from __future__ import annotations

from skills_learning.config import SkillAuthoringConfig
from skills_learning.embeddings import embed_skill_text
from skills_learning.models import FlowStep, FlowTemplateSkill, SkillMetadata
from skills_learning.retrieval import retrieve_candidate_skills
from skills_learning.store import SkillStore


class FakeEmbeddingClient:
    def __init__(self, *, fail: bool = False) -> None:
        self.fail = fail

    def embed(self, text: str, *, model: str) -> list[float]:
        if self.fail:
            raise TimeoutError("embedding timeout")
        lowered = text.lower()
        if "coffee" in lowered:
            return [1.0, 0.0]
        if "tea" in lowered:
            return [0.0, 1.0]
        return [0.5, 0.5]


def _skill(name: str, goal: str) -> FlowTemplateSkill:
    return FlowTemplateSkill(
        metadata=SkillMetadata(
            name=name,
            description=f"Learned flow for {goal}",
            originating_goal=goal,
        ),
        steps=[FlowStep("input_text", {"text": goal})],
        parameters={},
    )


def test_embed_skill_text_degrades_to_none_on_provider_failure() -> None:
    result = embed_skill_text(
        "coffee",
        client=FakeEmbeddingClient(fail=True),
        config=SkillAuthoringConfig(enabled=True),
    )

    assert result is None


def test_retrieve_candidate_skills_ranks_by_similarity_and_truncates_top_k() -> None:
    store = SkillStore()
    coffee = store.create_version(_skill("search coffee", "search coffee"))
    tea = store.create_version(_skill("search tea", "search tea"))
    store.store_embedding(coffee, [1.0, 0.0], model_name="fake")
    store.store_embedding(tea, [0.0, 1.0], model_name="fake")

    results = retrieve_candidate_skills(
        "find coffee",
        store=store,
        top_k=1,
        embedding_client=FakeEmbeddingClient(),
        config=SkillAuthoringConfig(enabled=True),
    )

    assert [result.skill.name for result in results] == ["search coffee"]


def test_retrieve_candidate_skills_returns_empty_without_embeddings() -> None:
    store = SkillStore()
    store.create_version(_skill("search coffee", "search coffee"))

    results = retrieve_candidate_skills(
        "find coffee",
        store=store,
        embedding_client=FakeEmbeddingClient(),
        config=SkillAuthoringConfig(enabled=True),
    )

    assert results == []


def test_skill_without_embedding_is_stored_but_excluded_from_retrieval() -> None:
    store = SkillStore()
    skill = store.create_version(_skill("search coffee", "search coffee"))

    results = retrieve_candidate_skills(
        "find coffee",
        store=store,
        embedding_client=FakeEmbeddingClient(),
        config=SkillAuthoringConfig(enabled=True),
    )

    assert store.get_by_id(skill.id) == skill
    assert results == []