"""Integration test: admin_model_overrides wire into routing.

Verifies that POST /admin/api/models/{model_id}/{provider}/availability
actually removes the model from /route candidates. The wired exclude set
(_admin_excluded_models) must intersect with select_candidates'
exclude_models filter so the overridden model never appears in the
route response.

Uses the pattern from test_route_decisions.py: temp DB, seeded models with
energy + proficiency so a specific model WOULD win, then override + /route
assertion.
"""

from __future__ import annotations

import json
import sqlite3
import time
from pathlib import Path
from typing import Any

import pytest
from openai import OpenAI
from starlette.testclient import TestClient

import dispatcher
from config import load_config

ROOT = Path(__file__).resolve().parent.parent
SCHEMA_SQL = (ROOT / "config" / "schema.sql").read_text()
CFG = load_config(str(ROOT / "config" / "config.yaml"))

ADMIN_TABLE_SQL = """
CREATE TABLE IF NOT EXISTS admin_model_overrides (
    model_id         TEXT NOT NULL,
    provider         TEXT NOT NULL,
    availability     TEXT NOT NULL,
    reason           TEXT,
    updated_at       TEXT NOT NULL,
    PRIMARY KEY (model_id, provider)
);
CREATE INDEX IF NOT EXISTS idx_admin_model_overrides_availability
    ON admin_model_overrides (availability);
"""

# A model we will seed with best proficiency + energy so it WOULD be picked.
WINNER_MODEL = "premium"
# A second model that would be runner-up.
RUNNER_MODEL = "mid"


def _completion(model_id: str) -> dict:
    """An OpenAI-compatible completion body for a *fake* provider response."""
    return {
        "id": f"chatcmpl-{model_id}",
        "object": "chat.completion",
        "model": model_id,
        "created": int(time.time()),
        "choices": [{"index": 0, "finish_reason": "stop", "message": {"role": "assistant", "content": "done"}}],
    }


class FakeResponse:
    """Minimal fake for requests.post() and httpx.Response."""

    def __init__(
        self,
        body: dict | None = None,
        lines: list[str] | None = None,
        headers: dict | None = None,
    ) -> None:
        self.body = body or _completion("dummy")
        self.lines = lines or []
        self.headers = headers or {"content-type": "application/json"}
        self.status_code = 200

    @property
    def text(self) -> str:
        return json.dumps(self.body)

    @property
    def content(self) -> bytes:
        return json.dumps(self.body).encode()

    def json(self) -> dict:
        return self.body


@pytest.fixture
def admin_override_router(tmp_path: Path, monkeypatch) -> tuple[TestClient, Path]:
    """A TestClient with a temp DB that has admin_model_overrides table,
    seeded with two models (WINNER_MODEL and RUNNER_MODEL) where WINNER
    has best proficiency + energy."""

    import admin

    db_path = tmp_path / "test.db"
    conn = sqlite3.connect(str(db_path))
    conn.executescript(SCHEMA_SQL)
    conn.executescript(ADMIN_TABLE_SQL)
    dispatcher.ensure_route_decisions(conn)

    # Seed two models: WINNER (tier 2, best) and RUNNER (tier 2).
    for mid, cost_prompt, cost_compl, prof_score in [
        (WINNER_MODEL, 0.50, 0.30, 0.95),
        (RUNNER_MODEL, 0.30, 0.15, 0.60),
    ]:
        conn.execute(
            """
            INSERT INTO models (
                model_id, provider, base_model_id, display_name, tier,
                context_window, effective_context_window, max_output_tokens,
                cost_per_1m_prompt, cost_per_1m_completion,
                supports_tools, supports_json_mode, supports_vision,
                supports_reasoning, reasoning_default_enabled,
                latency_class, reasoning_mode, context_variant,
                access_level, availability, last_updated
            ) VALUES (?, 'neuralwatt', ?, ?, ?, 262128, 192500, 16384,
                      ?, ?, 1, 1, 1, 1, 1,
                      'standard', 'default', 'full', 'public', 'active',
                      '2026-08-22T00:00:00+00:00')
            """,
            (
                mid,
                mid,
                mid,
                2,
                cost_prompt,
                cost_compl,
            ),
        )

    # Seed proficiency: WINNER has the best score for coding_general.
    conn.execute(
        "INSERT INTO proficiency (model_id, provider, category, "
        "blended_score, source, last_updated) "
        "VALUES (?, 'neuralwatt', 'coding_general', ?, 'self_eval_thin', "
        "'2026-01-01T00:00:00+00:00')",
        (WINNER_MODEL, 0.95),
    )
    conn.execute(
        "INSERT INTO proficiency (model_id, provider, category, "
        "blended_score, source, last_updated) "
        "VALUES (?, 'neuralwatt', 'coding_general', ?, 'self_eval_thin', "
        "'2026-01-01T00:00:00+00:00')",
        (RUNNER_MODEL, 0.60),
    )

    # Seed one energy observation so scoring works.
    now = "2026-08-22T00:00:00+00:00"
    for mid in (WINNER_MODEL, RUNNER_MODEL):
        conn.execute(
            """
            INSERT INTO energy_observations (
                model_id, provider, prompt_tokens, completion_tokens,
                energy_kwh, carbon_g_co2eq, cost_usd, observed_at
            ) VALUES (?, 'neuralwatt', 200, 500, 0.005, 2.0, 0.10, ?)
            """,
            (mid, now),
        )

    conn.commit()
    conn.close()

