import gradio as gr
import jax.numpy as jnp
from diffusers import FlaxStableDiffusionControlNetPipeline, FlaxControlNetModel
from diffusers import FlaxScoreSdeVeScheduler, FlaxDPMSolverMultistepScheduler
from transformers import CLIPImageProcessor
import torch
torch.backends.cuda.matmul.allow_tf32 = True
import torchvision
import torchvision.transforms as T
from flax.jax_utils import replicate
from flax.training.common_utils import shard
#from torchvision.transforms import v2 as T2
import cv2
import PIL
from PIL import Image
import numpy as np
import jax
import os

import torchvision.transforms.functional as F

output_res = (900,900)

conditioning_image_transforms = T.Compose(
    [
        #T2.ScaleJitter(target_size=output_res, scale_range=(0.5, 3.0))),
        T.RandomCrop(size=output_res, pad_if_needed=True, padding_mode="symmetric"),
        T.ToTensor(),
        #T.Normalize([0.5], [0.5]),
    ]
)
cnet, cnet_params = FlaxControlNetModel.from_pretrained("./models/catcon-controlnet-wd", dtype=jnp.bfloat16, from_flax=True)
pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
        "./models/wd-1-5-b2-flax", 
        controlnet=cnet,
        revision="flax",
        dtype=jnp.bfloat16,
        )
#scheduler, scheduler_state = FlaxDPMSolverMultistepScheduler.from_pretrained(
#    "Ryukijano/CatCon-One-Shot-Controlnet-SD-1-5-b2/wd-1-5-b2-flax",
#    subfolder="scheduler"
#)
#params["scheduler"] = scheduler_state

#scheduler = FlaxDPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
#pipe.enable_model_cpu_offload()

def get_random(seed):
    return jax.random.PRNGKey(seed)

# inference function takes prompt, negative prompt and image
def infer(prompt, negative_prompt, image):
    # implement your inference function here
    params["controlnet"] = cnet_params
    num_samples = 1

    inp = Image.fromarray(image)

    cond_input = conditioning_image_transforms(inp)
    cond_input = T.ToPILImage()(cond_input)

    cond_img_in = pipe.prepare_image_inputs([cond_input] * num_samples)
    cond_img_in = shard(cond_img_in)

    prompt_in = pipe.prepare_text_inputs([prompt] * num_samples)
    prompt_in = shard(prompt_in)

    n_prompt_in = pipe.prepare_text_inputs([negative_prompt] * num_samples)
    n_prompt_in = shard(n_prompt_in)

    rng = get_random(0)   
    rng = jax.random.split(rng, jax.device_count())

    p_params = replicate(params)
    
    output = pipe(
        prompt_ids=prompt_in,
        image=cond_img_in,
        params=p_params,
        prng_seed=rng,
        num_inference_steps=70,
        neg_prompt_ids=n_prompt_in,
        jit=True,
            ).images

    output_images = pipe.numpy_to_pil(np.asarray(output.reshape((num_samples,) + output.shape[-3:])))
    return output_images

gr.Interface(
    infer,
    inputs=[
        gr.Textbox(
            label="Enter prompt",
            max_lines=1,
            placeholder="1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, watercolor, night, turtleneck",
        ),
        gr.Textbox(
            label="Enter negative prompt",
            max_lines=1,
            placeholder="low quality",
        ),
        gr.Image(),
    ],
    outputs=gr.Gallery(columns=2, height="auto"),
    title="Generate controlled outputs with Categorical Conditioning on Waifu Diffusion 1.5 beta 2.",
    description="This Space uses image examples as style conditioning. Experimental proof of concept made for the [Huggingface JAX/Diffusers community sprint](https://github.com/huggingface/community-events/tree/main/jax-controlnet-sprint)[Demo available here](https://huggingface.co/spaces/Ryukijano/CatCon-One-Shot-Controlnet-SD-1-5-b2)[My teammate's demo is available here] (https://huggingface.co/spaces/Cognomen/CatCon-Controlnet-WD-1-5-b2) This is a controlnet for the Stable Diffusion checkpoint [Waifu Diffusion 1.5 beta 2](https://huggingface.co/waifu-diffusion/wd-1-5-beta2) which aims to guide image generation by conditioning outputs with patches of images from a common category of the training target examples. The current checkpoint has been trained for approx. 100k steps on a filtered subset of [Danbooru 2021](https://gwern.net/danbooru2021) using artists as the conditioned category with the aim of learning robust style transfer from an image example.Major limitations:- The current checkpoint was trained on 768x768 crops without aspect ratio checkpointing. Loss in coherence for non-square aspect ratios can be expected.- The training dataset is extremely noisy and used without filtering stylistic outliers from within each category, so performance may be less than ideal. A more diverse dataset with a larger variety of styles and categories would likely have better performance.- The Waifu Diffusion base model is a hybrid anime/photography model, and can unpredictably jump between those modalities.- As styling is sensitive to divergences in model checkpoints, the capabilities of this controlnet are not expected to predictably apply to other SD 2.X checkpoints.",
    
    examples=[
        ["1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, watercolor, night, turtleneck", "realistic, real life", "wikipe_cond_1.png"],
        ["1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, watercolor, night, turtleneck", "realistic, real life", "wikipe_cond_2.png"],
        ["1girl, green hair, sweater, looking at viewer, upper body, beanie, outdoors, watercolor, night, turtleneck", "realistic, real life", "wikipe_cond_3.png"]
        ],
    allow_flagging=False,
).launch(enable_queue=True)