Apply 2to3 to current paddle main python code

paddle/fluid/pybind/pybind.cc

@@ -394,8 +394,10 @@ All parameter, weight, gradient are variables in Paddle.
     InferenceOptimize(*(origin.Proto()), &pruned_desc);
     return new ProgramDesc(pruned_desc);
   });
-  m.def("empty_var_name", []() { return framework::kEmptyVarName; });
-  m.def("grad_var_suffix", []() { return framework::kGradVarSuffix; });
+  m.def("empty_var_name",
+        []() { return std::string(framework::kEmptyVarName); });
+  m.def("grad_var_suffix",
+        []() { return std::string(framework::kGradVarSuffix); });
   m.def_submodule(
        "var_names",
        "The module will return special predefined variable name in Paddle")

python/paddle/dataset/cifar.py

@@ -28,7 +28,7 @@ images per class.
 
 """
 
-import cPickle
+import pickle
 import itertools
 import numpy
 import paddle.dataset.common
@@ -48,7 +48,7 @@ def reader_creator(filename, sub_name, cycle=False):
         data = batch['data']
         labels = batch.get('labels', batch.get('fine_labels', None))
         assert labels is not None
-        for sample, label in itertools.izip(data, labels):
+        for sample, label in zip(data, labels):
             yield (sample / 255.0).astype(numpy.float32), int(label)
 
     def reader():
@@ -58,7 +58,7 @@ def reader_creator(filename, sub_name, cycle=False):
 
             while True:
                 for name in names:
-                    batch = cPickle.load(f.extractfile(name))
+                    batch = pickle.load(f.extractfile(name))
                     for item in read_batch(batch):
                         yield item
                 if not cycle:

python/paddle/dataset/common.py

@@ -20,9 +20,9 @@ import shutil
 import sys
 import importlib
 import paddle.dataset
-import cPickle
+import pickle
 import glob
-import cPickle as pickle
+import pickle as pickle
 
 __all__ = [
     'DATA_HOME',
@@ -75,13 +75,13 @@ def download(url, module_name, md5sum, save_name=None):
     retry_limit = 3
     while not (os.path.exists(filename) and md5file(filename) == md5sum):
         if os.path.exists(filename):
-            print "file md5", md5file(filename), md5sum
+            print(("file md5", md5file(filename), md5sum))
         if retry < retry_limit:
             retry += 1
         else:
             raise RuntimeError("Cannot download {0} within retry limit {1}".
                                format(url, retry_limit))
-        print "Cache file %s not found, downloading %s" % (filename, url)
+        print(("Cache file %s not found, downloading %s" % (filename, url)))
         r = requests.get(url, stream=True)
         total_length = r.headers.get('content-length')
 
@@ -104,8 +104,9 @@ def download(url, module_name, md5sum, save_name=None):
 
 
 def fetch_all():
-    for module_name in filter(lambda x: not x.startswith("__"),
-                              dir(paddle.dataset)):
+    for module_name in [
+            x for x in dir(paddle.dataset) if not x.startswith("__")
+    ]:
         if "fetch" in dir(
                 importlib.import_module("paddle.dataset.%s" % module_name)):
             getattr(
@@ -114,8 +115,9 @@ def fetch_all():
 
 
 def fetch_all_recordio(path):
-    for module_name in filter(lambda x: not x.startswith("__"),
-                              dir(paddle.dataset)):
+    for module_name in [
+            x for x in dir(paddle.dataset) if not x.startswith("__")
+    ]:
         if "convert" in dir(
                 importlib.import_module("paddle.dataset.%s" % module_name)) and \
                 not module_name == "common":
@@ -126,7 +128,7 @@ def fetch_all_recordio(path):
                 "convert")(ds_path)
 
 
-def split(reader, line_count, suffix="%05d.pickle", dumper=cPickle.dump):
+def split(reader, line_count, suffix="%05d.pickle", dumper=pickle.dump):
     """
     you can call the function as:
 
