import json
from uuid import uuid4
from open_webui.utils.misc import (
    openai_chat_chunk_message_template,
    openai_chat_completion_message_template,
)


def convert_ollama_tool_call_to_openai(tool_calls: dict) -> dict:
    openai_tool_calls = []
    for tool_call in tool_calls:
        openai_tool_call = {
            "index": tool_call.get("index", 0),
            "id": tool_call.get("id", f"call_{str(uuid4())}"),
            "type": "function",
            "function": {
                "name": tool_call.get("function", {}).get("name", ""),
                "arguments": json.dumps(
                    tool_call.get("function", {}).get("arguments", {})
                ),
            },
        }
        openai_tool_calls.append(openai_tool_call)
    return openai_tool_calls


def convert_response_ollama_to_openai(ollama_response: dict) -> dict:
    model = ollama_response.get("model", "ollama")
    message_content = ollama_response.get("message", {}).get("content", "")
    tool_calls = ollama_response.get("message", {}).get("tool_calls", None)
    openai_tool_calls = None

    if tool_calls:
        openai_tool_calls = convert_ollama_tool_call_to_openai(tool_calls)

    data = ollama_response
    usage = {
        "response_token/s": (
            round(
                (
                    (
                        data.get("eval_count", 0)
                        / ((data.get("eval_duration", 0) / 10_000_000))
                    )
                    * 100
                ),
                2,
            )
            if data.get("eval_duration", 0) > 0
            else "N/A"
        ),
        "prompt_token/s": (
            round(
                (
                    (
                        data.get("prompt_eval_count", 0)
                        / ((data.get("prompt_eval_duration", 0) / 10_000_000))
                    )
                    * 100
                ),
                2,
            )
            if data.get("prompt_eval_duration", 0) > 0
            else "N/A"
        ),
        "total_duration": data.get("total_duration", 0),
        "load_duration": data.get("load_duration", 0),
        "prompt_eval_count": data.get("prompt_eval_count", 0),
        "prompt_eval_duration": data.get("prompt_eval_duration", 0),
        "eval_count": data.get("eval_count", 0),
        "eval_duration": data.get("eval_duration", 0),
        "approximate_total": (lambda s: f"{s // 3600}h{(s % 3600) // 60}m{s % 60}s")(
            (data.get("total_duration", 0) or 0) // 1_000_000_000
        ),
    }

    response = openai_chat_completion_message_template(
        model, message_content, openai_tool_calls, usage
    )
    return response


async def convert_streaming_response_ollama_to_openai(ollama_streaming_response):
    async for data in ollama_streaming_response.body_iterator:
        data = json.loads(data)

        model = data.get("model", "ollama")
        message_content = data.get("message", {}).get("content", "")
        tool_calls = data.get("message", {}).get("tool_calls", None)
        openai_tool_calls = None

        if tool_calls:
            openai_tool_calls = convert_ollama_tool_call_to_openai(tool_calls)

        done = data.get("done", False)

        usage = None
        if done:
            usage = {
                "response_token/s": (
                    round(
                        (
                            (
                                data.get("eval_count", 0)
                                / ((data.get("eval_duration", 0) / 10_000_000))
                            )
                            * 100
                        ),
                        2,
                    )
                    if data.get("eval_duration", 0) > 0
                    else "N/A"
                ),
                "prompt_token/s": (
                    round(
                        (
                            (
                                data.get("prompt_eval_count", 0)
                                / ((data.get("prompt_eval_duration", 0) / 10_000_000))
                            )
                            * 100
                        ),
                        2,
                    )
                    if data.get("prompt_eval_duration", 0) > 0
                    else "N/A"
                ),
                "total_duration": data.get("total_duration", 0),
                "load_duration": data.get("load_duration", 0),
                "prompt_eval_count": data.get("prompt_eval_count", 0),
                "prompt_eval_duration": data.get("prompt_eval_duration", 0),
                "eval_count": data.get("eval_count", 0),
                "eval_duration": data.get("eval_duration", 0),
                "approximate_total": (
                    lambda s: f"{s // 3600}h{(s % 3600) // 60}m{s % 60}s"
                )((data.get("total_duration", 0) or 0) // 1_000_000_000),
            }

        data = openai_chat_chunk_message_template(
            model, message_content if not done else None, openai_tool_calls, usage
        )

        line = f"data: {json.dumps(data)}\n\n"
        yield line

    yield "data: [DONE]\n\n"