"""OpenAI 兼容模型凭证的最小连通测试。"""

from __future__ import annotations

import io
import time
import wave

import httpx

from schemas import CredentialTestRequest, CredentialTestResult

TEST_TIMEOUT_SECONDS = 10.0


def _endpoint(base_url: str, path: str) -> str:
    return f"{base_url.rstrip('/')}/{path.lstrip('/')}"


def _silent_wav() -> bytes:
    buffer = io.BytesIO()
    with wave.open(buffer, "wb") as wav:
        wav.setnchannels(1)
        wav.setsampwidth(2)
        wav.setframerate(16_000)
        wav.writeframes(b"\x00\x00" * 1_600)
    return buffer.getvalue()


def _error_detail(response: httpx.Response, api_key: str) -> str:
    try:
        body = response.json()
        detail = (
            body.get("error", {}).get("message")
            if isinstance(body, dict) and isinstance(body.get("error"), dict)
            else body.get("detail") if isinstance(body, dict) else None
        )
    except ValueError:
        detail = None
    text = str(detail or response.text or response.reason_phrase).strip()
    return text.replace(api_key, "***")[:300]


async def test_openai_credential(
    config: CredentialTestRequest,
) -> CredentialTestResult:
    started = time.perf_counter()
    headers = {"Authorization": f"Bearer {config.api_key}"}

    try:
        async with httpx.AsyncClient(timeout=TEST_TIMEOUT_SECONDS) as client:
            if config.type == "LLM":
                response = await client.post(
                    _endpoint(config.api_url, "chat/completions"),
                    headers=headers,
                    json={
                        "model": config.model_id,
                        "messages": [{"role": "user", "content": "Reply with OK."}],
                        "max_tokens": 1,
                        "stream": False,
                    },
                )
            elif config.type == "Embedding":
                response = await client.post(
                    _endpoint(config.api_url, "embeddings"),
                    headers=headers,
                    json={"model": config.model_id, "input": "ping"},
                )
            elif config.type == "ASR":
                response = await client.post(
                    _endpoint(config.api_url, "audio/transcriptions"),
                    headers=headers,
                    data={
                        "model": config.model_id,
                        **({"language": config.language} if config.language else {}),
                    },
                    files={"file": ("test.wav", _silent_wav(), "audio/wav")},
                )
            elif config.type == "TTS":
                response = await client.post(
                    _endpoint(config.api_url, "audio/speech"),
                    headers=headers,
                    json={
                        "model": config.model_id,
                        "input": "测试",
                        "voice": config.voice,
                        "speed": config.speed,
                    },
                )
            else:
                return CredentialTestResult(
                    ok=False,
                    message="暂不支持该资源类型的连通测试",
                    detail=f"当前仅支持 LLM、Embedding、ASR、TTS，收到 {config.type}",
                )

        latency_ms = round((time.perf_counter() - started) * 1000)
        if response.is_success:
            return CredentialTestResult(
                ok=True,
                latency_ms=latency_ms,
                message="连接成功",
                detail=f"OpenAI 兼容接口响应正常（HTTP {response.status_code}）",
            )
        return CredentialTestResult(
            ok=False,
            latency_ms=latency_ms,
            message=f"连接失败（HTTP {response.status_code}）",
            detail=_error_detail(response, config.api_key),
        )
    except httpx.TimeoutException:
        return CredentialTestResult(
            ok=False,
            latency_ms=round((time.perf_counter() - started) * 1000),
            message="连接超时",
            detail=f"服务未在 {TEST_TIMEOUT_SECONDS:g} 秒内响应",
        )
    except httpx.RequestError as exc:
        return CredentialTestResult(
            ok=False,
            latency_ms=round((time.perf_counter() - started) * 1000),
            message="无法连接到模型服务",
            detail=str(exc)[:300],
        )