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import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# Adjusting the line thickness to better match the provided example



fig, ax = plt.subplots( figsize=(5, 4))
# 64, 64sqrt2, 128, 128sqrt2, 256, 256sqrt2, 512
x_values = [2.8, 10.2, 36.9, 174.22, 366.9, 755.9] # 
y_values = [6.33, 5.52, 5.25, 5.19, 5.02, 5.02] # , 5.09
y_values = np.exp(y_values)
# 256sqrt2->700m
# 512->1.3billion

# Set line width for each line plot
line_width = 1.5
x = []
# Iterate over each subplot (task) and plot the lines with specified styles, markers, and adjusted line width
# for i, task in enumerate(tasks):

# ax.plot(x_values, y_values,  marker='o', linestyle='--', color='#1f78b4', linewidth=line_width)
# # for i, txt in enumerate(y_values):
# #     ax.annotate(f"{txt:.1f}", (x_values[i], y_values[i]), textcoords="offset points", xytext=(0,10), ha='center')
# ax.annotate(f"{y_values[-1]:.1f}", (x_values[-1], y_values[-1]), textcoords="offset points", xytext=(0,10), ha='center')


# # Set individual titles and axis labels for each subplot

# ax.set_xlabel("Model Parameters(M)", fontsize=14)
# ax.set_ylabel("Perplexity", fontsize=14)
# ax.set_ylim(0, 1)
fig, ax1 = plt.subplots(figsize=(5, 4))

INDEX = -2
# Plot Perplexity (left y-axis)
ax1.plot(x_values, y_values, marker='o', linestyle='-', color='#1f78b4', linewidth=line_width)
ax1.annotate(f"{y_values[INDEX]:.1f}", (x_values[INDEX], y_values[INDEX]), textcoords="offset points", xytext=(0, 10), ha='center')
ax1.set_xscale('log')
ax1.set_xlabel("Model Parameters(M)", fontsize=14)
ax1.set_ylabel("Perplexity", fontsize=14, color='#1f78b4')
ax1.tick_params(axis='y', labelcolor='#1f78b4')
# , 1.18

# Create a twin y-axis for controllability (right y-axis)
ax2 = ax1.twinx()
controllability_values = [0.11, 1.02, 1.07, 1.12, 1.87, 1.34]  #  Example values for controllability
ax2.plot(x_values, controllability_values, marker='s', linestyle='--', color='#006400', linewidth=line_width)
ax2.set_ylabel("Delta PSNR", fontsize=14, color='#006400')
ax2.set_ylim(0, np.max(controllability_values) + 0.2)
ax2.tick_params(axis='y', labelcolor='#006400')
ax2.annotate(f"{controllability_values[INDEX]:.1f}", (x_values[INDEX], controllability_values[INDEX]), textcoords="offset points", xytext=(0, 10), ha='center')

# Save the figure in high resolution
plt.tight_layout()
# plt.show()

plt.savefig(f"output/model_sizes.png", dpi=300)

# Adding a centralized legend that appears above the plot
# fig.legend(y_values, loc='upper center', bbox_to_anchor=(0.5, 1.05), ncol=3, frameon=False, markerscale=1.5)