import re, nltk, pickle
import numpy as np

nltk.download('stopwords')
from nltk.corpus import stopwords

# Paths for all resources for the bot.
RESOURCE_PATH = {
    'INTENT_RECOGNIZER': 'intent_recognizer.pkl',
    'TAG_CLASSIFIER': 'tag_classifier.pkl',
    'TFIDF_VECTORIZER': 'tfidf_vectorizer.pkl',
    'THREAD_EMBEDDINGS_FOLDER': 'thread_embeddings_by_tags',
    'WORD_EMBEDDINGS': 'data/word_embeddings.tsv',
}

def text_prepare(text):
    """Performs tokenization and simple preprocessing."""

    replace_by_space_re = re.compile('[/(){}\[\]\|@,;]')
    good_symbols_re = re.compile('[^0-9a-z #+_]')
    stopwords_set = set(stopwords.words('english'))

    text = text.lower()
    text = replace_by_space_re.sub(' ', text)
    text = good_symbols_re.sub('', text)
    text = ' '.join([x for x in text.split() if x and x not in stopwords_set])

    return text.strip()


def load_embeddings(path):
    """Loads pre-trained word embeddings from tsv file."""

    embeds = dict() # dictionary mapping words to vectors
    for line in open(path, encoding='utf-8'):
        row = line.strip().split('\t')
        embeds[row[0]] = np.array(row[1:], dtype=np.float32)

    embeddings_dim = embeds[list(embeds)[0]].shape[0]

    return embeds, embeddings_dim


def question_to_vec(question, embeddings, dim):
    """Transforms a string to an embedding by averaging word embeddings."""

    words = question.split()

    counter = 0
    res = np.zeros(dim)
    for word in words:
        if word in embeddings:
            res += np.array(embeddings[word])
            counter += 1
    if counter!=0:
        return res/counter # mean of all word embeddings
    else:
        return res # vector of zeros
        

def unpickle_file(filename):
    """Returns the result of unpickling the file content."""

    with open(filename, 'rb') as f:
        return pickle.load(f)

a52f5f347d47cfdc285f0e57a8ed2d7169c08039
<mask token>


def text_prepare(text):
    """Performs tokenization and simple preprocessing."""
    replace_by_space_re = re.compile('[/(){}\\[\\]\\|@,;]')
    good_symbols_re = re.compile('[^0-9a-z #+_]')
    stopwords_set = set(stopwords.words('english'))
    text = text.lower()
    text = replace_by_space_re.sub(' ', text)
    text = good_symbols_re.sub('', text)
    text = ' '.join([x for x in text.split() if x and x not in stopwords_set])
    return text.strip()


def load_embeddings(path):
    """Loads pre-trained word embeddings from tsv file."""
    embeds = dict()
    for line in open(path, encoding='utf-8'):
        row = line.strip().split('\t')
        embeds[row[0]] = np.array(row[1:], dtype=np.float32)
    embeddings_dim = embeds[list(embeds)[0]].shape[0]
    return embeds, embeddings_dim


def question_to_vec(question, embeddings, dim):
    """Transforms a string to an embedding by averaging word embeddings."""
    words = question.split()
    counter = 0
    res = np.zeros(dim)
    for word in words:
        if word in embeddings:
            res += np.array(embeddings[word])
            counter += 1
    if counter != 0:
        return res / counter
    else:
        return res


def unpickle_file(filename):
    """Returns the result of unpickling the file content."""
    with open(filename, 'rb') as f:
        return pickle.load(f)

<mask token>
nltk.download('stopwords')
<mask token>


def text_prepare(text):
    """Performs tokenization and simple preprocessing."""
    replace_by_space_re = re.compile('[/(){}\\[\\]\\|@,;]')
    good_symbols_re = re.compile('[^0-9a-z #+_]')
    stopwords_set = set(stopwords.words('english'))
    text = text.lower()
    text = replace_by_space_re.sub(' ', text)
    text = good_symbols_re.sub('', text)
    text = ' '.join([x for x in text.split() if x and x not in stopwords_set])
    return text.strip()


def load_embeddings(path):
    """Loads pre-trained word embeddings from tsv file."""
    embeds = dict()
    for line in open(path, encoding='utf-8'):
        row = line.strip().split('\t')
        embeds[row[0]] = np.array(row[1:], dtype=np.float32)
    embeddings_dim = embeds[list(embeds)[0]].shape[0]
    return embeds, embeddings_dim


def question_to_vec(question, embeddings, dim):
    """Transforms a string to an embedding by averaging word embeddings."""
    words = question.split()
    counter = 0
    res = np.zeros(dim)
    for word in words:
        if word in embeddings:
            res += np.array(embeddings[word])
            counter += 1
    if counter != 0:
        return res / counter
    else:
        return res


def unpickle_file(filename):
    """Returns the result of unpickling the file content."""
    with open(filename, 'rb') as f:
        return pickle.load(f)

