# flake8: noqa
# yapf: disable
import functools
import getpass
import math
import os
import os.path as osp
import shutil
from datetime import datetime
from typing import Any, Dict, List, Optional

import matplotlib.pyplot as plt
import mmengine
import numpy as np
import pandas as pd
import seaborn as sns
import tabulate
from matplotlib.colors import LinearSegmentedColormap
from mmengine import ConfigDict
from tqdm import tqdm

from opencompass.summarizers.default import (
    METRIC_BLACKLIST, METRIC_WHITELIST, DefaultSummarizer,
    model_abbr_from_cfg_used_in_summarizer)
from opencompass.utils import (LarkReporter, dataset_abbr_from_cfg,
                               get_infer_output_path, get_logger,
                               model_abbr_from_cfg)
from opencompass.utils.prompt import get_prompt_hash

model_name_mapping = {
    'llama-2-7b-chat-hf': 'LLaMA-2-7B',
    'llama-2-13b-chat-hf': 'LLaMA-2-13B',
    'llama-2-70b-chat-hf': 'LLaMA-2-70B',
    'baichuan2-7b-chat-hf': 'Baichuan2-7B',
    'baichuan2-13b-chat-hf': 'Baichuan2-13B',
    'yi-6b-chat-hf': 'Yi-6B',
    'yi-34b-chat-hf': 'Yi-34B',
    'deepseek-67b-chat-hf': 'DeepSeek-67B',
    'wizardlm-70b-v1.0-vllm': 'WizardLM-70B',
    'qwen-14b-chat-hf': 'Qwen-14B',
    'qwen-72b-chat-hf': 'Qwen-72B',
    'qwen-72b-chat-vllm': 'Qwen-72B-vLLM',
    'internlm2-chat-7b-turbomind': 'InternLM2-7B-200K',
    'internlm2-chat-20b-turbomind': 'InternLM2-20B-200K',
    'internlm2-chat-7b-hf': 'InternLM2-7B',
    'internlm2-chat-20b-hf': 'InternLM2-20B',
    'qwen-7b-chat-hf': 'Qwen-7B',
    'chatglm3-6b-hf': 'ChatGLM3-6B',
    'chatglm3-6b-32k-hf': 'ChatGLM3-6B-32K',
    'zephyr-7b-beta-vllm': 'Zephyr-7B Beta',
    'mistral-7b-instruct-v0.2-vllm': 'Mistral-7B Inst. v0.2',
    'mistral-7b-instruct-v0.1-vllm': 'Mistral-7B Inst. v0.1',
    'mixtral-8x7b-instruct-v0.1-vllm': 'Mixtral-8x7B Inst. v0.1',
    'orionstar-yi-34b-chat-hf': 'OrionStar-Yi-34B',
    'orionstar-14b-long-chat-vllm': 'Orion-14B-LongChat',
    'internlm-chat-7b-hf': 'InternLM-7B',
    'gemma-2b-it-hf': 'Gemma-2B',
    'gemma-7b-it-hf': 'Gemma-7B',
    'qwen1.5-0.5b-chat-hf': 'Qwen-1.5-0.5B',
    'qwen1.5-1.8b-chat-hf': 'Qwen-1.5-1.8B',
    'qwen1.5-4b-chat-hf': 'Qwen-1.5-4B',
    'qwen1.5-14b-chat-hf': 'Qwen-1.5-14B',
    'qwen1.5-72b-chat-hf': 'Qwen-1.5-72B',
    'qwen1.5-14b-chat-vllm': 'Qwen-1.5-14B-vLLM',
    'qwen1.5-72b-chat-vllm': 'Qwen-1.5-72B-vLLM',
    'glm4_notools': 'GLM-4',
    'claude-3-opus': 'Claude-3-Opus',
    # Add more mappings as necessary
}

dataset_mapping_dict = {}

needle_counts = ['2', '3', '4', '5']
languages = ['en', 'zh']
sizes = ['4k', '8k', '32k', '200k', '256k', '1000k']
types = ['origin', 'parallel']

