# Path: shorttext/utils/textpreprocessing.py
# def preprocess_text(text, pipeline):
# def text_preprocessor(pipeline):
# def oldschool_standard_text_preprocessor(stopwordsfile):
# def standard_text_preprocessor_1():
# def standard_text_preprocessor_2():
# 
# Path: shorttext/utils/compactmodel_io.py
# class CompactIOMachine:
#     """ Base class that implements compact model I/O.
# 
#     This is to replace the original :func:`compactio` decorator.
# 
#     """
#     def __init__(self, infodict, prefix, suffices):
#         """
# 
#         :param infodict: information about the model. Must contain the key 'classifier'.
#         :param prefix: prefix of names of the model file
#         :param suffices: suffices of the names of the model file
#         :type infodict: dict
#         :type prefix: str
#         :type suffices: list
#         """
#         self.infodict = infodict
#         self.prefix = prefix
#         self.suffices = suffices
# 
#     def savemodel(self, nameprefix):
#         """ Abstract method for `savemodel`.
# 
#         :param nameprefix: prefix of the model path
#         :type nameprefix: str
#         """
#         raise e.OperationNotDefinedException()
# 
#     def loadmodel(self, nameprefix):
#         """ Abstract method for `loadmodel`.
# 
#         :param nameprefix: prefix of the model path
#         :type nameprefix: str
#         """
#         raise e.OperationNotDefinedException()
# 
#     def save_compact_model(self, filename, *args, **kwargs):
#         """ Save the model in a compressed binary format.
# 
#         :param filename: name of the model file
#         :param args: arguments
#         :param kwargs: arguments
#         :type filename: str
#         :type args: dict
#         :type kwargs: dict
#         """
#         save_compact_model(filename, self.savemodel, self.prefix, self.suffices, self.infodict, *args, **kwargs)
# 
#     def load_compact_model(self, filename, *args, **kwargs):
#         """ Load the model in a compressed binary format.
# 
#         :param filename: name of the model file
#         :param args: arguments
#         :param kwargs: arguments
#         :type filename: str
#         :type args: dict
#         :type kwargs: dict
#         """
#         return load_compact_model(filename, self.loadmodel, self.prefix, self.infodict, *args, **kwargs)
# 
#     def get_info(self):
#         """ Getting information for the dressed machine.
# 
#         :return: dictionary of the information for the dressed machine.
#         :rtype: dict
#         """
#         return {'classifier': self.infodict['classifier'],
#                 'prefix': self.prefix,
#                 'suffices': self.suffices}


import json
import os
import warnings
import numpy as np
import shorttext.utils.kerasmodel_io as kerasio
import shorttext.utils.classification_exceptions as e
from shorttext.utils import tokenize
from shorttext.utils.compactmodel_io import CompactIOMachine


class VarNNEmbeddedVecClassifier(CompactIOMachine):
    """
    This is a wrapper for various neural network algorithms
    for supervised short text categorization.
    Each class label has a few short sentences, where each token is converted
    to an embedded vector, given by a pre-trained word-embedding model (e.g., Google Word2Vec model).
    The sentences are represented by a matrix, or rank-2 array.
    The type of neural network has to be passed when training, and it has to be of
    type :class:`keras.models.Sequential`. The number of outputs of the models has to match
    the number of class labels in the training data.
    To perform prediction, the input short sentences is converted to a unit vector
    in the same way. The score is calculated according to the trained neural network model.

    Examples of the models can be found in `frameworks`.

    A pre-trained Google Word2Vec model can be downloaded `here
    <https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit>`_.
    """
    def __init__(self, wvmodel, vecsize=None, maxlen=15, with_gensim=False):
        """ Initialize the classifier.

