import torch
import gradio as gr

import lora
import extra_networks_lora
import ui_extra_networks_lora
from modules import script_callbacks, ui_extra_networks, extra_networks, shared


def unload():
    torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora
    torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora


def before_ui():
    ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora())
    extra_networks.register_extra_network(extra_networks_lora.ExtraNetworkLora())


if not hasattr(torch.nn, 'Linear_forward_before_lora'):
    torch.nn.Linear_forward_before_lora = torch.nn.Linear.forward

if not hasattr(torch.nn, 'Conv2d_forward_before_lora'):
    torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward

torch.nn.Linear.forward = lora.lora_Linear_forward
torch.nn.Conv2d.forward = lora.lora_Conv2d_forward

script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
script_callbacks.on_script_unloaded(unload)
script_callbacks.on_before_ui(before_ui)


shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
    "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
    "lora_apply_to_outputs": shared.OptionInfo(False, "Apply Lora to outputs rather than inputs when possible (experimental)"),

}))
