import os
from typing import Any, Dict

from diffusers import FluxPipeline, FluxTransformer2DModel
from torchao.quantization import int8_weight_only, quantize_
from PIL.Image import Image
import torch

from huggingface_inference_toolkit.logging import logger

class EndpointHandler:
    def __init__(self, **kwargs: Any) -> None:  # type: ignore
        repo_id = "camenduru/FLUX.1-dev-diffusers"
        dtype = torch.bfloat16
        transformer = FluxTransformer2DModel.from_pretrained(repo_id, subfolder="transformer", torch_dtype=dtype)
        quantize_(transformer, int8_weight_only(), device="cuda")
        transformer.to(memory_format=torch.channels_last)
        transformer = torch.compile(transformer, mode="max-autotune", fullgraph=True)
        self.pipeline = FluxPipeline.from_pretrained(repo_id, transformer=transformer, torch_dtype=torch.bfloat16).to("cuda")
        self.pipeline.vae.to(memory_format=torch.channels_last)
        self.pipeline.vae.decode = torch.compile(self.pipeline.vae.decode, mode="max-autotune", fullgraph=True)

    def __call__(self, data: Dict[str, Any]) -> Image:
        logger.info(f"Received incoming request with {data=}")

        if "inputs" in data and isinstance(data["inputs"], str):
            prompt = data.pop("inputs")
        elif "prompt" in data and isinstance(data["prompt"], str):
            prompt = data.pop("prompt")
        else:
            raise ValueError(
                "Provided input body must contain either the key `inputs` or `prompt` with the"
                " prompt to use for the image generation, and it needs to be a non-empty string."
            )

        parameters = data.pop("parameters", {})

        num_inference_steps = parameters.get("num_inference_steps", 30)
        width = parameters.get("width", 1024)
        height = parameters.get("height", 768)
        guidance_scale = parameters.get("guidance_scale", 3.5)

        # seed generator (seed cannot be provided as is but via a generator)
        seed = parameters.get("seed", 0)
        generator = torch.manual_seed(seed)

        return self.pipeline(  # type: ignore
            prompt,
            height=height,
            width=width,
            guidance_scale=guidance_scale,
            num_inference_steps=num_inference_steps,
            generator=generator,
        ).images[0]