"""Integration tests for the extraction + triage pipeline (R8).

Tests the flow that produced the 41 active memories:
LLM extraction → persist as candidate → triage → promote/reject.
Uses mocked subprocess to avoid real claude -p calls.
"""

from __future__ import annotations

from unittest.mock import patch

import pytest

from atocore.memory.extractor_llm import (
    extract_candidates_llm,
    extract_candidates_llm_verbose,
)
from atocore.memory.service import create_memory, get_memories
from atocore.models.database import init_db
import atocore.memory.extractor_llm as extractor_llm


def _make_interaction(**kw):
    from atocore.interactions.service import Interaction

    return Interaction(
        id=kw.get("id", "test-pipe-1"),
        prompt=kw.get("prompt", "test prompt"),
        response=kw.get("response", ""),
        response_summary="",
        project=kw.get("project", ""),
        client="test",
        session_id="",
    )


class _FakeCompleted:
    def __init__(self, stdout, returncode=0):
        self.stdout = stdout
        self.stderr = ""
        self.returncode = returncode


def test_llm_extraction_persists_as_candidate(tmp_data_dir, monkeypatch):
    """Full flow: LLM extracts → caller persists as candidate → memory
    exists with status=candidate and correct project."""
    init_db()
    monkeypatch.setattr(extractor_llm, "_cli_available", lambda: True)
    monkeypatch.setattr(
        extractor_llm.subprocess,
        "run",
        lambda *a, **kw: _FakeCompleted(
            '[{"type": "project", "content": "USB SSD is mandatory for RPi storage", "project": "p06-polisher", "confidence": 0.6}]'
        ),
    )

    interaction = _make_interaction(
        response="We decided USB SSD is mandatory for the polisher RPi.",
        project="p06-polisher",
    )
    candidates = extract_candidates_llm(interaction)
    assert len(candidates) == 1
    assert candidates[0].content == "USB SSD is mandatory for RPi storage"

    mem = create_memory(
        memory_type=candidates[0].memory_type,
        content=candidates[0].content,
        project=candidates[0].project,
        confidence=candidates[0].confidence,
        status="candidate",
    )
    assert mem.status == "candidate"
    assert mem.project == "p06-polisher"

    # Verify it appears in the candidate queue
    queue = get_memories(status="candidate", project="p06-polisher", limit=10)
    assert any(m.id == mem.id for m in queue)


def test_llm_extraction_project_fallback(tmp_data_dir, monkeypatch):
    """R6+R9: when model returns empty project, candidate inherits
    the interaction's project."""
    init_db()
    monkeypatch.setattr(extractor_llm, "_cli_available", lambda: True)
    monkeypatch.setattr(
        extractor_llm.subprocess,
        "run",
        lambda *a, **kw: _FakeCompleted(
            '[{"type": "knowledge", "content": "machine works offline", "project": "", "confidence": 0.5}]'
        ),
    )

    interaction = _make_interaction(
        response="The machine works fully offline.",
        project="p06-polisher",
    )
    candidates = extract_candidates_llm(interaction)
    assert len(candidates) == 1
    assert candidates[0].project == "p06-polisher"


def test_promote_reject_flow(tmp_data_dir):
    """Candidate → promote and candidate → reject both work via the
    service layer (mirrors what auto_triage.py does via HTTP)."""
    from atocore.memory.service import promote_memory, reject_candidate_memory

    init_db()
    good = create_memory(
        memory_type="project",
        content="durable fact worth keeping",
        project="p06-polisher",
        confidence=0.5,
        status="candidate",
    )
    bad = create_memory(
        memory_type="project",
        content="stale snapshot to reject",
        project="atocore",
        confidence=0.5,
        status="candidate",
    )

    promote_memory(good.id)
    reject_candidate_memory(bad.id)

    active = get_memories(project="p06-polisher", active_only=True, limit=10)
    assert any(m.id == good.id for m in active)

    candidates = get_memories(status="candidate", limit=10)
    assert not any(m.id == good.id for m in candidates)
    assert not any(m.id == bad.id for m in candidates)


def test_duplicate_content_creates_separate_memory(tmp_data_dir):
    """create_memory allows duplicate content (dedup is the triage
    model's responsibility, not the DB layer). Both memories exist."""
    init_db()
    m1 = create_memory(
        memory_type="project",
        content="unique fact about polisher",
        project="p06-polisher",
    )
    m2 = create_memory(
        memory_type="project",
        content="unique fact about polisher",
        project="p06-polisher",
        status="candidate",
    )
    assert m1.id != m2.id


def test_llm_extraction_failure_returns_empty(tmp_data_dir, monkeypatch):
    """The full persist flow handles LLM extraction failure gracefully:
    0 candidates, nothing persisted, no raise."""
    init_db()
    monkeypatch.setattr(extractor_llm, "_cli_available", lambda: True)
    monkeypatch.setattr(
        extractor_llm.subprocess,
        "run",
        lambda *a, **kw: _FakeCompleted("", returncode=1),
    )

    interaction = _make_interaction(
        response="some real content that the LLM fails on",
        project="p06-polisher",
    )
    result = extract_candidates_llm_verbose(interaction)
    assert result.candidates == []
    assert "exit_1" in result.error

    # Nothing in the candidate queue
    queue = get_memories(status="candidate", limit=10)
    assert len(queue) == 0