# import html
#
# import gradio as gr
#
# import modules.textual_inversion.textual_inversion
# from modules import sd_hijack, shared
#
#
# def create_embedding(name, initialization_text, nvpt, overwrite_old):
#     filename = modules.textual_inversion.textual_inversion.create_embedding(name, nvpt, overwrite_old, init_text=initialization_text)
#
#     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
#
#     return gr.Dropdown.update(choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())), f"Created: {filename}", ""
# 
#
# def train_embedding(*args):
#
#     assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'
#
#     apply_optimizations = shared.opts.training_xattention_optimizations
#     try:
#         if not apply_optimizations:
#             sd_hijack.undo_optimizations()
#
#         embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args)
#
#         res = f"""
# Training {'interrupted' if shared.state.interrupted else 'finished'} at {embedding.step} steps.
# Embedding saved to {html.escape(filename)}
# """
#         return res, ""
#     except Exception:
#         raise
#     finally:
#         if not apply_optimizations:
#             sd_hijack.apply_optimizations()
#