import numpy as np
import PIL.Image
import torch
from typing import List
from diffusers.utils import numpy_to_pil
from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline
from diffusers.pipelines.wuerstchen import DEFAULT_STAGE_C_TIMESTEPS
from fastapi import FastAPI
import uvicorn
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import RedirectResponse, StreamingResponse
import io
import os
from pathlib import Path
from db import Database
import uuid
import logging
from fastapi import FastAPI, Request, HTTPException
from fastapi.middleware.cors import CORSMiddleware

logging.basicConfig(level=os.environ.get("LOGLEVEL", "INFO"))

MAX_SEED = np.iinfo(np.int32).max
USE_TORCH_COMPILE = os.environ.get("USE_TORCH_COMPILE", "0") == "1"
SPACE_ID = os.environ.get("SPACE_ID", "")
DEV = os.environ.get("DEV", "0") == "1"

DB_PATH = Path("/data/cache") if SPACE_ID else Path("./cache")
IMGS_PATH = DB_PATH / "imgs"
DB_PATH.mkdir(exist_ok=True, parents=True)
IMGS_PATH.mkdir(exist_ok=True, parents=True)

database = Database(DB_PATH)

dtype = torch.bfloat16
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if torch.cuda.is_available():
    prior_pipeline = StableCascadePriorPipeline.from_pretrained(
        "stabilityai/stable-cascade-prior", torch_dtype=dtype
    )  # .to(device)
    decoder_pipeline = StableCascadeDecoderPipeline.from_pretrained(
        "stabilityai/stable-cascade", torch_dtype=dtype
    )  # .to(device)
    prior_pipeline.to(device)
    decoder_pipeline.to(device)

    if USE_TORCH_COMPILE:
        prior_pipeline.prior = torch.compile(
            prior_pipeline.prior, mode="reduce-overhead", fullgraph=True
        )
        decoder_pipeline.decoder = torch.compile(
            decoder_pipeline.decoder, mode="max-autotune", fullgraph=True
        )


def generate(
    prompt: str,
    negative_prompt: str = "",
    seed: int = 0,
    width: int = 1024,
    height: int = 1024,
    prior_num_inference_steps: int = 20,
    prior_guidance_scale: float = 4.0,
    decoder_num_inference_steps: int = 10,
    decoder_guidance_scale: float = 0.0,
    num_images_per_prompt: int = 2,
) -> PIL.Image.Image:

    generator = torch.Generator().manual_seed(seed)
    prior_output = prior_pipeline(
        prompt=prompt,
        height=height,
        width=width,
        num_inference_steps=prior_num_inference_steps,
        timesteps=DEFAULT_STAGE_C_TIMESTEPS,
        negative_prompt=negative_prompt,
        guidance_scale=prior_guidance_scale,
        num_images_per_prompt=num_images_per_prompt,
        generator=generator,
    )
    decoder_output = decoder_pipeline(
        image_embeddings=prior_output.image_embeddings,
        prompt=prompt,
        num_inference_steps=decoder_num_inference_steps,
        # timesteps=decoder_timesteps,
        guidance_scale=decoder_guidance_scale,
        negative_prompt=negative_prompt,
        generator=generator,
        output_type="pil",
    ).images

    return decoder_output[0]


app = FastAPI()
origins = [
    "http://huggingface.co",
]

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


@app.middleware("http")
async def validate_origin(request: Request, call_next):
    if request.headers.get("referer") not in origins and not DEV:
        raise HTTPException(status_code=403, detail="Forbidden")
    response = await call_next(request)
    return response


@app.get("/image")
async def generate_image(prompt: str, negative_prompt: str = "", seed: int = 2134213213):
    cached_img = database.check(prompt, negative_prompt, seed)
    if cached_img:
        logging.info(f"Image found in cache: {cached_img[0]}")
        return StreamingResponse(open(cached_img[0], "rb"), media_type="image/jpeg")

    logging.info(f"Image not found in cache, generating new image")
    pil_image = generate(prompt, negative_prompt, seed)
    img_id = str(uuid.uuid4())
    img_path = IMGS_PATH / f"{img_id}.jpg"
    pil_image.save(img_path)
    img_io = io.BytesIO()
    pil_image.save(img_io, "JPEG")
    img_io.seek(0)
    database.insert(prompt, negative_prompt, str(img_path), seed)

    return StreamingResponse(img_io, media_type="image/jpeg")


@app.get("/")
async def main():
    # redirect to https://huggingface.co./spaces/multimodalart/stable-cascade
    return RedirectResponse(
        "https://multimodalart-stable-cascade.hf.space/?__theme=system"
    )


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