@@ -167,7 +169,7 @@ def split(reader, line_count, suffix="%05d.pickle", dumper=cPickle.dump):
 def cluster_files_reader(files_pattern,
                          trainer_count,
                          trainer_id,
-                         loader=cPickle.load):
+                         loader=pickle.load):
     """
     Create a reader that yield element from the given files, select
     a file set according trainer count and trainer_id
@@ -188,7 +190,7 @@ def cluster_files_reader(files_pattern,
         my_file_list = []
         for idx, fn in enumerate(file_list):
             if idx % trainer_count == trainer_id:
-                print "append file: %s" % fn
+                print(("append file: %s" % fn))
                 my_file_list.append(fn)
         for fn in my_file_list:
             with open(fn, "r") as f:
@@ -221,7 +223,7 @@ def convert(output_path, reader, line_count, name_prefix):
         for l in lines:
             # FIXME(Yancey1989):
             # dumps with protocol: pickle.HIGHEST_PROTOCOL
-            writer.write(cPickle.dumps(l))
+            writer.write(pickle.dumps(l))
         writer.close()
 
     lines = []

python/paddle/dataset/conll05.py

@@ -87,12 +87,12 @@ def corpus_reader(data_path, words_name, props_name):
             sentences = []
             labels = []
             one_seg = []
-            for word, label in itertools.izip(words_file, props_file):
+            for word, label in zip(words_file, props_file):
                 word = word.strip()
                 label = label.strip().split()
 
                 if len(label) == 0:  # end of sentence
-                    for i in xrange(len(one_seg[0])):
+                    for i in range(len(one_seg[0])):
                         a_kind_lable = [x[i] for x in one_seg]
                         labels.append(a_kind_lable)
 

python/paddle/dataset/flowers.py

@@ -28,10 +28,10 @@ Graphics and Image Processing (2008)
 http://www.robots.ox.ac.uk/~vgg/publications/papers/nilsback08.{pdf,ps.gz}.
 
 """
-import cPickle
+import pickle
 import itertools
 import functools
-from common import download
+from .common import download
 import tarfile
 import scipy.io as scio
 from paddle.dataset.image import *
@@ -116,10 +116,10 @@ def reader_creator(data_file,
                 file = file.strip()
                 batch = None
                 with open(file, 'r') as f:
-                    batch = cPickle.load(f)
+                    batch = pickle.load(f)
                 data = batch['data']
                 labels = batch['label']
-                for sample, label in itertools.izip(data, batch['label']):
+                for sample, label in zip(data, batch['label']):
                     yield sample, int(label) - 1
             if not cycle:
                 break

python/paddle/dataset/image.py

@@ -36,7 +36,7 @@ except ImportError:
     cv2 = None
 import os
 import tarfile
-import cPickle
+import pickle
 
 __all__ = [
     "load_image_bytes", "load_image", "resize_short", "to_chw", "center_crop",
@@ -86,10 +86,10 @@ def batch_images_from_tar(data_file,
                 output = {}
                 output['label'] = labels
                 output['data'] = data
-                cPickle.dump(
+                pickle.dump(
                     output,
                     open('%s/batch_%d' % (out_path, file_id), 'w'),
-                    protocol=cPickle.HIGHEST_PROTOCOL)
+                    protocol=pickle.HIGHEST_PROTOCOL)
                 file_id += 1
                 data = []
                 labels = []
@@ -97,10 +97,10 @@ def batch_images_from_tar(data_file,
         output = {}
         output['label'] = labels
         output['data'] = data
-        cPickle.dump(
+        pickle.dump(
             output,
             open('%s/batch_%d' % (out_path, file_id), 'w'),
-            protocol=cPickle.HIGHEST_PROTOCOL)
+            protocol=pickle.HIGHEST_PROTOCOL)
 
     with open(meta_file, 'a') as meta:
         for file in os.listdir(out_path):

python/paddle/dataset/imdb.py

@@ -42,13 +42,13 @@ def tokenize(pattern):
         # sequential access of member files, other than
         # tarfile.extractfile, which does random access and might
         # destroy hard disks.
-        tf = tarf.next()
+        tf = next(tarf)
         while tf != None:
             if bool(pattern.match(tf.name)):
                 # newline and punctuations removal and ad-hoc tokenization.
                 yield tarf.extractfile(tf).read().rstrip("\n\r").translate(
                     None, string.punctuation).lower().split()
-            tf = tarf.next()
+            tf = next(tarf)
 
 
 def build_dict(pattern, cutoff):
@@ -62,11 +62,11 @@ def build_dict(pattern, cutoff):
             word_freq[word] += 1
 
