import os
import json
import pandas as pd
from datasets import load_dataset

pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
os.makedirs("experiments/analysis/qualitative", exist_ok=True)

# baselines
target = {
    "flan-t5-xxl": "Flan-T5\textsubscript{XXL}",
    "opt-13b": "OPT\textsubscript{13B}",
    "davinci": "GPT-3\textsubscript{davinci}"
}
pretty_name = {
    'average': "Avg",
    'is competitor/rival of': "Rival",
    'is friend/ally of': "Ally",
    'is influenced by': "Inf",
    'is known for': "Know",
    'is similar to': "Sim"
}
p = 30
data = load_dataset("cardiffnlp/relentless_full", split="test")
for prompt in ['qa', 'lc']:
    output = []
    for d in data:
        for i in target.keys():
            with open(f"experiments/results/lm_{prompt}/{i}/ppl.{d['relation_type'].replace(' ', '_').replace('/', '__')}.jsonl") as f:
                ppl = [json.loads(x)['perplexity'] for x in f.read().split("\n") if len(x) > 0]
                rank_map = {p: n for n, p in enumerate(sorted(ppl), 1)}
                prediction = [rank_map[p] for p in ppl]

            # get index
            total_n = len(d['ranks'])
            p = int(total_n / 3)
            top_n = [0, int(total_n * p / 100) + 1]
            top_label = [x for x, _ in sorted(enumerate(d['ranks']), key=lambda x: x[1])[top_n[0]: top_n[1]]]
            bottom_n = [total_n - int(total_n * p / 100), total_n]
            bottom_label = [x for x, _ in sorted(enumerate(d['ranks']), key=lambda x: x[1])[bottom_n[0]: bottom_n[1]]]
            mid_n = [top_n[1], bottom_n[0]]
            mid_label = [x for x, _ in sorted(enumerate(d['ranks']), key=lambda x: x[1])[mid_n[0]: mid_n[1]]]

            # top
            top_pred = [x for x, _ in sorted(enumerate(prediction), key=lambda x: x[1])[top_n[0]: top_n[1]]]
            top_acc = len(set(top_pred).intersection(set(top_label))) / len(top_label) * 100
            # middle
            mid_pred = [x for x, _ in sorted(enumerate(prediction), key=lambda x: x[1])[mid_n[0]: mid_n[1]]]
            mid_acc = len(set(mid_pred).intersection(set(mid_label))) / len(mid_label) * 100
            # top
            bottom_pred = [x for x, _ in sorted(enumerate(prediction), key=lambda x: x[1])[bottom_n[0]: bottom_n[1]]]
            bottom_acc = len(set(bottom_pred).intersection(set(bottom_label))) / len(bottom_label) * 100

            # the index of bottom p percent
            output.append({
                "relation_type": d['relation_type'],
                "model": i,
                "top_pred_and_bottom_gold": [" : ".join(d['pairs'][x]) for x in set(top_pred).intersection(bottom_label)],
                "bottom_pred_and_top_gold": [" : ".join(d['pairs'][x]) for x in set(bottom_pred).intersection(top_label)],
            })

    df = pd.DataFrame(output)
    df.to_csv(f"experiments/analysis/qualitative/{prompt}.{p}.csv", index=False)
    # df.pop("top_num")
    # df.pop("bottom_num")
    df['relation_type'] = [pretty_name[i] for i in df['relation_type']]
    print(df)

    new_df = []
    for _, i in df.iterrows():
        top_pred_and_bottom_gold = i['top_pred_and_bottom_gold'][:min(len(i['top_pred_and_bottom_gold']), 4)]
        bottom_pred_and_top_gold = i['bottom_pred_and_top_gold'][:min(len(i['bottom_pred_and_top_gold']), 4)]
        for x in range(max(len(bottom_pred_and_top_gold), len(top_pred_and_bottom_gold))):
        # for x in range(max(len(bottom_pred_and_top_gold), len(top_pred_and_bottom_gold)) // 3):
            if len(top_pred_and_bottom_gold) >= x + 1:
                t = ", ".join(top_pred_and_bottom_gold[x * 1:min(len(top_pred_and_bottom_gold) + 1, (x + 1)*1)])
            else:
                t = ""
            if len(bottom_pred_and_top_gold) >= x + 1:
                b = ", ".join(bottom_pred_and_top_gold[x*1:min(len(bottom_pred_and_top_gold) + 1, (x + 1)*1)])
            else:
                b = ""
            new_df.append({"relation_type": i['relation_type'], "model": i['model'], "top": t, "bottom": b})
    df_new = pd.DataFrame(new_df)
    df_new['model'] = [target[i] for i in df_new['model']]
    df_new = df_new[['model', 'relation_type', 'top', 'bottom']]
    df_new = df_new.sort_values(by=['model', 'relation_type'])
    df_new.to_csv(f"experiments/analysis/qualitative/{prompt}.{p}.format.csv", index=False)
    with pd.option_context("max_colwidth", 1000):
        table = df_new.to_latex(index=False, escape=False)
        table = table.split(r"\midrule")[1].split(r"\bottomrule")[0]
        print(table)