes in PUBLICATIONS:
	counts = defaultdict(Counter)
	for split in SPLITS:
	table = pq.read_table(
	config_path(root, base, 1, split), columns=["author", "split"],
	)
	for row in table.to_pylist():
	counts[row["author"]][row["split"]] += 1
	assert counts
	for author, author_counts in counts.items():
	for split in SPLITS:
	assert author_counts[split] >= ELIGIBILITY_MINIMUM[split], (base, author, split)
	assert author_counts[split] % BOTTLENECK_TARGET == 0, (
	base, author, split,
	)


	def test_chunk_sizes_and_atomic_coverage_are_exact_in_every_split() -> None:
	for root, base, suffixes in PUBLICATIONS:
	atomic = {}
	for split in SPLITS:
	atomic[split] = {
	row["id"]: load_text_units(row["text"])
	for row in pq.read_table(
	config_path(root, base, 1, split), columns=["id", "text"],
	).to_pylist()
	}
	for suffix in suffixes[1:]:
	for split in SPLITS:
	# The metre publication names the column "conllu" in every one of
	# its tracks; the sentence publication carries two parse layers.
	conllu_column = "conllu" if root == METRE_ROOT else "conllu_human"
	rows = pq.read_table(
	config_path(root, base, suffix, split),
	columns=["text", conllu_column, "chunk_size", "constituent_ids"],
	).to_pylist()
	represented = [
	row_id for row in rows for row_id in row["constituent_ids"]
	]
	assert len(represented) == len(set(represented))
	assert set(represented) == set(atomic[split])
	for row in rows:
	assert row["chunk_size"] == suffix
	assert len(row["constituent_ids"]) == suffix
	assert len(load_text_units(row["text"])) == suffix
	assert len(load_conllu_units(row[conllu_column])) == suffix
	assert load_text_units(row["text"]) == [
	unit
	for row_id in row["constituent_ids"]
	for unit in atomic[split][row_id]
	]


	def test_sentence_publication_contains_no_hypotactic_fields_or_provenance() -> None:
	forbidden = {
	"metre", "metrical_lines", "syllables", "scansion",
	"alignment_component_id", "component_sentence_index",
	"component_line_index", "parent_sentence_ids", "hypotactic_file",
	"book", "poem_sequence", "line_number",
	}
	for suffix in (1, 5, 10):
	for split in SPLITS:
	path = config_path(REPO_ROOT, "sentence", suffix, split)
	schema = pq.ParquetFile(path).schema_arrow
	assert not (forbidden & set(schema.names))
	rows = pq.read_table(path, columns=["genre", "source_records"]).to_pylist()
	assert {row["genre"] for row in rows} <= {"prose", "verse"}
	for row in rows:
	assert all(
	record["source"] != "hypotactic"
	for record in row["source_records"]
	)


	def test_metre_publication_omits_redundant_genre_column() -> None:
	for suffix in (1, 5, 10):
	for split in SPLITS:
	path = config_path(METRE_ROOT, "verse", suffix, split)
	assert "genre" not in pq.ParquetFile(path).schema_arrow.names


	def test_metre_chunks_preserve_aligned_metre_and_syllable_line_units() -> None:
	atomic_syllables = {}
	atomic_metres = {}
	for split in SPLITS:
	rows = pq.read_table(
	config_path(METRE_ROOT, "verse", 1, split),
	columns=["id", "metre", "syllables"],
	).to_pylist()
	atomic_syllables[split] = {
	row["id"]: load_syllable_units(row["syllables"])
	for row in rows
	}
	atomic_metres[split] = {
	row["id"]: load_metre_units(row["metre"])
	for row in rows
	}
	assert all(len(units) == 1 for units in atomic_syllables[split].values())
	assert all(len(units) == 1 for units in atomic_metres[split].values())
	for suffix in (5, 10):
	for split in SPLITS:
	rows = pq.read_table(
	config_path(METRE_ROOT, "verse", suffix, split),
	columns=["constituent_ids", "metre", "syllables"],
	).to_pylist()
	for row in rows:
	assert load_metre_units(row["metre"]) == [
	metre
	for row_id in row["constituent_ids"]
	for metre in atomic_metres[split][row_id]
	]
	assert load_syllable_units(row["syllables"]) == [
	syllables
	for row_id in row["constituent_ids"]
	for syllables in atomic_syllables[split][row_id]
	]


