# EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction
# Han Cai, Junyan Li, Muyan Hu, Chuang Gan, Song Han
# International Conference on Computer Vision (ICCV), 2023

import os
from inspect import signature

import torch
import torch.nn as nn
import torch.nn.functional as F

__all__ = [
    "is_parallel",
    "get_device",
    "get_same_padding",
    "resize",
    "build_kwargs_from_config",
    "load_state_dict_from_file",
]


def is_parallel(model: nn.Module) -> bool:
    return isinstance(
        model, (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
    )


def get_device(model: nn.Module) -> torch.device:
    return model.parameters().__next__().device


def get_same_padding(kernel_size: int or tuple[int, ...]) -> int or tuple[int, ...]:
    if isinstance(kernel_size, tuple):
        return tuple([get_same_padding(ks) for ks in kernel_size])
    else:
        assert kernel_size % 2 > 0, "kernel size should be odd number"
        return kernel_size // 2


def resize(
    x: torch.Tensor,
    size: any or None = None,
    scale_factor: list[float] or None = None,
    mode: str = "bicubic",
    align_corners: bool or None = False,
) -> torch.Tensor:
    if mode in {"bilinear", "bicubic"}:
        return F.interpolate(
            x,
            size=size,
            scale_factor=scale_factor,
            mode=mode,
            align_corners=align_corners,
        )
    elif mode in {"nearest", "area"}:
        return F.interpolate(x, size=size, scale_factor=scale_factor, mode=mode)
    else:
        raise NotImplementedError(f"resize(mode={mode}) not implemented.")


def build_kwargs_from_config(config: dict, target_func: callable) -> dict[str, any]:
    valid_keys = list(signature(target_func).parameters)
    kwargs = {}
    for key in config:
        if key in valid_keys:
            kwargs[key] = config[key]
    return kwargs


def load_state_dict_from_file(
    file: str, only_state_dict=True
) -> dict[str, torch.Tensor]:
    file = os.path.realpath(os.path.expanduser(file))
    checkpoint = torch.load(file, map_location="cpu")
    if only_state_dict and "state_dict" in checkpoint:
        checkpoint = checkpoint["state_dict"]
    return checkpoint