import json
from datetime import UTC, datetime
from types import SimpleNamespace

from typer.testing import CliRunner

from tht.cli import app
from tht.config import load_config
from tht.jobs.dwh_pipeline import DwhPreprocessPipeline, config_dwh_binding
from tht.mschema.models import ColumnPhysical, PhysicalSchema, TablePhysical
from tht.ports.vector import VectorReadUnavailable
from tht.vectorstore.embeddings import EmbeddingsError


class _FakeEmbedder:
    def __init__(self):
        self.calls = 0

    def embed_query(self, text):
        self.calls += 1
        return [0.1, 0.2, 0.3]


class _FakeSearcher:
    def __init__(self):
        self.calls = []

    def search(self, vec, top_n, kinds=None):
        self.calls.append({"top_n": top_n, "kinds": kinds})
        if kinds == ["solved_question"]:
            return [SimpleNamespace(
                kind="memory", ref="s-1", id="m1", title="q solved",
                similarity=0.91, content="quanti pazienti nel 2024?",
                metadata={"session_id": "2026-01-01-000000-x", "sql": "SELECT 1",
                          "tables": ["fact_ablazione"], "question": "quanti pazienti nel 2024?"},
            )]
        if kinds == ["schema_table", "schema_column"]:
            return [SimpleNamespace(
                kind="schema_table", ref="fact_ablazione", id="t1",
                title="Tabella fact_ablazione", similarity=0.88,
                content="Tabella fact_ablazione", metadata={},
            )]
        if kinds == ["evidence"]:
            return [SimpleNamespace(
                kind="evidence", ref="ev1", id="ev1", title="Dominio ablazione",
                similarity=0.8, content="L'ablazione e' una procedura...",
                metadata={"status": "approved"},
            )]
        return []


def _workspace(tmp_path, with_session=None):
    physical = PhysicalSchema(
        database="d", schema="s", introspected_at=datetime(2026, 1, 1, tzinfo=UTC),
        tables={"fact_ablazione": TablePhysical(
            comment="Ablazioni", columns={"cod_paz": ColumnPhysical(type="bigint")})},
    )
    cfg = tmp_path / "workspace.yaml"
    cfg.write_text(
        "database: {database: d, schema: s, user: u, password: p, transport: direct}\n"
        "vector_db: {database: v, schema: public, user: u, password: p}\n"
        "embeddings: {base_url: 'http://localhost:11434', model: qwen3-embedding:0.6b, dim: 1024}\n"
        f"paths: {{artifacts: {tmp_path/'artifacts'}, indexes: {tmp_path/'i'}, "
        f"sessions: {tmp_path/'sessions'}}}\n"
    )
    binding = config_dwh_binding(load_config(cfg))
    DwhPreprocessPipeline(
        workspace_id=binding["workspace_id"], workspace_root=tmp_path,
        config_fingerprint=binding["config_fingerprint"],
        input_fingerprint=binding["input_fingerprint"],
        introspect=lambda output: physical.to_yaml(output),
        build_lsh=lambda _physical, output: [
            (output / name).write_text("index")
            for name in ("s_lsh.pkl", "s_minhashes.pkl", "s_meta.json")
        ],
        lsh_filenames=("s_lsh.pkl", "s_minhashes.pkl", "s_meta.json"),
    ).run()
    if with_session:
        sdir = tmp_path / "sessions" / with_session
        sdir.mkdir(parents=True)
        (sdir / "session_manifest.yaml").write_text(
            f"id: {with_session}\nquestion: q\ndatabase: d\nschema: s\n"
            "created_at: 2026-01-01T00:00:00+00:00\nstatus: open\n"
        )
    return cfg


def _patch(monkeypatch, embedder, searcher):
    import tht.cli.vector_cmd as vc

    monkeypatch.setattr(vc, "make_embedder", lambda _cfg: embedder)
    monkeypatch.setattr(vc, "open_searcher", lambda _cfg: searcher)


def test_pack_single_embed_and_sections(tmp_path, monkeypatch):
    cfg = _workspace(tmp_path)
    emb = _FakeEmbedder()
    searcher = _FakeSearcher()
    _patch(monkeypatch, emb, searcher)
    res = CliRunner().invoke(app, ["search", "pack", "quanti pazienti", "-c", str(cfg)])
    assert res.exit_code == 0, res.output
    assert emb.calls == 1  # UN solo embedding per le tre ricerche
    assert [call["kinds"] for call in searcher.calls] == [
        ["schema_table", "schema_column"],
        ["solved_question"],
    ]
    assert "fact_ablazione" in res.output and "Ablazioni" in res.output
    # Evidence is fail-closed until an ACTIVE corpus exists; legacy vector rows
    # must not leak into a new search pack.
    assert "Dominio ablazione" not in res.output
    assert "SELECT 1" in res.output


def test_pack_json_and_session_file(tmp_path, monkeypatch):
    sid = "2026-01-01-000000-test"
    cfg = _workspace(tmp_path, with_session=sid)
    _patch(monkeypatch, _FakeEmbedder(), _FakeSearcher())
    res = CliRunner().invoke(
        app, ["search", "pack", "q", "-c", str(cfg), "--session", sid, "--json"]
    )
    assert res.exit_code == 0, res.output
    data = json.loads(res.output)
    assert data["tables"][0]["name"] == "fact_ablazione"
    pack = tmp_path / "sessions" / sid / "retrieval_pack.md"
    assert pack.exists()
    assert "Retrieval pack" in pack.read_text()


def test_pack_degrades_direct_vector_read_error(tmp_path, monkeypatch):
    cfg = _workspace(tmp_path)

    class _BrokenSearcher:
        def search(self, vec, top_n, kinds=None):
            raise VectorReadUnavailable("Vector read operation unavailable")

    _patch(monkeypatch, _FakeEmbedder(), _BrokenSearcher())
    res = CliRunner().invoke(app, ["search", "pack", "q", "-c", str(cfg), "--json"])
    assert res.exit_code == 0, res.output
    data = json.loads(res.output[res.output.index("{"):])
    assert data["tables"] == [] and data["evidence"] == [] and data["solved"] == []
    assert len(data["warnings"]) == 2


def test_pack_degrades_gracefully(tmp_path, monkeypatch):
    cfg = _workspace(tmp_path)

    class _Broken:
        def embed_query(self, text):
            raise EmbeddingsError("ollama down")

    _patch(monkeypatch, _Broken(), _FakeSearcher())
    res = CliRunner().invoke(app, ["search", "pack", "q", "-c", str(cfg), "--json"])
    assert res.exit_code == 0, res.output
    data = json.loads(res.output[res.output.index("{"):])
    assert data["tables"] == [] and data["evidence"] == [] and data["solved"] == []
    assert any("retrieval non disponibile" in w for w in data["warnings"])