import io
import os

from pathlib import Path
import uvicorn
from fastapi import FastAPI, BackgroundTasks, HTTPException, UploadFile, Depends, status, Request
from fastapi.staticfiles import StaticFiles
from fastapi.middleware.cors import CORSMiddleware
from fastapi_utils.tasks import repeat_every

import numpy as np
import torch
from torch import autocast
from diffusers import StableDiffusionPipeline, StableDiffusionInpaintPipeline
from PIL import Image
import gradio as gr
import skimage
import skimage.measure
from utils import *
import boto3
import magic
import sqlite3
import requests
import uuid

AWS_ACCESS_KEY_ID = os.getenv('AWS_ACCESS_KEY_ID')
AWS_SECRET_KEY = os.getenv('AWS_SECRET_KEY')
AWS_S3_BUCKET_NAME = os.getenv('AWS_S3_BUCKET_NAME')
LIVEBLOCKS_SECRET = os.environ.get("LIVEBLOCKS_SECRET")
HF_TOKEN = os.environ.get("API_TOKEN") or True

if (AWS_ACCESS_KEY_ID == None or AWS_SECRET_KEY == None or AWS_S3_BUCKET_NAME == None or LIVEBLOCKS_SECRET == None):
    raise Exception("Missing environment variables")

FILE_TYPES = {
    'image/png': 'png',
    'image/jpeg': 'jpg',
}
DB_PATH = Path("rooms.db")

app = FastAPI()

print("DB_PATH", DB_PATH)


def get_db():
    db = sqlite3.connect(DB_PATH, check_same_thread=False)
    db.execute("CREATE TABLE IF NOT EXISTS rooms (id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL, room_id TEXT NOT NULL, users_count INTEGER NOT NULL DEFAULT 0)")
    print("Connected to database")
    db.commit()
    db.row_factory = sqlite3.Row
    try:
        yield db
    except Exception:
        db.rollback()
    finally:
        db.close()


s3 = boto3.client(service_name='s3',
                  aws_access_key_id=AWS_ACCESS_KEY_ID,
                  aws_secret_access_key=AWS_SECRET_KEY)
try:
    SAMPLING_MODE = Image.Resampling.LANCZOS
except Exception as e:
    SAMPLING_MODE = Image.LANCZOS


blocks = gr.Blocks().queue()
model = {}


def get_model():
    if "text2img" not in model:
        text2img = StableDiffusionPipeline.from_pretrained(
            "CompVis/stable-diffusion-v1-4",
            revision="fp16",
            torch_dtype=torch.float16,
            use_auth_token=HF_TOKEN,
        ).to("cuda")
        inpaint = StableDiffusionInpaintPipeline(
            vae=text2img.vae,
            text_encoder=text2img.text_encoder,
            tokenizer=text2img.tokenizer,
            unet=text2img.unet,
            scheduler=text2img.scheduler,
            safety_checker=text2img.safety_checker,
            feature_extractor=text2img.feature_extractor,
        ).to("cuda")

        # lms = LMSDiscreteScheduler(
        #     beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear")

        # img2img = StableDiffusionImg2ImgPipeline(
        #     vae=text2img.vae,
        #     text_encoder=text2img.text_encoder,
        #     tokenizer=text2img.tokenizer,
        #     unet=text2img.unet,
        #     scheduler=lms,
        #     safety_checker=text2img.safety_checker,
        #     feature_extractor=text2img.feature_extractor,
        # ).to("cuda")
        # try:
        #     total_memory = torch.cuda.get_device_properties(0).total_memory // (
        #         1024 ** 3
        #     )
        #     if total_memory <= 5:
        #         inpaint.enable_attention_slicing()
        # except:
        #     pass
        model["text2img"] = text2img
        model["inpaint"] = inpaint
        # model["img2img"] = img2img

    return model["text2img"], model["inpaint"]
    # model["img2img"]


# init model on startup
# get_model()


def run_outpaint(
    input_image,
    prompt_text,
    strength,
    guidance,
    step,
    fill_mode,
):
    text2img, inpaint = get_model()
    sel_buffer = np.array(input_image)
    img = sel_buffer[:, :, 0:3]
    mask = sel_buffer[:, :, -1]
    process_size = 512

    mask_sum = mask.sum()
    # if mask_sum >= WHITES:
    #     print("inpaiting with fixed Mask")
    #     mask = np.array(MASK)[:, :, 0]
    #     img, mask = functbl[fill_mode](img, mask)
    #     init_image = Image.fromarray(img)
    #     mask = 255 - mask
    #     mask = skimage.measure.block_reduce(mask, (8, 8), np.max)
    #     mask = mask.repeat(8, axis=0).repeat(8, axis=1)
    #     mask_image = Image.fromarray(mask)

    #     # mask_image=mask_image.filter(ImageFilter.GaussianBlur(radius = 8))
    #     with autocast("cuda"):
    #         images = inpaint(
    #             prompt=prompt_text,
    #             init_image=init_image.resize(
    #                 (process_size, process_size), resample=SAMPLING_MODE
    #             ),
    #             mask_image=mask_image.resize((process_size, process_size)),
    #             strength=strength,
    #             num_inference_steps=step,
    #             guidance_scale=guidance,
    #         )
    if mask_sum > 0:
        print("inpainting")
        img, mask = functbl[fill_mode](img, mask)
        init_image = Image.fromarray(img)
        mask = 255 - mask
        mask = skimage.measure.block_reduce(mask, (8, 8), np.max)
        mask = mask.repeat(8, axis=0).repeat(8, axis=1)
        mask_image = Image.fromarray(mask)

