Python codecs 模块，open() 实例源码

def test_zipfile_timestamp():
    # An environment variable can be used to influence the timestamp on
    # TarInfo objects inside the zip.  See issue #143.  TemporaryDirectory is
    # not a context manager under Python 3.
    with temporary_directory() as tempdir:
        for filename in ('one', 'two', 'three'):
            path = os.path.join(tempdir, filename)
            with codecs.open(path, 'w', encoding='utf-8') as fp:
                fp.write(filename + '\n')
        zip_base_name = os.path.join(tempdir, 'dummy')
        # The earliest date representable in TarInfos, 1980-01-01
        with environ('SOURCE_DATE_EPOCH', '315576060'):
            zip_filename = wheel.archive.make_wheelfile_inner(
                zip_base_name, tempdir)
        with readable_zipfile(zip_filename) as zf:
            for info in zf.infolist():
                assert info.date_time[:3] == (1980, 1, 1)

def read_text(filename,rel_hash):
    id_counter = 0
    nodes = {}
    f = codecs.open(filename, "r", "utf-8")
    #Add some default relations if none have been supplied (at least 1 rst and 1 multinuc)
    if len(rel_hash) < 2:
        rel_hash["elaboration_r"] = "rst"
        rel_hash["joint_m"] = "multinuc"

    rels = collections.OrderedDict(sorted(rel_hash.items()))

    for line in f:
        id_counter += 1
        nodes[str(id_counter)] = NODE(str(id_counter),id_counter,id_counter,"0",0,"edu",line.strip(),rels.keys()[0],rels.values()[0])

    return nodes

def shared_locations(self):
        """
        A dictionary of shared locations whose keys are in the set 'prefix',
        'purelib', 'platlib', 'scripts', 'headers', 'data' and 'namespace'.
        The corresponding value is the absolute path of that category for
        this distribution, and takes into account any paths selected by the
        user at installation time (e.g. via command-line arguments). In the
        case of the 'namespace' key, this would be a list of absolute paths
        for the roots of namespace packages in this distribution.

        The first time this property is accessed, the relevant information is
        read from the SHARED file in the .dist-info directory.
        """
        result = {}
        shared_path = os.path.join(self.path, 'SHARED')
        if os.path.isfile(shared_path):
            with codecs.open(shared_path, 'r', encoding='utf-8') as f:
                lines = f.read().splitlines()
            for line in lines:
                key, value = line.split('=', 1)
                if key == 'namespace':
                    result.setdefault(key, []).append(value)
                else:
                    result[key] = value
        return result

def compute_dt_dist(docs, labels, tags, model, max_len, batch_size, pad_id, idxvocab, output_file):
    #generate batches
    num_batches = int(math.ceil(float(len(docs)) / batch_size))
    dt_dist = []
    t = []
    combined = []
    docid = 0
    for i in xrange(num_batches):
        x, _, _, t, s = get_batch_doc(docs, labels, tags, i, max_len, cf.tag_len, batch_size, pad_id)
        attention, mean_topic = sess.run([model.attention, model.mean_topic], {model.doc: x, model.tag: t})
        dt_dist.extend(attention[:s])

        if debug:
            for si in xrange(s):
                d = x[si]
                print "\n\nDoc", docid, "=", " ".join([idxvocab[item] for item in d if (item != pad_id)])
                sorted_dist = matutils.argsort(attention[si], reverse=True)
                for ti in sorted_dist:
                    print "Topic", ti, "=", attention[si][ti]
                docid += 1

    np.save(open(output_file, "w"), dt_dist)

def gen_sent_on_topic(idxvocab, vocabxid, start_symbol, end_symbol, cf):
    output = codecs.open(args.gen_sent_on_topic, "w", "utf-8")
    topics, entropy = tm.get_topics(sess, topn=topn)
    with tf.variable_scope("model", reuse=True, initializer=initializer):
        mgen = LM(is_training=False, vocab_size=len(idxvocab), batch_size=1, num_steps=1, config=cf, \
            reuse_conv_variables=True)

    for t in range(cf.topic_number):
        output.write("\n" + "="*100 + "\n")
        output.write("Topic " +  str(t) + ":\n")
        output.write(" ".join([ idxvocab[item] for item in topics[t] ]) + "\n\n")

        output.write("\nSentence generation (greedy; argmax):" + "\n")
        s = mgen.generate_on_topic(sess, t, vocabxid[start_symbol], 0, cf.lm_sent_len+10, vocabxid[end_symbol])
        output.write("[0] " + " ".join([ idxvocab[item] for item in s ]) + "\n")

        for temp in gen_temps:
            output.write("\nSentence generation (random; temperature = " + str(temp) + "):\n")
            for i in xrange(gen_num):
                s = mgen.generate_on_topic(sess, t, vocabxid[start_symbol], temp, cf.lm_sent_len+10, \
                    vocabxid[end_symbol])
                output.write("[" + str(i) + "] " +  " ".join([ idxvocab[item] for item in s ]) + "\n")

