import os
import classla
# classla.download('sl', logging_level='WARNING')

from lemmagen3 import Lemmatizer

def lem_adj(gender, wrd):
    lem = Lemmatizer()
    if gender == 'm':
        lem.load_model(os.path.join('./model/lemmagen_models/kanon-adj-male.bin'))
    elif gender == 'f':
        lem.load_model(os.path.join('./model/lemmagen_models/kanon-adj-female.bin'))
    elif gender == 'n':
        lem.load_model(os.path.join('./model/lemmagen_models/kanon-adj-neutral.bin'))

    form = lem.lemmatize(wrd)
    return form


def process_nlp_pipeline(lang, text):
    nlp = classla.Pipeline(lang=lang, processors='tokenize,pos,lemma', tokenize_pretokenized=True, logging_level='WARNING')
    doc = nlp(text)
    return doc


def get_adj_msd(head, word):
    feats = head.feats
    feats_dict = {}
    feats = feats.strip().split('|')
    for f in feats:
        f = f.strip().split('=')
        feats_dict[f[0]] = f[1]
    gender = feats_dict['Gender']
    #print(gender)
    #gender = gender.strip().split('=')[1]
    if gender == 'Masc' and len(word.xpos) == 6:
        msd = word.xpos[:-1]+'ny'
    elif gender == 'Masc' and len(word.xpos) == 7:
        msd = word.xpos[:-1]+'y'
    elif gender == 'Fem':
        msd = word.xpos[:-1]+'n'
    elif gender == 'Neut':
        msd = word.xpos[:-1]+'n'
    else:
        msd = None
    return msd


def subfinder(mylist, pattern):
    matches = []
    for i in range(len(mylist)):
        if mylist[i].text.lower() == pattern[0] and [t.text.lower() for t in mylist[i:i+len(pattern)]] == pattern:
            matches.append(mylist[i:i+len(pattern)])
    return matches


def find_canon(term):
    head = None
    pre = []
    post = []
    for word in term.words:
        if word.upos == 'NOUN' or word.upos == 'PROPN':
            head = word
            break
    if head is None:
        if len(term.words) == 1:
            head2 = term.words[0]
            lem = Lemmatizer()
            lem.load_model(os.path.join('./model/lemmagen_models/kanon.bin'))
            head_form = lem.lemmatize(head2.text.lower())
            return head_form
        else:
            return ' '.join([w.text for w in term.words])  # just return the input because we do not cover such case
    else:
        for word in term.words:
            if word.id < head.id:
                pre.append(word)
            elif word.id > head.id:
                post.append(word)

    canon = []
    for el in pre:
        msd = get_adj_msd(head, el)
        if msd is None:
            canon.append(el.lemma.lower())
        else:
            if msd[0] == 'A' and msd[3] == 'm':
                form = lem_adj('m', el.text.lower())
                canon.append(form)
            elif msd[0] == 'A' and msd[3] == 'f':
                form = lem_adj('f', el.text.lower())
                canon.append(form)
            elif msd[0] == 'A' and msd[3] == 'n':
                form = lem_adj('n', el.text.lower())
                canon.append(form)

    lem = Lemmatizer()
    lem.load_model(os.path.join('./model/lemmagen_models/kanon.bin'))
    head_form = lem.lemmatize(head.text.lower())
    canon.append(head_form)
    for el in post:
        canon.append(el.text)
    return ' '.join(canon)

def process(forms):
    text = '\n'.join(forms)
    doc = process_nlp_pipeline('sl', text)
    return [find_canon(sent) for sent in doc.sentences]