import math

import pytest

from app.adapters.fake import FakeEmbeddingProvider, FakeReranker
from app.ports.model_providers import ModelProviderError


@pytest.mark.asyncio
async def test_fake_embedding_is_deterministic_normalized_and_1024_dimensional() -> None:
    provider = FakeEmbeddingProvider()

    first = await provider.embed_documents(["斑岩铜矿 钾化 绢英岩化"])
    second = await provider.embed_query("斑岩铜矿 钾化 绢英岩化")

    assert first.vectors[0] == second.vectors[0]
    assert len(first.vectors[0]) == 1024
    assert math.isclose(sum(value * value for value in first.vectors[0]), 1.0)


@pytest.mark.asyncio
async def test_fake_reranker_preserves_original_candidate_index() -> None:
    reranker = FakeReranker()
    documents = ["煤层测井对比", "斑岩铜矿钾化带", "铝土矿含矿层"]

    result = await reranker.rerank("斑岩铜矿的钾化带", documents, top_n=2)

    assert result.items[0].index == 1
    assert result.items[0].document == documents[1]
    assert result.items[0].relevance_score >= result.items[1].relevance_score


@pytest.mark.asyncio
async def test_fake_providers_reject_inputs_the_live_contract_rejects() -> None:
    embedder = FakeEmbeddingProvider()
    reranker = FakeReranker()

    with pytest.raises(ModelProviderError):
        await embedder.embed_documents([])
    with pytest.raises(ModelProviderError):
        await embedder.embed_documents(["x"] * 11)
    with pytest.raises(ModelProviderError):
        await reranker.rerank("", ["document"], top_n=1)
    with pytest.raises(ModelProviderError):
        await reranker.rerank("query", [], top_n=1)
    with pytest.raises(ModelProviderError):
        await reranker.rerank("query", ["document"], top_n=0)