import re
import sys
import numpy as np
import pandas as pd

# 用于记录每个度量的值
first_token_times_values = []
prompt_throughput_values = []
generation_throughput_values = []
running_values = []

# 从文件中读取数据
file_path = "server.log.valid"  # 替换成你的文件路径
with open(file_path, 'r') as file:
    # 遍历文件中的每一行进行统计
    for line in file:
        # 使用正则表达式提取Avg First Token times和Avg generation throughput以及Running的值
        match_first_token = re.search(r"Avg First Token times:([0-9.]+)", line)
        match_prompt_throughput = re.search(r"Avg prompt throughput: ([0-9.]+)", line)
        match_generation_throughput = re.search(r"Avg generation throughput: ([0-9.]+)", line)
        match_running = re.search(r"Running: (\d+)", line)

        # 统计Avg First Token times
        if match_first_token:
            first_token_times = float(match_first_token.group(1))
            if abs(first_token_times) > 1e-5:
                first_token_times_values.append(first_token_times)

        if match_prompt_throughput:
            prompt_throughput = float(match_prompt_throughput.group(1))
            if abs(prompt_throughput) > 1e-5:
                prompt_throughput_values.append(prompt_throughput)

        # 统计Avg generation throughput和Running
        if match_generation_throughput and match_running:
            generation_throughput = float(match_generation_throughput.group(1))
            running = int(match_running.group(1))
            if abs(generation_throughput) > 1e-5:
                generation_throughput_values.append(generation_throughput)
                running_values.append(running)

# 计算平均值
avg_first_token_times = np.mean(first_token_times_values) if len(first_token_times_values) > 0 else 0
max_first_token_times = np.max(first_token_times_values) if len(first_token_times_values) > 0 else 0
min_first_token_times = np.min(first_token_times_values) if len(first_token_times_values) > 0 else 0
p10_first_token_times = np.percentile(first_token_times_values, 10) if len(first_token_times_values) > 0 else 0
p90_first_token_times = np.percentile(first_token_times_values, 90) if len(first_token_times_values) > 0 else 0
p99_first_token_times = np.percentile(first_token_times_values, 99) if len(first_token_times_values) > 0 else 0
cnt_first_token_times = len(first_token_times_values) if len(first_token_times_values) > 0 else 0

avg_prompt_throughput = np.mean(prompt_throughput_values) if len(prompt_throughput_values) > 0 else 0
max_prompt_throughput = np.max(prompt_throughput_values) if len(prompt_throughput_values) > 0 else 0
min_prompt_throughput = np.min(prompt_throughput_values) if len(prompt_throughput_values) > 0 else 0
p10_prompt_throughput = np.percentile(prompt_throughput_values, 10) if len(prompt_throughput_values) > 0 else 0
p90_prompt_throughput = np.percentile(prompt_throughput_values, 90) if len(prompt_throughput_values) > 0 else 0
p99_prompt_throughput = np.percentile(prompt_throughput_values, 99) if len(prompt_throughput_values) > 0 else 0
cnt_prompt_throughput = len(prompt_throughput_values) if len(prompt_throughput_values) > 0 else 0

avg_generation_throughput = np.mean(generation_throughput_values) if len(generation_throughput_values) > 0 else 0
max_generation_throughput = np.max(generation_throughput_values) if len(generation_throughput_values) > 0 else 0
min_generation_throughput = np.min(generation_throughput_values) if len(generation_throughput_values) > 0 else 0
p10_generation_throughput = np.percentile(generation_throughput_values, 10) if len(generation_throughput_values) > 0 else 0
p90_generation_throughput = np.percentile(generation_throughput_values, 90) if len(generation_throughput_values) > 0 else 0
p99_generation_throughput = np.percentile(generation_throughput_values, 99) if len(generation_throughput_values) > 0 else 0
cnt_generation_throughput = len(generation_throughput_values) if len(generation_throughput_values) > 0 else 0

avg_running = np.mean(running_values) if len(running_values) > 0 else 0
max_running = np.max(running_values) if len(running_values) > 0 else 0
min_running = np.min(running_values) if len(running_values) > 0 else 0
p10_running = np.percentile(running_values, 10) if len(running_values) > 0 else 0
p90_running = np.percentile(running_values, 90) if len(running_values) > 0 else 0
p99_running = np.percentile(running_values, 99) if len(running_values) > 0 else 0
cnt_running = len(running_values) if len(running_values) > 0 else 0

# Create a DataFrame
data = {
    'avg': [avg_first_token_times, avg_prompt_throughput, avg_generation_throughput, avg_running],
    'max': [max_first_token_times, max_prompt_throughput, max_generation_throughput, max_running],
    'min': [min_first_token_times, min_prompt_throughput, min_generation_throughput, min_running],
    'p10': [p10_first_token_times, p10_prompt_throughput, p10_generation_throughput, p10_running],
    'p90': [p90_first_token_times, p90_prompt_throughput, p90_generation_throughput, p90_running],
    'p99': [p99_first_token_times, p99_prompt_throughput, p99_generation_throughput, p99_running],
    'num': [cnt_first_token_times, cnt_prompt_throughput, cnt_generation_throughput, cnt_running]
}

df = pd.DataFrame(data, index=['first_token_times', 'prompt_throughput', 'generation_throughput', 'running'])

# Display the DataFrame
print(df)