for needle_count in needle_counts:
    for language in languages:
        for size in sizes:
            key = f'{needle_count}needle_{language}_{size}'
            value = f'{needle_count}-Needle-Reasoning-{language.upper()}-{size.upper()}'
            dataset_mapping_dict[key] = value
for t in types:
    for language in languages:
        for size in sizes:
            if t == 'origin':
                key = f'{t}_{language}_{size}'
                value = f'Single-Needle-Retrieval-{language.upper()}-{size.upper()}'
            elif t == 'parallel':
                key = f'{t}_{language}_{size}'
                value = f'Multi-Needle-Retrieval-{language.upper()}-{size.upper()}'
            dataset_mapping_dict[key] = value


def calculate_elementwise_average(model_name, merged_df):
    score_columns = [col for col in merged_df.columns if col != 'dataset']

    origin_columns = [col for col in score_columns if 'origin' in col]
    parallel_columns = [col for col in score_columns if 'parallel' in col]
    multi_columns = [col for col in score_columns if 'needle' in col]

    if origin_columns and parallel_columns and multi_columns:
        origin_avg = merged_df[origin_columns].mean(axis=1) * 0.4
        parallel_avg = merged_df[parallel_columns].mean(axis=1) * 0.3
        multi_avg = merged_df[multi_columns].mean(axis=1) * 0.3
        merged_df[model_name] = origin_avg + parallel_avg + multi_avg
    else:
        relevant_columns = origin_columns or parallel_columns or multi_columns
        if relevant_columns:
            merged_df[model_name] = merged_df[relevant_columns].mean(axis=1)
        else:
            merged_df[model_name] = pd.Series([0] * len(merged_df))

    return merged_df.iloc[:, [0, -1]]

def read_after_specific_line_except_last(file_name, keyword, offset):
    with open(file_name, 'r', encoding='utf-8') as file:
        lines = file.readlines()

    for index, line in enumerate(lines):
        if keyword in line:
            start_index = index + offset + 1
            break
    else:
        return ''

    return ''.join(lines[start_index:-1])

def create_model_dataframe(nested_dict, model_name, dataset_abbr, parallel=False):
    if model_name not in nested_dict:
        print(f'Model {model_name} not found in the provided data.')
        return pd.DataFrame()

    model_data = nested_dict[model_name]
    data = []

    for key, value in model_data.items():
        if parallel:
            if dataset_abbr in key:
                new_key_base = key.replace(dataset_abbr, '').strip('_')
                for depth_key, score in value.items():
                    new_key = f'{new_key_base}{depth_key}'
                    if 'average_score' not in new_key:
                        data.append([new_key, score])
        else:
            if dataset_abbr in key:
                score = value.get('score', None)
                new_key = key.replace(dataset_abbr, '').strip('_')
                data.append([new_key, score])

    df = pd.DataFrame(data, columns=['dataset', model_name])
    return df

def convert_to_k(value):
    try:
        return f'{int(value) // 1000}k'
    except ValueError:
        return value

def parse_model_scores(text):
    lines = text.split('\n')

    result_dict = {}
    current_model = None

    for line in lines:
        if line.startswith('Model:'):
            current_model = line.split('Model:')[1].strip()
            result_dict[current_model] = {}
        elif current_model and ':' in line:
            dataset, score_str = line.split(':', 1)
            score_dict = eval(score_str.strip())
            result_dict[current_model][dataset] = score_dict

    return result_dict

def remove_empty_subfolders(plot_path):
    for folder_name in tqdm(os.listdir(plot_path),
                            desc='Deleting Empty folders'):
        folder_path = os.path.join(plot_path, folder_name)
        if os.path.isdir(folder_path):
            if not os.listdir(folder_path):
                shutil.rmtree(folder_path)