def write_predictions(self, inputs):
        '''
        Outputs predictions in a file named <model_name_prefix>.predictions.
        '''
        predictions = numpy.argmax(self.model.predict(inputs), axis=1)
        test_output_file = open("%s.predictions" % self.model_name_prefix, "w")
        for input_indices, prediction in zip(inputs, predictions):
            # The predictions are indices of words in padded sentences. We need to readjust them.
            padding_length = 0
            for index in input_indices:
                if numpy.all(index == 0):
                    padding_length += 1
                else:
                    break
            prediction = prediction - padding_length + 1  # +1 because the indices start at 1.
            print >>test_output_file, prediction

def process_train_data(self, input_file, onto_aware):
        print >>sys.stderr, "Reading training data"
        label_ind = []
        tagged_sentences = []
        for line in open(input_file):
            lnstrp = line.strip()
            label, tagged_sentence = lnstrp.split("\t")
            if label not in self.label_map:
                self.label_map[label] = len(self.label_map)
            label_ind.append(self.label_map[label])
            tagged_sentences.append(tagged_sentence)
        # Shuffling so that when Keras does validation split, it is not always at the end.
        sentences_and_labels = zip(tagged_sentences, label_ind)
        random.shuffle(sentences_and_labels)
        tagged_sentences, label_ind = zip(*sentences_and_labels)
        print >>sys.stderr, "Indexing training data"
        train_inputs = self.data_processor.prepare_paired_input(tagged_sentences, onto_aware=onto_aware,
                                                                for_test=False, remove_singletons=True)
        train_labels = self.data_processor.make_one_hot(label_ind)
        return train_inputs, train_labels

def process_test_data(self, input_file, onto_aware, is_labeled=True):
        if not self.model:
            raise RuntimeError, "Model not trained yet!"
        print >>sys.stderr, "Reading test data"
        label_ind = []
        tagged_sentences = []
        for line in open(input_file):
            lnstrp = line.strip()
            if is_labeled:
                label, tagged_sentence = lnstrp.split("\t")
                if label not in self.label_map:
                    self.label_map[label] = len(self.label_map)
                label_ind.append(self.label_map[label])
            else:
                tagged_sentence = lnstrp
            tagged_sentences.append(tagged_sentence)
        print >>sys.stderr, "Indexing test data"
        # Infer max sentence length if the model is trained
        input_shape = self.model.get_input_shape_at(0)[0]  # take the shape of the first of two inputs at 0.
        sentlenlimit = input_shape[1]  # (num_sentences, num_words, num_senses, num_hyps)
        test_inputs = self.data_processor.prepare_paired_input(tagged_sentences, onto_aware=onto_aware,
                                                               sentlenlimit=sentlenlimit, for_test=True)
        test_labels = self.data_processor.make_one_hot(label_ind)
        return test_inputs, test_labels

def test_zipfile_attributes():
    # With the change from ZipFile.write() to .writestr(), we need to manually
    # set member attributes.
    with temporary_directory() as tempdir:
        files = (('foo', 0o644), ('bar', 0o755))
        for filename, mode in files:
            path = os.path.join(tempdir, filename)
            with codecs.open(path, 'w', encoding='utf-8') as fp:
                fp.write(filename + '\n')
            os.chmod(path, mode)
        zip_base_name = os.path.join(tempdir, 'dummy')
        zip_filename = wheel.archive.make_wheelfile_inner(
            zip_base_name, tempdir)
        with readable_zipfile(zip_filename) as zf:
            for filename, mode in files:
                info = zf.getinfo(os.path.join(tempdir, filename))
                assert info.external_attr == (mode | 0o100000) << 16
                assert info.compress_type == zipfile.ZIP_DEFLATED

def copy_header(path):
    encoding = _get_encoding(path)
    try:
        file = codecs.open(path, "r", encoding)
    except:
        pass
    else:
        for row in file:
            if not row or row[0] != ';':
                break
            row = row.strip(" \n")
            if row == ';; okuri-ari entries.':
                break
            print(row)
        file.close()

# 2????????????????????????

def shared_locations(self):
        """
        A dictionary of shared locations whose keys are in the set 'prefix',
        'purelib', 'platlib', 'scripts', 'headers', 'data' and 'namespace'.
        The corresponding value is the absolute path of that category for
        this distribution, and takes into account any paths selected by the
        user at installation time (e.g. via command-line arguments). In the
        case of the 'namespace' key, this would be a list of absolute paths
        for the roots of namespace packages in this distribution.

        The first time this property is accessed, the relevant information is
        read from the SHARED file in the .dist-info directory.
        """
        result = {}
        shared_path = os.path.join(self.path, 'SHARED')
        if os.path.isfile(shared_path):
            with codecs.open(shared_path, 'r', encoding='utf-8') as f:
                lines = f.read().splitlines()
            for line in lines:
                key, value = line.split('=', 1)
                if key == 'namespace':
                    result.setdefault(key, []).append(value)
                else:
                    result[key] = value
        return result

def load(self, filename):
        """
        ?????????
        ????? ? ?? ??
        TrieNode??:
        {
            ?: [(??_1???_1), (??_2???_2)..],
            ...
        }
        ?? key = ? , value = [(??_1???_1), (??_2???_2)..]
        """
        with codecs.open(filename, 'r', 'utf-8') as f:
            for line in f.readlines():
                items = line.strip().split()
                if len(items) == 3:
                    self.setdefault(items[0], []
                                    ).append((int(items[1]), int(items[2])))
        return True

def process_file(self, filename, out_filename=None):
        """
        ?????????????????
        """
        results = {'words': [], 'tags': []}
        with codecs.open(filename, 'r', 'utf-8') as input_file:
            for line in input_file:
                print('PROCESS LINE:{}'.format(line))
                result = self.process(line.strip())
                print(self.format_result(result))
                results['words'].extend(result['words'])
                results['tags'].extend(result['tags'])

        if out_filename is None:
            return results
        else:
            with codecs.open(out_filename, 'w', 'utf-8') as output_file:
                output_file.write(self.format_result(results))
                output_file.write('\n')

def get_sqls(self):
        """This function extracts sqls from the java files with mybatis sqls.

