from kontextual_engine import SourcePayload, SourceReference, content_digest
from kontextual_engine.adapters.builtin_extractors import (
    CsvDatasetExtractor,
    DocumentPlaceholderExtractor,
    PlainTextExtractor,
)


def test_plain_text_extractor_emits_structural_units_and_links() -> None:
    payload = source_payload(
        "Intro line\nwith https://example.test/ref\n\nSecond paragraph\n",
        media_type="text/plain",
    )

    result = PlainTextExtractor().extract(payload)

    assert result.normalized.structure["kind"] == "plain_text"
    assert result.normalized.fields["line_count"] == 4
    assert result.normalized.fields["paragraph_count"] == 2
    assert result.normalized.links == [
        {
            "url": "https://example.test/ref",
            "line": 2,
            "start": 5,
            "end": 29,
        }
    ]
    assert result.metadata["link_count"] == 1


def test_csv_dataset_extractor_emits_table_schema_samples_and_links() -> None:
    payload = source_payload(
        "name,source\nalpha,https://example.test/a\nbeta,\n",
        media_type="text/csv",
        filename="metrics.csv",
    )

    result = CsvDatasetExtractor().extract(payload)

    assert result.normalized.structure["kind"] == "dataset"
    assert result.normalized.structure["columns"] == [
        {"name": "name", "index": 0},
        {"name": "source", "index": 1},
    ]
    assert result.normalized.tables[0]["row_count"] == 2
    assert result.normalized.tables[0]["rows"][0]["name"] == "alpha"
    assert result.normalized.links == [
        {
            "url": "https://example.test/a",
            "table": 0,
            "row": 0,
            "column": "source",
        }
    ]
    assert result.metadata["sample_rows"][0]["source"] == "https://example.test/a"


def test_document_placeholder_exposes_unsupported_structure() -> None:
    payload = source_payload(b"%PDF-1.7\n", media_type="application/pdf", filename="brief.pdf")

    result = DocumentPlaceholderExtractor().extract(payload)

    assert result.normalized.structure["kind"] == "pdf"
    assert result.normalized.fields["unsupported_count"] == 1
    assert result.normalized.fields["link_count"] == 0
    assert result.normalized.unsupported_elements[0]["reason"] == "deep_extraction_not_available"
    assert result.diagnostics[0].code == "extraction.depth_unsupported"


def source_payload(
    content: str | bytes,
    *,
    media_type: str,
    filename: str = "source.txt",
) -> SourcePayload:
    data = content.encode("utf-8") if isinstance(content, str) else content
    source_ref = SourceReference(
        source_system="test",
        path=filename,
        checksum=content_digest(data),
        connector_ref=f"test:{filename}",
    )
    return SourcePayload(
        connector_name="test",
        source_uri=filename,
        source_ref=source_ref,
        media_type=media_type,
        content=data,
        title=filename.rsplit(".", maxsplit=1)[0],
        metadata={"filename": filename},
    )