import pytest

from py.recipes.parsers.civitai_image import CivitaiApiMetadataParser


@pytest.mark.asyncio
async def test_parse_metadata_creates_loras_from_hashes(monkeypatch):
    async def fake_metadata_provider():
        return None

    monkeypatch.setattr(
        "py.recipes.parsers.civitai_image.get_default_metadata_provider",
        fake_metadata_provider,
    )

    parser = CivitaiApiMetadataParser()

    metadata = {
        "Size": "1536x2688",
        "seed": 3766932689,
        "Model": "indexed_v1",
        "steps": 30,
        "hashes": {
            "model": "692186a14a",
            "LORA:Jedst1": "fb4063c470",
            "LORA:HassaKu_style": "3ce00b926b",
            "LORA:DetailedEyes_V3": "2c1c3f889f",
            "LORA:jiaocha_illustriousXL": "35d3e6f8b0",
            "LORA:绪儿 厚涂构图光影质感增强V3": "d9b5900a59",
        },
        "prompt": "test",
        "Version": "ComfyUI",
        "sampler": "er_sde_ays_30",
        "cfgScale": 5,
        "clipSkip": 2,
        "resources": [
            {
                "hash": "692186a14a",
                "name": "indexed_v1",
                "type": "model",
            }
        ],
        "Model hash": "692186a14a",
        "negativePrompt": "bad",
        "username": "LumaRift",
        "baseModel": "Illustrious",
    }

    result = await parser.parse_metadata(metadata)

    assert result["base_model"] == "Illustrious"
    assert len(result["loras"]) == 5
    assert all(lora["weight"] == 1.0 for lora in result["loras"])
    assert {lora["name"] for lora in result["loras"]} == {
        "Jedst1",
        "HassaKu_style",
        "DetailedEyes_V3",
        "jiaocha_illustriousXL",
        "绪儿 厚涂构图光影质感增强V3",
    }


@pytest.mark.asyncio
async def test_parse_metadata_handles_nested_meta_and_lowercase_hashes(monkeypatch):
    async def fake_metadata_provider():
        return None

    monkeypatch.setattr(
        "py.recipes.parsers.civitai_image.get_default_metadata_provider",
        fake_metadata_provider,
    )

    parser = CivitaiApiMetadataParser()

    metadata = {
        "id": 106706587,
        "meta": {
            "prompt": "An enigmatic silhouette",
            "hashes": {
                "model": "ee75fd24a4",
                "lora:mj": "de49e1e98c",
                "LORA:Another_Earth_2": "dc11b64a8b",
            },
            "resources": [
                {
                    "hash": "ee75fd24a4",
                    "name": "stoiqoNewrealityFLUXSD35_f1DAlphaTwo",
                    "type": "model",
                }
            ],
        },
    }

    assert parser.is_metadata_matching(metadata)

    result = await parser.parse_metadata(metadata)

    assert result["gen_params"]["prompt"] == "An enigmatic silhouette"
    assert {l["name"] for l in result["loras"]} == {"mj", "Another_Earth_2"}
    assert {l["hash"] for l in result["loras"]} == {"de49e1e98c", "dc11b64a8b"}


@pytest.mark.asyncio
async def test_parse_metadata_populates_checkpoint_and_rewrites_thumbnails(monkeypatch):
    checkpoint_info = {
        "id": 222,
        "modelId": 111,
        "model": {"name": "Checkpoint Example", "type": "checkpoint"},
        "name": "Checkpoint Version",
        "images": [{"url": "https://image.civitai.com/checkpoints/original=true"}],
        "baseModel": "Illustrious",
        "downloadUrl": "https://civitai.com/checkpoint/download",
        "files": [
            {
                "type": "Model",
                "primary": True,
                "sizeKB": 1024,
                "name": "Checkpoint Example.safetensors",
                "hashes": {"SHA256": "FFAA0011"},
            }
        ],
    }

    lora_info = {
        "id": 444,
        "modelId": 333,
        "model": {"name": "Example Lora Model", "type": "lora"},
        "name": "Example Lora Version",
        "images": [{"url": "https://image.civitai.com/loras/original=true"}],
        "baseModel": "Illustrious",
        "downloadUrl": "https://civitai.com/lora/download",
        "files": [
            {
                "type": "Model",
                "primary": True,
                "sizeKB": 512,
                "hashes": {"SHA256": "abc123"},
            }
        ],
    }