        Returns:
           A list of :class:`SQL`. For example:
           [SQL('', u'select a.id, b.name from db.ac a join db.bc b on a.id=b.id or a.id=b.iid where a.cnt > 10')]

        """
        sqls = []
        for root, dirs, files in os.walk(self.dir):
            for file in files:
                if not file.endswith('.java'):
                    continue
                with codecs.open(os.path.join(root, file), 'r', encoding=self.encoding) as f:
                    sqls.extend(MybatisInlineSqlExtractor.get_selects_from_text(MybatisInlineSqlExtractor.remove_comment(f.read())))
        return sqls

def get_sqls(self):
        """This function extracts sqls from mysql general log file.


        Returns:
           A list of :class:`SQL`. For example:
           [SQL('', u'select a.id, b.name from db.ac a join db.bc b on a.id=b.id or a.id=b.iid where a.cnt > 10')]

        """
        general_log = open(self.log_path)
        log = GeneralQueryLog(general_log)
        session_db_map = {}
        sqls = []
        for entry in log:
            if entry['command'] == 'Connect':
                m = re.search('\s+on\s(?P<name>\w+)', entry['argument'])
                if m:
                    session_db_map[entry['session_id']] = m.groupdict()['name'].strip()
            elif entry['command'] == 'Init DB':
                session_db_map[entry['session_id']] = entry['argument'].strip()
            elif entry['command'] == 'Query':
                sql = entry['argument']
                if sql.strip()[:6].lower() == 'select':
                    yield SQL(session_db_map.get(entry['session_id'], ''), sql)

def __init__(self, filename, mode='a', encoding=None, delay=0):
        """
        Open the specified file and use it as the stream for logging.
        """
        #keep the absolute path, otherwise derived classes which use this
        #may come a cropper when the current directory changes
        if codecs is None:
            encoding = None
        self.baseFilename = os.path.abspath(filename)
        self.mode = mode
        self.encoding = encoding
        if delay:
            #We don't open the stream, but we still need to call the
            #Handler constructor to set level, formatter, lock etc.
            Handler.__init__(self)
            self.stream = None
        else:
            StreamHandler.__init__(self, self._open())

def test_save_svgz_filename():
    import gzip
    qr = segno.make_qr('test')
    f = tempfile.NamedTemporaryFile('wb', suffix='.svgz', delete=False)
    f.close()
    qr.save(f.name)
    f = open(f.name, mode='rb')
    expected = b'\x1f\x8b\x08'  # gzip magic number
    val = f.read(len(expected))
    f.close()
    f = gzip.open(f.name)
    try:
        content = f.read(6)
    finally:
        f.close()
    os.unlink(f.name)
    assert expected == val
    assert b'<?xml ' == content

def save(self):
        statCache = open(self.statCacheFilePath, 'w')
        self.cache['version'] = self.cacheVersion
        self.cache['date'] = self.startDate
        if not self.cache.has_key('players'):
            self.cache['players'] = {}
        self.cache['players'][self.playerName] = {
          'battles': [] if self.fastCache else self.battles,
          'account': self.account,
          'accountTanks': self.accountTanks,
          'session': self.session,
          'impact': self.impact,
          'tanks': self.tanks
        }
        if self.fastCache:
            statCache.write(json.dumps(self.cache))
        else:
            statCache.write(json.dumps(self.cache, sort_keys=True, indent=4, separators=(',', ': ')))
        statCache.close()

def _readTxt(fname):
    '''Returns array of words and word embedding matrix
    '''
    words, vectors = [], []
    hook = codecs.open(fname, 'r', 'utf-8')

    # get summary info about vectors file
    (numWords, dim) = (int(s.strip()) for s in hook.readline().split())

    for line in hook:
        chunks = line.split()
        word, vector = chunks[0].strip(), np.array([float(n) for n in chunks[1:]])
        words.append(word)
        vectors.append(vector)
    hook.close()

    assert len(words) == numWords
    for v in vectors: assert len(v) == dim

    return (words, vectors)

def read(analogy_file, setting, strings_only=False):
    multi_b = setting == settings.ALL_INFO
    multi_d = setting in [settings.ALL_INFO, settings.MULTI_ANSWER]

    analogies = {}
    with codecs.open(analogy_file, 'r', 'utf-8') as stream:
        cur_relation, cur_analogies = None, []
        for line in stream:
            # relation separators
            if line[0] == '#':
                if cur_relation:
                    analogies[cur_relation] = cur_analogies
                cur_relation = line[2:].strip()
                cur_analogies = []
            # everything else is an analogy
            else:
                analogy = _parseLine(line, multi_b, multi_d, strings_only)
                cur_analogies.append(analogy)
        analogies[cur_relation] = cur_analogies
    return analogies

def readme(path='README.rst'):
    """Try to read README.rst or return empty string if failed.

