# -*- coding: utf-8 -*-
import os
import sys
from typing import Any, Dict, Optional

if not os.getcwd() in sys.path:
    sys.path.append(os.getcwd())

from register.register import Registry, build_from_cfg
from torch.nn import Sequential


def build_model_from_cfg(cfg, registry, default_args=None):
    """Build a PyTorch model from config dict(s). Different from
    ``build_from_cfg``, if cfg is a list, a ``nn.Sequential`` will be built.
    Args:
        cfg (dict, list[dict]): The config of modules, is is either a config
            dict or a list of config dicts. If cfg is a list, a
            the built modules will be wrapped with ``nn.Sequential``.
        registry (:obj:`Registry`): A registry the module belongs to.
        default_args (dict, optional): Default arguments to build the module.
            Defaults to None.
    Returns:
        nn.Module: A built nn module.
    """
    if isinstance(cfg, list):
        modules = [build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg]
        return Sequential(*modules)
    else:
        return build_from_cfg(cfg, registry, default_args)


MODELS = Registry("model", build_func=build_model_from_cfg)
HEADS = MODELS
BACKBONES = MODELS
DETECTORS = MODELS
NECKS = MODELS


def build_model(
    cfg: Dict,
    model: Registry,
    build_func=build_model_from_cfg,
    default_args: Optional[Dict] = None,
) -> Any:
    return build_func(cfg, model, default_args)