<mask token>
nltk.download('stopwords')
<mask token>
RESOURCE_PATH = {'INTENT_RECOGNIZER': 'intent_recognizer.pkl',
    'TAG_CLASSIFIER': 'tag_classifier.pkl', 'TFIDF_VECTORIZER':
    'tfidf_vectorizer.pkl', 'THREAD_EMBEDDINGS_FOLDER':
    'thread_embeddings_by_tags', 'WORD_EMBEDDINGS': 'data/word_embeddings.tsv'}


def text_prepare(text):
    """Performs tokenization and simple preprocessing."""
    replace_by_space_re = re.compile('[/(){}\\[\\]\\|@,;]')
    good_symbols_re = re.compile('[^0-9a-z #+_]')
    stopwords_set = set(stopwords.words('english'))
    text = text.lower()
    text = replace_by_space_re.sub(' ', text)
    text = good_symbols_re.sub('', text)
    text = ' '.join([x for x in text.split() if x and x not in stopwords_set])
    return text.strip()


def load_embeddings(path):
    """Loads pre-trained word embeddings from tsv file."""
    embeds = dict()
    for line in open(path, encoding='utf-8'):
        row = line.strip().split('\t')
        embeds[row[0]] = np.array(row[1:], dtype=np.float32)
    embeddings_dim = embeds[list(embeds)[0]].shape[0]
    return embeds, embeddings_dim


def question_to_vec(question, embeddings, dim):
    """Transforms a string to an embedding by averaging word embeddings."""
    words = question.split()
    counter = 0
    res = np.zeros(dim)
    for word in words:
        if word in embeddings:
            res += np.array(embeddings[word])
            counter += 1
    if counter != 0:
        return res / counter
    else:
        return res


def unpickle_file(filename):
    """Returns the result of unpickling the file content."""
    with open(filename, 'rb') as f:
        return pickle.load(f)

import re, nltk, pickle
import numpy as np
nltk.download('stopwords')
from nltk.corpus import stopwords
RESOURCE_PATH = {'INTENT_RECOGNIZER': 'intent_recognizer.pkl',
    'TAG_CLASSIFIER': 'tag_classifier.pkl', 'TFIDF_VECTORIZER':
    'tfidf_vectorizer.pkl', 'THREAD_EMBEDDINGS_FOLDER':
    'thread_embeddings_by_tags', 'WORD_EMBEDDINGS': 'data/word_embeddings.tsv'}


def text_prepare(text):
    """Performs tokenization and simple preprocessing."""
    replace_by_space_re = re.compile('[/(){}\\[\\]\\|@,;]')
    good_symbols_re = re.compile('[^0-9a-z #+_]')
    stopwords_set = set(stopwords.words('english'))
    text = text.lower()
    text = replace_by_space_re.sub(' ', text)
    text = good_symbols_re.sub('', text)
    text = ' '.join([x for x in text.split() if x and x not in stopwords_set])
    return text.strip()


def load_embeddings(path):
    """Loads pre-trained word embeddings from tsv file."""
    embeds = dict()
    for line in open(path, encoding='utf-8'):
        row = line.strip().split('\t')
        embeds[row[0]] = np.array(row[1:], dtype=np.float32)
    embeddings_dim = embeds[list(embeds)[0]].shape[0]
    return embeds, embeddings_dim


def question_to_vec(question, embeddings, dim):
    """Transforms a string to an embedding by averaging word embeddings."""
    words = question.split()
    counter = 0
    res = np.zeros(dim)
    for word in words:
        if word in embeddings:
            res += np.array(embeddings[word])
            counter += 1
    if counter != 0:
        return res / counter
    else:
        return res


def unpickle_file(filename):
    """Returns the result of unpickling the file content."""
    with open(filename, 'rb') as f:
        return pickle.load(f)

import re, nltk, pickle
import numpy as np

nltk.download('stopwords')
from nltk.corpus import stopwords

# Paths for all resources for the bot.
RESOURCE_PATH = {
    'INTENT_RECOGNIZER': 'intent_recognizer.pkl',
    'TAG_CLASSIFIER': 'tag_classifier.pkl',
    'TFIDF_VECTORIZER': 'tfidf_vectorizer.pkl',
    'THREAD_EMBEDDINGS_FOLDER': 'thread_embeddings_by_tags',
    'WORD_EMBEDDINGS': 'data/word_embeddings.tsv',
}

def text_prepare(text):
    """Performs tokenization and simple preprocessing."""

    replace_by_space_re = re.compile('[/(){}\[\]\|@,;]')
    good_symbols_re = re.compile('[^0-9a-z #+_]')
    stopwords_set = set(stopwords.words('english'))

    text = text.lower()
    text = replace_by_space_re.sub(' ', text)
    text = good_symbols_re.sub('', text)
    text = ' '.join([x for x in text.split() if x and x not in stopwords_set])

    return text.strip()


def load_embeddings(path):
    """Loads pre-trained word embeddings from tsv file."""

    embeds = dict() # dictionary mapping words to vectors
    for line in open(path, encoding='utf-8'):
        row = line.strip().split('\t')
        embeds[row[0]] = np.array(row[1:], dtype=np.float32)

    embeddings_dim = embeds[list(embeds)[0]].shape[0]

    return embeds, embeddings_dim


def question_to_vec(question, embeddings, dim):
    """Transforms a string to an embedding by averaging word embeddings."""

    words = question.split()

    counter = 0
    res = np.zeros(dim)
    for word in words:
        if word in embeddings:
            res += np.array(embeddings[word])
            counter += 1
    if counter!=0:
        return res/counter # mean of all word embeddings
    else:
        return res # vector of zeros
        

def unpickle_file(filename):
    """Returns the result of unpickling the file content."""

    with open(filename, 'rb') as f:
        return pickle.load(f)
