import torch
from PIL.Image import Image
from diffusers import StableDiffusionXLPipeline

from pipelines.models import TextToImageRequest
from diffusers import DDIMScheduler
from torch import Generator
from loss import SchedulerWrapper, LoadSDXLQuantization

from onediffx import compile_pipe, save_pipe, load_pipe

def callback_dynamic_cfg(pipe, step_index, timestep, callback_kwargs):
  if step_index == int(pipe.num_timesteps * 0.78):
    callback_kwargs['prompt_embeds'] = callback_kwargs['prompt_embeds'].chunk(2)[-1]
    callback_kwargs['add_text_embeds'] = callback_kwargs['add_text_embeds'].chunk(2)[-1]
    callback_kwargs['add_time_ids'] = callback_kwargs['add_time_ids'].chunk(2)[-1]
    pipe._guidance_scale = 0.1

  return callback_kwargs

def load_pipeline(pipeline=None) -> StableDiffusionXLPipeline:
    if not pipeline:
        pipeline = StableDiffusionXLPipeline.from_pretrained(
            "stablediffusionapi/newdream-sdxl-20",
            torch_dtype=torch.float16,
        )
    
    pipeline.scheduler = SchedulerWrapper(DDIMScheduler.from_config(pipeline.scheduler.config))
    quantizer = LoadSDXLQuantization(pipeline.unet)
    quantizer.load_model()
    pipeline.to("cuda")

    pipeline = compile_pipe(pipeline)
    load_pipe(pipeline, dir="/home/sandbox/.cache/huggingface/hub/models--RobertML--cached-pipe-02/snapshots/58d70deae87034cce351b780b48841f9746d4ad7")

    for _ in range(1):
        deepcache_output = pipeline(prompt="polypterid, fattenable, geoparallelotropic, Galeus, galipine, peritoneum, malappropriate, Sekar", output_type="pil", num_inference_steps=20)
    pipeline.scheduler.prepare_loss()
    for _ in range(1):
        pipeline(prompt="polypterid, fattenable, geoparallelotropic, Galeus, galipine, peritoneum, malappropriate, Sekar", output_type="pil", num_inference_steps=20)
    return pipeline

def infer(request: TextToImageRequest, pipeline: StableDiffusionXLPipeline) -> Image:
    if request.seed is None:
        generator = None
    else:
        generator = Generator(pipeline.device).manual_seed(request.seed)

    return pipeline(
        prompt=request.prompt,
        negative_prompt=request.negative_prompt,
        width=request.width,
        height=request.height,
        generator=generator,
        num_inference_steps=13,
        cache_interval=1,
        cache_layer_id=1,
        cache_block_id=0,
        eta=1.0,
        guidance_scale = 5.0,
        guidance_rescale = 0.0,
        callback_on_step_end=callback_dynamic_cfg,
        callback_on_step_end_tensor_inputs=['prompt_embeds', 'add_text_embeds', 'add_time_ids'],
    ).images[0]