"""OpenAI-compatible proxy server for chat.z.ai."""

from __future__ import annotations

import asyncio
import json
import os
import time
import uuid
from contextlib import asynccontextmanager

import uvicorn
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse, StreamingResponse

from main import ZaiClient

# ── Session Pool ─────────────────────────────────────────────────────


def _env_float(name: str, default: float) -> float:
    raw = os.getenv(name)
    if raw is None:
        return default
    try:
        return float(raw)
    except ValueError:
        return default


AUTH_REFRESH_MIN_INTERVAL_SECONDS = _env_float(
    "ZAI_AUTH_REFRESH_MIN_INTERVAL_SECONDS", 2.0
)


class SessionPool:
    """Manages a single ZaiClient instance with automatic auth refresh."""

    def __init__(self) -> None:
        self._client = ZaiClient()
        self._lock = asyncio.Lock()
        self._authed = False
        self._last_auth_refresh_at = 0.0
        self._refresh_min_interval = max(0.0, AUTH_REFRESH_MIN_INTERVAL_SECONDS)

    async def close(self) -> None:
        await self._client.close()

    async def ensure_auth(self) -> None:
        """Authenticate if not already done."""
        if self._authed:
            return
        async with self._lock:
            if self._authed:
                return
            await self._client.auth_as_guest()
            self._authed = True
            self._last_auth_refresh_at = time.monotonic()

    async def refresh_auth(self, *, force: bool = False) -> None:
        """Refresh the guest token with single-flight behavior."""
        now = time.monotonic()
        if (
            not force
            and self._authed
            and now - self._last_auth_refresh_at < self._refresh_min_interval
        ):
            return
        async with self._lock:
            now = time.monotonic()
            if (
                not force
                and self._authed
                and now - self._last_auth_refresh_at < self._refresh_min_interval
            ):
                return
            await self._client.auth_as_guest()
            self._authed = True
            self._last_auth_refresh_at = time.monotonic()

    async def get_models(self) -> list | dict:
        await self.ensure_auth()
        return await self._client.get_models()

    async def create_chat(self, user_message: str, model: str) -> dict:
        await self.ensure_auth()
        return await self._client.create_chat(user_message, model)

    def chat_completions(
        self,
        chat_id: str,
        messages: list[dict],
        prompt: str,
        *,
        model: str,
        tools: list[dict] | None = None,
    ):
        return self._client.chat_completions(
            chat_id=chat_id,
            messages=messages,
            prompt=prompt,
            model=model,
            tools=tools,
        )


pool = SessionPool()

# ── FastAPI app ──────────────────────────────────────────────────────


@asynccontextmanager
async def lifespan(_app: FastAPI):
    await pool.ensure_auth()
    yield
    await pool.close()


app = FastAPI(lifespan=lifespan)

# ── Helpers ──────────────────────────────────────────────────────────


def _make_id() -> str:
    return f"chatcmpl-{uuid.uuid4().hex[:29]}"


def _openai_chunk(
    completion_id: str,
    model: str,
    *,
    content: str | None = None,
    reasoning_content: str | None = None,
    finish_reason: str | None = None,
) -> dict:
    delta: dict = {}
    if content is not None:
        delta["content"] = content
    if reasoning_content is not None:
        delta["reasoning_content"] = reasoning_content
    return {
        "id": completion_id,
        "object": "chat.completion.chunk",
        "created": int(time.time()),
        "model": model,
        "choices": [
            {
                "index": 0,
                "delta": delta,
                "finish_reason": finish_reason,
            }
        ],
    }


def _openai_completion(
    completion_id: str,
    model: str,
    content: str,
    reasoning_content: str,
) -> dict:
    message: dict = {"role": "assistant", "content": content}
    if reasoning_content:
        message["reasoning_content"] = reasoning_content
    return {
        "id": completion_id,
        "object": "chat.completion",
        "created": int(time.time()),
        "model": model,
        "choices": [
            {
                "index": 0,
                "message": message,
                "finish_reason": "stop",
            }
        ],
        "usage": {
            "prompt_tokens": 0,
            "completion_tokens": 0,
            "total_tokens": 0,
        },
    }


