"""End-to-end milestone against real models: plan with the planner role,
approve through the gate, then let the executor role actually create and test
a file on disk.

Gated on NISSE_LIVE_PLANNER_MODEL like the planning test — it needs a model
that holds the tool-call contract on both ends.
"""

import io
import os
from pathlib import Path

import pytest

from nisse_dev.control import (
    AWAIT_ARCH_SIGNOFF,
    AWAIT_INPUT,
    AWAIT_MILESTONE_SIGNOFF,
    EXECUTING,
    Orchestrator,
)
from nisse_dev.envfile import load_env_file
from nisse_dev.executor import LLMExecutor
from nisse_dev.llm import LiteLLMClient, RoleMap
from nisse_dev.oplog import OpLog
from nisse_dev.planner import LLMPlanningAgent
from nisse_dev.state import StateStore
from nisse_dev.tools import Workspace
from nisse_dev.transport import InMemoryTransport

load_env_file(Path(__file__).resolve().parents[2] / ".env")

SENTINEL = "NISSE_LIVE_PLANNER_MODEL"

pytestmark = [
    pytest.mark.integration,
    pytest.mark.skipif(
        not os.environ.get(SENTINEL),
        reason=f"no live planner route configured ({SENTINEL} unset)",
    ),
]


def _drive_to_execution(
    store: StateStore, transport: InMemoryTransport, max_steps: int = 10
) -> None:
    """Play the operator. A scripted approve/approve/start sequence is not
    robust against a live model: ask_human is a legitimate move at any gate,
    and a question shifts the state machine under a fixed script. So respond
    to whatever the agent actually did."""
    for _ in range(max_steps):
        state = store.get_state()
        assert state is not None
        if state.state == EXECUTING:
            return
        gate = state.data.get("gate") or {}
        if state.state == AWAIT_INPUT:
            transport.receive("don", "Use your best judgement and proceed.")
        elif state.state == AWAIT_MILESTONE_SIGNOFF and gate.get("approved"):
            transport.receive("don", "start milestone 1")
        elif state.state in (AWAIT_ARCH_SIGNOFF, AWAIT_MILESTONE_SIGNOFF):
            transport.receive("don", "approve")
        else:
            raise AssertionError(f"unexpected state {state.state}")
    raise AssertionError(
        f"never reached execution; last message: {transport.outbound[-1]}"
    )


def test_plan_approve_execute_produces_a_real_file(tmp_path: Path) -> None:
    assert os.environ.get("NISSE_LLM_TOKEN"), (
        f"{SENTINEL} is set, so the live path is expected to work, "
        "but NISSE_LLM_TOKEN is missing"
    )
    # Both roles use the capable model here: the point is the whole gated
    # loop over a real proxy, not local-model competence (measured separately).
    model = os.environ[SENTINEL]
    roles = RoleMap(executor=model, planner=model, delegate=model, consultant=model)
    client = LiteLLMClient.from_env()
    store = StateStore(tmp_path / "state.db", oplog=OpLog("nisse-dev", io.StringIO()))
    workspace = Workspace(tmp_path / "work", timeout_s=60)
    transport = InMemoryTransport()
    orchestrator = Orchestrator(
        store,
        transport,
        LLMPlanningAgent(client, store, roles),
        operators={"don"},
        roles=roles,
        executor=LLMExecutor(client, store, roles, workspace, max_turns=10),
    )
    orchestrator.start()

    transport.receive(
        "don",
        "use greet. Milestone 1: create greet.py in the workspace root defining "
        "greet(name) returning 'Hello, <name>!', and verify it by running python.",
    )
    _drive_to_execution(store, transport)

    state = store.get_state()
    assert state is not None
    assert state.state in (EXECUTING, AWAIT_INPUT)

    created = list(workspace.root.rglob("*.py"))
    assert created, f"executor created no Python file; said: {transport.outbound[-1]}"
    combined = "\n".join(p.read_text() for p in created)
    assert "def greet" in combined