    :param str path: Path to README file.

    :return: File contents.
    :rtype: str
    """
    path = os.path.realpath(os.path.join(os.path.dirname(__file__), path))
    handle = None
    url_prefix = 'https://raw.githubusercontent.com/Robpol86/{name}/v{version}/'.format(name=NAME, version=VERSION)
    try:
        handle = codecs.open(path, encoding='utf-8')
        return handle.read(131072).replace('.. image:: docs', '.. image:: {0}docs'.format(url_prefix))
    except IOError:
        return ''
    finally:
        getattr(handle, 'close', lambda: None)()

def shared_locations(self):
        """
        A dictionary of shared locations whose keys are in the set 'prefix',
        'purelib', 'platlib', 'scripts', 'headers', 'data' and 'namespace'.
        The corresponding value is the absolute path of that category for
        this distribution, and takes into account any paths selected by the
        user at installation time (e.g. via command-line arguments). In the
        case of the 'namespace' key, this would be a list of absolute paths
        for the roots of namespace packages in this distribution.

        The first time this property is accessed, the relevant information is
        read from the SHARED file in the .dist-info directory.
        """
        result = {}
        shared_path = os.path.join(self.path, 'SHARED')
        if os.path.isfile(shared_path):
            with codecs.open(shared_path, 'r', encoding='utf-8') as f:
                lines = f.read().splitlines()
            for line in lines:
                key, value = line.split('=', 1)
                if key == 'namespace':
                    result.setdefault(key, []).append(value)
                else:
                    result[key] = value
        return result

def shared_locations(self):
        """
        A dictionary of shared locations whose keys are in the set 'prefix',
        'purelib', 'platlib', 'scripts', 'headers', 'data' and 'namespace'.
        The corresponding value is the absolute path of that category for
        this distribution, and takes into account any paths selected by the
        user at installation time (e.g. via command-line arguments). In the
        case of the 'namespace' key, this would be a list of absolute paths
        for the roots of namespace packages in this distribution.

        The first time this property is accessed, the relevant information is
        read from the SHARED file in the .dist-info directory.
        """
        result = {}
        shared_path = os.path.join(self.path, 'SHARED')
        if os.path.isfile(shared_path):
            with codecs.open(shared_path, 'r', encoding='utf-8') as f:
                lines = f.read().splitlines()
            for line in lines:
                key, value = line.split('=', 1)
                if key == 'namespace':
                    result.setdefault(key, []).append(value)
                else:
                    result[key] = value
        return result

def _download(args):
    url, folderName, index = args
    session = setupSession()
    try:
        # time out is another parameter tuned
        # fit for the network about 10Mb
        image = session.get(url, timeout = 5)
        imageName = str(index)
        with open(os.path.join(folderName, imageName),'wb') as fout:
            fout.write(image.content)
        fileExtension = imghdr.what(os.path.join(folderName, imageName))
        if fileExtension is None:
            os.remove(os.path.join(folderName, imageName))
        else:
            newName = imageName + '.' + str(fileExtension)
            os.rename(os.path.join(folderName, imageName), os.path.join(folderName, newName))

    except Exception as e:
        print ("failed to download one pages with url of " + str(url))

# basic funciton to get id list

def __init__(self):
        self.file = codecs.open('article.json', 'w', encoding="utf-8")

def __init__(self):
        self.file = open('articleexport.json', 'wb')
        self.exporter = JsonItemExporter(self.file, encoding="utf-8", ensure_ascii=False)
        self.exporter.start_exporting()

def handle(self, *args, **options):
        schema = getattr(settings, 'SWAGGER_SCHEMA', None)
        module = getattr(settings, 'SWAGGER_MODULE', None)

        if not schema:
            raise ImproperlyConfigured('You have to provide SWAGGER_SCHEMA setting pointing to desired schema')
        if not module:
            raise ImproperlyConfigured('You have to specify desired controller module name in SWAGGER_MODULE setting')

        router = SwaggerRouter()

        print('Inspecting available controllers...')

        router.update(True)
        router.process()

        print()
        print('Following classes and methods are going to be generated:')

        enum = router.get_enum()

        for name in enum:
            print("{} : {}".format(name, [x['method'] for x in enum[name]['methods']]))

        if(options['generate']):
            template = Template()
            filename = module.split('.')[-1] + '.py'
            structure = [{ 'name' : name, 'data' : data } for name, data in six.iteritems(enum)]

            print('Generating handlers ({})...'.format(filename))

            with codecs.open(filename, 'w', 'utf-8') as f:
                f.write(template.render(template_name = 'view.jinja', names = structure))

            print('Done.')
        else:
            print()
            print('Use --generate option to create them')

def load_constraints(self, constraints_filepath):
        """
        This methods reads a collection of constraints from the specified file, and returns a set with
        all constraints for which both of their constituent words are in the specified vocabulary.
        """
        constraints_filepath.strip()
        constraints = set()