     # Not sure if we should prune less-frequent words here.
-    word_freq = filter(lambda x: x[1] > cutoff, word_freq.items())
+    word_freq = [x for x in list(word_freq.items()) if x[1] > cutoff]
 
     dictionary = sorted(word_freq, key=lambda x: (-x[1], x[0]))
     words, _ = list(zip(*dictionary))
-    word_idx = dict(zip(words, xrange(len(words))))
+    word_idx = dict(list(zip(words, list(range(len(words))))))
     word_idx['<unk>'] = len(words)
     return word_idx
 

python/paddle/dataset/imikolov.py

@@ -64,11 +64,11 @@ def build_dict(min_word_freq=50):
             # remove <unk> for now, since we will set it as last index
             del word_freq['<unk>']
 
-        word_freq = filter(lambda x: x[1] > min_word_freq, word_freq.items())
+        word_freq = [x for x in list(word_freq.items()) if x[1] > min_word_freq]
 
         word_freq_sorted = sorted(word_freq, key=lambda x: (-x[1], x[0]))
         words, _ = list(zip(*word_freq_sorted))
-        word_idx = dict(zip(words, xrange(len(words))))
+        word_idx = dict(list(zip(words, list(range(len(words))))))
         word_idx['<unk>'] = len(words)
 
     return word_idx

python/paddle/dataset/mnist.py

@@ -65,7 +65,7 @@ def reader_creator(image_filename, label_filename, buffer_size):
 
                 images = images / 255.0 * 2.0 - 1.0
 
-                for i in xrange(buffer_size):
+                for i in range(buffer_size):
                     yield images[i, :], int(labels[i])
         finally:
             try:

python/paddle/dataset/movielens.py

@@ -187,7 +187,7 @@ def max_movie_id():
     Get the maximum value of movie id.
     """
     __initialize_meta_info__()
-    return reduce(__max_index_info__, MOVIE_INFO.viewvalues()).index
+    return reduce(__max_index_info__, list(MOVIE_INFO.values())).index
 
 
 def max_user_id():
@@ -195,7 +195,7 @@ def max_user_id():
     Get the maximum value of user id.
     """
     __initialize_meta_info__()
-    return reduce(__max_index_info__, USER_INFO.viewvalues()).index
+    return reduce(__max_index_info__, list(USER_INFO.values())).index
 
 
 def __max_job_id_impl__(a, b):
@@ -210,7 +210,7 @@ def max_job_id():
     Get the maximum value of job id.
     """
     __initialize_meta_info__()
-    return reduce(__max_job_id_impl__, USER_INFO.viewvalues()).job_id
+    return reduce(__max_job_id_impl__, list(USER_INFO.values())).job_id
 
 
 def movie_categories():
@@ -243,7 +243,7 @@ def unittest():
     for test_count, _ in enumerate(test()()):
         pass
 
-    print train_count, test_count
+    print((train_count, test_count))
 
 
 def fetch():

python/paddle/dataset/mq2007.py

@@ -26,7 +26,7 @@ http://research.microsoft.com/en-us/um/beijing/projects/letor/LETOR4.0/Data/MQ20
 import os
 import functools
 import rarfile
-from common import download
+from .common import download
 import numpy as np
 
 # URL = "http://research.microsoft.com/en-us/um/beijing/projects/letor/LETOR4.0/Data/MQ2007.rar"
@@ -330,4 +330,4 @@ if __name__ == "__main__":
     mytest = functools.partial(
         __reader__, filepath="MQ2007/MQ2007/Fold1/sample", format="listwise")
     for label, query in mytest():
-        print label, query
+        print((label, query))

python/paddle/dataset/sentiment.py

@@ -43,11 +43,11 @@ def download_data_if_not_yet():
             nltk.data.path.append(paddle.dataset.common.DATA_HOME)
         movie_reviews.categories()
     except LookupError:
-        print "Downloading movie_reviews data set, please wait....."
+        print("Downloading movie_reviews data set, please wait.....")
         nltk.download(
             'movie_reviews', download_dir=paddle.dataset.common.DATA_HOME)
-        print "Download data set success....."
-        print "Path is " + nltk.data.find('corpora/movie_reviews').path
+        print("Download data set success.....")
+        print(("Path is " + nltk.data.find('corpora/movie_reviews').path))
 
