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import torch
from torch.utils.data import DataLoader
from unittest.mock import patch
from mentioned.data import (
mentions_by_sentence,
flatten_to_sentences,
LitBankMentionDataset,
collate_fn,
make_litbank,
extract_spans_from_bio,
flatten_entities,
LitBankEntityDataset,
entity_collate_fn,
make_litbank_entity,
)
# --- FIXTURES ---
@pytest.fixture
def mock_raw_example():
"""Simulates a raw entry from LitBank before flattening."""
return {
"sentences": [["The", "cat", "sat", "."], ["It", "was", "happy", "."]],
"coref_chains": [
[[0, 0, 1], [1, 0, 0]] # "The cat" (0,0-1) and "It" (1,0-0)
],
}
@pytest.fixture
def mock_flattened_data():
"""Simulates the output of the HF map functions."""
return [
{"sentence": ["The", "cat", "sat", "."], "mentions": [[0, 1]]},
{"sentence": ["It", "was", "happy", "."], "mentions": [[0, 0]]},
{"sentence": ["No", "mentions"], "mentions": []},
]
# --- UNIT TESTS ---
def test_extract_spans_from_bio_simple():
sentence = [
{"token": "John", "bio_tags": ["B-PER"]},
{"token": "Smith", "bio_tags": ["I-PER"]},
{"token": "works", "bio_tags": ["O"]},
{"token": "at", "bio_tags": ["O"]},
{"token": "Google", "bio_tags": ["B-ORG"]},
]
spans, labels = extract_spans_from_bio(sentence)
# inclusive indexing
assert spans == [(0, 1), (4, 4)]
assert labels == ["PER", "ORG"]
def test_extract_spans_handles_single_token_entity():
sentence = [
{"token": "Paris", "bio_tags": ["B-LOC"]},
{"token": "is", "bio_tags": ["O"]},
]
spans, labels = extract_spans_from_bio(sentence)
assert spans == [(0, 0)]
assert labels == ["LOC"]
def test_litbank_entity_dataset_getitem():
fake_dataset = [
{
"sentence": ["John", "works"],
"entity_spans": [(0, 1)],
"entity_labels": ["PER"],
}
]
ds = LitBankEntityDataset(fake_dataset)
item = ds[0]
assert item["sentence"] == ["John", "works"]
assert torch.equal(item["starts"], torch.tensor([1, 0]))
assert item["entity_spans"] == [(0, 1)]
assert item["entity_labels"] == ["PER"]
assert item["task_id"] == 1
def test_flatten_entities():
batch = {
"entities": [
[ # document 1
[
{"token": "John", "bio_tags": ["B-PER"]},
{"token": "Smith", "bio_tags": ["I-PER"]},
]
]
]
}
output = flatten_entities(batch)
assert output["sentence"] == [["John", "Smith"]]
assert output["entity_spans"] == [[(0, 1)]]
assert output["entity_labels"] == [["PER"]]
def test_entity_collate_fn_basic():
batch = [
{
"sentence": ["John", "works"],
"starts": torch.tensor([1, 0]),
"entity_spans": [(0, 1)],
"entity_labels": ["PER"],
"task_id": 1,
}
]
output = entity_collate_fn(batch)
assert output["starts"].shape == (1, 2)
assert output["spans"].shape == (1, 2, 2)
assert output["spans"][0, 0, 1] == 1
assert output["gold_labels"][0] == {(0, 1): "PER"}
assert output["task_id"].shape == (1,)
def test_mentions_by_sentence_grouping(mock_raw_example):
"""Verify coref chains are correctly mapped to sentence indices as strings."""
result = mentions_by_sentence(mock_raw_example)
assert "mentions" in result
# Sentence 0 has mention (0, 1)
assert (0, 1) in result["mentions"]["0"]
# Sentence 1 has mention (0, 0)
assert (0, 0) in result["mentions"]["1"]
def test_flatten_to_sentences_alignment(mock_raw_example):
"""Verify flattening expands 1 doc -> N sentences with correct mention alignment."""