def save_results_to_plots(txt_results_save_path):
    content = read_after_specific_line_except_last(txt_results_save_path, 'raw format', 2)
    parsed_data = parse_model_scores(content)
    model_names = get_dict_model_names(parsed_data)
    numbers = [2, 3, 4, 5]
    languages = ['en', 'zh']
    size_exists = []
    sizes_origin = ['_4k', '_8k', '_32k', '_128k', '_200k', '_256k', '_1000k']

    for size in sizes_origin:
        if size in content:
            size_exists.append(size)

    multi_dataset_abbrs = [f'{num}needle_{lang}{size}' for num in numbers for lang in languages for size in size_exists]
    origin_dataset_abbrs = [f'origin_{lang}{size}' for lang in languages for size in size_exists]
    parallel_dataset_abbrs = [f'parallel_{lang}{size}' for lang in languages for size in size_exists]

    dataset_abbrs = multi_dataset_abbrs + origin_dataset_abbrs + \
                        parallel_dataset_abbrs
    base_path = os.path.dirname(txt_results_save_path)
    plot_path = os.path.join(base_path, 'plots')

    model_scores = {}

    for model_name in tqdm(model_names):
        model_datasets_scores = {}  # Dictionary to store scores for each dataset for the current model
        for dataset_abbr in dataset_abbrs:
            parallel_flag = 'parallel' in dataset_abbr

            folder_path = os.path.join(plot_path, dataset_mapping_dict[dataset_abbr])
            ensure_directory(folder_path)

            save_path = os.path.join(folder_path, f'{model_name}.png')

            df = create_model_dataframe(parsed_data, model_name, dataset_abbr, parallel=parallel_flag)

            score = visualize(df, save_path, model_name, dataset_abbr)

            model_datasets_scores[dataset_abbr] = '{:.02f}'.format(score)

        overall_dataset_abbrs = multi_dataset_abbrs + origin_dataset_abbrs + parallel_dataset_abbrs
        overall_score_pic_path = os.path.join(plot_path, f'{model_name}_overall.png')
        merged_df = merge_dataframes(model_name, overall_dataset_abbrs, parsed_data)
        averaged_df = calculate_elementwise_average(model_name, merged_df)
        overall_score = visualize(averaged_df, overall_score_pic_path, model_name, 'Overall Score')

        # Single-Retrieval
        single_retrieval_score_pic_path = os.path.join(plot_path, f'{model_name}_single_retrieval_overall.png')
        single_retrieval_merged_df = merge_dataframes(model_name, origin_dataset_abbrs, parsed_data)
        single_retrieval_averaged_df = calculate_elementwise_average(model_name, single_retrieval_merged_df)
        single_retrieval_overall_score = visualize(single_retrieval_averaged_df, single_retrieval_score_pic_path, model_name, 'Single-Retrieval Overall Score')

        # Multi-Retrieval
        multi_retrieval_score_pic_path = os.path.join(plot_path, f'{model_name}_multi_retrieval_overall.png')
        multi_retrieval_merged_df = merge_dataframes(model_name, parallel_dataset_abbrs, parsed_data)
        multi_retrieval_averaged_df = calculate_elementwise_average(model_name, multi_retrieval_merged_df)
        multi_retrieval_overall_score = visualize(multi_retrieval_averaged_df, multi_retrieval_score_pic_path, model_name, 'Multi-Retrieval Overall Score')

        # Multi-Reasoning
        multi_reasoning_score_pic_path = os.path.join(plot_path, f'{model_name}_multi_reasoning_overall.png')
        multi_reasoning_merged_df = merge_dataframes(model_name, multi_dataset_abbrs, parsed_data)
        multi_reasoning_averaged_df = calculate_elementwise_average(model_name, multi_reasoning_merged_df)
        multi_reasoning_overall_score = visualize(multi_reasoning_averaged_df, multi_reasoning_score_pic_path, model_name, 'Multi-Reasoning Overall Score')

        model_scores[model_name] = averaged_df
    remove_empty_subfolders(plot_path)
    return model_scores

