import gradio as gr
import requests
import os
import shutil
from pathlib import Path
import tempfile
from tempfile import TemporaryDirectory


from typing import Optional

import torch
from io import BytesIO

from huggingface_hub import CommitInfo, Discussion, HfApi, hf_hub_download
from huggingface_hub.file_download import repo_folder_name
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
    download_from_original_stable_diffusion_ckpt, download_controlnet_from_original_ckpt
)
from transformers import CONFIG_MAPPING


COMMIT_MESSAGE = " This PR adds fp32 and fp16 weights in PyTorch and safetensors format to {}"


def convert_single(model_id: str, token:str, filename: str, model_type: str, sample_size: int, scheduler_type: str, extract_ema: bool, folder: str, progress):
    from_safetensors = filename.endswith(".safetensors")

    progress(0, desc="Downloading model")
    local_file = os.path.join(model_id, filename)
    ckpt_file = local_file if os.path.isfile(local_file) else hf_hub_download(repo_id=model_id, filename=filename, token=token)

    if model_type == "v1":
        config_url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
    elif model_type == "v2":
        if sample_size == 512:
            config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference.yaml"
        else:
            config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml"
    elif model_type == "ControlNet":
        config_url = (Path(model_id)/"resolve/main"/filename).with_suffix(".yaml")
        config_url = "https://huggingface.co./" + str(config_url)

    #config_file = BytesIO(requests.get(config_url).content)
    
    response = requests.get(config_url)
    with tempfile.NamedTemporaryFile(delete=False, mode='wb') as tmp_file:
        tmp_file.write(response.content)
        temp_config_file_path = tmp_file.name
        
    if model_type == "ControlNet":
        progress(0.2, desc="Converting ControlNet Model")
        pipeline = download_controlnet_from_original_ckpt(ckpt_file, temp_config_file_path, image_size=sample_size, from_safetensors=from_safetensors, extract_ema=extract_ema)
        to_args = {"dtype": torch.float16}
    else:
        progress(0.1, desc="Converting Model")
        pipeline = download_from_original_stable_diffusion_ckpt(ckpt_file, temp_config_file_path, image_size=sample_size, scheduler_type=scheduler_type, from_safetensors=from_safetensors, extract_ema=extract_ema)
        to_args = {"torch_dtype": torch.float16}

    pipeline.save_pretrained(folder)
    pipeline.save_pretrained(folder, safe_serialization=True)

    pipeline = pipeline.to(**to_args)
    pipeline.save_pretrained(folder, variant="fp16")
    pipeline.save_pretrained(folder, safe_serialization=True, variant="fp16")

    return folder


def previous_pr(api: "HfApi", model_id: str, pr_title: str) -> Optional["Discussion"]:
    try:
        discussions = api.get_repo_discussions(repo_id=model_id)
    except Exception:
        return None
    for discussion in discussions:
        if discussion.status == "open" and discussion.is_pull_request and discussion.title == pr_title:
            details = api.get_discussion_details(repo_id=model_id, discussion_num=discussion.num)
            if details.target_branch == "refs/heads/main":
                return discussion


def convert(token: str, model_id: str, filename: str, model_type: str, sample_size: int = 512, scheduler_type: str = "pndm", extract_ema: bool = True, progress=gr.Progress()):
    api = HfApi()

    pr_title = "Adding `diffusers` weights of this model"

    with TemporaryDirectory() as d:
        folder = os.path.join(d, repo_folder_name(repo_id=model_id, repo_type="models"))
        os.makedirs(folder)
        new_pr = None
        try:
            folder = convert_single(model_id, token, filename, model_type, sample_size, scheduler_type, extract_ema, folder, progress)
            progress(0.7, desc="Uploading to Hub")
            new_pr  = api.upload_folder(folder_path=folder, path_in_repo="./", repo_id=model_id, repo_type="model", token=token, commit_message=pr_title, commit_description=COMMIT_MESSAGE.format(model_id), create_pr=True)
            pr_number = new_pr.split("%2F")[-1].split("/")[0]
            link = f"Pr created at: {'https://huggingface.co./' + os.path.join(model_id, 'discussions', pr_number)}"
            progress(1, desc="Done")
        except Exception as e:
            raise gr.exceptions.Error(str(e))
        finally:
            shutil.rmtree(folder)

        return link