        with codecs.open(constraints_filepath, "r", "utf-8") as f:
            for line in f:
                word_pair = line.split()
                if word_pair[0] in self.vocabulary and word_pair[1] in self.vocabulary and word_pair[0] != word_pair[1]:
                    constraints |= {(self.vocab_index[word_pair[0]], self.vocab_index[word_pair[1]])}

        return constraints

def load_word_vectors(file_destination):
    """
    This method loads the word vectors from the supplied file destination. 
    It loads the dictionary of word vectors and prints its size and the vector dimensionality. 
    """
    print "Loading pretrained word vectors from", file_destination
    word_dictionary = {}

    try:

        f = codecs.open(file_destination, 'r', 'utf-8') 

        for line in f:

            line = line.split(" ", 1)   
            key = unicode(line[0].lower())
            word_dictionary[key] = numpy.fromstring(line[1], dtype="float32", sep=" ")

    except:

        print "Word vectors could not be loaded from:", file_destination
        return {}

    print len(word_dictionary), "vectors loaded from", file_destination     

    return word_dictionary

def print_word_vectors(word_vectors, write_path):
    """
    This function prints the collection of word vectors to file, in a plain textual format. 
    """

    f_write = codecs.open(write_path, 'w', 'utf-8')

    for key in word_vectors:
        print >>f_write, key, " ".join(map(unicode, numpy.round(word_vectors[key], decimals=6))) 

    print "Printed", len(word_vectors), "word vectors to:", write_path

def read_relfile(filename):
    f = codecs.open(filename, "r", "utf-8")
    rels = {}
    for line in f:
        if line.find("\t") > 0:
            rel_data = line.split("\t")
            if rel_data[1].strip() == "rst":
                rels[rel_data[0].strip()+"_r"]="rst"
            elif rel_data[1].strip() == "multinuc":
                rels[rel_data[0].strip()+"_m"]="multinuc"

    return rels

def rs3topng(rs3_filepath, png_filepath=None):
    """Convert a RS3 file into a PNG image of the RST tree.

    If no output filename is given, the PNG image is returned
    as a string (which is useful for embedding).
    """
    try:
        from selenium import webdriver
        from selenium.common.exceptions import WebDriverException
    except ImportError:
        raise ImportError(
            'Please install selenium: pip install selenium')

    html_str = rs3tohtml(rs3_filepath)

    temp = tempfile.NamedTemporaryFile(suffix='.html', delete=False)
    temp.write(html_str.encode('utf8'))
    temp.close()

    try:
        driver = webdriver.PhantomJS()
    except WebDriverException as err:
        raise WebDriverException(
           'Please install phantomjs: http://phantomjs.org/\n' + err.msg)

    driver.get(temp.name)
    os.unlink(temp.name)

    png_str = driver.get_screenshot_as_png()
    if png_filepath:
        with open(png_filepath, 'w') as png_file:
            png_file.write(png_str)
    else:
        return png_str

def cli(argv=sys.argv[1:]):
    parser = argparse.ArgumentParser(
    description="Convert an RS3 file into an HTML file containing the RST tree.")
    parser.add_argument('rs3_file')
    parser.add_argument('output_file', nargs='?')
    parser.add_argument(
        '-f', '--output-format', nargs='?', default='html',
        help="output format: html (default), png")
    parser.add_argument(
        '-d', '--debug', action='store_true',
        help="output format: html (default), png")

    args = parser.parse_args(argv)

    if args.debug:
        import pudb; pudb.set_trace()

    if args.output_format == 'png':
        if args.output_file:
            rs3topng(args.rs3_file, args.output_file)
            sys.exit(0)
        else:
            sys.stderr.write("No PNG output file given.\n")
            sys.exit(1)

    if args.output_file:
        with codecs.open(args.output_file, 'w', 'utf8') as outfile:
            outfile.write(rs3tohtml(args.rs3_file))
    else:
        sys.stdout.write(rs3tohtml(args.rs3_file).encode('utf8'))

def alias_script(new_Locator):
    with codecs.open(textfile, 'r', encoding='UTF-8') as f:
        for line in f:
            #array.append(line)
            x= line.split(";")
            #Starting
            value="<alias_def>\n"
            new_Locator.write(value)
            for name in x[1:11]:
                if len(name)>1:
                    try:
                        name=name.replace("&","und")
                    except:
                        continue
                    new_Locator.write("<alt>" + name + "</alt> \n")
                    laenge=len(name)
                    z=4
                    while z <= laenge:
                        try:
                            value1="<alt>"+name[0:z]+"</alt>\n"
                            new_Locator.write(value1)
                        except:
                            continue
                        z=z+1


            value="</alias_def>\n"
            new_Locator.write(value)

def gen_sent_on_doc(docs, tags, idxvocab, vocabxid, start_symbol, end_symbol, cf):
    topics, _ = tm.get_topics(sess, topn=topn)
    topics = [ " ".join([idxvocab[w] for w in t]) for t in topics ]
    doc_text = [ item.replace("\t", "\n") for item in codecs.open(args.input_doc, "r", "utf-8").readlines() ]
    output = codecs.open(args.gen_sent_on_doc, "w", "utf-8")
    with tf.variable_scope("model", reuse=True, initializer=initializer):
        mgen = LM(is_training=False, vocab_size=len(idxvocab), batch_size=1, num_steps=1, config=cf, \
            reuse_conv_variables=True)