        # mask_image=mask_image.filter(ImageFilter.GaussianBlur(radius = 8))
        with autocast("cuda"):
            images = inpaint(
                prompt=prompt_text,
                init_image=init_image.resize(
                    (process_size, process_size), resample=SAMPLING_MODE
                ),
                mask_image=mask_image.resize((process_size, process_size)),
                strength=strength,
                num_inference_steps=step,
                guidance_scale=guidance,
            )
    else:
        print("text2image")
        with autocast("cuda"):
            images = text2img(
                prompt=prompt_text, height=process_size, width=process_size,
            )

    return images['sample'][0], images["nsfw_content_detected"][0]


with blocks as demo:

    with gr.Row():

        with gr.Column(scale=3, min_width=270):
            sd_prompt = gr.Textbox(
                label="Prompt", placeholder="input your prompt here", lines=4
            )
        with gr.Column(scale=2, min_width=150):
            sd_strength = gr.Slider(
                label="Strength", minimum=0.0, maximum=1.0, value=0.75, step=0.01
            )
        with gr.Column(scale=1, min_width=150):
            sd_step = gr.Number(label="Step", value=50, precision=0)
            sd_guidance = gr.Number(label="Guidance", value=7.5)
    with gr.Row():
        with gr.Column(scale=4, min_width=600):
            init_mode = gr.Radio(
                label="Init mode",
                choices=[
                    "patchmatch",
                    "edge_pad",
                    "cv2_ns",
                    "cv2_telea",
                    "gaussian",
                    "perlin",
                ],
                value="patchmatch",
                type="value",
            )

    model_input = gr.Image(label="Input", type="pil", image_mode="RGBA")
    proceed_button = gr.Button("Proceed", elem_id="proceed")
    model_output = gr.Image(label="Output")
    is_nsfw = gr.JSON()

    proceed_button.click(
        fn=run_outpaint,
        inputs=[
            model_input,
            sd_prompt,
            sd_strength,
            sd_guidance,
            sd_step,
            init_mode,
        ],
        outputs=[model_output, is_nsfw],
    )


blocks.config['dev_mode'] = False


def generateAuthToken():
    response = requests.get(f"https://liveblocks.io/api/authorize",
                            headers={"Authorization": f"Bearer {LIVEBLOCKS_SECRET}"})
    if response.status_code == 200:
        data = response.json()
        return data["token"]
    else:
        raise Exception(response.status_code, response.text)


def get_room_count(room_id: str, jwtToken: str = ''):
    response = requests.get(
        f"https://liveblocks.net/api/v1/room/{room_id}/users", headers={"Authorization": f"Bearer {jwtToken}", "Content-Type": "application/json"})
    if response.status_code == 200:
        res = response.json()
        if "data" in res:
            return len(res["data"])
        else:
            return 0
    raise Exception("Error getting room count")


@app.on_event("startup")
@repeat_every(seconds=60)
async def sync_rooms():
    print("Syncing rooms")
    try:
        jwtToken = generateAuthToken()
        for db in get_db():
            rooms = db.execute("SELECT * FROM rooms").fetchall()
            for row in rooms:
                room_id = row["room_id"]
                users_count = get_room_count(room_id, jwtToken)
                cursor = db.cursor()
                cursor.execute(
                    "UPDATE rooms SET users_count = ? WHERE room_id = ?", (users_count, room_id))
                db.commit()
    except Exception as e:
        print(e)
        print("Rooms update failed")


@app.get('/api/rooms')
async def get_rooms(db: sqlite3.Connection = Depends(get_db)):
    rooms = db.execute("SELECT * FROM rooms").fetchall()
    return rooms


@app.post('/api/auth')
async def autorize(request: Request, db: sqlite3.Connection = Depends(get_db)):
    data = await request.json()
    room = data["room"]
    payload = {
        "userId": str(uuid.uuid4()),
        "userInfo": {
            "name": "Anon"
        }}

    response = requests.post(f"https://api.liveblocks.io/v2/rooms/{room}/authorize",
                             headers={"Authorization": f"Bearer {LIVEBLOCKS_SECRET}"}, json=payload)
    if response.status_code == 200:
        # user in, incremente room count
        # cursor = db.cursor()
        # cursor.execute(
        #     "UPDATE rooms SET users_count = users_count + 1 WHERE room_id = ?", (room,))
        # db.commit()
        sync_rooms()
        return response.json()
    else:
        raise Exception(response.status_code, response.text)


@ app.post('/api/uploadfile/')
async def create_upload_file(background_tasks: BackgroundTasks, file: UploadFile):
    contents = await file.read()
    file_size = len(contents)
    if not 0 < file_size < 2E+06:
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail='Supported file size is less than 2 MB'
        )
    file_type = magic.from_buffer(contents, mime=True)
    if file_type.lower() not in FILE_TYPES:
        raise HTTPException(
            status_code=status.HTTP_400_BAD_REQUEST,
            detail=f'Unsupported file type {file_type}. Supported types are {FILE_TYPES}'
        )
    temp_file = io.BytesIO()
    temp_file.write(contents)
    temp_file.seek(0)
    s3.upload_fileobj(Fileobj=temp_file, Bucket=AWS_S3_BUCKET_NAME, Key="uploads/" +
                      file.filename, ExtraArgs={"ContentType": file.content_type, "CacheControl": "max-age=31536000"})
    temp_file.close()

    return {"url": f'https://d26smi9133w0oo.cloudfront.net/uploads/{file.filename}', "filename": file.filename}


app.mount("/", StaticFiles(directory="../static", html=True), name="static")

origins = ["*"]

app.add_middleware(
    CORSMiddleware,
    allow_origins=origins,
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

app = gr.mount_gradio_app(app, blocks, "/gradio",
                          gradio_api_url="http://0.0.0.0:7860/gradio/")


if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860,
                log_level="debug", reload=False)