def visualize(df_raw, save_path: str,model_name: str ,dataset_type:str):
    df = df_raw.copy()
    if df.empty:
        return -1
    df['Context Length'] = df['dataset'].apply(
        lambda x: int(x.split('Length')[1].split('Depth')[0]))
    df['Document Depth'] = df['dataset'].apply(
        lambda x: float(x.split('Depth')[1].split('_')[0]))

    model_columns = [
        col for col in df.columns
        if col not in ['Context Length', 'Document Depth']
    ]

    for model_name in model_columns[1:]:
        model_df = df[['Document Depth', 'Context Length',
                        model_name]].copy()
        model_df.rename(columns={model_name: 'Score'}, inplace=True)
        pivot_table = pd.pivot_table(model_df,
                                    values='Score',
                                    index=['Document Depth'],
                                    columns=['Context Length'],
                                    aggfunc='mean')

        mean_scores = pivot_table.mean().values
        overall_score = mean_scores.mean()
        plt.figure(figsize=(10, 6))
        ax = plt.gca()
        cmap = LinearSegmentedColormap.from_list(
            'custom_cmap', ['#F0496E', '#EBB839', '#0CD79F'])

        sns.heatmap(pivot_table,
                    cmap=cmap,
                    ax=ax,
                    vmin=0,
                    vmax=100)
        cbar = ax.collections[0].colorbar
        x_data = [i + 0.5 for i in range(len(mean_scores))]
        y_data = mean_scores

        ax2 = ax.twinx()
        ax2.plot(x_data,
                y_data,
                color='white',
                marker='o',
                linestyle='-',
                linewidth=2,
                markersize=8,
                label='Average Depth Score'
                )
        for x_value, y_value in zip(x_data, y_data):
            ax2.text(x_value, y_value, f'{y_value:.2f}', ha='center', va='top')

        ax2.set_ylim(0, 100)

        ax2.set_yticklabels([])
        ax2.set_yticks([])

        ax2.legend(loc='lower left')

        if model_name in model_name_mapping:
            title_name = model_name_mapping[model_name]
        else:
            title_name = model_name

        ax.set_title(title_name, fontsize=12, fontweight='bold', pad=15)

        if dataset_type in dataset_mapping_dict:
            dataset_name = dataset_mapping_dict[dataset_type]
        else:
            dataset_name = dataset_type

        ax.text(0.5, 1.005, f'{dataset_name}:{overall_score:.2f}',
                transform=ax.transAxes,
                ha='center',
                fontsize=12,
                fontweight='normal')
        ax.set_xlabel('Token Length', fontsize=13, fontweight='normal', labelpad=1)
        ax.set_ylabel('Depth Percent(%)', fontsize=13, fontweight='normal', labelpad=1)
        converted_labels = [convert_to_k(value) for value in pivot_table.columns.values]

        ax.tick_params(axis='both', which='major', length=1, pad=1)
        ax.tick_params(axis='both', which='minor', length=1, pad=1)
        ax.set_xticklabels(converted_labels, rotation=45)
        index_length = len(pivot_table.index)

        selected_indices = pivot_table.index.values[::2]
        labels = [str(int(index)) for index in selected_indices]
        ax.set_yticks(np.arange(0, len(pivot_table.index), 2))
        ax.set_yticklabels(labels, rotation=0)
        for spine in ax.spines.values():
            spine.set_visible(False)
        for spine in ax2.spines.values():
            spine.set_visible(False)

        plt.tight_layout()
        plt.draw()
        directory_path, original_filename = os.path.split(save_path)

        filename_suffix = (title_name+'_'+dataset_name).replace(' ', '_')
        new_filename = f'{filename_suffix}.png'

        new_save_path = os.path.join(directory_path, new_filename)

        plt.savefig(new_save_path, format='png', bbox_inches='tight', pad_inches=0)
        print(f'Saved: {new_save_path}')

        plt.close()

    return overall_score


def ensure_directory(path):
    if not os.path.exists(path):
        os.makedirs(path)

def get_dict_model_names(nested_dict):
    model_names = []
    for first_level_key in nested_dict:
        model_names.append(first_level_key)
    return model_names

