# Path: neuronlp/io/data_utils.py
# ROOT = b"_ROOT"
# ROOT_POS = b"_ROOT_POS"
# ROOT_TYPE = b"_<ROOT>"
# ROOT_CHAR = b"_ROOT_CHAR"
# PAD = b"_PAD"
# PAD_POS = b"_PAD_POS"
# PAD_TYPE = b"_<PAD>"
# PAD_CHAR = b"_PAD_CHAR"
# _START_VOCAB = [ROOT, PAD]
# UNK_ID = 0
# ROOT_ID = 1
# PAD_ID = 2
# MAX_CHAR_LENGTH = 45
# DIGIT_RE = re.compile(br"\d")
# def create_alphabets(alphabet_directory, data_paths, max_vocabulary_size, min_occurence=1, normalize_digits=True):
# def read_data(source_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet, max_size=None,
#               normalize_digits=True):
# def get_batch(data, batch_size):
# def iterate_batch(data, batch_size, shuffle=False):


import logging
import sys
import pickle
import numpy as np
import gzip
import theano
from gensim.models.word2vec import Word2Vec
from .io import data_utils
    """
    load word embeddings from file
    :param embedding:
    :param embedding_path:
    :return: embedding dict, embedding dimention, caseless
    """
    print "loading embedding: %s from %s" % (embedding, embedding_path)
    if embedding == 'word2vec':
        # loading word2vec
        word2vec = Word2Vec.load_word2vec_format(embedding_path, binary=True)
        embedd_dim = word2vec.vector_size
        return word2vec, embedd_dim, False
    elif embedding == 'glove':
        # loading GloVe
        embedd_dim = -1
        embedd_dict = dict()
        with gzip.open(embedding_path, 'r') as file:
            for line in file:
                line = line.strip()
                line = line.decode('utf-8')
                if len(line) == 0:
                    continue

                tokens = line.split()
                if embedd_dim < 0:
                    embedd_dim = len(tokens) - 1
                else:
                    assert (embedd_dim + 1 == len(tokens))
                embedd = np.empty([1, embedd_dim], dtype=theano.config.floatX)
                embedd[:] = tokens[1:]

# Path: neuronlp/io/data_utils.py
# ROOT = b"_ROOT"
# ROOT_POS = b"_ROOT_POS"
# ROOT_TYPE = b"_<ROOT>"
# ROOT_CHAR = b"_ROOT_CHAR"
# PAD = b"_PAD"
# PAD_POS = b"_PAD_POS"
# PAD_TYPE = b"_<PAD>"
# PAD_CHAR = b"_PAD_CHAR"
# _START_VOCAB = [ROOT, PAD]
# UNK_ID = 0
# ROOT_ID = 1
# PAD_ID = 2
# MAX_CHAR_LENGTH = 45
# DIGIT_RE = re.compile(br"\d")
# def create_alphabets(alphabet_directory, data_paths, max_vocabulary_size, min_occurence=1, normalize_digits=True):
# def read_data(source_path, word_alphabet, char_alphabet, pos_alphabet, type_alphabet, max_size=None,
#               normalize_digits=True):
# def get_batch(data, batch_size):
# def iterate_batch(data, batch_size, shuffle=False):


# Path: neuronlp/utils.py
import logging
import sys
import pickle
import numpy as np
import gzip
import theano
from gensim.models.word2vec import Word2Vec
from .io import data_utils

    """
    load word embeddings from file
    :param embedding:
    :param embedding_path:
    :return: embedding dict, embedding dimention, caseless
    """
    print "loading embedding: %s from %s" % (embedding, embedding_path)
    if embedding == 'word2vec':
        # loading word2vec
        word2vec = Word2Vec.load_word2vec_format(embedding_path, binary=True)
        embedd_dim = word2vec.vector_size
        return word2vec, embedd_dim, False
    elif embedding == 'glove':
        # loading GloVe
        embedd_dim = -1
        embedd_dict = dict()
        with gzip.open(embedding_path, 'r') as file:
            for line in file:
                line = line.strip()
                line = line.decode('utf-8')
                if len(line) == 0:
                    continue

                tokens = line.split()
                if embedd_dim < 0:
                    embedd_dim = len(tokens) - 1
                else:
                    assert (embedd_dim + 1 == len(tokens))
                embedd = np.empty([1, embedd_dim], dtype=theano.config.floatX)
                embedd[:] = tokens[1:]

                word = data_utils.DIGIT_RE.sub(b"0", tokens[0]) if normalize_digits else tokens[0]