import asyncio
import json
import logging
import traceback
from pydantic import BaseModel

from fastapi import FastAPI, WebSocket, HTTPException, WebSocketDisconnect
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import (
    StreamingResponse,
    JSONResponse,
    HTMLResponse,
    FileResponse,
)

from diffusers import DiffusionPipeline, LCMScheduler, AutoencoderTiny
from compel import Compel, ReturnedEmbeddingsType
import torch

try:
    import intel_extension_for_pytorch as ipex
except:
    pass
from PIL import Image
import numpy as np
import gradio as gr
import io
import uuid
import os
import time
import psutil


MAX_QUEUE_SIZE = int(os.environ.get("MAX_QUEUE_SIZE", 0))
TIMEOUT = float(os.environ.get("TIMEOUT", 0))
SAFETY_CHECKER = os.environ.get("SAFETY_CHECKER", None)
TORCH_COMPILE = os.environ.get("TORCH_COMPILE", None)

WIDTH = 768
HEIGHT = 768
# disable tiny autoencoder for better quality speed tradeoff
USE_TINY_AUTOENCODER = False

# check if MPS is available OSX only M1/M2/M3 chips
mps_available = hasattr(torch.backends, "mps") and torch.backends.mps.is_available()
xpu_available = hasattr(torch, "xpu") and torch.xpu.is_available()
device = torch.device(
    "cuda" if torch.cuda.is_available() else "xpu" if xpu_available else "cpu"
)
torch_device = device
# change to torch.float16 to save GPU memory
torch_dtype = torch.float16

print(f"TIMEOUT: {TIMEOUT}")
print(f"SAFETY_CHECKER: {SAFETY_CHECKER}")
print(f"MAX_QUEUE_SIZE: {MAX_QUEUE_SIZE}")
print(f"device: {device}")

if mps_available:
    device = torch.device("mps")
    torch_device = "cpu"
    torch_dtype = torch.float32

model_id = "stabilityai/stable-diffusion-xl-base-1.0"

if SAFETY_CHECKER == "True":
    pipe = DiffusionPipeline.from_pretrained(model_id)
else:
    pipe = DiffusionPipeline.from_pretrained(model_id, safety_checker=None)
    
if USE_TINY_AUTOENCODER:
    pipe.vae = AutoencoderTiny.from_pretrained(
        "madebyollin/taesd", torch_dtype=torch_dtype, use_safetensors=True
    )
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.set_progress_bar_config(disable=True)
pipe.to(device=torch_device, dtype=torch_dtype).to(device)
pipe.unet.to(memory_format=torch.channels_last)

# check if computer has less than 64GB of RAM using sys or os
if psutil.virtual_memory().total < 64 * 1024**3:
    pipe.enable_attention_slicing()

if TORCH_COMPILE:
    pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
    pipe.vae = torch.compile(pipe.vae, mode="reduce-overhead", fullgraph=True)

    pipe(prompt="warmup", num_inference_steps=1, guidance_scale=8.0)

# Load LCM LoRA
pipe.load_lora_weights("lcm-sd/lcm-sdxl-lora", weight_name="lcm_sdxl_lora.safetensors", adapter_name="lcm")

compel_proc = Compel(
    tokenizer=[pipe.tokenizer, pipe.tokenizer_2],
    text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
    returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
    requires_pooled=[False, True],
)
user_queue_map = {}


class InputParams(BaseModel):
    seed: int = 2159232
    prompt: str
    guidance_scale: float = 0.5
    strength: float = 0.5
    steps: int = 4
    lcm_steps: int = 50
    width: int = WIDTH
    height: int = HEIGHT


def predict(params: InputParams):
    generator = torch.manual_seed(params.seed)
    prompt_embeds, pooled_prompt_embeds = compel_proc(params.prompt)
    results = pipe(
        prompt_embeds=prompt_embeds,
        pooled_prompt_embeds=pooled_prompt_embeds,
        generator=generator,
        num_inference_steps=params.steps,
        guidance_scale=params.guidance_scale,
        width=params.width,
        height=params.height,
        # original_inference_steps=params.lcm_steps,
        output_type="pil",
    )
    nsfw_content_detected = (
        results.nsfw_content_detected[0]
        if "nsfw_content_detected" in results
        else False
    )
    if nsfw_content_detected:
        return None
    return results.images[0]


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


@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
    await websocket.accept()
    if MAX_QUEUE_SIZE > 0 and len(user_queue_map) >= MAX_QUEUE_SIZE:
        print("Server is full")
        await websocket.send_json({"status": "error", "message": "Server is full"})
        await websocket.close()
        return

    try:
        uid = str(uuid.uuid4())
        print(f"New user connected: {uid}")
        await websocket.send_json(
            {"status": "success", "message": "Connected", "userId": uid}
        )
        user_queue_map[uid] = {
            "queue": asyncio.Queue(),
        }
        await websocket.send_json(
            {"status": "start", "message": "Start Streaming", "userId": uid}
        )
        await handle_websocket_data(websocket, uid)
    except WebSocketDisconnect as e:
        logging.error(f"WebSocket Error: {e}, {uid}")
        traceback.print_exc()
    finally:
        print(f"User disconnected: {uid}")
        queue_value = user_queue_map.pop(uid, None)
        queue = queue_value.get("queue", None)
        if queue:
            while not queue.empty():
                try:
                    queue.get_nowait()
                except asyncio.QueueEmpty:
                    continue


@app.get("/queue_size")
async def get_queue_size():
    queue_size = len(user_queue_map)
    return JSONResponse({"queue_size": queue_size})


@app.get("/stream/{user_id}")
async def stream(user_id: uuid.UUID):
    uid = str(user_id)
    try:
        user_queue = user_queue_map[uid]
        queue = user_queue["queue"]

        async def generate():
            while True:
                params = await queue.get()
                if params is None:
                    continue

                image = predict(params)
                if image is None:
                    continue
                frame_data = io.BytesIO()
                image.save(frame_data, format="JPEG")
                frame_data = frame_data.getvalue()
                if frame_data is not None and len(frame_data) > 0:
                    yield b"--frame\r\nContent-Type: image/jpeg\r\n\r\n" + frame_data + b"\r\n"

                await asyncio.sleep(1.0 / 120.0)

        return StreamingResponse(
            generate(), media_type="multipart/x-mixed-replace;boundary=frame"
        )
    except Exception as e:
        logging.error(f"Streaming Error: {e}, {user_queue_map}")
        traceback.print_exc()
        return HTTPException(status_code=404, detail="User not found")


async def handle_websocket_data(websocket: WebSocket, user_id: uuid.UUID):
    uid = str(user_id)
    user_queue = user_queue_map[uid]
    queue = user_queue["queue"]
    if not queue:
        return HTTPException(status_code=404, detail="User not found")
    last_time = time.time()
    try:
        while True:
            params = await websocket.receive_json()
            params = InputParams(**params)
            while not queue.empty():
                try:
                    queue.get_nowait()
                except asyncio.QueueEmpty:
                    continue
            await queue.put(params)
            if TIMEOUT > 0 and time.time() - last_time > TIMEOUT:
                await websocket.send_json(
                    {
                        "status": "timeout",
                        "message": "Your session has ended",
                        "userId": uid,
                    }
                )
                await websocket.close()
                return

    except Exception as e:
        logging.error(f"Error: {e}")
        traceback.print_exc()


@app.get("/", response_class=HTMLResponse)
async def root():
    return FileResponse("./static/txt2imglora.html")