def merge_dataframes(model_name, dataset_abbrs, parsed_data):
    dfs = []
    for dataset_abbr in dataset_abbrs:
        parallel_flag = 'parallel' in dataset_abbr
        df = create_model_dataframe(parsed_data, model_name, dataset_abbr, parallel=parallel_flag)

        if not df.empty and len(df.columns) > 1:
            score_column = df.columns[-1]
            df.rename(columns={score_column: dataset_abbr}, inplace=True)

        dfs.append(df)

    from functools import reduce
    merged_df = reduce(lambda left, right: pd.merge(left, right, on='dataset', how='outer'), dfs)

    if merged_df.isnull().any().any():
        print('Warning: Some rows were filtered out due to NaN values. '
              'This is often due to mismatched row counts among DataFrames.')
        merged_df = merged_df.dropna()
    return merged_df

class NeedleBenchSummarizer(DefaultSummarizer):
    """NeedleBench summarizer in OpenCompass.

    Args:
        config (ConfigDict): The configuration object of the evaluation task. It's expected to be filled out at runtime.
        dataset_abbrs (list[str], optional): Dataset abbreviations to be listed in the summary.
        summary_groups (list): The dataset groups whose results need to be averaged out. For example, mmlu. Each item it a dict with
            'name' (str) and 'subsets' (list of dataset abbrs), and optionally
            'weights' if weighted average is needed.
        prompt_db: A deprecated field.
    """
    def _format_table(self, parsed_results, dataset_metrics, dataset_eval_mode):
        dataset_abbrs = [dataset_abbr_from_cfg(dataset) for dataset in self.dataset_cfgs]
        prompt_version = {dataset_abbr_from_cfg(d): get_prompt_hash(d)[:6] for d in self.dataset_cfgs}

        summarizer_dataset_abbrs = []
        if self.dataset_abbrs is None:
            for dataset_abbr in dataset_abbrs:
                if dataset_abbr in dataset_metrics:
                    for metric in dataset_metrics[dataset_abbr]:
                        summarizer_dataset_abbrs.append((dataset_abbr, metric))
                else:
                    summarizer_dataset_abbrs.append((dataset_abbr, None))
            for dataset_abbr in dataset_metrics:
                for metric in dataset_metrics[dataset_abbr]:
                    if (dataset_abbr, metric) not in summarizer_dataset_abbrs:
                        summarizer_dataset_abbrs.append((dataset_abbr, metric))
        else:
            for item in self.dataset_abbrs:
                if isinstance(item, str):
                    summarizer_dataset_abbrs.append((item, None))
                elif isinstance(item, (list, tuple)):
                    summarizer_dataset_abbrs.append((item[0], item[1]))

        table = []
        header = ['dataset', 'version', 'metric', 'mode'] + self.model_abbrs
        table.append(header)

        for key in dataset_metrics:
            dataset_metrics[key] = list(set(dataset_metrics[key]))

        for dataset_abbr, metric in summarizer_dataset_abbrs:
            if dataset_abbr not in dataset_metrics:

                table.append([dataset_abbr, '-', '-', '-'] + ['-'] * len(self.model_abbrs))
                table.append(header)
                continue
            if len(dataset_metrics[dataset_abbr]) >= 10:
                metric = 'average_score'

            if metric is None:
                metric = dataset_metrics[dataset_abbr][0]
            elif metric in dataset_metrics[dataset_abbr]:
                pass
            else:
                table.append([dataset_abbr, '-', '-', '-'] + ['-'] * len(self.model_abbrs))
                continue

            row = [dataset_abbr, prompt_version.get(dataset_abbr, '-'), metric, dataset_eval_mode.get(dataset_abbr, '-')]
            for model_abbr in self.model_abbrs:
                if dataset_abbr in parsed_results[model_abbr]:
                    row.append('{:.02f}'.format(parsed_results[model_abbr][dataset_abbr][metric]))
                else:
                    row.append('-')

