"""Per-task backend profile resolution and provider dispatch."""

import importlib

import pytest


@pytest.fixture()
def config(monkeypatch):
    # Clear any default LLM_* so the default backend is deterministic.
    for k in ("LLM_PROVIDER", "LLM_BASE_URL", "LLM_MODEL", "LLM_API_KEY", "ANTHROPIC_API_KEY"):
        monkeypatch.delenv(k, raising=False)
    import app.config as config
    return importlib.reload(config)


def test_default_backend(config):
    be = config.backend(None)
    assert be.provider == "openai"
    assert be.base_url == "https://api.anthropic.com/v1/"
    assert be.model == "claude-haiku-4-5"
    assert config.backend("") is config.DEFAULT_BACKEND


def test_named_backend_resolves(config, monkeypatch):
    monkeypatch.setenv("LLM_OUMI_PROVIDER", "anthropic")
    monkeypatch.setenv("LLM_OUMI_BASE_URL", "https://api.oumi.ai/inference")
    monkeypatch.setenv("LLM_OUMI_MODEL", "projects/X/deployments/22")
    monkeypatch.setenv("LLM_OUMI_API_KEY", "oumi_test")
    be = config.backend("oumi")
    assert be.provider == "anthropic"
    assert be.base_url == "https://api.oumi.ai/inference"
    assert be.model == "projects/X/deployments/22"
    assert be.api_key == "oumi_test"
    assert be.name == "oumi"


def test_named_provider_defaults_to_openai(config, monkeypatch):
    monkeypatch.setenv("LLM_QWEN_BASE_URL", "https://api.oumi.ai/inference/v1/")
    monkeypatch.setenv("LLM_QWEN_MODEL", "projects/X/deployments/99")
    monkeypatch.setenv("LLM_QWEN_API_KEY", "oumi_test")
    assert config.backend("qwen").provider == "openai"


def test_unconfigured_named_backend_falls_back_to_default(config, monkeypatch):
    monkeypatch.setenv("LLM_OUMI_BASE_URL", "https://api.oumi.ai/inference")
    # MODEL and API_KEY missing -> not fully configured -> default
    assert config.backend("oumi") is config.DEFAULT_BACKEND
    # ...and the trace says "default", not "oumi", so the fallback isn't silent.
    assert config.backend("oumi").name == "default"


def test_task_backend_defaults_none():
    from app.tasks.base import Task

    t = Task(
        id="x", title="X", tagline="", system_prompt="", ui={},
        sample=lambda: None, lookup_truth=lambda i: None,
        parse_output=lambda s: s, score=lambda tr, p: {}, present=lambda p, tr: {},
    )
    assert t.backend is None


def test_runner_dispatch_by_provider():
    from app import engine
    from app.config import Backend

    oa = Backend("openai", "u", "m", "k")
    an = Backend("anthropic", "u", "m", "k")
    assert engine._runner_for(oa) is engine._run_openai
    assert engine._runner_for(an) is engine._run_anthropic


class _Block:
    """Stands in for an anthropic content block."""

    def __init__(self, **kw):
        self.__dict__.update(kw)

    def model_dump(self):
        return dict(self.__dict__)


def _resp(stop_reason, content):
    return _Block(
        stop_reason=stop_reason,
        content=content,
        usage=_Block(input_tokens=1, output_tokens=2),
    )


def test_anthropic_run_emits_openai_format_trajectory(monkeypatch):
    """The anthropic path must return the same standard chat record as the
    openai path — it's the training-data artifact the UI downloads."""
    monkeypatch.setenv("LANGFUSE_TRACING_ENABLED", "false")
    monkeypatch.setenv("LANGFUSE_PUBLIC_KEY", "pk-lf-test")
    monkeypatch.setenv("LANGFUSE_SECRET_KEY", "sk-lf-test")
    from app import engine
    from app.config import Backend
    from app.tasks.base import Task

    task = Task(
        id="t", title="T", tagline="", system_prompt="SYS", ui={},
        sample=lambda: None, lookup_truth=lambda i: None,
        parse_output=lambda s: s, score=lambda tr, p: {}, present=lambda p, tr: {},
        tools=[{
            "type": "function",
            "function": {
                "name": "lookup",
                "description": "d",
                "parameters": {"type": "object", "properties": {"q": {"type": "string"}}},
            },
        }],
        execute_tool=lambda name, args: "TOOL_RESULT",
    )
    turns = [
        _resp("tool_use", [_Block(type="tool_use", id="tu_1", name="lookup", input={"q": "x"})]),
        _resp("end_turn", [_Block(type="text", text="DONE")]),
    ]
    client = _Block(messages=_Block(create=lambda **kw: turns.pop(0)))
    monkeypatch.setattr(engine, "_anthropic", lambda be: client)

    final, transcript, messages = engine._run_anthropic(
        task, "hello", Backend("anthropic", "u", "m", "k")
    )

    assert final == "DONE"
    assert transcript == [{"tool": "lookup", "args": {"q": "x"}, "result": "TOOL_RESULT"}]
    assert [m["role"] for m in messages] == ["system", "user", "assistant", "tool", "assistant"]
    assert messages[0]["content"] == "SYS" and messages[1]["content"] == "hello"
    assert messages[2]["tool_calls"] == [{
        "id": "tu_1",
        "type": "function",
        "function": {"name": "lookup", "arguments": '{"q": "x"}'},
    }]
    assert messages[3] == {"role": "tool", "tool_call_id": "tu_1", "content": "TOOL_RESULT"}
    assert messages[-1] == {"role": "assistant", "content": "DONE"}