        :param wvmodel: Word2Vec model
        :param vecsize: length of the embedded vectors in the model (Default: None, directly extracted from word-embedding model)
        :param maxlen: maximum number of words in a sentence (Default: 15)
        :type wvmodel: gensim.models.keyedvectors.KeyedVectors
        :type vecsize: int
        :type maxlen: int
        """

# Path: shorttext/utils/textpreprocessing.py
# def preprocess_text(text, pipeline):
# def text_preprocessor(pipeline):
# def oldschool_standard_text_preprocessor(stopwordsfile):
# def standard_text_preprocessor_1():
# def standard_text_preprocessor_2():
# 
# Path: shorttext/utils/compactmodel_io.py
# class CompactIOMachine:
#     """ Base class that implements compact model I/O.
# 
#     This is to replace the original :func:`compactio` decorator.
# 
#     """
#     def __init__(self, infodict, prefix, suffices):
#         """
# 
#         :param infodict: information about the model. Must contain the key 'classifier'.
#         :param prefix: prefix of names of the model file
#         :param suffices: suffices of the names of the model file
#         :type infodict: dict
#         :type prefix: str
#         :type suffices: list
#         """
#         self.infodict = infodict
#         self.prefix = prefix
#         self.suffices = suffices
# 
#     def savemodel(self, nameprefix):
#         """ Abstract method for `savemodel`.
# 
#         :param nameprefix: prefix of the model path
#         :type nameprefix: str
#         """
#         raise e.OperationNotDefinedException()
# 
#     def loadmodel(self, nameprefix):
#         """ Abstract method for `loadmodel`.
# 
#         :param nameprefix: prefix of the model path
#         :type nameprefix: str
#         """
#         raise e.OperationNotDefinedException()
# 
#     def save_compact_model(self, filename, *args, **kwargs):
#         """ Save the model in a compressed binary format.
# 
#         :param filename: name of the model file
#         :param args: arguments
#         :param kwargs: arguments
#         :type filename: str
#         :type args: dict
#         :type kwargs: dict
#         """
#         save_compact_model(filename, self.savemodel, self.prefix, self.suffices, self.infodict, *args, **kwargs)
# 
#     def load_compact_model(self, filename, *args, **kwargs):
#         """ Load the model in a compressed binary format.
# 
#         :param filename: name of the model file
#         :param args: arguments
#         :param kwargs: arguments
#         :type filename: str
#         :type args: dict
#         :type kwargs: dict
#         """
#         return load_compact_model(filename, self.loadmodel, self.prefix, self.infodict, *args, **kwargs)
# 
#     def get_info(self):
#         """ Getting information for the dressed machine.
# 
#         :return: dictionary of the information for the dressed machine.
#         :rtype: dict
#         """
#         return {'classifier': self.infodict['classifier'],
#                 'prefix': self.prefix,
#                 'suffices': self.suffices}


# Path: shorttext/classifiers/embed/nnlib/VarNNEmbedVecClassification.py
import json
import os
import warnings
import numpy as np
import shorttext.utils.kerasmodel_io as kerasio
import shorttext.utils.classification_exceptions as e
from shorttext.utils import tokenize
from shorttext.utils.compactmodel_io import CompactIOMachine



class VarNNEmbeddedVecClassifier(CompactIOMachine):
    """
    This is a wrapper for various neural network algorithms
    for supervised short text categorization.
    Each class label has a few short sentences, where each token is converted
    to an embedded vector, given by a pre-trained word-embedding model (e.g., Google Word2Vec model).
    The sentences are represented by a matrix, or rank-2 array.
    The type of neural network has to be passed when training, and it has to be of
    type :class:`keras.models.Sequential`. The number of outputs of the models has to match
    the number of class labels in the training data.
    To perform prediction, the input short sentences is converted to a unit vector
    in the same way. The score is calculated according to the trained neural network model.

    Examples of the models can be found in `frameworks`.

    A pre-trained Google Word2Vec model can be downloaded `here
    <https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit>`_.
    """
    def __init__(self, wvmodel, vecsize=None, maxlen=15, with_gensim=False):
        """ Initialize the classifier.

        :param wvmodel: Word2Vec model
        :param vecsize: length of the embedded vectors in the model (Default: None, directly extracted from word-embedding model)
        :param maxlen: maximum number of words in a sentence (Default: 15)
        :type wvmodel: gensim.models.keyedvectors.KeyedVectors
        :type vecsize: int
        :type maxlen: int
        """

        CompactIOMachine.__init__(self, {'classifier': 'nnlibvec'}, 'nnlibvec', ['_classlabels.txt', '.json', '.h5', '_config.json'])