            table.append(row)
        for i in range(len(table)):
            if i == 0 or table[i][0].startswith('---------'):
                table[i] = [table[i][0]] + table[i][4:]
            else:
                table[i] = [table[i][0]] + table[i][4:]

        return table

    def _format_raw_txt(self, raw_results):
        raw_dataset_abbrs = []
        for model_abbr in self.model_abbrs:
            for dataset_abbr in raw_results[model_abbr]:
                if dataset_abbr not in raw_dataset_abbrs:
                    raw_dataset_abbrs.append(dataset_abbr)
        raw_txts = []
        for model_abbr in self.model_abbrs:
            raw_txts.append('-------------------------------')
            raw_txts.append(f'Model: {model_abbr}')
            for dataset_abbr in raw_dataset_abbrs:
                result = raw_results[model_abbr].get(dataset_abbr, '{}')
                raw_txts.append(f'{dataset_abbr}: {result}')
        raw_txts = '\n'.join(raw_txts)
        return raw_txts

    def _output_to_file(self, output_path, time_str, table, raw_txts):
        if output_path is None:
            output_path = osp.join(self.work_dir, 'summary', f'summary_{time_str}.txt')
            output_csv_path = osp.join(self.work_dir, 'summary', f'summary_{time_str}.csv')
        else:
            output_csv_path = output_path.replace('.txt', '.csv')

        output_dir = osp.split(output_path)[0]
        mmengine.mkdir_or_exist(output_dir)
        with open(output_path, 'w', encoding='utf-8') as f:
            text = f'{time_str}\n' + \
                    'tabulate format\n' + \
                    '^' * 128 + '\n' + \
                    tabulate.tabulate(table, headers='firstrow') + '\n' + \
                    '$' * 128 + '\n\n' + \
                    '-' * 128 + ' THIS IS A DIVIDER ' + '-' * 128 + '\n\n' + \
                    'csv format\n' + \
                    '^' * 128 + '\n' + \
                    '\n'.join([','.join(row) for row in table]) + '\n' + \
                    '$' * 128 + '\n\n' + \
                    '-' * 128 + ' THIS IS A DIVIDER ' + '-' * 128 + '\n\n' + \
                    'raw format\n' + \
                    '^' * 128 + '\n' + \
                    raw_txts + '\n' + \
                    '$' * 128 + '\n'
            f.write(text)
        self.logger.info(f'write summary to {osp.abspath(output_path)}')

        with open(output_csv_path, 'w', encoding='utf-8') as f:
            f.write('\n'.join([','.join(row) for row in table]) + '\n')
        self.logger.info(f'write csv to {osp.abspath(output_csv_path)}')


    def summarize(
        self,
        output_path: str = None,
        time_str: str = datetime.now().strftime('%Y%m%d_%H%M%S')):  # noqa

        raw_results, parsed_results, dataset_metrics, dataset_eval_mode = self._pick_up_results()
        raw_results, parsed_results, dataset_metrics, dataset_eval_mode = \
            self._calculate_group_metrics(raw_results, parsed_results, dataset_metrics, dataset_eval_mode)
        table = self._format_table(parsed_results, dataset_metrics, dataset_eval_mode)
        raw_txts = self._format_raw_txt(raw_results)
        print(tabulate.tabulate(table, headers='firstrow'))
        self._output_to_file(output_path, time_str, table, raw_txts)
        if self.lark_reporter:
            content = f'{getpass.getuser()} 的'
            content += f'详细评测汇总已输出至 {osp.abspath(output_path)}'
            self.lark_reporter.post(content)

        if output_path is None:
            output_path = osp.join(self.work_dir, 'summary', f'summary_{time_str}.txt')
        # plot to show visualize results
        save_results_to_plots(output_path)

class NeedleBenchATCSummarizer(DefaultSummarizer):
    """NeedleBench-ATC summarizer in OpenCompass.

