app.py

import gradio as gr
import torch
from diffusers import DiffusionPipeline
from transformers import AutoTokenizer, CLIPTokenizerFast, T5TokenizerFast


def load_tokenizers(model_id: str) -> list[CLIPTokenizerFast | T5TokenizerFast | None]:
    config = DiffusionPipeline.load_config(model_id)
    num_tokenizers = sum("tokenizer" in key for key in config.keys())

    if not 1 <= num_tokenizers <= 3:
        raise gr.Error(f"Invalid number of tokenizers: {num_tokenizers}")

    tokenizers = [
        AutoTokenizer.from_pretrained(
            model_id, subfolder=f'tokenizer{"" if i == 0 else f"_{i + 1}"}'
        )
        for i in range(num_tokenizers)
    ]

    # Pad the list with None if there are fewer than 3 tokenizers
    tokenizers.extend([None] * (3 - num_tokenizers))

    return tokenizers


@torch.no_grad()
def inference(model_id: str, input_text: str):
    tokenizers = load_tokenizers(model_id)

    text_pairs_components = []
    special_tokens_components = []
    for i, tokenizer in enumerate(tokenizers):
        if tokenizer:
            label_text = f"Tokenizer {i + 1}: {tokenizer.__class__.__name__}"

            # テキストとトークンIDのペアを作成
            input_ids = tokenizer(
                text=input_text,
                truncation=True,
                return_length=False,
                return_overflowing_tokens=False,
            ).input_ids
            decoded_tokens = [tokenizer.decode(id_) for id_ in input_ids]
            token_pairs = [
                (str(token), str(id_)) for token, id_ in zip(decoded_tokens, input_ids)
            ]
            output_text_pair_component = gr.HighlightedText(
                label=label_text,
                value=token_pairs,
                visible=True,
                show_legend=True,
            )

            # スペシャルトークンを追加
            special_tokens = []
            for k, v in tokenizer.special_tokens_map.items():
                if k == "additional_special_tokens":
                    continue
                special_token_map = (str(k), str(v))
                special_tokens.append(special_token_map)
            output_special_tokens_component = gr.HighlightedText(
                label=label_text,
                value=special_tokens,
                visible=True,
                show_legend=True,
            )
        else:
            output_text_pair_component = gr.HighlightedText(visible=False)
            output_special_tokens_component = gr.HighlightedText(visible=False)

        text_pairs_components.append(output_text_pair_component)
        special_tokens_components.append(output_special_tokens_component)

    return text_pairs_components + special_tokens_components


if __name__ == "__main__":
    theme = gr.themes.Soft(
        primary_hue=gr.themes.colors.emerald,
        secondary_hue=gr.themes.colors.emerald,
    )
    with gr.Blocks(theme=theme) as demo:
        with gr.Column():
            input_model_id = gr.Dropdown(
                label="Model ID",
                choices=[
                    "black-forest-labs/FLUX.1-dev",
                    "black-forest-labs/FLUX.1-schnell",
                    "stabilityai/stable-diffusion-3-medium-diffusers",
                    "stabilityai/stable-diffusion-xl-base-1.0",
                    "stable-diffusion-v1-5/stable-diffusion-v1-5",
                    "stabilityai/japanese-stable-diffusion-xl",
                    "rinna/japanese-stable-diffusion",
                ],
                value="black-forest-labs/FLUX.1-dev",
            )
            input_text = gr.Textbox(
                label="Input Text",
                placeholder="Enter text here",
            )

            with gr.Tab(label="Tokenization Outputs"):
                with gr.Column():
                    output_highlighted_text_1 = gr.HighlightedText()
                    output_highlighted_text_2 = gr.HighlightedText()
                    output_highlighted_text_3 = gr.HighlightedText()
            with gr.Tab(label="Special Tokens"):
                with gr.Column():
                    output_special_tokens_1 = gr.HighlightedText()
                    output_special_tokens_2 = gr.HighlightedText()
                    output_special_tokens_3 = gr.HighlightedText()

            with gr.Row():
                clear_button = gr.ClearButton(components=[input_text])
                submit_button = gr.Button("Run", variant="primary")

        all_inputs = [input_model_id, input_text]
        all_output = [
            output_highlighted_text_1,
            output_highlighted_text_2,
            output_highlighted_text_3,
            output_special_tokens_1,
            output_special_tokens_2,
            output_special_tokens_3,
        ]
        submit_button.click(fn=inference, inputs=all_inputs, outputs=all_output)

        examples = gr.Examples(
            fn=inference,
            inputs=all_inputs,
            outputs=all_output,
            examples=[
                ["black-forest-labs/FLUX.1-dev", "a photo of cat"],
                [
                    "stabilityai/stable-diffusion-3-medium-diffusers",
                    'cat holding sign saying "I am a cat"',
                ],
                ["rinna/japanese-stable-diffusion", "空を飛んでいるネコの写真　油絵"],
            ],
            cache_examples=True,
        )

    demo.queue().launch()