    for d in range(len(docs)):
        output.write("\n" + "="*100 + "\n")
        output.write("Doc " +  str(d) +":\n")
        output.write(doc_text[d])

        doc, _, _, t, _ = get_batch_doc(docs, None, tags, d, cf.doc_len, cf.tag_len, 1, vocabxid[pad_symbol])
        best_topics, best_words = mgen.get_topics_on_doc(sess, doc, t, topn)

        output.write("\nRepresentative topics:\n")
        output.write("\n".join([ ("[%.3f] %s: %s" % (item[1],str(item[0]).zfill(3),topics[item[0]])) \
            for item in best_topics ]) + "\n")

        output.write("\nRepresentative words:\n")
        output.write("\n".join([ ("[%.3f] %s" % (item[1], idxvocab[item[0]])) for item in best_words ]) + "\n")

        output.write("\nSentence generation (greedy; argmax):" + "\n")
        s = mgen.generate_on_doc(sess, doc, t, vocabxid[start_symbol], 0, cf.lm_sent_len+10, vocabxid[end_symbol])
        output.write("[0] " + " ".join([ idxvocab[item] for item in s ]) + "\n")

        for temp in gen_temps:
            output.write("\nSentence generation (random; temperature = " + str(temp) + "):\n")

            for i in xrange(gen_num):
                s = mgen.generate_on_doc(sess, doc, t, vocabxid[start_symbol], temp, cf.lm_sent_len+10, \
                    vocabxid[end_symbol])
                output.write("[" + str(i) + "] " + " ".join([ idxvocab[item] for item in s ]) + "\n")
######
#main#
######

#load the vocabulary

def gen_vocab(dummy_symbols, corpus, stopwords, vocab_minfreq, vocab_maxfreq, verbose):
    idxvocab = []
    vocabxid = defaultdict(int)
    vocab_freq = defaultdict(int)
    for line_id, line in enumerate(codecs.open(corpus, "r", "utf-8")):
        for word in line.strip().split():
            vocab_freq[word] += 1
        if line_id % 1000 == 0 and verbose:
            sys.stdout.write(str(line_id) + " processed\r")
            sys.stdout.flush()

    #add in dummy symbols into vocab
    for s in dummy_symbols:
        update_vocab(s, idxvocab, vocabxid)

    #remove low fequency words
    for w, f in sorted(vocab_freq.items(), key=operator.itemgetter(1), reverse=True):
        if f < vocab_minfreq:
            break
        else:
            update_vocab(w, idxvocab, vocabxid)

    #ignore stopwords, frequent words and symbols for the document input for topic model
    stopwords = set([item.strip().lower() for item in open(stopwords)])
    freqwords = set([item[0] for item in sorted(vocab_freq.items(), key=operator.itemgetter(1), \
        reverse=True)[:int(float(len(vocab_freq))*vocab_maxfreq)]]) #ignore top N% most frequent words for topic model
    alpha_check = re.compile("[a-zA-Z]")
    symbols = set([ w for w in vocabxid.keys() if ((alpha_check.search(w) == None) or w.startswith("'")) ])
    ignore = stopwords | freqwords | symbols | set(dummy_symbols) | set(["n't"])
    ignore = set([vocabxid[w] for w in ignore if w in vocabxid])

    return idxvocab, vocabxid, ignore

def read(fname):
    file_path = os.path.join(os.path.dirname(__file__), fname)
    return codecs.open(file_path, encoding='utf-8').read()

def process_data(self, input_file, onto_aware, for_test=False):
        '''
        Reads an input file and makes input for training or testing.
        '''
        dataset_type = "test" if for_test else "training"
        print >>sys.stderr, "Reading %s data" % dataset_type
        label_ind = []
        tagged_sentences = []
        max_sentence_length = 0
        all_sentence_lengths = []
        for line in open(input_file):
            lnstrp = line.strip()
            label, tagged_sentence = lnstrp.split("\t")
            sentence_length = len(tagged_sentence.split())
            all_sentence_lengths.append(sentence_length)
            if sentence_length > max_sentence_length:
                max_sentence_length = sentence_length
            label_ind.append(int(label))
            tagged_sentences.append(tagged_sentence)
        if for_test:
            if not self.model:
                raise RuntimeError("Model not trained yet!")
            input_shape = self.model.get_input_shape_at(0)  # (num_sentences, num_words, ...)
            sentlenlimit = input_shape[1]
        else:
            sentlenlimit = max_sentence_length
        # We need to readjust the labels because padding would affect the sentence indices.
        for i in range(len(label_ind)):
            length = all_sentence_lengths[i]
            label_ind[i] += sentlenlimit - length
        if not for_test:
            # Shuffling so that when Keras does validation split, it is not always at the end.
            sentences_and_labels = zip(tagged_sentences, label_ind)
            random.shuffle(sentences_and_labels)
            tagged_sentences, label_ind = zip(*sentences_and_labels)
        print >>sys.stderr, "Indexing %s data" % dataset_type
        inputs = self.data_processor.prepare_input(tagged_sentences, onto_aware=onto_aware,
                                                   sentlenlimit=sentlenlimit, for_test=for_test,
                                                   remove_singletons=False)
        labels = self.data_processor.make_one_hot(label_ind)
        return inputs, labels

