import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import argparse

parser = argparse.ArgumentParser()
parser.add_argument('file', nargs='+')
args = parser.parse_args()

df = None

for jsonl_file in args.file:
    # Read JSONL file into DataFrame
    df_part = pd.read_json(jsonl_file, lines=True)
    df_part['label'] = jsonl_file
    if df is None:
        df = df_part
    else:
        df = pd.concat([df, df_part])

# Group by model and n_kv, calculate mean and std for both speed metrics
df_grouped = df.groupby(['label', 'n_kv']).agg({
    'speed_pp': ['mean', 'std'],
    'speed_tg': ['mean', 'std']
}).reset_index()

# Flatten multi-index columns
df_grouped.columns = ['label', 'n_kv', 'speed_pp_mean', 'speed_pp_std', 
                      'speed_tg_mean', 'speed_tg_std']

# Replace NaN with 0 (std for a single sample is NaN)

df_grouped['speed_pp_std'] = df_grouped['speed_pp_std'].fillna(0)
df_grouped['speed_tg_std'] = df_grouped['speed_tg_std'].fillna(0)

# Prepare ticks values for X axis (prune for readability)
x_ticks = df['n_kv'].unique()
while len(x_ticks) > 16:
    x_ticks = x_ticks[::2]

# Get unique labels and color map
labels = df_grouped['label'].unique()
colors = plt.cm.rainbow(np.linspace(0, 1, len(labels)))

# Create prompt processing plot
plt.figure(figsize=(10, 6))
ax1 = plt.gca()

plt.grid()

ax1.set_xticks(x_ticks)

# Plot each label's data
for label, color in zip(labels, colors):
    label_data = df_grouped[df_grouped['label'] == label].sort_values('n_kv')
    
    # Plot prompt processing
    pp = ax1.errorbar(label_data['n_kv'], label_data['speed_pp_mean'], 
                     yerr=label_data['speed_pp_std'], color=color, 
                     marker='o', linestyle='-', label=label)
    
# Add labels and title
ax1.set_xlabel('Context Length (tokens)')
ax1.set_ylabel('Prompt Processing Rate (t/s)')
plt.title('Prompt Processing Performance Comparison')

ax1.legend(loc='upper right')

# Adjust layout and save
plt.tight_layout()
plt.savefig('performance_comparison_pp.png', bbox_inches='tight')
plt.close()

# Create token generation plot
plt.figure(figsize=(10, 6))
ax1 = plt.gca()

plt.grid()
ax1.set_xticks(x_ticks)

# Plot each model's data
for label, color in zip(labels, colors):
    label_data = df_grouped[df_grouped['label'] == label].sort_values('n_kv')
    
    # Plot token generation
    tg = ax1.errorbar(label_data['n_kv'], label_data['speed_tg_mean'],
                     yerr=label_data['speed_tg_std'], color=color, 
                     marker='s', linestyle='-', label=label)

# Add labels and title
ax1.set_xlabel('Context Length (n_kv)')
ax1.set_ylabel('Token Generation Rate (t/s)')
plt.title('Token Generation Performance Comparison')

ax1.legend(loc='upper right')

# Adjust layout and save
plt.tight_layout()
plt.savefig('performance_comparison_tg.png', bbox_inches='tight')
plt.close()