    async def fake_metadata_provider():
        class Provider:
            async def get_model_version_info(self, version_id):
                if version_id == "222":
                    return checkpoint_info, None
                if version_id == "444":
                    return lora_info, None
                return None, "Model not found"

        return Provider()

    monkeypatch.setattr(
        "py.recipes.parsers.civitai_image.get_default_metadata_provider",
        fake_metadata_provider,
    )

    parser = CivitaiApiMetadataParser()

    metadata = {
        "prompt": "test prompt",
        "negativePrompt": "test negative prompt",
        "civitaiResources": [
            {
                "type": "checkpoint",
                "modelId": 111,
                "modelVersionId": 222,
                "modelName": "Checkpoint Example",
                "modelVersionName": "Checkpoint Version",
            },
            {
                "type": "lora",
                "modelId": 333,
                "modelVersionId": 444,
                "modelName": "Example Lora",
                "modelVersionName": "Lora Version",
                "weight": 0.7,
            },
        ],
    }

    result = await parser.parse_metadata(metadata)

    assert result["model"] is not None
    assert result["model"]["name"] == "Checkpoint Example"
    assert result["model"]["type"] == "checkpoint"
    assert (
        result["model"]["thumbnailUrl"]
        == "https://image.civitai.com/checkpoints/width=450,optimized=true"
    )
    assert result["model"]["modelId"] == 111
    assert result["model"]["size"] == 1024 * 1024
    assert result["model"]["hash"] == "ffaa0011"
    assert result["model"]["file_name"] == "Checkpoint Example"

    assert result["loras"]
    assert result["loras"][0]["name"] == "Example Lora Model"
    assert (
        result["loras"][0]["thumbnailUrl"]
        == "https://image.civitai.com/loras/width=450,optimized=true"
    )
    assert result["loras"][0]["hash"] == "abc123"


@pytest.mark.asyncio
async def test_parse_metadata_handles_modelVersionIds(monkeypatch):
    """Test that modelVersionIds from Civitai image API are properly processed."""
    lora_info_1 = {
        "id": 2398829,
        "modelId": 123456,
        "model": {"name": "Dance LoRA 1", "type": "lora"},
        "name": "Version 1.0",
        "images": [{"url": "https://image.civitai.com/lora1/original=true"}],
        "baseModel": "SDXL",
        "downloadUrl": "https://civitai.com/lora1/download",
        "files": [
            {
                "type": "Model",
                "primary": True,
                "sizeKB": 10240,
                "name": "dance_lora_1.safetensors",
                "hashes": {"SHA256": "aabbccdd0011"},
            }
        ],
    }

    lora_info_2 = {
        "id": 2398838,
        "modelId": 123457,
        "model": {"name": "Style LoRA 2", "type": "lora"},
        "name": "Version 2.0",
        "images": [{"url": "https://image.civitai.com/lora2/original=true"}],
        "baseModel": "SDXL",
        "downloadUrl": "https://civitai.com/lora2/download",
        "files": [
            {
                "type": "Model",
                "primary": True,
                "sizeKB": 20480,
                "name": "style_lora_2.safetensors",
                "hashes": {"SHA256": "aabbccdd0022"},
            }
        ],
    }

    async def fake_metadata_provider():
        class Provider:
            async def get_model_version_info(self, version_id):
                if version_id == "2398829":
                    return lora_info_1, None
                if version_id == "2398838":
                    return lora_info_2, None
                return None, "Model not found"

        return Provider()

    monkeypatch.setattr(
        "py.recipes.parsers.civitai_image.get_default_metadata_provider",
        fake_metadata_provider,
    )

    parser = CivitaiApiMetadataParser()

    # This simulates the metadata from Civitai image API where modelVersionIds
    # is at the root level and meta only contains basic prompt info
    metadata = {
        "id": 109882763,
        "meta": {
            "id": 109882763,
            "meta": {"prompt": "A woman does the hip bump dance."},
        },
        "modelVersionIds": [2398829, 2398838],
    }

    assert parser.is_metadata_matching(metadata)

    result = await parser.parse_metadata(metadata)

    # Verify both LoRAs were created from modelVersionIds
    assert len(result["loras"]) == 2

    # Check first LoRA
    lora1 = result["loras"][0]
    assert lora1["id"] == 2398829
    assert lora1["name"] == "Dance LoRA 1"
    assert lora1["type"] == "lora"
    assert lora1["hash"] == "aabbccdd0011"
    assert lora1["baseModel"] == "SDXL"
    assert (
        lora1["thumbnailUrl"]
        == "https://image.civitai.com/lora1/width=450,optimized=true"
    )

