dev(narugo): optimize model loader

import os

import threading

from collections import defaultdict

from contextlib import contextmanager

from threading import Lock

from typing import List, Optional, Tuple, Union

                               f'Please install it with `pip install dghs-imgutils[demo]`.')

_MODEL_LOAD_LOCKS = defaultdict(Lock)

_G_ML_LOCK = Lock()

@contextmanager

def _model_load_lock():

    """

    Context manager for thread-safe model loading operations.

    This context manager ensures that model loading operations are thread-safe by using

    process-specific locks. It prevents concurrent model loading operations which could

    lead to race conditions.

    :yields: None

    """

    with _G_ML_LOCK:

        lock = _MODEL_LOAD_LOCKS[os.getpid()]

    with lock:

        yield

def _v_fix(v):

    """

    Round and convert a float value to an integer.

        self._model_types = {}

        self._hf_token = hf_token

        self._global_lock = Lock()

        self._model_load_locks = defaultdict(Lock)

        self._model_meta_lock = Lock()

        self._model_exec_locks = defaultdict(Lock)

    def _get_hf_token(self) -> Optional[str]:

        """

        :rtype: tuple

        """

        cache_key = os.getpid(), threading.get_ident(), model_name

        with self._model_load_locks[os.getpid()]:

        with _model_load_lock():

            if cache_key not in self._models:

                self._check_model_name(model_name)

                model = open_onnx_model(hf_hub_download(

                    max_infer_size = 640

                names_map = _safe_eval_names_str(model_metadata.custom_metadata_map['names'])

                labels = [names_map[i] for i in range(len(names_map))]

                self._models[cache_key] = (model, max_infer_size, labels, self._model_exec_locks[cache_key])

                self._models[cache_key] = (model, max_infer_size, labels, Lock())

        return self._models[cache_key]