import matplotlib
matplotlib.use('Agg')

import functools

import gradio as gr
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd


# benchmark order: pytorch, tf eager, tf xla; units = ms
BENCHMARK_DATA = {
    "Greedy Search": {
        "DistilGPT2": {
            "T4": [336.22, 3976.23, 115.84],
            "3090": [158.38, 1835.82, 46.56],
            "A100": [371.49, 4073.84, 60.94],
        },
        "GPT2": {
            "T4": [607.31, 7140.23, 185.12],
            "3090": [297.03, 3308.31, 76.68],
            "A100": [691.75, 7323.60, 110.72],
        },
        "OPT-1.3B": {
            "T4": [1303.41, 15939.07, 1488.15],
            "3090": [428.33, 7259.43, 468.37],
            "A100": [1125.00, 16713.63, 384.52],
        },
        "GPTJ-6B": {
            "T4": [0, 0, 0],
            "3090": [0, 0, 0],
            "A100": [2664.28, 32783.09, 1440.06],
        },
        "T5 Small": {
            "T4": [99.88, 1527.73, 18.78],
            "3090": [55.09, 665.70, 9.25],
            "A100": [124.91, 1642.07, 13.72],
        },
        "T5 Base": {
            "T4": [416.56, 6095.05, 106.12],
            "3090": [223.00, 2503.28, 46.67],
            "A100": [550.76, 6504.11, 64.57],
        },
        "T5 Large": {
            "T4": [645.05, 9587.67, 225.17],
            "3090": [377.74, 4216.41, 97.92],
            "A100": [944.17, 10572.43, 116.52],
        },
        "T5 3B": {
            "T4": [1493.61, 13629.80, 1494.80],
            "3090": [694.75, 6316.79, 489.33],
            "A100": [1801.68, 16707.71, 411.93],
        },
    },
    "Sample": {
        "DistilGPT2": {
            "T4": [],
            "3090": [],
            "A100": [],
        },
        "GPT2": {
            "T4": [],
            "3090": [],
            "A100": [],
        },
        "OPT-1.3B": {
            "T4": [],
            "3090": [],
            "A100": [],
        },
        "GPTJ-6B": {
            "T4": [],
            "3090": [],
            "A100": [],
        },
        "T5 Small": {
            "T4": [],
            "3090": [],
            "A100": [],
        },
        "T5 Base": {
            "T4": [],
            "3090": [],
            "A100": [],
        },
        "T5 Large": {
            "T4": [],
            "3090": [],
            "A100": [],
        },
        "T5 3B": {
            "T4": [],
            "3090": [],
            "A100": [],
        },
    },
    "Beam Search": {
        "DistilGPT2": {
            "T4": [],
            "3090": [],
            "A100": [],
        },
        "GPT2": {
            "T4": [],
            "3090": [],
            "A100": [],
        },
        "OPT-1.3B": {
            "T4": [],
            "3090": [],
            "A100": [],
        },
        "GPTJ-6B": {
            "T4": [],
            "3090": [],
            "A100": [],
        },
        "T5 Small": {
            "T4": [],
            "3090": [],
            "A100": [],
        },
        "T5 Base": {
            "T4": [],
            "3090": [],
            "A100": [],
        },
        "T5 Large": {
            "T4": [],
            "3090": [],
            "A100": [],
        },
        "T5 3B": {
            "T4": [],
            "3090": [],
            "A100": [],
        },
    },
}


def get_plot(model_name, generate_type):
    df = pd.DataFrame(BENCHMARK_DATA[generate_type][model_name])
    df["framework"] = ["PyTorch", "TF (Eager Execition)", "TF (XLA)"]
    df = pd.melt(df, id_vars=["framework"], value_vars=["T4", "3090", "A100"])

    # fig = plt.figure(figsize=(100, 6), dpi=200)
    g = sns.catplot(
        data=df, kind="bar",
        x="variable", y="value", hue="framework",
        ci="sd", palette="dark", alpha=.6, height=6
    )
    g.despine(left=True)
    g.set_axis_labels("GPU", "Generation time (ms)")
    g.legend.set_title("Framework")
    return plt.gcf()

demo = gr.Blocks()

with demo:
    gr.Markdown(
        """
        # TensorFlow XLA Text Generation Benchmark
        Pick a tab for the type of generation (or other information), and then select a model from the dropdown menu.
        You can also ommit results from TensorFlow Eager Execution, if you wish to better compare the performance of
        PyTorch to TensorFlow with XLA.
        """
    )
    with gr.Tabs():
        with gr.TabItem("Greedy Search"):
            model_selector = gr.Dropdown(
                choices=["DistilGPT2", "GPT2", "OPT-1.3B", "GPTJ-6B", "T5 Small", "T5 Base", "T5 Large", "T5 3B"],
                value="T5 Small",
                label="Model",
                interactive=True,
            )
            plot_fn = functools.partial(get_plot, generate_type="Greedy Search")
            plot = gr.Plot(value=plot_fn("T5 Small"))  # Show plot when the gradio app is initialized
            model_selector.change(fn=plot_fn, inputs=model_selector, outputs=plot)
        with gr.TabItem("Sample"):
            gr.Button("New Tiger")
        with gr.TabItem("Beam Search"):
            gr.Button("New Tiger")
        with gr.TabItem("Benchmark Information"):
            gr.Dataframe(
                headers=["Parameter", "Value"],
                value=[
                    ["Transformers Version", "4.22.dev0"],
                    ["TensorFlow Version", "2.9.1"],
                    ["Pytorch Version", "1.11.0"],
                    ["OS", "22.04 LTS (3090) / Debian 10 (other GPUs)"],
                    ["CUDA", "11.6 (3090) / 11.3 (others GPUs)"],
                    ["Number of Runs", "100 (the first run was discarded to ignore compilation time)"],
                    ["Is there code to reproduce?", "Yes -- https://gist.github.com/gante/f0017e3f13ac11b0c02e4e4db351f52f"],
                ],
            )

demo.launch()