# Pre-process with mention mapping first
processed = mentions_by_sentence(mock_raw_example)
# Mocking HF batch behavior (dict of lists)
batch = {k: [v] for k, v in processed.items()}
flattened = flatten_to_sentences(batch)
assert len(flattened["sentence"]) == 2
assert flattened["mentions"][0] == [(0, 1)] # "The cat"
assert flattened["mentions"][1] == [(0, 0)] # "It"
def test_dataset_tensor_logic(mock_flattened_data):
"""Verify the 2D span_labels are correctly populated (inclusive indexing)."""
ds = LitBankMentionDataset(mock_flattened_data)
# Check sentence with a multi-token mention (0, 1)
item = ds[0]
assert item["starts"][0] == 1
assert item["span_labels"][0, 1] == 1
assert item["span_labels"].sum() == 1 # Only one mention
# Check empty sentence
empty_item = ds[2]
assert empty_item["starts"].sum() == 0
assert empty_item["span_labels"].sum() == 0
def test_collate_masking_and_shapes(mock_flattened_data):
"""Verify the 2D mask logic: upper triangle + is_start."""
ds = LitBankMentionDataset(mock_flattened_data)
# Batch size 3: [len 4, len 4, len 2]
batch = [ds[0], ds[1], ds[2]]
collated = collate_fn(batch)
B, N = 3, 4
assert collated["starts"].shape == (B, N)
assert collated["spans"].shape == (B, N, N)
# Check span_loss_mask
# For the first sentence: mention at (0,1). Start is 1 at index 0.
# Therefore, the mask should allow calculations for row 0.
mask = collated["span_loss_mask"]
# Row 0 (starts with 'The') should be mostly True (for j >= 0)
assert mask[0, 0, 0] == True
assert mask[0, 0, 1] == True
# Row 2 (starts with 'sat') should be False because starts[2] == 0
assert torch.all(mask[0, 2, :] == False)
def test_out_of_bounds_guard():
"""Ensure indexing doesn't crash if data has an error."""
bad_data = [{"sentence": ["Short"], "mentions": [[0, 10]]}]
ds = LitBankMentionDataset(bad_data)
# Should not raise IndexError
item = ds[0]
assert item["span_labels"].sum() == 0
# --- INTEGRATION TEST ---
def test_make_litbank_integration():
"""Check if the real pipeline loads and provides a valid batch."""
try:
data = make_litbank(tag="split_0")
batch = next(iter(data.train_loader))
assert "sentences" in batch
assert "span_loss_mask" in batch
assert isinstance(batch["spans"], torch.Tensor)
except Exception as e:
pytest.fail(f"Integration test failed: {e}")
@patch("mentioned.data.load_dataset")
def test_make_litbank_entity(mock_load_dataset):
# -----------------------------
# Fake HF split
# -----------------------------
class FakeSplit(list):
@property
def column_names(self):
return list(self[0].keys()) if self else []
# -----------------------------
# Fake HF dataset dict
# -----------------------------
class DummyDataset(dict):
def map(self, fn, batched=False, remove_columns=None):
mapped = {}
for split_name, split_data in self.items():
if not split_data:
mapped[split_name] = FakeSplit([])
continue
if batched:
batch = {
"entities": [item["entities"] for item in split_data]
}
result = fn(batch)
new_split = []
for i in range(len(result["sentence"])):
new_split.append({
"sentence": result["sentence"][i],
"entity_spans": result["entity_spans"][i],
"entity_labels": result["entity_labels"][i],
})
mapped[split_name] = FakeSplit(new_split)
else:
mapped[split_name] = FakeSplit(split_data)
return DummyDataset(mapped)
# -----------------------------
# Fake data
# -----------------------------
fake_data = DummyDataset({
"train": FakeSplit([
{
"entities": [
[
{"token": "John", "bio_tags": ["B-PER"]},
{"token": "Smith", "bio_tags": ["I-PER"]},
]
]
}
]),
"validation": FakeSplit([]),
"test": FakeSplit([]),
})
mock_load_dataset.return_value = fake_data
# -----------------------------
# Run function
# -----------------------------
data = make_litbank_entity()
batch = next(iter(data.train_loader))
print(batch)
# -----------------------------
# Assertions
# -----------------------------
assert "starts" in batch
assert "spans" in batch
assert "gold_labels" in batch
# ensure entity span is present
assert batch["spans"].sum() > 0
assert batch["gold_labels"][0] == {(0, 1): "PER"}
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