def print_attention_values(self, input_file, test_inputs, output_file):
        sent_attention_outputs = self.get_attention(test_inputs)
        tagged_sentences = [x.strip().split("\t")[1] for x in codecs.open(input_file).readlines()]
        outfile = codecs.open(output_file, "w", "utf-8")
        full_json_struct = []
        for sent_attention, tagged_sentence in zip(sent_attention_outputs, tagged_sentences):
            sent_json = {}
            sent_json["input"] = tagged_sentence
            sent_json["tokens"] = []
            tagged_words = tagged_sentence.split()
            for tagged_word, word_attention in zip(tagged_words, sent_attention):
                token_json = {}
                token_json["surface_form"] = tagged_word
                token_json["senses"] = []
                for sense_num, sense_attention in enumerate(word_attention):
                    if len(sense_attention) == 0:
                        continue
                    sense_json = {}
                    sense_json["id"] = sense_num
                    sense_json["hypernyms"] = []
                    for hyp_name, hyp_att in sense_attention:
                        if isinstance(hyp_att, tuple):
                            # Averaging forward and backward attention
                            sense_json["hypernyms"].append({hyp_name: {"forward": float(hyp_att[0]),
                                                                       "backward": float(hyp_att[1])}})
                        else:
                            sense_json["hypernyms"].append({hyp_name: float(hyp_att)})
                    token_json["senses"].append(sense_json)
                sent_json["tokens"].append(token_json)
            full_json_struct.append(sent_json)
        print >>outfile, json.dumps(full_json_struct, indent=2)
        outfile.close()

def load_model(self, epoch=None):
        '''
        Loads a saved model. If epoch id is provided, will load the corresponding model. Or else,
        will load the best model.
        '''
        if not epoch:
            self.model = load_model("%s.model" % self.model_name_prefix,
                                    custom_objects=self.custom_objects)
        else:
            self.model = load_model("%s_%d.model" % (self.model_name_prefix, epoch),
                                    custom_objects=self.custom_objects)
        self.data_processor = pickle.load(open("%s.dataproc" % self.model_name_prefix, "rb"))
        self.label_map = pickle.load(open("%s.labelmap" % self.model_name_prefix, "rb"))

def print_attention_values(self, input_file, test_inputs, output_file):
        onto_aware = True
        sent1_attention_outputs = self.get_attention(test_inputs[0])
        sent2_attention_outputs = self.get_attention(test_inputs[1])
        tagged_sentences = [x.strip().split("\t")[1] for x in codecs.open(input_file).readlines()]
        outfile = codecs.open(output_file, "w", "utf-8")
        for sent1_attention, sent2_attention, tagged_sentence in zip(sent1_attention_outputs, sent2_attention_outputs, tagged_sentences):
            print >>outfile, tagged_sentence
            print >>outfile, "Sentence 1:"
            for word_attention in sent1_attention:
                for sense_attention in word_attention:
                    print >>outfile, " ".join(["%s:%f" % (hyp, hyp_att) for hyp, hyp_att in sense_attention])
                print >>outfile
            print >>outfile, "\nSentence 2:"
            for word_attention in sent2_attention:
                for sense_attention in word_attention:
                    print >>outfile, " ".join(["%s:%f" % (hyp, hyp_att) for hyp, hyp_att in sense_attention])
                print >>outfile
        outfile.close()

def fetch_all_transitions(self, language, ngram_length):
        """ Generate a dict of counts for transitions for all n-grams in the language word list """
        wordlist = os.path.join(os.path.dirname(__file__), "wordlists/{0}.txt".format(language))
        if not os.path.exists(wordlist):
            raise SystemError("Language '{0}' does not exist".format(language))

        all_grams = []
        with codecs.open(wordlist, 'r', encoding='utf-8') as f:
            for line in f:
                words = line.strip('\n').lower().split()
                ngrams = reduce(lambda x, y: x + y, map(lambda word: self.find_ngrams(word, ngram_length), words))
                all_grams += ngrams
        return dict(Counter(all_grams))

def _init_posix(vars):
    """Initialize the module as appropriate for POSIX systems."""
    # load the installed Makefile:
    makefile = get_makefile_filename()
    try:
        _parse_makefile(makefile, vars)
    except IOError as e:
        msg = "invalid Python installation: unable to open %s" % makefile
        if hasattr(e, "strerror"):
            msg = msg + " (%s)" % e.strerror
        raise IOError(msg)
    # load the installed pyconfig.h:
    config_h = get_config_h_filename()
    try:
        with open(config_h) as f:
            parse_config_h(f, vars)
    except IOError as e:
        msg = "invalid Python installation: unable to open %s" % config_h
        if hasattr(e, "strerror"):
            msg = msg + " (%s)" % e.strerror
        raise IOError(msg)
    # On AIX, there are wrong paths to the linker scripts in the Makefile
    # -- these paths are relative to the Python source, but when installed
    # the scripts are in another directory.
    if _PYTHON_BUILD:
        vars['LDSHARED'] = vars['BLDSHARED']

def write_exports(self, exports):
        """
        Write a dictionary of exports to a file in .ini format.
        :param exports: A dictionary of exports, mapping an export category to
                        a list of :class:`ExportEntry` instances describing the
                        individual export entries.
        """
        rf = self.get_distinfo_file(EXPORTS_FILENAME)
        with open(rf, 'w') as f:
            write_exports(exports, f)

def write_installed_files(self, paths, prefix, dry_run=False):
        """
        Writes the ``RECORD`` file, using the ``paths`` iterable passed in. Any
        existing ``RECORD`` file is silently overwritten.

