from huggingface_hub.constants import HF_HUB_CACHE
from transformers import T5EncoderModel, T5TokenizerFast, CLIPTokenizer, CLIPTextModel
import torch
import torch._dynamo
import gc
from PIL import Image as img
from PIL.Image import Image
from pipelines.models import TextToImageRequest
from torch import Generator
from diffusers import FluxTransformer2DModel, DiffusionPipeline
import os

os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"

Pipeline = None

ckpt_id = "black-forest-labs/FLUX.1-schnell"
ckpt_revision = "741f7c3ce8b383c54771c7003378a50191e9efe9"


def empty_cache():
    gc.collect()
    torch.cuda.empty_cache()
    torch.cuda.reset_max_memory_allocated()
    torch.cuda.reset_peak_memory_stats()


def load_pipeline() -> Pipeline:
    empty_cache()

    dtype, device = torch.bfloat16, "cuda"
    text_encoder_2 = T5EncoderModel.from_pretrained(
        "escort321/FLUX.1-schnell1-up",
        revision="d7e70e3a8fbc36ec3c47e78913e9c7142bc87b7b",
        subfolder="text_encoder_2",
        torch_dtype=torch.bfloat16,
    )

    path = os.path.join(
        HF_HUB_CACHE,
        "models--escort321--FLUX.1-schnell1-up/snapshots/d7e70e3a8fbc36ec3c47e78913e9c7142bc87b7b/transformer",
    )
    transformer = FluxTransformer2DModel.from_pretrained(
        path, torch_dtype=torch.bfloat16, use_safetensors=False
    )
    pipeline = DiffusionPipeline.from_pretrained(
        ckpt_id,
        revision=ckpt_revision,
        transformer=transformer,
        text_encoder_2=text_encoder_2,
        torch_dtype=dtype,
    ).to(device)
    # quantize_(pipeline.vae, int8_weight_only())
    pipeline(
        prompt="wordcraft, radiance, ethereal, cartilaginous, tuner, fruity, dullard, existence",
        width=1024,
        height=1024,
        guidance_scale=0.0,
        num_inference_steps=4,
        max_sequence_length=256,
    )

    empty_cache()
    return pipeline


@torch.no_grad()
def infer(
    request: TextToImageRequest, pipeline: Pipeline, generator: Generator
) -> Image:
    return pipeline(
        request.prompt,
        generator=generator,
        guidance_scale=0.0,
        num_inference_steps=4,
        max_sequence_length=256,
        height=request.height,
        width=request.width,
        output_type="pil",
    ).images[0]