import zipfile
import requests
import os
import json
from statistics import mean
import pandas as pd
from gensim.models import fasttext
from datasets import load_dataset


# load fasttext
def load_model():
    os.makedirs('./cache', exist_ok=True)
    path = './cache/crawl-300d-2M-subword.bin'
    if not os.path.exists(path):
        url = 'https://dl.fbaipublicfiles.com/fasttext/vectors-english/crawl-300d-2M-subword.zip'
        filename = os.path.basename(url)
        _path = f"./cache/{filename}"
        with open(_path, "wb") as f:
            r = requests.get(url)
            f.write(r.content)
        with zipfile.ZipFile(_path, 'r') as zip_ref:
            zip_ref.extractall("./cache")
        os.remove(_path)
    return fasttext.load_facebook_model(path)


def cosine_similarity(a, b):
    norm_a = sum(map(lambda x: x * x, a)) ** 0.5
    norm_b = sum(map(lambda x: x * x, b)) ** 0.5
    return sum(map(lambda x: x[0] * x[1], zip(a, b)))/(norm_a * norm_b)


def get_vector(_model, _word_a, _word_b):
    # return np.mean([_model[_x] for _x in _word_a.split(" ")], axis=0) - np.mean([_model[_x] for _x in _word_b.split(" ")], axis=0)
    return _model[_word_a] - _model[_word_b]


# load dataset
data = load_dataset("cardiffnlp/relentless", split="test")
full_result = []
os.makedirs("results/word_embedding/fasttext", exist_ok=True)
scorer = None
for d in data:
    ppl_file = f"results/word_embedding/fasttext/ppl.{d['relation_type'].replace(' ', '_').replace('/', '__')}.jsonl"

    anchor_embeddings = [(a, b) for a, b in d['prototypical_examples']]
    option_embeddings = [(x, y) for x, y in d['pairs']]

    if not os.path.exists(ppl_file):

        if scorer is None:
            scorer = load_model()

        anchor_embeddings = [get_vector(scorer, a, b) for a, b in d['prototypical_examples']]
        option_embeddings = [get_vector(scorer, x, y) for x, y in d['pairs']]
        similarity = [[cosine_similarity(a, b) for b in anchor_embeddings] for a in option_embeddings]
        output = [{"similarity": s} for s in similarity]
        with open(ppl_file, "w") as f:
            f.write("\n".join([json.dumps(i) for i in output]))

    with open(ppl_file) as f:
        similarity = [json.loads(i)['similarity'] for i in f.read().split("\n") if len(i) > 0]

    true_rank = d['ranks']
    assert len(true_rank) == len(similarity), f"Mismatch in number of examples: {len(true_rank)} vs {len(similarity)}"
    prediction = [max(s) for s in similarity]
    rank_map = {p: n for n, p in enumerate(sorted(prediction, reverse=True), 1)}
    prediction_max = [rank_map[p] for p in prediction]

    prediction = [min(s) for s in similarity]
    rank_map = {p: n for n, p in enumerate(sorted(prediction, reverse=True), 1)}
    prediction_min = [rank_map[p] for p in prediction]

    prediction = [mean(s) for s in similarity]
    rank_map = {p: n for n, p in enumerate(sorted(prediction, reverse=True), 1)}
    prediction_mean = [rank_map[p] for p in prediction]

    tmp = pd.DataFrame([true_rank, prediction_max, prediction_min, prediction_mean]).T
    cor_max = tmp.corr("spearman").values[0, 1]
    cor_min = tmp.corr("spearman").values[0, 2]
    cor_mean = tmp.corr("spearman").values[0, 3]
    full_result.append({"model": "fastText\textsubscript{pair}", "relation_type": d['relation_type'], "correlation": cor_max})

df = pd.DataFrame(full_result)
df = df.pivot(columns="relation_type", index="model", values="correlation")
df['average'] = df.mean(1)
df.to_csv("results/word_embedding/fasttext.csv")
df = (100 * df).round()
print(df.to_markdown())
print(df.to_latex())