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import numpy as np
import tqdm

from tokenspeed.runtime.engine.async_llm import AsyncLLM
from tokenspeed.runtime.engine.io_struct import GenerateReqInput
from tokenspeed.runtime.utils import get_colorful_logger

logger = get_colorful_logger(__name__)

_warmup_registry = {}


def warmup(name: str) -> callable:
    def decorator(fn: callable):
        _warmup_registry[name] = fn
        return fn

    return decorator


async def execute_warmups(warmup_names: list[str], tokenizer_manager: AsyncLLM):
    for warmup_name in warmup_names:
        if warmup_name not in _warmup_registry:
            logger.warning("Could not find custom warmup %s", warmup_name)
            continue
        logger.info("Running warmup %s", warmup_name)
        await _warmup_registry[warmup_name](tokenizer_manager)


@warmup("voice_chat")
async def voice_chat(tokenizer_manager: AsyncLLM):
    # this warms up the fused_moe triton kernels and caches them
    # if we don't do this we break real time inference for voice chat
    for i in tqdm.trange(1, 512):
        size = i * 4
        generate_req_input = GenerateReqInput(
            input_ids=(np.random.randint(2**16, size=[size])).tolist(),
            sampling_params={
                "max_new_tokens": 30,
                "temperature": 0.8,
                "stop_token_ids": [1],
                "min_p": 0.0,
            },
        )
        await tokenizer_manager.generate_request(generate_req_input).__anext__()