Revert "isort"

This reverts commit 3f03344.
huggingface · Feb 10, 2020 · 808bbd5 · 808bbd5
1 parent 3f03344
commit 808bbd5
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Showing 135 changed files with 281 additions and 1,210 deletions.
diff --git a/examples/benchmarks.py b/examples/benchmarks.py
@@ -22,15 +22,10 @@
 import csv
 import timeit
 from time import time
-
-from transformers import (
-    AutoConfig,
-    AutoTokenizer,
-    is_tf_available,
-    is_torch_available,
-)
 from typing import List
 
+from transformers import AutoConfig, AutoTokenizer, is_tf_available, is_torch_available
+
 
 if is_tf_available():
     import tensorflow as tf

diff --git a/examples/contrib/run_openai_gpt.py b/examples/contrib/run_openai_gpt.py
@@ -35,17 +35,9 @@
 
 import numpy as np
 import torch
-from torch.utils.data import (
-    DataLoader,
-    RandomSampler,
-    SequentialSampler,
-    TensorDataset,
-)
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
+from tqdm import tqdm, trange
 
-from tqdm import (
-    tqdm,
-    trange,
-)
 from transformers import (
     CONFIG_NAME,
     WEIGHTS_NAME,

diff --git a/examples/contrib/run_swag.py b/examples/contrib/run_swag.py
@@ -27,18 +27,10 @@
 
 import numpy as np
 import torch
-from torch.utils.data import (
-    DataLoader,
-    RandomSampler,
-    SequentialSampler,
-    TensorDataset,
-)
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
 from torch.utils.data.distributed import DistributedSampler
+from tqdm import tqdm, trange
 
-from tqdm import (
-    tqdm,
-    trange,
-)
 from transformers import (
     WEIGHTS_NAME,
     AdamW,

diff --git a/examples/contrib/run_transfo_xl.py b/examples/contrib/run_transfo_xl.py
@@ -28,10 +28,7 @@
 
 import torch
 
-from transformers import (
-    TransfoXLCorpus,
-    TransfoXLLMHeadModel,
-)
+from transformers import TransfoXLCorpus, TransfoXLLMHeadModel
 
 
 logging.basicConfig(

diff --git a/examples/distillation/distiller.py b/examples/distillation/distiller.py
@@ -24,19 +24,12 @@
 import torch.nn as nn
 import torch.nn.functional as F
 from torch.optim import AdamW
-from torch.utils.data import (
-    BatchSampler,
-    DataLoader,
-    RandomSampler,
-)
+from torch.utils.data import BatchSampler, DataLoader, RandomSampler
 from torch.utils.data.distributed import DistributedSampler
+from tqdm import tqdm
 
-from grouped_batch_sampler import (
-    GroupedBatchSampler,
-    create_lengths_groups,
-)
+from grouped_batch_sampler import GroupedBatchSampler, create_lengths_groups
 from lm_seqs_dataset import LmSeqsDataset
-from tqdm import tqdm
 from transformers import get_linear_schedule_with_warmup
 from utils import logger
 

diff --git a/examples/distillation/grouped_batch_sampler.py b/examples/distillation/grouped_batch_sampler.py
@@ -19,10 +19,7 @@
 from collections import defaultdict
 
 import numpy as np
-from torch.utils.data.sampler import (
-    BatchSampler,
-    Sampler,
-)
+from torch.utils.data.sampler import BatchSampler, Sampler
 
 from utils import logger
 

diff --git a/examples/distillation/run_squad_w_distillation.py b/examples/distillation/run_squad_w_distillation.py
@@ -26,17 +26,10 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from torch.utils.data import (
-    DataLoader,
-    RandomSampler,
-    SequentialSampler,
-)
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
 from torch.utils.data.distributed import DistributedSampler
+from tqdm import tqdm, trange
 
-from tqdm import (
-    tqdm,
-    trange,
-)
 from transformers import (
     WEIGHTS_NAME,
     AdamW,
@@ -60,11 +53,7 @@
     compute_predictions_logits,
     squad_evaluate,
 )
-from transformers.data.processors.squad import (
-    SquadResult,
-    SquadV1Processor,
-    SquadV2Processor,
-)
+from transformers.data.processors.squad import SquadResult, SquadV1Processor, SquadV2Processor
 
 
 try:

diff --git a/examples/distillation/scripts/binarized_data.py b/examples/distillation/scripts/binarized_data.py
@@ -23,11 +23,7 @@
 
 import numpy as np
 
-from transformers import (
-    BertTokenizer,
-    GPT2Tokenizer,
-    RobertaTokenizer,
-)
+from transformers import BertTokenizer, GPT2Tokenizer, RobertaTokenizer
 
 
 logging.basicConfig(

diff --git a/examples/distillation/scripts/extract.py b/examples/distillation/scripts/extract.py
@@ -20,10 +20,7 @@
 
 import torch
 
-from transformers import (
-    GPT2LMHeadModel,
-    RobertaForMaskedLM,
-)
+from transformers import GPT2LMHeadModel, RobertaForMaskedLM
 
 
 if __name__ == "__main__":

diff --git a/examples/distillation/train.py b/examples/distillation/train.py
@@ -41,12 +41,7 @@
     RobertaForMaskedLM,
     RobertaTokenizer,
 )
-from utils import (
-    git_log,
-    init_gpu_params,
-    logger,
-    set_seed,
-)
+from utils import git_log, init_gpu_params, logger, set_seed
 
 
 MODEL_CLASSES = {

diff --git a/examples/hans/hans_processors.py b/examples/hans/hans_processors.py
@@ -19,11 +19,7 @@
 import os
 
 from transformers.file_utils import is_tf_available
-from utils_hans import (
-    DataProcessor,
-    InputExample,
-    InputFeatures,
-)
+from utils_hans import DataProcessor, InputExample, InputFeatures
 
 
 if is_tf_available():

diff --git a/examples/hans/test_hans.py b/examples/hans/test_hans.py
@@ -15,11 +15,7 @@
 # limitations under the License.
 """ Finetuning the library models for sequence classification on GLUE (Bert, XLM, XLNet, RoBERTa)."""
 
