import json
import os
from itertools import combinations
from random import shuffle, seed

import pandas as pd
from datasets import load_dataset


def get_stats(filename):
    with open(filename) as f:
        _data = [json.loads(i) for i in f.read().splitlines()]
    return len(_data), list(set([len(i['choice']) for i in _data])), len(list(set([i['prefix'] for i in _data])))


def lexical_overlap(word_a, word_b):
    for a in word_a.split(" "):
        for b in word_b.split(" "):
            if a.lower() == b.lower():
                return True
    return False


def create_analogy(_data, output_path, negative_per_relation, instance_per_relation=100):
    # if os.path.exists(output_path):
    #     return
    df = _data.to_pandas()
    analogy_data = []
    for _, i in df.iterrows():
        target = [(q.tolist(), c.tolist()) for q, c in combinations(i['positives'], 2)
                  if not any(lexical_overlap(c[0], y) or lexical_overlap(c[1], y) for y in q)]
        if len(target) == 0:
            continue
        if len(target) > instance_per_relation:
            seed(42)
            shuffle(target)
            target = target[:instance_per_relation]
        for m, (q, c) in enumerate(target):
            negative = []
            for r in df['relation_type']:
                if r == i['relation_type']:
                    continue
                target_per_relation = [y.tolist() for y in df[df['relation_type'] == r]['positives'].values[0]]
                shuffle(target_per_relation)
                negative += target_per_relation[:negative_per_relation]
            analogy_data.append({
                "stem": q,
                "choice": [c, c[::-1]] + negative,
                "answer": 0,
                "prefix": i["relation_type"]
            })
    os.makedirs(os.path.dirname(output_path), exist_ok=True)
    with open(output_path, "w") as f:
        f.write("\n".join([json.dumps(i) for i in analogy_data]))

stat = []
###################################################################
# create analogy from `relbert/semeval2012_relational_similarity` #
###################################################################
if not os.path.exists("dataset/semeval2012_relational_similarity/valid.jsonl"):
    data = load_dataset("relbert/semeval2012_relational_similarity", split="validation")
    analogy_data = [{
        "stem": i['positives'][0], "choice": i["negatives"] + [i['positives'][1]], "answer": 2, "prefix": i["relation_type"]
    } for i in data]
    os.makedirs("dataset/semeval2012_relational_similarity", exist_ok=True)
    with open("dataset/semeval2012_relational_similarity/valid.jsonl", "w") as f:
        f.write("\n".join([json.dumps(i) for i in analogy_data]))

v_size, v_num_choice, v_relation_type = get_stats("dataset/semeval2012_relational_similarity/valid.jsonl")
stat.append({
    "name": "`semeval2012_relational_similarity`",
    "Size (valid/test)": f"{v_size}/-",
    "Num of choice (valid/test)": f"{','.join([str(n) for n in v_num_choice])}/-",
    "Num of relation group (valid/test)": f"{v_relation_type}/-",
    "Original Reference": "[relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity)"
})


#############################################################
# create analogy from `relbert/t_rex_relational_similarity` #
#############################################################
data = load_dataset("relbert/t_rex_relational_similarity", "filter_unified.min_entity_1_max_predicate_100", split="test")
create_analogy(data, "dataset/t_rex_relational_similarity/test.jsonl", negative_per_relation=2)
data = load_dataset("relbert/t_rex_relational_similarity", "filter_unified.min_entity_4_max_predicate_100", split="validation")
create_analogy(data, "dataset/t_rex_relational_similarity/valid.jsonl", negative_per_relation=1)

t_size, t_num_choice, t_relation_type = get_stats("dataset/t_rex_relational_similarity/test.jsonl")
v_size, v_num_choice, v_relation_type = get_stats("dataset/t_rex_relational_similarity/valid.jsonl")
stat.append({
    "name": "`t_rex_relational_similarity`",
    "Size (valid/test)": f"{v_size}/{t_size}",
    "Num of choice (valid/test)": f"{','.join([str(n) for n in v_num_choice])}/{','.join([str(n) for n in t_num_choice])}",
    "Num of relation group (valid/test)": f"{v_relation_type}/{t_relation_type}",
    "Original Reference": "[relbert/t_rex_relational_similarity](https://huggingface.co/datasets/relbert/t_rex_relational_similarity)"
})

##################################################################
# create analogy from `relbert/conceptnet_relational_similarity` #
##################################################################
data = load_dataset("relbert/conceptnet_relational_similarity", split="test")
create_analogy(data, "dataset/conceptnet_relational_similarity/test.jsonl", negative_per_relation=1)
data = load_dataset("relbert/conceptnet_relational_similarity", split="validation")
create_analogy(data, "dataset/conceptnet_relational_similarity/valid.jsonl", negative_per_relation=1)
t_size, t_num_choice, t_relation_type = get_stats("dataset/conceptnet_relational_similarity/test.jsonl")
v_size, v_num_choice, v_relation_type = get_stats("dataset/conceptnet_relational_similarity/valid.jsonl")
stat.append({
    "name": "`conceptnet_relational_similarity`",
    "Size (valid/test)": f"{v_size}/{t_size}",
    "Num of choice (valid/test)": f"{','.join([str(n) for n in v_num_choice])}/{','.join([str(n) for n in t_num_choice])}",
    "Num of relation group (valid/test)": f"{v_relation_type}/{t_relation_type}",
    "Original Reference": "[relbert/conceptnet_relational_similarity](https://huggingface.co/datasets/relbert/conceptnet_relational_similarity)"
})

##################################################################
# create analogy from `relbert/conceptnet_relational_similarity` #
##################################################################
data = load_dataset("relbert/nell_relational_similarity", split="test")
create_analogy(data, "dataset/nell_relational_similarity/test.jsonl", negative_per_relation=1)
data = load_dataset("relbert/nell_relational_similarity", split="validation")
create_analogy(data, "dataset/nell_relational_similarity/valid.jsonl", negative_per_relation=1)
t_size, t_num_choice, t_relation_type = get_stats("dataset/nell_relational_similarity/test.jsonl")
v_size, v_num_choice, v_relation_type = get_stats("dataset/nell_relational_similarity/valid.jsonl")
stat.append({
    "name": "`nell_relational_similarity`",
    "Size (valid/test)": f"{v_size}/{t_size}",
    "Num of choice (valid/test)": f"{','.join([str(n) for n in v_num_choice])}/{','.join([str(n) for n in t_num_choice])}",
    "Num of relation group (valid/test)": f"{v_relation_type}/{t_relation_type}",
    "Original Reference": "[relbert/nell_relational_similarity](https://huggingface.co/datasets/relbert/nell_relational_similarity)"
})
print(pd.DataFrame(stat).to_markdown(index=False))