    Args:
        config (ConfigDict): The configuration object of the evaluation task. It's expected to be filled out at runtime.
        dataset_abbrs (list[str], optional): Dataset abbreviations to be listed in the summary.
        summary_groups (list): The dataset groups whose results need to be averaged out. For example, mmlu. Each item it a dict with
            'name' (str) and 'subsets' (list of dataset abbrs), and optionally
            'weights' if weighted average is needed.
        prompt_db: A deprecated field.
    """

    def _format_table(self, parsed_results, dataset_metrics, dataset_eval_mode):
        dataset_abbrs = [dataset_abbr_from_cfg(dataset) for dataset in self.dataset_cfgs]
        prompt_version = {dataset_abbr_from_cfg(d): get_prompt_hash(d)[:6] for d in self.dataset_cfgs}

        summarizer_dataset_abbrs = []
        if self.dataset_abbrs is None:
            # display all dataset metrics included in the config
            for dataset_abbr in dataset_abbrs:
                if dataset_abbr in dataset_metrics:
                    for metric in dataset_metrics[dataset_abbr]:
                        summarizer_dataset_abbrs.append((dataset_abbr, metric))
                else:
                    summarizer_dataset_abbrs.append((dataset_abbr, None))
            # along with all possible group metrics
            for dataset_abbr in dataset_metrics:
                for metric in dataset_metrics[dataset_abbr]:
                    if (dataset_abbr, metric) not in summarizer_dataset_abbrs:
                        summarizer_dataset_abbrs.append((dataset_abbr, metric))
        else:
            # follow the required order
            for item in self.dataset_abbrs:
                if isinstance(item, str):
                    summarizer_dataset_abbrs.append((item, None))
                elif isinstance(item, (list, tuple)):
                    summarizer_dataset_abbrs.append((item[0], item[1]))

        table = []
        header = ['dataset', 'version', 'metric', 'mode'] + self.model_abbrs
        table.append(header)

        for key in dataset_metrics:
            dataset_metrics[key] = list(set(dataset_metrics[key]))

        for dataset_abbr, metric in summarizer_dataset_abbrs:
            if dataset_abbr not in dataset_metrics:
                table.append([dataset_abbr, '-', '-', '-'] + ['-'] * len(self.model_abbrs))
                table.append(header)
                continue
            if len(dataset_metrics[dataset_abbr]) >= 10:
                metric = 'average_score'

            if metric is None:
                metric = dataset_metrics[dataset_abbr][0]
            elif metric in dataset_metrics[dataset_abbr]:
                pass
            else:
                table.append([dataset_abbr, '-', '-', '-'] + ['-'] * len(self.model_abbrs))
                continue

            row = [dataset_abbr, prompt_version.get(dataset_abbr, '-'), metric, dataset_eval_mode.get(dataset_abbr, '-')]
            for model_abbr in self.model_abbrs:
                if dataset_abbr in parsed_results[model_abbr]:
                    row.append('{:.02f}'.format(parsed_results[model_abbr][dataset_abbr][metric]))
                else:
                    row.append('-')

            table.append(row)
        for i in range(len(table)):
            if i == 0 or table[i][0].startswith('---------'):
                table[i] = [table[i][0]] + table[i][4:]
            else:
                table[i] = [table[i][0]] + table[i][4:]

        return table

    def _read_and_sort_dataframe(self, file_path):
        # Read the file without treating the first row as a header
        data = pd.read_csv(file_path)
        # print(data)
        # Correct the extraction of needle counts for all settings
        data['needle_count'] = data['dataset'].str.extract(r'needle_(\d+)_').astype(float)
        data['needle_count'] = data['needle_count'].astype(int)

        # Define experimental settings groups
        experimental_settings = {
            'en': '_en$',
            'zh': '_zh$',
            'en_ordered': '_en_ordered',
            'zh_ordered': '_zh_ordered',
        }