-from __future__ import (
-    absolute_import,
-    division,
-    print_function,
-)
+from __future__ import absolute_import, division, print_function
 
 import argparse
 import glob
@@ -29,21 +25,13 @@
 
 import numpy as np
 import torch
-from torch.utils.data import (
-    DataLoader,
-    RandomSampler,
-    SequentialSampler,
-    TensorDataset,
-)
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
 from torch.utils.data.distributed import DistributedSampler
+from tqdm import tqdm, trange
 
 from hans_processors import glue_output_modes as output_modes
 from hans_processors import glue_processors as processors
 from hans_processors import hans_convert_examples_to_features as convert_examples_to_features
-from tqdm import (
-    tqdm,
-    trange,
-)
 from transformers import (
     WEIGHTS_NAME,
     AdamW,

diff --git a/examples/mm-imdb/run_mmimdb.py b/examples/mm-imdb/run_mmimdb.py
@@ -27,17 +27,10 @@
 import torch
 import torch.nn as nn
 from sklearn.metrics import f1_score
-from torch.utils.data import (
-    DataLoader,
-    RandomSampler,
-    SequentialSampler,
-)
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
 from torch.utils.data.distributed import DistributedSampler
+from tqdm import tqdm, trange
 
-from tqdm import (
-    tqdm,
-    trange,
-)
 from transformers import (
     WEIGHTS_NAME,
     AdamW,
@@ -63,13 +56,7 @@
     XLNetTokenizer,
     get_linear_schedule_with_warmup,
 )
-from utils_mmimdb import (
-    ImageEncoder,
-    JsonlDataset,
-    collate_fn,
-    get_image_transforms,
-    get_mmimdb_labels,
-)
+from utils_mmimdb import ImageEncoder, JsonlDataset, collate_fn, get_image_transforms, get_mmimdb_labels
 
 
 try:

diff --git a/examples/pplm/run_pplm.py b/examples/pplm/run_pplm.py
@@ -26,23 +26,18 @@
 import argparse
 import json
 from operator import add
+from typing import List, Optional, Tuple, Union
 
 import numpy as np
 import torch
 import torch.nn.functional as F
 from torch.autograd import Variable
+from tqdm import trange
 
 from pplm_classification_head import ClassificationHead
-from tqdm import trange
 from transformers import GPT2Tokenizer
 from transformers.file_utils import cached_path
 from transformers.modeling_gpt2 import GPT2LMHeadModel
-from typing import (
-    List,
-    Optional,
-    Tuple,
-    Union,
-)
 
 
 PPLM_BOW = 1

diff --git a/examples/pplm/run_pplm_discrim_train.py b/examples/pplm/run_pplm_discrim_train.py
@@ -29,16 +29,10 @@
 from nltk.tokenize.treebank import TreebankWordDetokenizer
 from torchtext import data as torchtext_data
 from torchtext import datasets
+from tqdm import tqdm, trange
 
 from pplm_classification_head import ClassificationHead
-from tqdm import (
-    tqdm,
-    trange,
-)
-from transformers import (
-    GPT2LMHeadModel,
-    GPT2Tokenizer,
-)
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
 
 
 torch.manual_seed(0)

diff --git a/examples/run_bertology.py b/examples/run_bertology.py
@@ -26,20 +26,11 @@
 
 import numpy as np
 import torch
-from torch.utils.data import (
-    DataLoader,
-    SequentialSampler,
-    Subset,
-)
+from torch.utils.data import DataLoader, SequentialSampler, Subset
 from torch.utils.data.distributed import DistributedSampler
-
-from run_glue import (
-    ALL_MODELS,
-    MODEL_CLASSES,
-    load_and_cache_examples,
-    set_seed,
-)
 from tqdm import tqdm
+
+from run_glue import ALL_MODELS, MODEL_CLASSES, load_and_cache_examples, set_seed
 from transformers import glue_compute_metrics as compute_metrics
 from transformers import glue_output_modes as output_modes
 from transformers import glue_processors as processors

diff --git a/examples/run_glue.py b/examples/run_glue.py
@@ -25,22 +25,10 @@
 
 import numpy as np
 import torch
-from torch.utils.data import (
-    DataLoader,
-    RandomSampler,
-    SequentialSampler,
-    TensorDataset,
-)
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
 from torch.utils.data.distributed import DistributedSampler
+from tqdm import tqdm, trange
 
-from tqdm import (
-    tqdm,
-    trange,
-)
-from transformers import glue_compute_metrics as compute_metrics
-from transformers import glue_convert_examples_to_features as convert_examples_to_features
-from transformers import glue_output_modes as output_modes
-from transformers import glue_processors as processors
 from transformers import (
     WEIGHTS_NAME,
     AdamW,
@@ -70,6 +58,10 @@
     XLNetTokenizer,
     get_linear_schedule_with_warmup,
 )
+from transformers import glue_compute_metrics as compute_metrics
+from transformers import glue_convert_examples_to_features as convert_examples_to_features
+from transformers import glue_output_modes as output_modes
+from transformers import glue_processors as processors
 
 
 try: