import streamlit as st

import numpy as np
from PIL import Image

# constants
HF_REPO_NAME_DIFFUSION = 'nostalgebraist/nostalgebraist-autoresponder-diffusion'
model_path_diffusion = 'nostalgebraist-autoresponder-diffusion'
timestep_respacing_sres1 = '20' # '90,60,60,20,20'
timestep_respacing_sres2 = '20' # '250'

DIFFUSION_DEFAULTS = dict(
    batch_size=1,
    n_samples=1,
    clf_free_guidance=False,
    clf_free_guidance_sres=False,
    guidance_scale=1,
    guidance_scale_sres=0,
    yield_intermediates=True
)

@st.cache
def setup():
    import os, subprocess, sys
    os.system("git clone https://github.com/nostalgebraist/improved-diffusion.git && cd improved-diffusion && git fetch origin nbar-space && git checkout nbar-dev && pip install -e .")
    os.system("pip install tokenizers x-transformers==0.22.0 axial-positional-embedding")
    os.system("pip install einops==0.3.2")
    sys.path.append("improved-diffusion")
    from improved_diffusion import pipeline
    # import improved_diffusion.pipeline
    # from transformer_utils.util.tfm_utils import get_local_path_from_huggingface_cdn
    #
    # if not os.path.exists(model_path_diffusion):
    #     model_tar_name = 'model.tar'
    #     model_tar_path = get_local_path_from_huggingface_cdn(
    #         HF_REPO_NAME_DIFFUSION, model_tar_name
    #     )
    #     subprocess.run(f"tar -xf {model_tar_path} && rm {model_tar_path}", shell=True)
    #
    # checkpoint_path_sres1 = os.path.join(model_path_diffusion, "sres1.pt")
    # config_path_sres1 = os.path.join(model_path_diffusion, "config_sres1.json")
    #
    # checkpoint_path_sres2 = os.path.join(model_path_diffusion, "sres2.pt")
    # config_path_sres2 = os.path.join(model_path_diffusion, "config_sres2.json")
    #
    # # load
    # sampling_model_sres1 = improved_diffusion.pipeline.SamplingModel.from_config(
    #     checkpoint_path=checkpoint_path_sres1,
    #     config_path=config_path_sres1,
    #     timestep_respacing=timestep_respacing_sres1
    # )
    #
    # sampling_model_sres2 = improved_diffusion.pipeline.SamplingModel.from_config(
    #     checkpoint_path=checkpoint_path_sres2,
    #     config_path=config_path_sres2,
    #     timestep_respacing=timestep_respacing_sres2
    # )
    #
    # pipeline = improved_diffusion.pipeline.SamplingPipeline(sampling_model_sres1, sampling_model_sres2)
    return pipeline

pipeline = setup()

def handler(text, ts1, ts2, gs1):
    # # a = np.random.randint(0, 255, (128, 128, 3)).astype(np.uint8)
    # data = {'text': text[:380], 'guidance_scale': gs1}
    # args = {k: v for k, v in DIFFUSION_DEFAULTS.items()}
    # args.update(data)
    #
    # print(f"running: {args}")
    #
    # pipeline.base_model.set_timestep_respacing(str(ts1))
    # pipeline.super_res_model.set_timestep_respacing(str(ts2))
    #
    # return pipeline.sample(**args)


text = st.text_area('asdf')

if st.button('rweerew'):
    for s, xs in handler(text, 20, 20, 0):
        pass

# x = st.slider('Select a value')
# st.write(x, 'squared is', x * x)