    # Redirect the dispatcher to the temp DB.
    monkeypatch.setattr(dispatcher.cfg.database, "path", str(db_path))
    monkeypatch.setattr(dispatcher.cfg.verification, "local_llm_enabled", False)
    monkeypatch.setattr(dispatcher.cfg.freshness, "exclude_stale", True)
    monkeypatch.setattr(dispatcher.cfg.freshness, "exclude_deprecated", True)
    monkeypatch.setattr(dispatcher.cfg.routing, "require_vision", False)
    monkeypatch.setattr(dispatcher.cfg.logging, "log_route_decisions", True)
    # Exploration defaults to enabled; this fixture asserts a deterministic
    # winner on tier-2 routes, so pin epsilon to 0. Leave the enabled flag as
    # configured so the production default is still exercised structurally.
    monkeypatch.setattr(dispatcher.cfg.exploration, "epsilon", 0.0)
    monkeypatch.setenv("NEURALWATT_API_KEY", "test-key")

    # Ensure admin tables are wired into the startup migration.
    from admin import ensure_admin_tables

    temp_conn = sqlite3.connect(str(db_path))
    temp_conn.row_factory = sqlite3.Row
    try:
        ensure_admin_tables(conn=temp_conn)
    except Exception:
        pass  # may already exist
    finally:
        temp_conn.close()

    # Stub the classifier — always returns coding_general, tier 2.
    monkeypatch.setattr(
        dispatcher,
        "classify",
        lambda task, context: dispatcher.Classification(
            task_category="coding_general",
            task_tier=2,
            required_context_tokens=100,
            confidence=0.9,
        ),
    )

    # Stub the provider call so the /route endpoint doesn't actually call
    # NeuralWatt. It just returns a minimal response.
    def fake_post(url: str, headers: Any = None, json: Any = None,
                  stream: bool = False, timeout: int = 600):
        if stream:
            return FakeResponse(lines=["data: ..."])
        return FakeResponse(_completion(json["model"] if json else "dummy"))

    monkeypatch.setattr(dispatcher.requests, "post", fake_post)

    app = dispatcher.app

    with TestClient(app) as client:
        yield client, db_path


def test_admin_override_excludes_model_from_route(admin_override_router):
    """When an admin override marks WINNER_MODEL as deprecated, POST /route
    must NOT pick it. The selected model should be RUNNER_MODEL instead,
    and candidates_considered should be 1 (not 2).

    This is the CRITICAL integration test: override → router exclusion.
    """
    client, db_path = admin_override_router

    # First, verify that WITHOUT an override, the router picks WINNER.
    resp = client.post("/route", json={"task": "write a python function"})
    assert resp.status_code == 200
    body = resp.json()
    assert body["selected"]["model_id"] == WINNER_MODEL
    assert body["candidates_considered"] == 2

    # Now mark WINNER as deprecated via admin override.
    override_resp = client.post(
        f"/admin/api/models/{WINNER_MODEL}/neuralwatt/availability",
        json={"availability": "deprecated", "reason": "failing verification"},
    )
    assert override_resp.status_code == 200
    override_data = override_resp.json()
    assert override_data["is_overridden"] is True
    assert override_data["effective_availability"] == "deprecated"

    # POST /route again: the router must NOT pick the overridden model.
    resp = client.post("/route", json={"task": "write a python function"})
    assert resp.status_code == 200
    body = resp.json()
    assert body["selected"]["model_id"] == RUNNER_MODEL
    assert body["candidates_considered"] == 1
    # Verify the override model is in the excluded set via the decision log.
    conn = sqlite3.connect(str(db_path))
    conn.row_factory = sqlite3.Row
    decision_row = conn.execute(
        "SELECT * FROM route_decisions ORDER BY id DESC LIMIT 1"
    ).fetchone()
    conn.close()
    assert decision_row["selected_model"] == RUNNER_MODEL


def test_admin_override_revert_includes_model_again(admin_override_router):
    """After DELETE on the admin override, the model must become routable
    again and be picked if it still has best scores."""
    client, db_path = admin_override_router

    # Mark as deprecated.
    client.post(
        f"/admin/api/models/{WINNER_MODEL}/neuralwatt/availability",
        json={"availability": "deprecated", "reason": "test"},
    )

    # Verify route picks RUNNER.
    resp = client.post("/route", json={"task": "write a python function"})
    assert resp.json()["selected"]["model_id"] == RUNNER_MODEL

    # Delete override.
    delete_resp = client.delete(
        f"/admin/api/models/{WINNER_MODEL}/neuralwatt/availability"
    )
    assert delete_resp.status_code == 200
    del_data = delete_resp.json()
    assert del_data["is_overridden"] is False

    # Route again: WINNER should be back on the radar.
    resp = client.post("/route", json={"task": "write a python function"})
    assert resp.status_code == 200
    body = resp.json()
    assert body["selected"]["model_id"] == WINNER_MODEL
    assert body["candidates_considered"] == 2