import gradio as gr
from utils.compression_util import get_compression_leaderboard
from utils.compression_util import common_corpuses

with gr.Blocks() as demo:
    # gr.Markdown("## Convertor")
    # with gr.Accordion("Convertor", open=False):
    #     gr.Markdown("Tokenize {} corpus")
    #     with gr.Row(elem_classes="no-border"):
    #         gr.Button("File Size", min_width=50)
    #         file_size = gr.Textbox(
    #             show_label=False,
    #             min_width=50,
    #             # elem_classes="textbox-as-text"
    #         )
    #         gr.Dropdown(
    #             choices=['MB', 'GB', 'TB'],
    #             show_label=False,
    #             min_width=15,
    #             # elem_classes="textbox-as-text"
    #         )
    #         # gr.Markdown('<h2 align="center">≈</h2>')
    #         # gr.HTML('<h2 style="margin: auto;">≈</h2>')
    #         gr.Button(
    #             "≈",
    #             min_width=10,
    #             elem_classes="button-white h2-font"
    #
    #         )
    #
    #         gr.Button(
    #             "Tokens",
    #             min_width=50
    #         )
    #         gr.Textbox(
    #             show_label=False,
    #             min_width=50
    #         )
    #         gr.Dropdown(
    #             ['million', 'billion', 'trillion'],
    #             show_label=False,
    #             min_width=15,
    #             elem_classes="button-white"
    #         )

    gr.Markdown("## 🛠️ Setting")  # ⚙
    with gr.Accordion("Please select corpus and measure of compression rate ...", open=True):
        # file size 💽 🖴, tokens 🧮
        # gr.Markdown(
        #     "Please select corpus and measure of compression rate.\n"
            #"`num_of_trillion_tokens`  `num_of_billion_tokens`\n"
            # "- `b_tokens/g_bytes` measures how many billion tokens per gigabytes corpus. \n"
            # "- `t_tokens/t_bytes` measures how many trillion tokens per terabytes corpus. \n"
            # "- `n_chars/n_tokens` measures how many chars per token in the current corpus. \n\n"
            # "All the above measures are depend on corpus. You can reproduce this "
            # "procedure at [github](https://github.com/xu-song/tokenizer-arena/)."
        # )

        with gr.Row():
            compress_rate_corpus = gr.Dropdown(
                common_corpuses,  # , "code"
                value=["cc100-en", "cc100-zh-Hans"],
                label="corpus",
                multiselect=True
                # info=""
            )


            # unit of file_size: gigabyte terabyte
            # unit of token_num: million billion trillion
            # The most common units of measurement include length (meter, inch, foot), weight (gram, kilogram, pound), volume (liter, gallon, milliliter), time (second, minute, hour)
            compress_rate_unit = gr.Radio(
                ["b_tokens/g_bytes", "t_tokens/t_bytes"],
                value="b_tokens/g_bytes",
                label="measure",
            )

        gr.Markdown(
            # "`num_of_trillion_tokens`  `num_of_billion_tokens`\n"
            "- `b_tokens/g_bytes` measures how many billion tokens per gigabytes corpus. \n"
            "- `t_tokens/t_bytes` measures how many trillion tokens per terabytes corpus. \n"
            "- `n_chars/n_tokens` measures how many chars per token in the tokenized corpus. \n"
            # "\nAll the above measures are depend on corpus. You can reproduce this "
            # "procedure at [github](https://github.com/xu-song/tokenizer-arena/)."
        )

    gr.Markdown("## 🏆 Compression Rate Leaderboard")
    search_bar = gr.Textbox(
        placeholder="🔍 Search tokenizers(e.g., 'llama') and press ENTER...",
        show_label=False,
        elem_id="search-bar",
    )
    compress_rate_table = gr.Dataframe()

    # func call
    compress_rate_corpus.change(
        get_compression_leaderboard,
        inputs=[compress_rate_corpus, compress_rate_unit],
        outputs=compress_rate_table
    )
    compress_rate_unit.change(
        get_compression_leaderboard,
        inputs=[compress_rate_corpus, compress_rate_unit],
        outputs=compress_rate_table
    )
    # file_size.change(
    #     get_all_compress_rate,
    #     outputs=compress_rate_table
    # )

    search_bar.submit(
        get_compression_leaderboard,
        inputs=[
            compress_rate_corpus,
            compress_rate_unit,
            search_bar,
        ],
        outputs=compress_rate_table
    )

    demo.load(
        get_compression_leaderboard,
        inputs=[compress_rate_corpus, compress_rate_unit],
        outputs=compress_rate_table
    )
if __name__ == "__main__":
    demo.launch()