# ── /v1/models ───────────────────────────────────────────────────────


@app.get("/v1/models")
async def list_models():
    models_resp = await pool.get_models()
    # Normalize to list
    if isinstance(models_resp, dict) and "data" in models_resp:
        models_list = models_resp["data"]
    elif isinstance(models_resp, list):
        models_list = models_resp
    else:
        models_list = []

    data = []
    for m in models_list:
        mid = m.get("id") or m.get("name", "unknown")
        data.append(
            {
                "id": mid,
                "object": "model",
                "created": 0,
                "owned_by": "z.ai",
            }
        )
    return {"object": "list", "data": data}


# ── /v1/chat/completions ────────────────────────────────────────────


async def _do_request(
    messages: list[dict],
    model: str,
    prompt: str,
    tools: list[dict] | None = None,
):
    """Create a new chat and return (chat_id, async generator).

    Raises on Zai errors so the caller can retry.
    """
    chat = await pool.create_chat(prompt, model)
    chat_id = chat["id"]
    gen = pool.chat_completions(
        chat_id=chat_id,
        messages=messages,
        prompt=prompt,
        model=model,
        tools=tools,
    )
    return chat_id, gen


async def _stream_response(
    messages: list[dict],
    model: str,
    prompt: str,
    tools: list[dict] | None = None,
):
    """SSE generator with one retry on error."""
    completion_id = _make_id()
    retried = False

    while True:
        try:
            _chat_id, gen = await _do_request(messages, model, prompt, tools)

            # Send initial role chunk
            role_chunk = {
                "id": completion_id,
                "object": "chat.completion.chunk",
                "created": int(time.time()),
                "model": model,
                "choices": [
                    {
                        "index": 0,
                        "delta": {"role": "assistant"},
                        "finish_reason": None,
                    }
                ],
            }
            yield f"data: {json.dumps(role_chunk, ensure_ascii=False)}\n\n"

            tool_call_idx = 0
            async for data in gen:
                phase = data.get("phase", "")
                delta = data.get("delta_content", "")

                # Tool call events from Zai
                if data.get("tool_calls"):
                    for tc in data["tool_calls"]:
                        tc_chunk = {
                            "id": completion_id,
                            "object": "chat.completion.chunk",
                            "created": int(time.time()),
                            "model": model,
                            "choices": [
                                {
                                    "index": 0,
                                    "delta": {
                                        "tool_calls": [
                                            {
                                                "index": tool_call_idx,
                                                "id": tc.get("id", f"call_{uuid.uuid4().hex[:24]}"),
                                                "type": "function",
                                                "function": {
                                                    "name": tc.get("function", {}).get("name", ""),
                                                    "arguments": tc.get("function", {}).get("arguments", ""),
                                                },
                                            }
                                        ]
                                    },
                                    "finish_reason": None,
                                }
                            ],
                        }
                        yield f"data: {json.dumps(tc_chunk, ensure_ascii=False)}\n\n"
                        tool_call_idx += 1
                elif phase == "thinking" and delta:
                    chunk = _openai_chunk(
                        completion_id, model, reasoning_content=delta
                    )
                    yield f"data: {json.dumps(chunk, ensure_ascii=False)}\n\n"
                elif phase == "answer" and delta:
                    chunk = _openai_chunk(completion_id, model, content=delta)
                    yield f"data: {json.dumps(chunk, ensure_ascii=False)}\n\n"
                elif phase == "done":
                    break

            # Send finish chunk
            finish_reason = "tool_calls" if tool_call_idx > 0 else "stop"
            finish_chunk = _openai_chunk(
                completion_id, model, finish_reason=finish_reason
            )
            yield f"data: {json.dumps(finish_chunk, ensure_ascii=False)}\n\n"
            yield "data: [DONE]\n\n"
            return

        except Exception:
            if retried:
                # Already retried once — yield error and stop
                error = {
                    "error": {
                        "message": "Upstream Zai error after retry",
                        "type": "server_error",
                    }
                }
                yield f"data: {json.dumps(error)}\n\n"
                yield "data: [DONE]\n\n"
                return
            retried = True
            await pool.refresh_auth()
            # Loop back and retry


