"""
Tests for the two-gate contradiction detector — no live LLM required.

* The ``--demo`` / ``pinned_target_id`` path bypasses both gates and must return a
  deterministic high-confidence verdict (this is what keeps the live demo reliable).
* The Gate-2 path is exercised with a monkeypatched ``LLMGateway`` so we test the
  accept/reject thresholding without any API key.
"""

from __future__ import annotations

import pytest

from falsify import graph_ops
from falsify.tasks import detect_contradictions
from falsify.tasks.detect_contradictions import ContradictionJudgement

pytestmark = pytest.mark.asyncio


async def test_pinned_target_is_deterministic():
    """Pinned mode returns exactly one high-confidence 'contradicts' verdict."""
    out = await detect_contradictions("any new fact", pinned_target_id="E_qa")
    assert len(out) == 1
    assert out[0].target_id == "E_qa"
    assert out[0].relation == "contradicts"
    assert out[0].confidence >= 0.6


class _FakeHit:
    def __init__(self, _id, score, payload):
        self.id = _id
        self.score = score
        self.payload = payload


class _FakeVectorEngine:
    def __init__(self, hits):
        self._hits = hits

    async def search(self, collection, query_text=None, limit=15, include_payload=False, **kw):
        return self._hits


async def test_gate2_accepts_high_confidence_contradiction(monkeypatch):
    """An on-topic candidate judged 'contradicts' with conf>=0.6 is returned."""
    hits = [_FakeHit("E_qa", 0.10, {"claim": "March QA report documents the defect"})]
    monkeypatch.setattr(graph_ops, "get_vector_engine", lambda: _FakeVectorEngine(hits))

    async def fake_llm(text_input, system_prompt, response_model, **kw):
        return ContradictionJudgement(relation="contradicts", confidence=0.9, rationale="back-dated")

    from cognee.infrastructure.llm.LLMGateway import LLMGateway
    monkeypatch.setattr(LLMGateway, "acreate_structured_output", staticmethod(fake_llm))

    out = await detect_contradictions("the report was back-dated")
    assert len(out) == 1
    assert out[0].target_id == "E_qa"
    assert out[0].relation == "contradicts"


async def test_gate1_filters_off_topic(monkeypatch):
    """A candidate beyond the distance threshold is filtered before the LLM runs."""
    hits = [_FakeHit("E_far", 0.90, {"claim": "unrelated topic"})]
    monkeypatch.setattr(graph_ops, "get_vector_engine", lambda: _FakeVectorEngine(hits))

    called = {"llm": False}

    async def fake_llm(text_input, system_prompt, response_model, **kw):
        called["llm"] = True
        return ContradictionJudgement(relation="contradicts", confidence=1.0)

    from cognee.infrastructure.llm.LLMGateway import LLMGateway
    monkeypatch.setattr(LLMGateway, "acreate_structured_output", staticmethod(fake_llm))

    out = await detect_contradictions("some fact", distance_threshold=0.35)
    assert out == []
    assert called["llm"] is False  # Gate-1 rejected it; LLM never consulted


async def test_gate2_rejects_low_confidence(monkeypatch):
    """On-topic but low-confidence verdicts are not treated as contradictions."""
    hits = [_FakeHit("E_qa", 0.10, {"claim": "March QA report"})]
    monkeypatch.setattr(graph_ops, "get_vector_engine", lambda: _FakeVectorEngine(hits))

    async def fake_llm(text_input, system_prompt, response_model, **kw):
        return ContradictionJudgement(relation="contradicts", confidence=0.2, rationale="unsure")

    from cognee.infrastructure.llm.LLMGateway import LLMGateway
    monkeypatch.setattr(LLMGateway, "acreate_structured_output", staticmethod(fake_llm))

    out = await detect_contradictions("weak signal")
    assert out == []