# -*- coding: utf-8 -*-
# Copyright (C) 2016-2023 PyThaiNLP Project
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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from pythainlp.augment.word2vec.core import Word2VecAug
from pythainlp.corpus import get_corpus_path
from pythainlp.tokenize import word_tokenize
from typing import List, Tuple
[docs]class LTW2VAug:
    """
    Text Augment using word2vec from LTW2V
    LTW2V:
    `github.com/PyThaiNLP/large-thaiword2vec <https://github.com/PyThaiNLP/large-thaiword2vec>`_
    """
[docs]    def __init__(self):
        self.ltw2v_wv = get_corpus_path("ltw2v")
        self.load_w2v() 
[docs]    def tokenizer(self, text: str) -> List[str]:
        """
        :param str text: thai text
        :rtype: List[str]
        """
        return word_tokenize(text, engine="newmm") 
[docs]    def load_w2v(self):  # insert substitute
        """
        Load ltw2v word2vec model
        """
        self.aug = Word2VecAug(self.ltw2v_wv, self.tokenizer, type="binary") 
[docs]    def augment(
        self, sentence: str, n_sent: int = 1, p: float = 0.7
    ) -> List[Tuple[str]]:
        """
        Text Augment using word2vec from Thai2Fit
        :param str sentence: thai sentence
        :param int n_sent: number sentence
        :param float p: Probability of word
        :return: list of text augment
        :rtype: List[Tuple[str]]
        :Example:
        ::
            from pythainlp.augment.word2vec import LTW2VAug
            aug = LTW2VAug()
            aug.augment("ผมเรียน", n_sent=2, p=0.5)
            # output: [('เขา', 'เรียนหนังสือ'), ('เขา', 'สมัครเรียน')]
        """
        return self.aug.augment(sentence, n_sent, p)