from dataclasses import FrozenInstanceError
from unittest.mock import MagicMock

import pytest

from tht.adapters.vector.qdrant import QdrantVectorStore
from tht.ports.vector import (
    VectorReadUnavailable,
    VectorStore,
    VectorStoreError,
)


def test_vector_contract_is_exported_from_public_packages():
    from tht.adapters.vector import QdrantVectorStore as PublicQdrantStore
    from tht.ports import VectorReadUnavailable as PublicVectorReadUnavailable
    from tht.ports import VectorStore as PublicVectorStore

    assert PublicQdrantStore is QdrantVectorStore
    assert PublicVectorStore is VectorStore
    assert PublicVectorReadUnavailable is VectorReadUnavailable

    capabilities = store_capabilities = QdrantVectorStore(
        base_url="http://qdrant:6333",
        collection="workspace-semantic",
        workspace_id="demo",
        expected_dimension=1024,
        request=lambda *args, **kwargs: MagicMock(
            ok=True,
            status_code=200,
            text='{"result":{"config":{"params":{"vectors":{"size":1024,"distance":"Cosine"}}},"payload_schema":{}}}',
            json=lambda: {
                "result": {
                    "config": {"params": {"vectors": {"size": 1024, "distance": "Cosine"}}},
                    "payload_schema": {},
                }
            },
        ),
    ).capabilities
    assert capabilities.search is True
    with pytest.raises(FrozenInstanceError):
        store_capabilities.search = False


@pytest.mark.parametrize("limit", [True, False, 1.0, 0, -1])
def test_qdrant_search_requires_a_strict_positive_integer_limit(limit):
    store = QdrantVectorStore(
        base_url="http://qdrant:6333",
        collection="workspace-semantic",
        workspace_id="demo",
        expected_dimension=1024,
        request=lambda *args, **kwargs: MagicMock(
            ok=True,
            status_code=200,
            text='{"result":{"points":[]}}',
            json=lambda: {"result": {"points": []}},
        ),
    )

    with pytest.raises(ValueError, match="positive integer"):
        store.search(["memory"], [0.1] * 1024, limit=limit)


def test_qdrant_search_rejects_dimension_mismatches_before_transport():
    seen = []
    store = QdrantVectorStore(
        base_url="http://qdrant:6333",
        collection="workspace-semantic",
        workspace_id="demo",
        expected_dimension=1024,
        request=lambda *args, **kwargs: seen.append((args, kwargs)),
    )

    with pytest.raises(VectorStoreError, match="dimension"):
        store.search(["memory"], [0.1], limit=1)

    assert seen == []


def test_qdrant_store_is_runtime_vector_store():
    store = QdrantVectorStore(
        base_url="http://qdrant:6333",
        collection="workspace-semantic",
        workspace_id="demo",
        expected_dimension=1024,
        request=lambda *args, **kwargs: MagicMock(
            ok=True,
            status_code=200,
            text='{"result":{"config":{"params":{"vectors":{"size":1024,"distance":"Cosine"}}},"payload_schema":{}}}',
            json=lambda: {
                "result": {
                    "config": {"params": {"vectors": {"size": 1024, "distance": "Cosine"}}},
                    "payload_schema": {},
                }
            },
        ),
    )
    assert isinstance(store, VectorStore)