	def parse_boundary_fixture(separator: str) -> list[dict]:
	parser = HypotacticParser("test")
	parser.feed(
	'<div class="poem" data-author="Test" data-work="Test" data-number="1">'
	'<div class="line hexameter" data-number="1" data-metre="hexameter">'
	'<span class="word"><span class="syll long">μῆ</span></span>'
	f'{separator}<span class="word"><span class="syll long">ἄει</span></span>'
	f'{separator}<span class="word"><span class="syll short hiatus">θεά</span></span>'
	'</div></div>'
	)
	return parser.lines[0]["syllables"]


	def test_hypotactic_word_boundaries_are_serialization_invariant() -> None:
	minified = parse_boundary_fixture("")
	spaced = parse_boundary_fixture(" \n ")
	assert minified == spaced
	assert [syllable["features"] for syllable in minified] == [
	["caesura", "hiatus"], ["caesura"], [],
	]


	def test_hypotactic_upstream_hiatus_is_replaced_by_derived_hiatus() -> None:
	syllables = parse_boundary_fixture("")
	assert "hiatus" not in syllables[-1]["features"]


	def test_bare_hypotactic_diastole_is_long() -> None:
	parser = HypotacticParser("test")
	parser.feed(
	'<div class="poem" data-author="Test" data-work="Test" data-number="1">'
	'<div class="line hexameter" data-number="1" data-metre="hexameter">'
	'<span class="word"><span class="syll diastole">ας</span></span>'
	'</div></div>'
	)
	assert parser.lines[0]["syllables"][0]["quantity"] == "long"


	def test_line_features_use_half_morae_and_listed_caesura_positions() -> None:
	syllables = [
	{"text": "μῆ", "quantity": "long", "features": []},
	{"text": "νι", "quantity": "short", "features": []},
	{
	"text": "ν",
	"quantity": "short",
	"features": ["caesura", "hiatus"],
	},
	{"text": "ἄει", "quantity": "long", "features": []},
	]
	assert syllable_line(syllables) == {
	"features": {
	"hiatus": 1,
	"longa": 2,
	"brevia": 2,
	"morae": 3,
	"caesurae": [{"isochronic": 2, "isosyllabic": 3}],
	},
	"syllables": syllables,
	}


	def test_unquantified_syllable_rejects_line_features() -> None:
	with pytest.raises(ValueError, match="cannot derive line time"):
	line_features([
	{"text": "?", "quantity": "unknown", "features": []},
	])


	def test_upstream_hypotactic_syllable_classes_are_not_published() -> None:
	parser = HypotacticParser("test")
	parser.feed(
	'<div class="poem" data-author="Test" data-work="Test" data-number="1">'
	'<div class="line hexameter" data-number="1" data-metre="hexameter">'
	'<span class="word"><span class="syll long lbn correption">μῆ</span></span>'
	'<span class="word"><span class="syll short fixme context-menu-active">νι</span>'
	'<span class="syll long synizesis">ν</span></span>'
	'</div></div>'
	)
	assert [syllable["features"] for syllable in parser.lines[0]["syllables"]] == [
	["caesura"], [], [],
	]


	def test_published_boundary_features_cover_every_author_but_never_line_final() -> None:
	feature_authors = defaultdict(set)
	authors = set()
	for split in SPLITS:
	for row in pq.read_table(
	config_path(METRE_ROOT, "verse", 1, split),
	columns=["author", "syllables"],
	).to_pylist():
	authors.add(row["author"])
	for line in load_syllable_units(row["syllables"]):
	syllables = line["syllables"]
	assert not ({"caesura", "hiatus"} & set(syllables[-1]["features"]))
	for syllable in syllables[:-1]:
	for feature in {"caesura", "hiatus"} & set(syllable["features"]):
	feature_authors[feature].add(row["author"])
	assert feature_authors["caesura"] == authors
	assert feature_authors["hiatus"] == authors


