from __future__ import annotations

import json
import logging
from collections.abc import AsyncGenerator

import httpx

logger = logging.getLogger(__name__)


async def stream_chat(
    base_url: str,
    model: str,
    messages: list[dict],
    **kwargs,
) -> AsyncGenerator[str, None]:
    """Proxy Ollama chat streaming, converting NDJSON to OpenAI SSE format."""
    payload = {
        "model": model,
        "messages": messages,
        "stream": True,
        **{k: v for k, v in kwargs.items() if v is not None},
    }

    async with httpx.AsyncClient(timeout=120.0) as client:
        async with client.stream(
            "POST",
            f"{base_url}/api/chat",
            json=payload,
        ) as resp:
            if resp.status_code != 200:
                body = await resp.aread()
                error_msg = body.decode("utf-8", errors="replace")
                yield _error_event(f"Ollama error: {error_msg}")
                return

            async for line in resp.aiter_lines():
                if not line.strip():
                    continue
                try:
                    chunk = json.loads(line)
                except json.JSONDecodeError:
                    continue

                if chunk.get("done"):
                    # Final chunk — send [DONE]
                    yield "data: [DONE]\n\n"
                    return

                content = chunk.get("message", {}).get("content", "")
                if content:
                    openai_chunk = {
                        "id": "chatcmpl-gateway",
                        "object": "chat.completion.chunk",
                        "model": model,
                        "choices": [
                            {
                                "index": 0,
                                "delta": {"content": content},
                                "finish_reason": None,
                            }
                        ],
                    }
                    yield f"data: {json.dumps(openai_chunk)}\n\n"


async def complete_chat(
    base_url: str,
    model: str,
    messages: list[dict],
    **kwargs,
) -> dict:
    """Non-streaming Ollama chat, returns OpenAI-compatible response."""
    payload = {
        "model": model,
        "messages": messages,
        "stream": False,
        **{k: v for k, v in kwargs.items() if v is not None},
    }

    async with httpx.AsyncClient(timeout=120.0) as client:
        resp = await client.post(f"{base_url}/api/chat", json=payload)
        resp.raise_for_status()
        data = resp.json()

    return {
        "id": "chatcmpl-gateway",
        "object": "chat.completion",
        "model": model,
        "choices": [
            {
                "index": 0,
                "message": {
                    "role": "assistant",
                    "content": data.get("message", {}).get("content", ""),
                },
                "finish_reason": "stop",
            }
        ],
        "usage": {
            "prompt_tokens": data.get("prompt_eval_count", 0),
            "completion_tokens": data.get("eval_count", 0),
            "total_tokens": data.get("prompt_eval_count", 0)
            + data.get("eval_count", 0),
        },
    }


async def generate_embedding(
    base_url: str,
    model: str,
    input_text: str | list[str],
) -> dict:
    """Ollama embedding, returns OpenAI-compatible response."""
    texts = [input_text] if isinstance(input_text, str) else input_text

    async with httpx.AsyncClient(timeout=60.0) as client:
        resp = await client.post(
            f"{base_url}/api/embed",
            json={"model": model, "input": texts},
        )
        resp.raise_for_status()
        data = resp.json()

    embeddings_data = []
    raw_embeddings = data.get("embeddings", [])
    for i, emb in enumerate(raw_embeddings):
        embeddings_data.append({
            "object": "embedding",
            "embedding": emb,
            "index": i,
        })

    return {
        "object": "list",
        "data": embeddings_data,
        "model": model,
        "usage": {"prompt_tokens": 0, "total_tokens": 0},
    }


def _error_event(message: str) -> str:
    error = {
        "id": "chatcmpl-gateway",
        "object": "chat.completion.chunk",
        "model": "error",
        "choices": [
            {
                "index": 0,
                "delta": {"content": f"[Error] {message}"},
                "finish_reason": "stop",
            }
        ],
    }
    return f"data: {json.dumps(error)}\n\ndata: [DONE]\n\n"