#!/usr/bin/env python3
import torch
import os
from huggingface_hub import HfApi
from pathlib import Path
from diffusers.utils import load_image
import cv2
from PIL import Image
import numpy as np

from diffusers import (
    ControlNetModel,
    StableDiffusionControlNetImg2ImgPipeline,
    StableDiffusionControlNetInpaintPipeline,
    DiffusionPipeline,
    UniPCMultistepScheduler,
)
import sys

checkpoint = sys.argv[1]

# image = load_image(
#     "https://huggingface.co./lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"
# )

img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
image = load_image(img_url).resize((512, 512))
mask_image = load_image(mask_url).resize((512, 512))

np_image = np.array(image)

low_threshold = 100
high_threshold = 200

np_image = cv2.Canny(np_image, low_threshold, high_threshold)
np_image = np_image[:, :, None]
np_image = np.concatenate([np_image, np_image, np_image], axis=2)
canny_image = Image.fromarray(np_image)

controlnet = ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16)
# pipe = DiffusionPipeline.from_pretrained(
#    "runwayml/stable-diffusion-inpainting", controlnet=controlnet, torch_dtype=torch.float16, custom_pipeline="stable_diffusion_controlnet_inpaint"
# )
pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
    "runwayml/stable-diffusion-inpainting",
    controlnet=controlnet,
    torch_dtype=torch.float16,
)

pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()

generator = torch.manual_seed(0)
text_prompt="a blue dog"
# out_image = pipe("A blue dog", num_inference_steps=50, generator=generator, image=image, mask_image=mask_image, controlnet_conditioning_image=canny_image).images[0]
out_image = pipe(
    text_prompt,
    num_inference_steps=20,
    generator=generator,
    image=image,
    mask_image=mask_image,
    control_image=canny_image,
).images[0]

path = os.path.join(Path.home(), "images", "aa.png")
out_image.save(path)

api = HfApi()

api.upload_file(
    path_or_fileobj=path,
    path_in_repo=path.split("/")[-1],
    repo_id="patrickvonplaten/images",
    repo_type="dataset",
)
print("https://huggingface.co./datasets/patrickvonplaten/images/blob/main/aa.png")