	def test_metre_syntax_origin_is_explicit_and_provenance_matches() -> None:
	atomic_origins = set()
	for split in SPLITS:
	rows = pq.read_table(
	config_path(METRE_ROOT, "verse", 1, split),
	columns=["syntax_annotation", "treebank_source", "source_records"],
	).to_pylist()
	for row in rows:
	origin = row["syntax_annotation"]
	atomic_origins.add(origin)
	sources = {record["source"] for record in row["source_records"]}
	assert "hypotactic" in sources
	assert origin == "predicted"
	assert row["treebank_source"] == "stoicheia_tagger_parser"
	assert "stoicheia_tagger_parser" in sources
	assert atomic_origins == {"predicted"}

	for suffix in (5, 10):
	for split in SPLITS:
	origins = set(pq.read_table(
	config_path(METRE_ROOT, "verse", suffix, split),
	columns=["syntax_annotation"],
	)["syntax_annotation"].to_pylist())
	assert origins == {"predicted"}


	def test_predicted_metre_trees_use_reconstructed_sentence_context() -> None:
	report = json.loads((METRE_ROOT / "metadata/build_report.json").read_text())
	syntax = report["automatic_syntax"]
	assert syntax["inference_unit"] == (
	"punctuation-delimited context with punctuation removed before parsing"
	)
	assert syntax["publication_unit"] == (
	"metrical line cropped from its inferred sentence tree"
	)
	assert 0 < syntax["context_sentences"] < syntax["predictions_in_cache"]
	assert 0 < syntax["context_sentences_spanning_multiple_lines"]

	found_prediction = False
	for split in SPLITS:
	rows = pq.read_table(
	config_path(METRE_ROOT, "verse", 1, split),
	columns=["syntax_annotation", "source_records"],
	).to_pylist()
	for row in rows:
	if row["syntax_annotation"] != "predicted":
	continue
	found_prediction = True
	records = row["source_records"]
	model_record = next(
	record for record in records
	if record["source"] == "stoicheia_tagger_parser"
	)
	assert "removing all editorial punctuation" in (
	model_record["annotation_provenance"]
	)
	break
	if found_prediction:
	break
	assert found_prediction


	def test_gold_is_diagnostic_only_and_quality_metrics_are_reported() -> None:
	report = json.loads((METRE_ROOT / "metadata/build_report.json").read_text())
	syntax = report["automatic_syntax"]
	quality = syntax["gold_reference_quality"]
	assert syntax["machine_predictions_replacing_prior_gold"] > 0
	assert quality["comparison_lines"] == syntax["gold_reference_lines"]
	assert quality["matched_character_span_tokens"] > 0
	for name in (
	"token_boundary_precision",
	"token_boundary_recall",
	"exact_tokenization_line_rate",
	"lemma_exact_nfc_accuracy_on_matched_tokens",
	"upos_accuracy_on_matched_tokens",
	"deprel_accuracy_on_matched_tokens",
	"uas_on_matched_tokens",
	"las_on_matched_tokens",
	):
	assert 0 <= quality[name] <= 1


	def test_scansion_is_not_published() -> None:
	for suffix in (1, 5, 10):
	for split in SPLITS:
	path = config_path(METRE_ROOT, "verse", suffix, split)
	assert "scansion" not in pq.ParquetFile(path).schema_arrow.names


	def test_metrical_line_sanitizer_drops_all_provenance_and_identifiers() -> None:
	unsafe = json.dumps([{
	"text": "μῆνιν ἄειδε",
	"metre": "hexameter",
	"syllables": [{"text": "μῆ", "quantity": "long", "features": []}],
	"hypotactic_file": "iliad1.html",
	"hypotactic_author": "Homer",
	"hypotactic_work": "Iliad",
	"book": "1",
	"poem_sequence": "1",
	"number": "1",
	"speaker": "Achilles",
	"id": "vm-secret",
	"coverage_in_sentence": "full",
	"scansion": "–",
	}], ensure_ascii=False)
	lines = sanitize_metrical_lines(unsafe)
	assert len(lines) == 1
	assert set(lines[0]) == PUBLIC_METRICAL_LINE_FIELD_SET