import json
import os
from itertools import product

import pandas as pd
from random import shuffle, seed


parameters_min_e_freq = [1, 2, 3, 4]
parameters_max_p_freq = [100, 50, 25, 10]


def get_test_predicate(_data):
    tmp_df = pd.DataFrame(_data)
    predicates_count = tmp_df.groupby("predicate")['text'].count().sort_values(ascending=False).to_dict()
    total_num = sum(predicates_count.values())
    pre_k = list(predicates_count.keys())
    seed(42)
    shuffle(pre_k)
    predicates_train = []
    for k in pre_k:
        predicates_train.append(k)
        if sum([predicates_count[i] for i in predicates_train]) > total_num * 0.8:
            break
    predicates_test = sorted([i for i in pre_k if i not in predicates_train])
    return predicates_test


if not os.path.exists("data/t_rex.filter_unified.test.jsonl"):
    with open(f"data/t_rex.filter_unified.min_entity_{max(parameters_min_e_freq)}_max_predicate_{min(parameters_max_p_freq)}.jsonl") as f:
        data = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
    pred_test = get_test_predicate(data)
    data_test = [i for i in data if i['predicate'] in pred_test]
    f_writer = open("data/t_rex.filter_unified.test.jsonl", 'w')
    for n, i in enumerate(data_test):
        print(f"\n[{n+1}/{len(data_test)}]")
        print(f"{json.dumps(i, indent=4)}")
        flag = input(">>> (enter to add to test)")
        if flag == '':
            f_writer.write(json.dumps(i) + '\n')
    f_writer.close()

with open("data/t_rex.filter_unified.test.jsonl") as f:
    data_test = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
    test_predicate = set([i['predicate'] for i in data_test])


seed(42)
for min_e_freq, max_p_freq in product(parameters_min_e_freq, parameters_max_p_freq):
    with open(f"data/t_rex.filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}.jsonl") as f:
        data = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
    data = [i for i in data if i['predicate'] not in test_predicate]
    shuffle(data)
    data_train = data[:int(len(data) * 0.9)]
    data_valid = data[int(len(data) * 0.9):]
    with open(f"data/t_rex.filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}.train.jsonl", "w") as f:
        f.write('\n'.join([json.dumps(i) for i in data_train]))
    with open(f"data/t_rex.filter_unified.min_entity_{min_e_freq}_max_predicate_{max_p_freq}.validation.jsonl", "w") as f:
        f.write('\n'.join([json.dumps(i) for i in data_valid]))


#
# # make test split
# with open(f"data/t_rex.filter_unified.jsonl") as f:
#     data = [json.loads(i) for i in f.read().split('\n') if len(i) > 0]
# train_data, validation_data, test_data = create_split(data)