import gradio as gr
import requests
import subprocess
import os
import torch
from huggingface_hub import whoami
from huggingface_hub import HfApi
from huggingface_hub import login
import random
import time

api=HfApi()
REPO_TYPES = ["model", "dataset", "space"]

def duplicate(source_url_model, source_url_vae, dst_repo, token, new_name, dst_repo_path, repo_type):
    try:
        _ = whoami(token)
        # ^ this will throw if token is invalid

        # make sure the user fills out the other required paths.
        if not dst_repo_path[len(dst_repo_path)-1] == '/':
                raise Exception("Your destination path *must* end with a /")
        if not source_url_model:
                raise Exception("You haven't chosen a model file to download!")
        if not source_url_vae:
                raise Exception("You haven't chosen a VAE file to download!")
        if not dst_repo:
                raise Exception("You haven't chosen a repo to download to")
        login(token=token)

        # keep things separate, partly in case people download different files with same name (`download.zip`). Especially, it also allows saving filename to work
        dir="/home/user/apps/downloads/"+str(int(time.time()))+str(random.getrandbits(8))+"/"
        subprocess.check_call([r"mkdir","-p",dir])
        subprocess.check_call([r"aria2c","-x16","--split=16","-o","source.ckpt",source_url_model,"--dir="+dir])
        subprocess.check_call([r"aria2c","-x16","--split=16","-o","vae.ckpt",source_url_vae,"--dir="+dir])

        #USE AT YOUR OWN RISK

        #local path to runwayML SD 1.5 checkpoint (https://huggingface.co/runwayml/stable-diffusion-v1-5)
        ckpt_15 = dir+"source.ckpt"

        #local path to StabilityAI finetuned autoencoder (https://huggingface.co/stabilityai/sd-vae-ft-mse)
        ckpt_vae = dir+"vae.ckpt"

        #path to save merged model to
        ckpt_out = dir+"source_vae.ckpt"

        pl_sd = torch.load(ckpt_15, map_location="cpu")
        sd = pl_sd["state_dict"]

        over_sd = torch.load(ckpt_vae,map_location="cpu")["state_dict"]

        sdk = sd.keys()

        for key in over_sd.keys():
            if "first_stage_model."+key in sdk:
                sd["first_stage_model."+key] = over_sd[key]
                print(key,"overwritten")

        torch.save(pl_sd,ckpt_out)

        if new_name:
                dst_repo_path=dst_repo_path
        else:
                dst_repo_path=dst_repo_path+"model+vae.ckpt"

        api.upload_file(
                path_or_fileobj=dir+"source_vae.ckpt",
                path_in_repo=dst_repo_path,
                repo_id=dst_repo,
                repo_type=repo_type
        )

        # now clean up
        os.remove(dir+files[0])
        os.rmdir(dir)
        match repo_type:
                case "space":
                        repo_url=f"https://hf.co/spaces/{dst_repo}"
                case "dataset":
                        repo_url=f"https://hf.co/datasets/{dst_repo}"
                case "model":
                        repo_url=f"https://hf.co/{dst_repo}"
        return (
            f'Find your repo <a href=\'{repo_url}\' target="_blank" style="text-decoration:underline">here</a>',
            "sp.jpg",
        )

    except Exception as e:
        blames=["grandma","my boss","your boss","God","you","you. It's *all* your fault.","the pope"]
        blameweights=(1,1,1,1,4,2,1)
        excuses=["I blame it all on "+random.choices(blames,weights=blameweights)[0],"It's my fault, sorry.","I did it on purpose.","That file doesn't want to be downloaded.","You nincompoop!"]
        excusesweights=(12,1,1,2,3)
        excuse=random.choices(excuses,weights=excusesweights)[0]
        return (
            f"""
        ### Error 😢😢😢

        {e}



        <i>""" + excuse+"</i>",
            None,
        )


interface = gr.Interface(
    fn=duplicate,
    inputs=[
        gr.Textbox(placeholder="Source URL for model (e.g. civitai.com/api/download/models/4324322534)"),
        gr.Textbox(placeholder="Source URL for VAE (e.g. civitai.com/api/download/models/4324322534)"),
        gr.Textbox(placeholder="Destination repository (e.g. osanseviero/dst)"),
        gr.Textbox(placeholder="Write access token", type="password"),
        gr.Textbox(placeholder="Post-download name of your file, if you want it changed (e.g. stupidmodel_stupidvae.safetensors)"),
        gr.Textbox(placeholder="Destination for your file within your repo. Don't include the filename, end path with a / (e.g. /models/Stable-diffusion/)"),
        gr.Dropdown(choices=REPO_TYPES, value="model"),
    ],
    outputs=[
        gr.Markdown(label="output"),
        gr.Image(show_label=False),
    ],
    title="Merge a VAE with a model!",
    description="Merge a VAE with your model, and export to your Hugging Face repository! You need to specify a write token obtained in https://hf.co/settings/tokens. This Space is a an experimental demo. CKPT format only; I just ripped off someone else's script, I have no idea how this works...",
    article="<p>credit to <a href='https://gist.github.com/Quasimondo/f344659f57dc15bd7892a969bd58ac67'>Quasimodo's script</a></p><p>Find your write token at <a href='https://huggingface.co/settings/tokens' target='_blank'>token settings</a></p>",
    allow_flagging="never",
    live=False,
)
interface.launch(enable_queue=True)