"""Tests for the prompt-cache bootstrap script (mocked adapt/LLM)."""

import asyncio
import json

from pydantic import BaseModel

import scripts.build_judge_prompt_cache as builder


class _In(BaseModel):
    question: str


class _Out(BaseModel):
    statements: list[str]


class _FakeAdapted:
    """Fake adapted prompt returned by prompt.adapt()."""

    def __init__(self):
        self.instruction = "中文指令。"
        self.language = "chinese"
        self.examples = [(_In(question="问题"), _Out(statements=["陈述"]))]


class _FakePrompt:
    """Fake prompt with async adapt() that returns _FakeAdapted."""

    def __init__(self):
        self.input_model = _In
        self.output_model = _Out
        self.instruction = "English."
        self.examples = [(_In(question="q"), _Out(statements=["s"]))]
        self.language = "english"

    async def adapt(self, target_language, llm, adapt_instruction=False):
        assert target_language == "chinese"
        assert adapt_instruction is True
        return _FakeAdapted()


def test_serialize_prompt_shape():
    """serialize_prompt emits the expected committed cache schema."""
    data = builder.serialize_prompt(
        _FakeAdapted(), "hash123", "context_recall", "prompt", "zh", "0.4.3"
    )
    assert data["metric"] == "context_recall"
    assert data["prompt_attr"] == "prompt"
    assert data["language"] == "chinese"
    assert data["source_hash"] == "hash123"
    assert data["instruction"] == "中文指令。"
    assert data["examples"] == [
        {"input": {"question": "问题"}, "output": {"statements": ["陈述"]}}
    ]


def test_build_cache_writes_all_files(tmp_path, monkeypatch):
    """build_cache writes one JSON per (metric, attr) under CACHE_ROOT/<language>/."""
    # Build a fake registry where every prompt attr is a _FakePrompt.
    fake_registry = {name: type("M", (), {})() for name in builder.METRIC_PROMPT_ATTRS}
    for name, attrs in builder.METRIC_PROMPT_ATTRS.items():
        for attr in attrs:
            setattr(fake_registry[name], attr, _FakePrompt())

    monkeypatch.setattr(builder, "build_models", lambda j, e, s: (object(), object()))
    monkeypatch.setattr(builder, "build_metric_registry", lambda llm, emb: fake_registry)
    monkeypatch.setattr(builder, "CACHE_ROOT", tmp_path)

    written = asyncio.run(builder.build_cache("zh", "gpt-5", "emb", object()))

    expected_count = sum(len(a) for a in builder.METRIC_PROMPT_ATTRS.values())
    assert len(written) == expected_count

    sample = tmp_path / "zh" / "context_recall__prompt.json"
    assert sample.exists()
    data = json.loads(sample.read_text(encoding="utf-8"))
    assert data["instruction"] == "中文指令。"
    assert data["metric"] == "context_recall"