 
 def get_word_dict():
@@ -64,7 +64,7 @@ def get_word_dict():
         for field in movie_reviews.fileids(category):
             for words in movie_reviews.words(field):
                 word_freq_dict[words] += 1
-    words_sort_list = word_freq_dict.items()
+    words_sort_list = list(word_freq_dict.items())
     words_sort_list.sort(cmp=lambda a, b: b[1] - a[1])
     for index, word in enumerate(words_sort_list):
         words_freq_sorted.append((word[0], index))
@@ -80,7 +80,8 @@ def sort_files():
     files_list = list()
     neg_file_list = movie_reviews.fileids('neg')
     pos_file_list = movie_reviews.fileids('pos')
-    files_list = list(chain.from_iterable(zip(neg_file_list, pos_file_list)))
+    files_list = list(
+        chain.from_iterable(list(zip(neg_file_list, pos_file_list))))
     return files_list
 
 

python/paddle/dataset/tests/common_test.py

@@ -36,7 +36,7 @@ class TestCommon(unittest.TestCase):
     def test_split(self):
         def test_reader():
             def reader():
-                for x in xrange(10):
+                for x in range(10):
                     yield x
 
             return reader
@@ -49,7 +49,7 @@ class TestCommon(unittest.TestCase):
 
     def test_cluster_file_reader(self):
         _, temp_path = tempfile.mkstemp()
-        for x in xrange(5):
+        for x in range(5):
             with open(temp_path + '/%05d.test' % x) as f:
                 f.write('%d\n' % x)
         reader = paddle.dataset.common.cluster_files_reader(
@@ -63,7 +63,7 @@ class TestCommon(unittest.TestCase):
 
         def test_reader():
             def reader():
-                for x in xrange(record_num):
+                for x in range(record_num):
                     yield x
 
             return reader

python/paddle/dataset/tests/imikolov_test.py

@@ -59,7 +59,7 @@ class TestMikolov(unittest.TestCase):
         self.assertEqual(first_line, read_line)
 
     def test_total(self):
-        _, idx = zip(*WORD_DICT.items())
+        _, idx = list(zip(*list(WORD_DICT.items())))
         self.assertEqual(sorted(idx)[-1], len(WORD_DICT) - 1)
 
 

python/paddle/dataset/tests/test_sentiment.py

@@ -24,9 +24,8 @@ from nltk.corpus import movie_reviews
 class TestSentimentMethods(unittest.TestCase):
     def test_get_word_dict(self):
         word_dict = st.get_word_dict()[0:10]
-        test_word_list = [(u',', 0), (u'the', 1), (u'.', 2), (u'a', 3),
-                          (u'and', 4), (u'of', 5), (u'to', 6), (u"'", 7),
-                          (u'is', 8), (u'in', 9)]
+        test_word_list = [(',', 0), ('the', 1), ('.', 2), ('a', 3), ('and', 4),
+                          ('of', 5), ('to', 6), ("'", 7), ('is', 8), ('in', 9)]
         for idx, each in enumerate(word_dict):
             self.assertEqual(each, test_word_list[idx])
         self.assertTrue("/root/.cache/paddle/dataset" in nltk.data.path)

python/paddle/dataset/uci_housing.py

@@ -49,9 +49,12 @@ def feature_range(maximums, minimums):
     import matplotlib.pyplot as plt
     fig, ax = plt.subplots()
     feature_num = len(maximums)
-    ax.bar(range(feature_num), maximums - minimums, color='r', align='center')
+    ax.bar(list(range(feature_num)),
+           maximums - minimums,
+           color='r',
+           align='center')
     ax.set_title('feature scale')
-    plt.xticks(range(feature_num), feature_names)
+    plt.xticks(list(range(feature_num)), feature_names)
     plt.xlim([-1, feature_num])
     fig.set_figheight(6)
     fig.set_figwidth(10)
@@ -71,7 +74,7 @@ def load_data(filename, feature_num=14, ratio=0.8):
     maximums, minimums, avgs = data.max(axis=0), data.min(axis=0), data.sum(
         axis=0) / data.shape[0]
     feature_range(maximums[:-1], minimums[:-1])
-    for i in xrange(feature_num - 1):
+    for i in range(feature_num - 1):
         data[:, i] = (data[:, i] - avgs[i]) / (maximums[i] - minimums[i])
     offset = int(data.shape[0] * ratio)
     UCI_TRAIN_DATA = data[:offset]