        # Function to calculate maximum needles
        def calculate_max_needles(dataset):
            max_needles = {model: None for model in dataset.columns if 'b' in model}
            for model in max_needles.keys():
                consecutive_low_scores = 0
                previous_needle_count = 0
                for index, row in dataset.sort_values(by='needle_count').iterrows():
                    try:
                        score = float(row[model])
                    except ValueError as e:
                        score = -1
                    if score < 60:
                        consecutive_low_scores += 1
                        if consecutive_low_scores == 1:
                            max_needles[model] = previous_needle_count
                    else:
                        consecutive_low_scores = 0
                    previous_needle_count = row['needle_count']
                max_needle_count_seen = dataset['needle_count'].max()
                max_needles[model] = max_needle_count_seen if max_needles[model] is None else max_needles[model]
            return max_needles

        # Calculate max needles for each group and organize results in a DataFrame
        results = {}
        for setting, regex in experimental_settings.items():
            filtered_data = data[data['dataset'].str.contains(regex)]
            results[setting] = calculate_max_needles(filtered_data)

        # Convert results to DataFrame and transpose it
        results_df = pd.DataFrame(results).transpose()

        # Return the sorted DataFrame
        results_df.index.name = 'ATC Experiment Type'
        return results_df

    def _output_to_file(self, output_path, time_str, table, raw_txts):
        # output to file
        if output_path is None:
            output_path = osp.join(self.work_dir, 'summary', f'summary_{time_str}.txt')
            output_csv_path = osp.join(self.work_dir, 'summary', f'summary_{time_str}.csv')
        else:
            output_csv_path = output_path.replace('.txt', '.csv')

        output_dir = osp.split(output_path)[0]
        mmengine.mkdir_or_exist(output_dir)
        with open(output_path, 'w', encoding='utf-8') as f:
            text = f'{time_str}\n' + \
                    'tabulate format\n' + \
                    '^' * 128 + '\n' + \
                    tabulate.tabulate(table, headers='firstrow') + '\n' + \
                    '$' * 128 + '\n\n' + \
                    '-' * 128 + ' THIS IS A DIVIDER ' + '-' * 128 + '\n\n' + \
                    'csv format\n' + \
                    '^' * 128 + '\n' + \
                    '\n'.join([','.join(row) for row in table]) + '\n' + \
                    '$' * 128 + '\n\n' + \
                    '-' * 128 + ' THIS IS A DIVIDER ' + '-' * 128 + '\n\n' + \
                    'raw format\n' + \
                    '^' * 128 + '\n' + \
                    raw_txts + '\n' + \
                    '$' * 128 + '\n'
            f.write(text)
        self.logger.info(f'write summary to {osp.abspath(output_path)}')

        with open(output_csv_path, 'w', encoding='utf-8') as f:
            f.write('\n'.join([','.join(row) for row in table]) + '\n')
        # self.logger.info(f'write csv to {osp.abspath(output_csv_path)}')

        df_sorted = self._read_and_sort_dataframe(output_csv_path)

        df_sorted.to_csv(output_csv_path)

        self.logger.info(f'write sorted csv to {output_csv_path}')


    def summarize(
        self,
        output_path: str = None,
        time_str: str = datetime.now().strftime('%Y%m%d_%H%M%S')):  # noqa

        # pick up results
        raw_results, parsed_results, dataset_metrics, dataset_eval_mode = self._pick_up_results()

        # calculate group metrics
        raw_results, parsed_results, dataset_metrics, dataset_eval_mode = \
            self._calculate_group_metrics(raw_results, parsed_results, dataset_metrics, dataset_eval_mode)

        # format table
        table = self._format_table(parsed_results, dataset_metrics, dataset_eval_mode)

        # format raw txt
        raw_txts = self._format_raw_txt(raw_results)

        # output to .text / .csv files
        self._output_to_file(output_path, time_str, table, raw_txts)

        if self.lark_reporter:
            content = f'{getpass.getuser()} 的'
            content += f'详细评测汇总已输出至 {osp.abspath(output_path)}'
            self.lark_reporter.post(content)

        if output_path is None:
            output_path = osp.join(self.work_dir, 'summary', f'summary_{time_str}.txt')
