"""Provider tests for ImagePropertiesProvider."""

from __future__ import annotations

import cv2
import numpy as np
import pytest

from config.settings import Settings
from pipeline.feature_extraction import PipelineOutput
from providers.image_analysis.image_properties import ImagePropertiesProvider


@pytest.fixture
def properties_provider():
    return ImagePropertiesProvider(settings=Settings(environment="test", db_path=":memory:"))


@pytest.fixture
def pipeline_output(sample_image_bytes):
    img = cv2.imdecode(np.frombuffer(sample_image_bytes, np.uint8), cv2.IMREAD_COLOR)
    return PipelineOutput(
        image=img, image_hash="h", width=img.shape[1], height=img.shape[0], source="bytes",
        original_bytes=sample_image_bytes, original_format=".jpg",
    )


class TestImagePropertiesProvider:
    def test_name(self, properties_provider):
        assert properties_provider.name == "image_properties"

    def test_capability(self, properties_provider):
        from models.providers import ProviderCapability
        assert properties_provider.capability == ProviderCapability.IMAGE_ANALYSIS

    def test_execute_returns_properties(self, properties_provider, pipeline_output):
        result = properties_provider.execute(pipeline_output)
        assert result.success is True
        n = result.normalized
        assert n["width"] == 200
        assert n["height"] == 200
        assert n["channels"] == 3
        assert n["color_profile"] == "BGR"
        assert isinstance(n["dominant_colors"], list)
        assert len(n["dominant_colors"]) > 0
        # Each color should be a hex string
        for c in n["dominant_colors"]:
            assert c.startswith("#")

    def test_aspect_ratio(self, properties_provider):
        img = np.zeros((100, 200, 3), dtype=np.uint8)
        po = PipelineOutput(image=img, image_hash="h", width=200, height=100, source="bytes")
        result = properties_provider.execute(po)
        assert result.normalized["aspects"]["aspect_ratio"] == 2.0

    def test_grayscale_image(self, properties_provider):
        img = np.zeros((200, 200), dtype=np.uint8)
        po = PipelineOutput(image=img, image_hash="h", width=200, height=200, source="bytes")
        result = properties_provider.execute(po)
        # The preprocessor converts to BGR before reaching here, but if we
        # bypass it (which we do here), the provider should still handle it
        # via the color_profile guess
        assert result.success is True