python/paddle/dataset/wmt14.py

@@ -154,8 +154,8 @@ def get_dict(dict_size, reverse=True):
     tar_file = paddle.dataset.common.download(URL_TRAIN, 'wmt14', MD5_TRAIN)
     src_dict, trg_dict = __read_to_dict(tar_file, dict_size)
     if reverse:
-        src_dict = {v: k for k, v in src_dict.items()}
-        trg_dict = {v: k for k, v in trg_dict.items()}
+        src_dict = {v: k for k, v in list(src_dict.items())}
+        trg_dict = {v: k for k, v in list(trg_dict.items())}
     return src_dict, trg_dict
 
 

python/paddle/dataset/wmt16.py

@@ -70,7 +70,9 @@ def __build_dict(tar_file, dict_size, save_path, lang):
         fout.write("%s\n%s\n%s\n" % (START_MARK, END_MARK, UNK_MARK))
         for idx, word in enumerate(
                 sorted(
-                    word_dict.iteritems(), key=lambda x: x[1], reverse=True)):
+                    iter(list(word_dict.items())),
+                    key=lambda x: x[1],
+                    reverse=True)):
             if idx + 3 == dict_size: break
             fout.write("%s\n" % (word[0]))
 

python/paddle/fluid/__init__.py

@@ -14,49 +14,49 @@
 
 from __future__ import print_function
 # import all class inside framework into fluid module
-import framework
-from framework import *
+from . import framework
+from .framework import *
 # import all class inside executor into fluid module
-import executor
-from executor import *
-
-import trainer
-from trainer import Trainer
-from trainer import BeginEpochEvent
-from trainer import EndEpochEvent
-from trainer import BeginStepEvent
-from trainer import EndStepEvent
-from trainer import CheckpointConfig
-
-import inferencer
-from inferencer import Inferencer
-
-import io
-import evaluator
-import initializer
-import layers
-import contrib
-import nets
-import optimizer
-import backward
-import regularizer
-import average
-import metrics
-import transpiler
-from param_attr import ParamAttr, WeightNormParamAttr
-from data_feeder import DataFeeder
-from core import LoDTensor, LoDTensorArray, CPUPlace, CUDAPlace, CUDAPinnedPlace, Scope
-from transpiler import DistributeTranspiler, InferenceTranspiler, \
+from . import executor
+from .executor import *
+
+from . import trainer
+from .trainer import Trainer
+from .trainer import BeginEpochEvent
+from .trainer import EndEpochEvent
+from .trainer import BeginStepEvent
+from .trainer import EndStepEvent
+from .trainer import CheckpointConfig
+
+from . import inferencer
+from .inferencer import Inferencer
+
+from . import io
+from . import evaluator
+from . import initializer
+from . import layers
+from . import contrib
+from . import nets
+from . import optimizer
+from . import backward
+from . import regularizer
+from . import average
+from . import metrics
+from . import transpiler
+from .param_attr import ParamAttr, WeightNormParamAttr
+from .data_feeder import DataFeeder
+from .core import LoDTensor, LoDTensorArray, CPUPlace, CUDAPlace, CUDAPinnedPlace, Scope
+from .transpiler import DistributeTranspiler, InferenceTranspiler, \
     memory_optimize, release_memory, DistributeTranspilerConfig
-from concurrency import (Go, make_channel, channel_send, channel_recv,
-                         channel_close, Select)
-from lod_tensor import create_lod_tensor, create_random_int_lodtensor
-import clip
-import profiler
-import unique_name
-import recordio_writer
-import parallel_executor
-from parallel_executor import *
+from .concurrency import (Go, make_channel, channel_send, channel_recv,
+                          channel_close, Select)
+from .lod_tensor import create_lod_tensor, create_random_int_lodtensor
+from . import clip
+from . import profiler
+from . import unique_name
+from . import recordio_writer
+from . import parallel_executor
+from .parallel_executor import *
 from paddle.fluid.layers.math_op_patch import monkey_patch_variable
 