        prefix is used to determine when to write absolute paths.
        """
        prefix = os.path.join(prefix, '')
        base = os.path.dirname(self.path)
        base_under_prefix = base.startswith(prefix)
        base = os.path.join(base, '')
        record_path = self.get_distinfo_file('RECORD')
        logger.info('creating %s', record_path)
        if dry_run:
            return None
        with CSVWriter(record_path) as writer:
            for path in paths:
                if os.path.isdir(path) or path.endswith(('.pyc', '.pyo')):
                    # do not put size and hash, as in PEP-376
                    hash_value = size = ''
                else:
                    size = '%d' % os.path.getsize(path)
                    with open(path, 'rb') as fp:
                        hash_value = self.get_hash(fp.read())
                if path.startswith(base) or (base_under_prefix and
                                             path.startswith(prefix)):
                    path = os.path.relpath(path, base)
                writer.writerow((path, hash_value, size))

            # add the RECORD file itself
            if record_path.startswith(base):
                record_path = os.path.relpath(record_path, base)
            writer.writerow((record_path, '', ''))
        return record_path

def check_installed_files(self):
        """
        Checks that the hashes and sizes of the files in ``RECORD`` are
        matched by the files themselves. Returns a (possibly empty) list of
        mismatches. Each entry in the mismatch list will be a tuple consisting
        of the path, 'exists', 'size' or 'hash' according to what didn't match
        (existence is checked first, then size, then hash), the expected
        value and the actual value.
        """
        mismatches = []
        base = os.path.dirname(self.path)
        record_path = self.get_distinfo_file('RECORD')
        for path, hash_value, size in self.list_installed_files():
            if not os.path.isabs(path):
                path = os.path.join(base, path)
            if path == record_path:
                continue
            if not os.path.exists(path):
                mismatches.append((path, 'exists', True, False))
            elif os.path.isfile(path):
                actual_size = str(os.path.getsize(path))
                if size and actual_size != size:
                    mismatches.append((path, 'size', size, actual_size))
                elif hash_value:
                    if '=' in hash_value:
                        hasher = hash_value.split('=', 1)[0]
                    else:
                        hasher = None

                    with open(path, 'rb') as f:
                        actual_hash = self.get_hash(f.read(), hasher)
                        if actual_hash != hash_value:
                            mismatches.append((path, 'hash', hash_value, actual_hash))
        return mismatches

def list_installed_files(self):
        """
        Iterates over the ``installed-files.txt`` entries and returns a tuple
        ``(path, hash, size)`` for each line.

        :returns: a list of (path, hash, size)
        """

        def _md5(path):
            f = open(path, 'rb')
            try:
                content = f.read()
            finally:
                f.close()
            return hashlib.md5(content).hexdigest()

        def _size(path):
            return os.stat(path).st_size

        record_path = os.path.join(self.path, 'installed-files.txt')
        result = []
        if os.path.exists(record_path):
            with codecs.open(record_path, 'r', encoding='utf-8') as f:
                for line in f:
                    line = line.strip()
                    p = os.path.normpath(os.path.join(self.path, line))
                    # "./" is present as a marker between installed files
                    # and installation metadata files
                    if not os.path.exists(p):
                        logger.warning('Non-existent file: %s', p)
                        if p.endswith(('.pyc', '.pyo')):
                            continue
                        #otherwise fall through and fail
                    if not os.path.isdir(p):
                        result.append((p, _md5(p), _size(p)))
            result.append((record_path, None, None))
        return result

def list_distinfo_files(self, absolute=False):
        """
        Iterates over the ``installed-files.txt`` entries and returns paths for
        each line if the path is pointing to a file located in the
        ``.egg-info`` directory or one of its subdirectories.

        :parameter absolute: If *absolute* is ``True``, each returned path is
                          transformed into a local absolute path. Otherwise the
                          raw value from ``installed-files.txt`` is returned.
        :type absolute: boolean
        :returns: iterator of paths
        """
        record_path = os.path.join(self.path, 'installed-files.txt')
        skip = True
        with codecs.open(record_path, 'r', encoding='utf-8') as f:
            for line in f:
                line = line.strip()
                if line == './':
                    skip = False
                    continue
                if not skip:
                    p = os.path.normpath(os.path.join(self.path, line))
                    if p.startswith(self.path):
                        if absolute:
                            yield p
                        else:
                            yield line

def read(self, filepath):
        """Read the metadata values from a file path."""
        fp = codecs.open(filepath, 'r', encoding='utf-8')
        try:
            self.read_file(fp)
        finally:
            fp.close()

def write(self, filepath, skip_unknown=False):
        """Write the metadata fields to filepath."""
        fp = codecs.open(filepath, 'w', encoding='utf-8')
        try:
            self.write_file(fp, skip_unknown)
        finally:
            fp.close()