async def _sync_response(
    messages: list[dict],
    model: str,
    prompt: str,
    tools: list[dict] | None = None,
) -> dict:
    """Non-streaming response with one retry on error."""
    completion_id = _make_id()

    for attempt in range(2):
        try:
            _chat_id, gen = await _do_request(messages, model, prompt, tools)

            content_parts: list[str] = []
            reasoning_parts: list[str] = []
            tool_calls: list[dict] = []

            async for data in gen:
                phase = data.get("phase", "")
                delta = data.get("delta_content", "")

                if data.get("tool_calls"):
                    for tc in data["tool_calls"]:
                        tool_calls.append(
                            {
                                "id": tc.get("id", f"call_{uuid.uuid4().hex[:24]}"),
                                "type": "function",
                                "function": {
                                    "name": tc.get("function", {}).get("name", ""),
                                    "arguments": tc.get("function", {}).get("arguments", ""),
                                },
                            }
                        )
                elif phase == "thinking" and delta:
                    reasoning_parts.append(delta)
                elif phase == "answer" and delta:
                    content_parts.append(delta)
                elif phase == "done":
                    break

            if tool_calls:
                message: dict = {"role": "assistant", "content": None, "tool_calls": tool_calls}
                if reasoning_parts:
                    message["reasoning_content"] = "".join(reasoning_parts)
                return {
                    "id": completion_id,
                    "object": "chat.completion",
                    "created": int(time.time()),
                    "model": model,
                    "choices": [
                        {
                            "index": 0,
                            "message": message,
                            "finish_reason": "tool_calls",
                        }
                    ],
                    "usage": {
                        "prompt_tokens": 0,
                        "completion_tokens": 0,
                        "total_tokens": 0,
                    },
                }

            return _openai_completion(
                completion_id,
                model,
                "".join(content_parts),
                "".join(reasoning_parts),
            )

        except Exception:
            if attempt == 0:
                await pool.refresh_auth()
                continue
            return {
                "error": {
                    "message": "Upstream Zai error after retry",
                    "type": "server_error",
                }
            }

    # Unreachable, but satisfy type checker
    return {"error": {"message": "Unexpected error", "type": "server_error"}}


@app.post("/v1/chat/completions")
async def chat_completions(request: Request):
    body = await request.json()

    model: str = body.get("model", "glm-5")
    messages: list[dict] = body.get("messages", [])
    stream: bool = body.get("stream", False)
    tools: list[dict] | None = body.get("tools")

    # Extract the last user message as the prompt for signature
    prompt = ""
    for msg in reversed(messages):
        if msg.get("role") == "user":
            content = msg.get("content", "")
            if isinstance(content, str):
                prompt = content
            elif isinstance(content, list):
                # Handle multimodal content array
                prompt = " ".join(
                    p.get("text", "")
                    for p in content
                    if isinstance(p, dict) and p.get("type") == "text"
                )
            break

    # Zai ignores multi-turn context — flatten all messages into a single
    # user message with <ROLE> tags so the model sees the full conversation.
    parts: list[str] = []
    for msg in messages:
        role = msg.get("role", "user")
        content = msg.get("content", "") or ""
        parts.append(f"<{role.upper()}>{content}</{role.upper()}>")
    flat_content = "\n".join(parts)
    messages = [{"role": "user", "content": flat_content}]

    if not prompt:
        return JSONResponse(
            status_code=400,
            content={
                "error": {
                    "message": "No user message found in messages",
                    "type": "invalid_request_error",
                }
            },
        )

    if stream:
        return StreamingResponse(
            _stream_response(messages, model, prompt, tools),
            media_type="text/event-stream",
            headers={
                "Cache-Control": "no-cache",
                "Connection": "keep-alive",
                "X-Accel-Buffering": "no",
            },
        )
    else:
        result = await _sync_response(messages, model, prompt, tools)
        if "error" in result:
            return JSONResponse(status_code=502, content=result)
        return result


# ── Entry point ──────────────────────────────────────────────────────

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)