"""Tests for pipeline.ai_provider_wrapper — no monkeypatching, no mocks."""

import pipeline.ai_provider_wrapper as w
from tests.conftest import FakeEmbedder


def test_empty_input_returns_empty():
    """Empty text list always returns empty list — no embedder call needed."""
    result = w.get_embeddings_with_retry([])
    assert result == []


def test_successful_embeddings(mem_db):
    """Real embedder returns vectors aligned with input texts."""
    embedder = FakeEmbedder()
    result = w.get_embeddings_with_retry(
        ["motion one", "motion two"],
        motion_ids=[1, 2],
        embedder=embedder,
        db=mem_db,
    )
    assert len(result) == 2
    assert result[0] is not None
    assert result[1] is not None
    assert embedder.call_count >= 1


def test_transient_failure_retries(mem_db):
    """A transient failure (first call fails, second succeeds) triggers retry."""

    class TransientEmbedder:
        def __init__(self):
            self.call_count = 0

        def __call__(self, texts, model=None, batch_size=50):
            self.call_count += 1
            if self.call_count == 1:
                raise RuntimeError("Transient network error")
            return [[0.5] * 8 for _ in texts]

    embedder = TransientEmbedder()
    result = w.get_embeddings_with_retry(
        ["motion text"],
        motion_ids=[42],
        embedder=embedder,
        db=mem_db,
        retries=3,
    )
    # After retry, should succeed
    assert result[0] is not None
    assert embedder.call_count >= 2


def test_permanent_failure_returns_none_sentinel(mem_db):
    """A permanently failing embedder returns None in the result list."""
    always_fails = FakeEmbedder(fail_indices={0})

    result = w.get_embeddings_with_retry(
        ["failing motion"],
        motion_ids=[99],
        embedder=always_fails,
        db=mem_db,
        retries=2,
    )
    # Result entry is None for the failed item
    assert result == [None]