 Tensor = LoDTensor
@@ -99,8 +99,8 @@ def __bootstrap__():
         None
     """
     import sys
-    import core
     import os
+    from . import core
 
     in_test = 'unittest' in sys.modules
 

python/paddle/fluid/annotations.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from __future__ import print_function
 import functools
 import sys
 
@@ -28,7 +29,7 @@ def deprecated(since, instead, extra_message=""):
 
         @functools.wraps(func)
         def wrapper(*args, **kwargs):
-            print >> sys.stderr, err_msg
+            print(err_msg, file=sys.stderr)
             return func(*args, **kwargs)
 
         wrapper.__doc__ += "\n    "

python/paddle/fluid/clip.py

@@ -15,8 +15,8 @@
 import copy
 
 import functools
-import layers
-import framework
+from . import layers
+from . import framework
 from . import core
 
 __all__ = [
@@ -80,8 +80,7 @@ def error_clip_callback(block, context):
     # the context is a grad_to_var map
     grad_to_var = context
     op_desc = block.desc.op(block.desc.op_size() - 1)
-    for grad_n in filter(lambda n: grad_to_var.has_key(n),
-                         op_desc.output_arg_names()):
+    for grad_n in [n for n in op_desc.output_arg_names() if n in grad_to_var]:
         fwd_var = block._var_recursive(grad_to_var[grad_n])
         error_clip = getattr(fwd_var, "error_clip", None)
         if not (error_clip is None or isinstance(error_clip,
@@ -247,7 +246,7 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
     """
 
     def __init__(self, clip_norm, group_name="default_group"):
-        if not isinstance(group_name, basestring):
+        if not isinstance(group_name, str):
             raise TypeError("'group_name' must be a basestring.")
 
         self.clip_norm = clip_norm
@@ -284,7 +283,7 @@ class GradientClipByGlobalNorm(BaseGradientClipAttr):
                 x=clip_var,
                 y=layers.elementwise_max(
                     x=clip_var, y=group_norm_var))
-            assert group_scale_var.shape == (1L, )
+            assert group_scale_var.shape == (1, )
             self.context[group_scale_name] = group_scale_var
 
         new_grad = layers.elementwise_mul(
@@ -313,7 +312,7 @@ def set_gradient_clip(clip, param_list=None, program=None):
         program = framework.default_main_program()
     if param_list is None:
         param_list = program.block(0).all_parameters()
-    if all(isinstance(elem, basestring) for elem in param_list):
+    if all(isinstance(elem, str) for elem in param_list):
         param_list = [program.block(0).var(elem) for elem in param_list]
     if not all(isinstance(elem, framework.Parameter) for elem in param_list):
         raise TypeError(

python/paddle/fluid/concurrency.py

@@ -12,11 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from layers.control_flow import BlockGuard, equal
+from .layers.control_flow import BlockGuard, equal
 from .framework import Operator
-from layer_helper import LayerHelper, unique_name
-from layers import fill_constant
-import core
+from .layer_helper import LayerHelper, unique_name
+from .layers import fill_constant
+from . import core
 
 __all__ = [
     'Go', 'make_channel', 'channel_send', 'channel_recv', 'channel_close',

python/paddle/fluid/data_feeder.py

@@ -12,14 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from __future__ import print_function
-import core
+from . import core
 import numpy
 import os
 import six.moves as six
 import multiprocessing
 
-from framework import Variable, default_main_program
+from .framework import Variable, default_main_program
 
 __all__ = ['DataFeeder']
 
@@ -142,7 +141,7 @@ class DataFeeder(object):
         if program is None:
             program = default_main_program()
         for each_var in feed_list:
-            if isinstance(each_var, basestring):
+            if isinstance(each_var, str):
                 each_var = program.block(0).var(each_var)
             if not isinstance(each_var, Variable):
                 raise TypeError("Feed list should contain a list of variable")

python/paddle/fluid/debugger.py

@@ -14,8 +14,8 @@
 
 import sys
 import re
-from graphviz import GraphPreviewGenerator
-import proto.framework_pb2 as framework_pb2
+from .graphviz import GraphPreviewGenerator
+from .proto import framework_pb2
 from google.protobuf import text_format
 
 _vartype2str_ = [