    # Check second LoRA
    lora2 = result["loras"][1]
    assert lora2["id"] == 2398838
    assert lora2["name"] == "Style LoRA 2"
    assert lora2["type"] == "lora"
    assert lora2["hash"] == "aabbccdd0022"
    assert lora2["baseModel"] == "SDXL"


@pytest.mark.asyncio
async def test_parse_metadata_extracts_checkpoint_from_resources_model_type(monkeypatch):
    """resources entries with type:"model" should be captured as the checkpoint,
    not skipped (which was the old buggy behavior), and not mixed into loras."""
    captured_hashes = []

    async def fake_metadata_provider():
        class Provider:
            async def get_model_by_hash(self, model_hash):
                captured_hashes.append(model_hash)
                if model_hash == "a1b2c3d4e5":
                    return ({
                        "id": 999,
                        "modelId": 888,
                        "name": "v1.0",
                        "model": {"name": "Real Checkpoint", "type": "Checkpoint"},
                        "baseModel": "SDXL 1.0",
                        "images": [{"url": "https://image.civitai.com/cp/original=true"}],
                        "files": [{"type": "Model", "primary": True, "sizeKB": 1024, "name": "cp.safetensors"}]
                    }, None)
                return None, "Model not found"

        return Provider()

    monkeypatch.setattr(
        "py.recipes.parsers.civitai_image.get_default_metadata_provider",
        fake_metadata_provider,
    )

    parser = CivitaiApiMetadataParser()

    metadata = {
        "prompt": "test",
        "resources": [
            {"hash": "a1b2c3d4e5", "name": "Real Checkpoint", "type": "model"},
            {"hash": "f6g7h8i9j0", "name": "Some LoRA", "type": "lora", "weight": 0.8},
        ],
        "Model hash": "a1b2c3d4e5",
    }

    result = await parser.parse_metadata(metadata)

    # The type:"model" resource should be in result["model"], not in result["loras"]
    assert result["model"] is not None, "checkpoint model should be extracted"
    assert result["model"]["name"] == "Real Checkpoint"
    assert result["model"]["hash"] == "a1b2c3d4e5"
    assert result["model"]["type"] == "model"

    # The LoRA resource should be in result["loras"]
    assert len(result["loras"]) == 1
    assert result["loras"][0]["name"] == "Some LoRA"

    # The checkpoint hash should have triggered a lookup
    assert "a1b2c3d4e5" in captured_hashes


@pytest.mark.asyncio
async def test_parse_metadata_resources_model_type_does_not_duplicate_checkpoint_in_loras(monkeypatch):
    """When a resources entry has type:"model", it should NOT also appear in loras.
    Regression test for the bug where the checkpoint model appeared in both places."""
    async def fake_metadata_provider():
        class Provider:
            async def get_model_by_hash(self, model_hash):
                if model_hash == "cp123hash":
                    return ({
                        "id": 100,
                        "modelId": 200,
                        "name": "v2",
                        "model": {"name": "My Checkpoint", "type": "Checkpoint"},
                        "baseModel": "SDXL",
                        "files": [{"type": "Model", "primary": True, "sizeKB": 1024, "name": "cp.safetensors"}]
                    }, None)
                if model_hash == "lora1hash":
                    return ({
                        "id": 300,
                        "modelId": 400,
                        "name": "v1",
                        "model": {"name": "Style LoRA", "type": "LORA"},
                        "baseModel": "SDXL",
                        "files": [{"type": "Model", "primary": True, "sizeKB": 512, "name": "style.safetensors"}]
                    }, None)
                return None, "Model not found"

        return Provider()

    monkeypatch.setattr(
        "py.recipes.parsers.civitai_image.get_default_metadata_provider",
        fake_metadata_provider,
    )

    parser = CivitaiApiMetadataParser()
    metadata = {
        "resources": [
            {"hash": "cp123hash", "name": "My Checkpoint", "type": "model"},
            {"hash": "lora1hash", "name": "Style LoRA", "type": "lora", "weight": 0.5},
        ],
    }

    result = await parser.parse_metadata(metadata)

    # Checkpoint must NOT appear in loras
    lora_names = {l["name"] for l in result["loras"]}
    assert "My Checkpoint" not in lora_names
    assert "Style LoRA" in lora_names

    # Checkpoint must be in result["model"]
    assert result["model"] is not None
    assert result["model"]["name"] == "My Checkpoint"