from __future__ import annotations

import logging
from typing import TYPE_CHECKING, List

import numpy as np
import tqdm

from sglang.srt.disaggregation.utils import FAKE_BOOTSTRAP_HOST
from sglang.srt.managers.io_struct import GenerateReqInput

if TYPE_CHECKING:
    from sglang.srt.managers.tokenizer_manager import TokenizerManager

logger = logging.getLogger(__file__)

_warmup_registry = {}


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

    return decorator


async def execute_warmups(
    disaggregation_mode: str,
    warmup_names: List[str],
    tokenizer_manager: TokenizerManager,
):
    for warmup_name in warmup_names:
        if warmup_name not in _warmup_registry:
            logger.warning(f"Could not find custom warmup {warmup_name}")
            continue
        logger.info(f"Running warmup {warmup_name}")
        await _warmup_registry[warmup_name](disaggregation_mode, tokenizer_manager)


@warmup("voice_chat")
async def voice_chat(disaggregation_mode: str, tokenizer_manager: TokenizerManager):
    # 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,
            },
        )
        if disaggregation_mode != "null":
            generate_req_input.bootstrap_room = 0
            generate_req_input.bootstrap_host = FAKE_BOOTSTRAP_HOST

        await tokenizer_manager.generate_request(generate_req_input, None).__anext__()