import regex as re
from sklearn.feature_extraction.text import HashingVectorizer
import torch


token_root_len = 7


class Analyzer(object):

    def __init__(self):
        self.token_pat = re.compile(r'(?:\p{L}|\d)+')

    def __call__(self, doc):
        return [tok[0:token_root_len] for tok in self.token_pat.findall(doc)]


# hiperparametr - liczba bitów hasza
vector_length = 2**18


vectorizer = HashingVectorizer(n_features=vector_length, analyzer=Analyzer())

midpoint = 1913.0


def vectorize_text(content):
    # musimy przekonwertować macierz sklearn => macierz numpy => tensor pytorcha
    return (torch.from_numpy(vectorizer.fit_transform([content]).toarray()))[0]


def vectorize_batch(contents):
    # musimy przekonwertować macierz sklearn => macierz numpy => tensor pytorcha
    return (torch.from_numpy(vectorizer.fit_transform(contents).toarray()))



def process_line(line):
    fields = line.strip('\n').split('\t')

    year_from, year_to, _, _, content = fields
    # normalizujemy lata do wartości (-1,1)
    year_normalized = ((float(year_from) + float(year_to)) / 2 - midpoint) / 100.0
    return (content, torch.tensor(year_normalized))