Sort imports for optional third-party libraries.

These libraries aren't always installed in the virtual environment where
isort is running. Declaring them properly avoids mixing these
third-party imports with local imports.

examples/distillation/distiller.py

@@ -19,6 +19,7 @@ import math
 import os
 import time
 
+import psutil
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -27,7 +28,6 @@ from torch.utils.data import BatchSampler, DataLoader, RandomSampler
 from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm
 
-import psutil
 from grouped_batch_sampler import GroupedBatchSampler, create_lengths_groups
 from lm_seqs_dataset import LmSeqsDataset
 from transformers import get_linear_schedule_with_warmup

examples/distillation/utils.py

@@ -20,11 +20,10 @@ import logging
 import os
 import socket
 
+import git
 import numpy as np
 import torch
 
-import git
-
 
 logging.basicConfig(
     format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d -  %(message)s",

examples/mm-imdb/utils_mmimdb.py

@@ -20,11 +20,10 @@ from collections import Counter
 
 import torch
 import torch.nn as nn
-from torch.utils.data import Dataset
-
 import torchvision
 import torchvision.transforms as transforms
 from PIL import Image
+from torch.utils.data import Dataset
 
 
 POOLING_BREAKDOWN = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}

examples/pplm/run_pplm_discrim_train.py

@@ -26,12 +26,12 @@ import torch
 import torch.nn.functional as F
 import torch.optim as optim
 import torch.utils.data as data
-from tqdm import tqdm, trange
-
 from nltk.tokenize.treebank import TreebankWordDetokenizer
-from pplm_classification_head import ClassificationHead
 from torchtext import data as torchtext_data
 from torchtext import datasets
+from tqdm import tqdm, trange
+
+from pplm_classification_head import ClassificationHead
 from transformers import GPT2LMHeadModel, GPT2Tokenizer
 
 

examples/run_ner.py

@@ -25,13 +25,13 @@ import random
 
 import numpy as np
 import torch
+from seqeval.metrics import f1_score, precision_score, recall_score
 from tensorboardX import SummaryWriter
 from torch.nn import CrossEntropyLoss
 from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
 from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm, trange
 
-from seqeval.metrics import f1_score, precision_score, recall_score
 from transformers import (
     WEIGHTS_NAME,
     AdamW,

examples/run_tf_glue.py

@@ -1,8 +1,8 @@
 import os
 
 import tensorflow as tf
-
 import tensorflow_datasets
+
 from transformers import (
     BertConfig,
     BertForSequenceClassification,

examples/run_tf_ner.py

@@ -9,9 +9,9 @@ import re
 import numpy as np
 import tensorflow as tf
 from absl import app, flags, logging
-
 from fastprogress import master_bar, progress_bar
 from seqeval import metrics
+
 from transformers import (
     TF2_WEIGHTS_NAME,
     BertConfig,