| import os |
| import textwrap |
|
|
| import pyarrow as pa |
| import pytest |
|
|
| from datasets import ClassLabel, Features, Image |
| from datasets.packaged_modules.csv.csv import Csv |
|
|
| from ..utils import require_pil |
|
|
|
|
| @pytest.fixture |
| def csv_file(tmp_path): |
| filename = tmp_path / "file.csv" |
| data = textwrap.dedent( |
| """\ |
| header1,header2 |
| 1,2 |
| 10,20 |
| """ |
| ) |
| with open(filename, "w") as f: |
| f.write(data) |
| return str(filename) |
|
|
|
|
| @pytest.fixture |
| def malformed_csv_file(tmp_path): |
| filename = tmp_path / "malformed_file.csv" |
| data = textwrap.dedent( |
| """\ |
| header1,header2 |
| 1,2 |
| 10,20, |
| """ |
| ) |
| with open(filename, "w") as f: |
| f.write(data) |
| return str(filename) |
|
|
|
|
| @pytest.fixture |
| def csv_file_with_image(tmp_path, image_file): |
| filename = tmp_path / "csv_with_image.csv" |
| data = textwrap.dedent( |
| f"""\ |
| image |
| {image_file} |
| """ |
| ) |
| with open(filename, "w") as f: |
| f.write(data) |
| return str(filename) |
|
|
|
|
| @pytest.fixture |
| def csv_file_with_label(tmp_path): |
| filename = tmp_path / "csv_with_label.csv" |
| data = textwrap.dedent( |
| """\ |
| label |
| good |
| bad |
| good |
| """ |
| ) |
| with open(filename, "w") as f: |
| f.write(data) |
| return str(filename) |
|
|
|
|
| @pytest.fixture |
| def csv_file_with_int_list(tmp_path): |
| filename = tmp_path / "csv_with_int_list.csv" |
| data = textwrap.dedent( |
| """\ |
| int_list |
| 1 2 3 |
| 4 5 6 |
| 7 8 9 |
| """ |
| ) |
| with open(filename, "w") as f: |
| f.write(data) |
| return str(filename) |
|
|
|
|
| def test_csv_generate_tables_raises_error_with_malformed_csv(csv_file, malformed_csv_file, caplog): |
| csv = Csv() |
| generator = csv._generate_tables([[csv_file, malformed_csv_file]]) |
| with pytest.raises(ValueError, match="Error tokenizing data"): |
| for _ in generator: |
| pass |
| assert any( |
| record.levelname == "ERROR" |
| and "Failed to read file" in record.message |
| and os.path.basename(malformed_csv_file) in record.message |
| for record in caplog.records |
| ) |
|
|
|
|
| @require_pil |
| def test_csv_cast_image(csv_file_with_image): |
| with open(csv_file_with_image, encoding="utf-8") as f: |
| image_file = f.read().splitlines()[1] |
| csv = Csv(encoding="utf-8", features=Features({"image": Image()})) |
| generator = csv._generate_tables([[csv_file_with_image]]) |
| pa_table = pa.concat_tables([table for _, table in generator]) |
| assert pa_table.schema.field("image").type == Image()() |
| generated_content = pa_table.to_pydict()["image"] |
| assert generated_content == [{"path": image_file, "bytes": None}] |
|
|
|
|
| def test_csv_cast_label(csv_file_with_label): |
| with open(csv_file_with_label, encoding="utf-8") as f: |
| labels = f.read().splitlines()[1:] |
| csv = Csv(encoding="utf-8", features=Features({"label": ClassLabel(names=["good", "bad"])})) |
| generator = csv._generate_tables([[csv_file_with_label]]) |
| pa_table = pa.concat_tables([table for _, table in generator]) |
| assert pa_table.schema.field("label").type == ClassLabel(names=["good", "bad"])() |
| generated_content = pa_table.to_pydict()["label"] |
| assert generated_content == [ClassLabel(names=["good", "bad"]).str2int(label) for label in labels] |
|
|
|
|
| def test_csv_convert_int_list(csv_file_with_int_list): |
| csv = Csv(encoding="utf-8", sep=",", converters={"int_list": lambda x: [int(i) for i in x.split()]}) |
| generator = csv._generate_tables([[csv_file_with_int_list]]) |
| pa_table = pa.concat_tables([table for _, table in generator]) |
| assert pa.types.is_list(pa_table.schema.field("int_list").type) |
| generated_content = pa_table.to_pydict()["int_list"] |
| assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]] |
|
|
