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from diffusers import AutoencoderKL
from transformers import CLIPTextModel, CLIPTokenizer
import torch

prompt = "Draw a dog"
vae = AutoencoderKL.from_pretrained("CompVis/stable-diffusion-v1-4",
                                    subfolder="vae",
                                    cache_dir='hf_cache')

tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14",cache_dir='hf_cache')
text_encoder = CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14",cache_dir='hf_cache')

vae.forward = vae.decode
torch.onnx.export(
    vae,
    (torch.randn(1, 4, 64, 64), False),
    "vae.onnx",
    input_names=["latent_sample", "return_dict"],
    output_names=["sample"],
    dynamic_axes={
        "latent_sample": {
            0: "batch",
            1: "channels",
            2: "height",
            3: "width"
        },
    },
    do_constant_folding=True,
    opset_version=14,
)

text_input = tokenizer(
    prompt,
    padding="max_length",
    max_length=tokenizer.model_max_length,
    truncation=True,
    return_tensors="pt",
)
print('Here is the shape of the input -----------------------------------------------------')
print(text_input.input_ids.shape)
torch.onnx.export(
    text_encoder,
    (text_input.input_ids.to(torch.int32)),
    "encoder.onnx",
    input_names=["input_ids"],
    output_names=["last_hidden_state", "pooler_output"],
    dynamic_axes={
        "input_ids": {
            0: "batch",
            1: "sequence"
        },
    },
    opset_version=14,
    do_constant_folding=True,
)