import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
import math
from datetime import datetime
from matplotlib.ticker import FuncFormatter

# Predefined hyperparameter sets
PARAM_SETS = {
    "Stack-V2-Python": {"E": 0.69123678, "A": 0.01130616 * 1e9, "k": 0.393463, "alpha": 0.18937067},
    "Pile": {"E": 1.28254036, "A": 0.2035367 * 1e9, "k": 0.33027934, "alpha": 0.19479807}
}

def pred_loss(E, A, k, alpha, n, p):
    return E + (A / (n * (1 + np.log(p) * k))) ** alpha

def generate_plot(E, A, k, alpha):
    plt.clf()
    colors = ['#2B83BA', '#7BB7D6', '#ED7D5F', '#D7191C']
    ax = plt.gca()
    for i, p in enumerate([1, 2, 4, 8]):
        x_plot = np.linspace(535813376 * 0.9, 4353203200 * 1.1, 100)
        y_plot = pred_loss(E, A, k, alpha, x_plot, p)
        ax.plot(x_plot, y_plot, marker=None, markersize=1, linewidth=3, color=colors[int(math.log(p, 2))], label=f"$P={p}$")

    ax.legend(fontsize=12)
    # ax.set_xscale("log")
    # ax.set_yscale("log")

    def billions(x, pos):
        if x < 1e9:
            result = ""
        else:
            result = f'{x * 1e-9:.1f}B'
        return result

    ax.xaxis.set_major_formatter(FuncFormatter(billions))
    ax.xaxis.set_minor_formatter(FuncFormatter(billions))
    ax.yaxis.set_major_formatter(FuncFormatter(lambda x, pos: f"{x:.2f}"))
    ax.yaxis.set_minor_formatter(FuncFormatter(lambda x, pos: f"{x:.2f}"))
    ax.set_xlim(535813376 * 0.9, 4353203200 * 1.1)
    ax.set_ylim(ax.get_ylim()[0] * 1, ax.get_ylim()[1] * 1.01)

    ax.text(0.03, 0.03, f"$E={E}$\n$A={A}$\n$k={k}$\n$\\alpha={alpha}$", transform=ax.transAxes, fontsize=10, verticalalignment='bottom', multialignment='left')

    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)

    ax.set_xlabel('Parameters (Non-Embedding)', fontsize=12)
    ax.set_ylabel(f'Loss', fontsize=12)
    return plt


OUTPUT_TEMPLATE = """Loss for a {n}B model when P={p} is: **{loss:.5f}**. It is equivalant to:

- A **{n1}B** model with **P=1**;
- A **{n2}B** model with **P=2**;
- A **{n4}B** model with **P=4**;
- A **{n8}B** model with **P=8**;

Note: The equivalent parameters are for reference only. In some reasoning tasks, scaling the parallel streams will obtain more performance gains than the loss benefits!

Enjoy it! 😊"""

def process_inputs(E, A, k, alpha, n, p):
    """Process inputs and return results"""
    n = n * 1e9
    plot = generate_plot(E, A, k, alpha)
    loss = pred_loss(E, A, k, alpha, n, p)

    n1 = n * (k * np.log(p) + 1) / (k * np.log(1) + 1) / 1e9
    n2 = n * (k * np.log(p) + 1) / (k * np.log(2) + 1) / 1e9
    n4 = n * (k * np.log(p) + 1) / (k * np.log(4) + 1) / 1e9
    n8 = n * (k * np.log(p) + 1) / (k * np.log(8) + 1) / 1e9

    print(f"[{datetime.now()}] {E = }, {A = }, {k = }, {alpha = }, {n = }, {p = }")
    
    return plot, OUTPUT_TEMPLATE.format(n=round(n / 1e9, 2), p=p, n1=round(n1, 2), n2=round(n2, 2), n4=round(n4, 2), n8=round(n8, 2), loss=loss)

# Create interface

HEAD = """<div align="center">

# Parallel Scaling Law Visualization

[![Paper](https://img.shields.io/badge/arXiv-2505.10475-red)](https://arxiv.org/abs/2505.10475)
</div>
"""

with gr.Blocks() as demo:
    gr.Markdown(HEAD)
    
    with gr.Row():
        with gr.Column():

            gr.Markdown("""$$
\\text{Loss}=E+\\left(
    \\frac{A}{\\text{Parameters}\\times (1+k\\log P)}
\\right)^{\\alpha}
$$""")
            
            # Input values
            N = gr.Number(value=2.8, label="N: Number of Non-Embedding Model Parameters (in Billion)")
            P = gr.Number(value=4, label="P: Number of Parallel Streams")

            gr.Markdown("---")

            # Hyperparameter selection section
            param_set = gr.Dropdown(
                choices=["Custom"] + list(PARAM_SETS.keys()),
                value=list(PARAM_SETS.keys())[0],
                label="Select our pre-fitted parameters for two datasets"
            )
            
            # Custom parameter inputs
            param_E = gr.Number(value=PARAM_SETS["Stack-V2-Python"]['E'], label="E")
            param_A = gr.Number(value=PARAM_SETS["Stack-V2-Python"]['A'], label="A")
            param_k = gr.Number(value=PARAM_SETS["Stack-V2-Python"]['k'], label="k")
            param_alpha = gr.Number(value=PARAM_SETS["Stack-V2-Python"]['alpha'], label="alpha")
            
        

        plot, output = process_inputs(PARAM_SETS["Stack-V2-Python"]['E'], PARAM_SETS["Stack-V2-Python"]['A'], PARAM_SETS["Stack-V2-Python"]['k'], PARAM_SETS["Stack-V2-Python"]['alpha'], 2.8, 4)
        with gr.Column():

            submit_btn = gr.Button("Calculate")
            # Output section
            plot_output = gr.Plot(label="Scaling Law Curve", value=plot)
            result_output = gr.Markdown(label="Result", value=output)
            
    
    # Auto-fill parameters when selecting predefined sets
    def update_params(param_set):
        if param_set in PARAM_SETS:
            params = PARAM_SETS[param_set]
            return [params["E"], params["A"], params["k"], params["alpha"]]
        return [gr.skip(), gr.skip(), gr.skip(), gr.skip()]
    
    param_set.change(
        update_params,
        inputs=[param_set],
        outputs=[param_E, param_A, param_k, param_alpha]
    )
    
    # Submit button event
    click_event = submit_btn.click(
        process_inputs,
        inputs=[param_E, param_A, param_k, param_alpha,
                N, P],
        outputs=[plot_output, result_output]
    )


demo.launch()