python 2 compatibility

examples/run_swag.py

@@ -15,22 +15,25 @@
 # limitations under the License.
 """BERT finetuning runner."""
 
+import argparse
+import csv
 import logging
 import os
-import argparse
 import random
-from tqdm import tqdm, trange
-import csv
+import sys
+from io import open
 
 import numpy as np
 import torch
-from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
+from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
+                              TensorDataset)
 from torch.utils.data.distributed import DistributedSampler
+from tqdm import tqdm, trange
 
-from pytorch_pretrained_bert.tokenization import BertTokenizer
+from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
 from pytorch_pretrained_bert.modeling import BertForMultipleChoice
 from pytorch_pretrained_bert.optimization import BertAdam
-from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
+from pytorch_pretrained_bert.tokenization import BertTokenizer
 
 logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
                     datefmt = '%m/%d/%Y %H:%M:%S',
@@ -65,17 +68,17 @@ class SwagExample(object):
 
     def __repr__(self):
         l = [
-            f"swag_id: {self.swag_id}",
-            f"context_sentence: {self.context_sentence}",
-            f"start_ending: {self.start_ending}",
-            f"ending_0: {self.endings[0]}",
-            f"ending_1: {self.endings[1]}",
-            f"ending_2: {self.endings[2]}",
-            f"ending_3: {self.endings[3]}",
+            "swag_id: {}".format(self.swag_id),
+            "context_sentence: {}".format(self.context_sentence),
+            "start_ending: {}".format(self.start_ending),
+            "ending_0: {}".format(self.endings[0]),
+            "ending_1: {}".format(self.endings[1]),
+            "ending_2: {}".format(self.endings[2]),
+            "ending_3: {}".format(self.endings[3]),
         ]
 
         if self.label is not None:
-            l.append(f"label: {self.label}")
+            l.append("label: {}".format(self.label))
 
         return ", ".join(l)
 
@@ -102,7 +105,11 @@ class InputFeatures(object):
 def read_swag_examples(input_file, is_training):
     with open(input_file, 'r', encoding='utf-8') as f:
         reader = csv.reader(f)
-        lines = list(reader)
+        lines = []
+        for line in reader:
+            if sys.version_info[0] == 2:
+                line = list(unicode(cell, 'utf-8') for cell in line)
+            lines.append(line)
 
     if is_training and lines[0][-1] != 'label':
         raise ValueError(
@@ -184,15 +191,15 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
         label = example.label
         if example_index < 5:
             logger.info("*** Example ***")
-            logger.info(f"swag_id: {example.swag_id}")
+            logger.info("swag_id: {}".format(example.swag_id))
             for choice_idx, (tokens, input_ids, input_mask, segment_ids) in enumerate(choices_features):
-                logger.info(f"choice: {choice_idx}")
-                logger.info(f"tokens: {' '.join(tokens)}")
-                logger.info(f"input_ids: {' '.join(map(str, input_ids))}")
-                logger.info(f"input_mask: {' '.join(map(str, input_mask))}")
-                logger.info(f"segment_ids: {' '.join(map(str, segment_ids))}")
+                logger.info("choice: {}".format(choice_idx))
+                logger.info("tokens: {}".format(' '.join(tokens)))
+                logger.info("input_ids: {}".format(' '.join(map(str, input_ids))))
+                logger.info("input_mask: {}".format(' '.join(map(str, input_mask))))
+                logger.info("segment_ids: {}".format(' '.join(map(str, segment_ids))))
             if is_training:
-                logger.info(f"label: {label}")
+                logger.info("label: {}".format(label))
 
         features.append(
             InputFeatures(
@@ -349,7 +356,8 @@ def main():
 
     if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
         raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
-    os.makedirs(args.output_dir, exist_ok=True)
+    if not os.path.exists(args.output_dir):
+        os.makedirs(args.output_dir)
 
     tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
 
@@ -362,7 +370,7 @@ def main():
 
     # Prepare model
     model = BertForMultipleChoice.from_pretrained(args.bert_model,
-        cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank),
+        cache_dir=os.path.join(PYTORCH_PRETRAINED_BERT_CACHE, 'distributed_{}'.format(args.local_rank)),
         num_choices=4)
     if args.fp16:
         model.half()