import asyncio

from src.preprocess import resize_preserve_aspect


def test_resize_preserve_aspect():
    from PIL import Image

    img = Image.new("RGB", (1024, 512), color="white")
    resized = resize_preserve_aspect(img, max_side=256)
    assert max(resized.size) == 256


def test_pipeline_writes_captions_to_db(monkeypatch):
    """Ensure pipeline calls write_captions with non-empty captions."""

    # Stub out download to avoid network
    async def fake_download_image(url, session, retries=2):
        return object()

    monkeypatch.setattr("src.download.download_image", fake_download_image)

    # Stub out preprocess to return a dummy tensor-like object (skip Pillow / torch entirely)
    def fake_preprocess_image(img, device="cpu"):
        return object()

    monkeypatch.setattr("src.preprocess.preprocess_image", fake_preprocess_image)

    # Stub out model load + generate
    def fake_load(self):
        self.model = True
        self.processor = True

    def fake_generate_captions(self, images, prompt, **kwargs):
        return [f"caption-for-{i}" for i in range(len(images))]

    monkeypatch.setattr("src.model.Florence2CaptionModel.load", fake_load)
    monkeypatch.setattr(
        "src.model.Florence2CaptionModel.generate_captions", fake_generate_captions
    )

    # Capture writes
    written = []

    async def fake_write_captions(updates, batch_size=64):
        written.extend(updates)

    monkeypatch.setattr("src.db.write_captions", fake_write_captions)

    # Run pipeline with a small dummy URL list
    from src import pipeline

    urls = [(1, "http://example.com/image1.jpg"), (2, "http://example.com/image2.jpg")]
    asyncio.run(pipeline.run_pipeline(urls, dry_run=True))

    assert len(written) == 2
    assert all(isinstance(c, str) and len(c) > 0 for _, c in written)