"""Miscellaneous neural network functionality."""
import importlib
from pathlib import Path
from typing import Dict, Type

from loguru import logger
import torch
from torch import nn


def activation_function(activation: str) -> Type[nn.Module]:
    """Returns the callable activation function."""
    activation_fns = nn.ModuleDict(
        [
            ["elu", nn.ELU(inplace=True)],
            ["gelu", nn.GELU()],
            ["glu", nn.GLU()],
            ["leaky_relu", nn.LeakyReLU(negative_slope=1.0e-2, inplace=True)],
            ["none", nn.Identity()],
            ["relu", nn.ReLU(inplace=True)],
            ["selu", nn.SELU(inplace=True)],
        ]
    )
    return activation_fns[activation.lower()]


def configure_backbone(backbone: str, backbone_args: Dict) -> Type[nn.Module]:
    """Loads a backbone network."""
    network_module = importlib.import_module("text_recognizer.networks")
    backbone_ = getattr(network_module, backbone)

    if "pretrained" in backbone_args:
        logger.info("Loading pretrained backbone.")
        checkpoint_file = Path(__file__).resolve().parents[2] / backbone_args.pop(
            "pretrained"
        )

        # Loading state directory.
        state_dict = torch.load(checkpoint_file)
        network_args = state_dict["network_args"]
        weights = state_dict["model_state"]

        freeze = False
        if "freeze" in backbone_args and backbone_args["freeze"] is True:
            backbone_args.pop("freeze")
            freeze = True
        network_args = backbone_args

        # Initializes the network with trained weights.
        backbone = backbone_(**network_args)
        backbone.load_state_dict(weights)
        if freeze:
            for params in backbone.parameters():
                params.requires_grad = False
    else:
        backbone_ = getattr(network_module, backbone)
        backbone = backbone_(**backbone_args)

    if "remove_layers" in backbone_args and backbone_args["remove_layers"] is not None:
        backbone = nn.Sequential(
            *list(backbone.children())[:][: -backbone_args["remove_layers"]]
        )

    return backbone