Sort imports with isort.

This is the result of:

    $ isort --recursive examples templates transformers utils hubconf.py setup.py

transformers/convert_xlm_original_pytorch_checkpoint_to_pytorch.py

@@ -18,15 +18,15 @@ from __future__ import absolute_import, division, print_function
 
 import argparse
 import json
+import logging
 from io import open
 
-import torch
 import numpy
+import torch
 
 from transformers import CONFIG_NAME, WEIGHTS_NAME
 from transformers.tokenization_xlm import VOCAB_FILES_NAMES
 
-import logging
 
 logging.basicConfig(level=logging.INFO)
 

transformers/convert_xlnet_original_tf_checkpoint_to_pytorch.py

@@ -14,24 +14,25 @@
 # limitations under the License.
 """Convert BERT checkpoint."""
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
+from __future__ import absolute_import, division, print_function
 
-import os
 import argparse
+import logging
+import os
+
 import torch
 
 from transformers import (
     CONFIG_NAME,
     WEIGHTS_NAME,
     XLNetConfig,
-    XLNetLMHeadModel,
     XLNetForQuestionAnswering,
     XLNetForSequenceClassification,
+    XLNetLMHeadModel,
     load_tf_weights_in_xlnet,
 )
 
+
 GLUE_TASKS_NUM_LABELS = {
     "cola": 2,
     "mnli": 3,
@@ -44,7 +45,6 @@ GLUE_TASKS_NUM_LABELS = {
     "wnli": 2,
 }
 
-import logging
 
 logging.basicConfig(level=logging.INFO)
 

transformers/data/__init__.py

+from .metrics import is_sklearn_available
 from .processors import (
+    DataProcessor,
     InputExample,
     InputFeatures,
-    DataProcessor,
-    SquadFeatures,
     SingleSentenceClassificationProcessor,
+    SquadExample,
+    SquadFeatures,
+    SquadV1Processor,
+    SquadV2Processor,
+    glue_convert_examples_to_features,
+    glue_output_modes,
+    glue_processors,
+    glue_tasks_num_labels,
+    squad_convert_examples_to_features,
+    xnli_output_modes,
+    xnli_processors,
+    xnli_tasks_num_labels,
 )
-from .processors import glue_output_modes, glue_processors, glue_tasks_num_labels, glue_convert_examples_to_features
-from .processors import squad_convert_examples_to_features, SquadExample, SquadV1Processor, SquadV2Processor
-from .processors import xnli_output_modes, xnli_processors, xnli_tasks_num_labels
 
-from .metrics import is_sklearn_available
 
 if is_sklearn_available():
     from .metrics import glue_compute_metrics, xnli_compute_metrics

transformers/data/metrics/__init__.py

@@ -15,8 +15,9 @@
 # limitations under the License.
 
 import csv
-import sys
 import logging
+import sys
+
 
 logger = logging.getLogger(__name__)
 

transformers/data/metrics/squad_metrics.py

@@ -8,17 +8,19 @@ that a question is unanswerable.
 """
 
 
+import collections
 import json
 import logging
 import math
-import collections
+import re
+import string
 from io import open
+
 from tqdm import tqdm
-import string
-import re
 
 from transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize
 
+
 logger = logging.getLogger(__name__)
 
 

transformers/data/processors/__init__.py

-from .utils import InputExample, InputFeatures, DataProcessor, SingleSentenceClassificationProcessor
-from .glue import glue_output_modes, glue_processors, glue_tasks_num_labels, glue_convert_examples_to_features
-from .squad import squad_convert_examples_to_features, SquadFeatures, SquadExample, SquadV1Processor, SquadV2Processor
+from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
+from .squad import SquadExample, SquadFeatures, SquadV1Processor, SquadV2Processor, squad_convert_examples_to_features
+from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
 from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels

transformers/data/processors/glue.py

@@ -18,8 +18,9 @@
 import logging
 import os
 
-from .utils import DataProcessor, InputExample, InputFeatures
 from ...file_utils import is_tf_available
+from .utils import DataProcessor, InputExample, InputFeatures
+
 
 if is_tf_available():
     import tensorflow as tf

transformers/data/processors/squad.py

-from tqdm import tqdm
 import collections
+import json
 import logging
 import os
-import json
-import numpy as np
-from multiprocessing import Pool
-from multiprocessing import cpu_count
 from functools import partial
+from multiprocessing import Pool, cpu_count
+
+import numpy as np
+from tqdm import tqdm
 
+from ...file_utils import is_tf_available, is_torch_available
 from ...tokenization_bert import BasicTokenizer, whitespace_tokenize
 from .utils import DataProcessor, InputExample, InputFeatures
-from ...file_utils import is_tf_available, is_torch_available
+
 
 if is_torch_available():
     import torch

transformers/data/processors/utils.py

@@ -14,14 +14,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import csv
-import sys
 import copy
+import csv
 import json
 import logging
+import sys
 
 from ...file_utils import is_tf_available, is_torch_available
 
+
 logger = logging.getLogger(__name__)
 
 

transformers/data/processors/xnli.py

@@ -22,6 +22,7 @@ import os
 
 from .utils import DataProcessor, InputExample
 
+
 logger = logging.getLogger(__name__)
 
 

transformers/file_utils.py

@@ -5,26 +5,27 @@ Copyright by the AllenNLP authors.
 """
 from __future__ import absolute_import, division, print_function, unicode_literals
 
-import sys
+import fnmatch
 import json
 import logging
 import os
-import six
+import sys
 import tempfile
-import fnmatch
+from contextlib import contextmanager
 from functools import partial, wraps
 from hashlib import sha256
 from io import open
 
 import boto3
+import requests
+import six
 from botocore.config import Config
 from botocore.exceptions import ClientError
-import requests
+from filelock import FileLock
 from tqdm.auto import tqdm
-from contextlib import contextmanager
+
 from . import __version__
 
-from filelock import FileLock
 
 logger = logging.getLogger(__name__)  # pylint: disable=invalid-name
 

transformers/hf_api.py

@@ -22,6 +22,7 @@ import six
 from requests.exceptions import HTTPError
 from tqdm import tqdm
 
+
 ENDPOINT = "https://huggingface.co"
 
 

transformers/modelcard.py

@@ -23,15 +23,14 @@ import os
 from io import open
 
 from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP
-
 from .file_utils import (
     CONFIG_NAME,
     MODEL_CARD_NAME,
-    WEIGHTS_NAME,
     TF2_WEIGHTS_NAME,
+    WEIGHTS_NAME,
     cached_path,
-    is_remote_url,
     hf_bucket_url,
+    is_remote_url,
 )
 
 

transformers/modeling_albert.py

@@ -14,17 +14,21 @@
 # limitations under the License.
 """PyTorch ALBERT model. """
 
-import os
-import math
 import logging
+import math
+import os
+
 import torch
 import torch.nn as nn
 from torch.nn import CrossEntropyLoss, MSELoss
-from transformers.modeling_utils import PreTrainedModel
+
 from transformers.configuration_albert import AlbertConfig
-from transformers.modeling_bert import BertEmbeddings, BertSelfAttention, prune_linear_layer, ACT2FN
+from transformers.modeling_bert import ACT2FN, BertEmbeddings, BertSelfAttention, prune_linear_layer
+from transformers.modeling_utils import PreTrainedModel
+
 from .file_utils import add_start_docstrings
 
+
 logger = logging.getLogger(__name__)
 
 

transformers/modeling_auto.py

@@ -29,80 +29,78 @@ from .configuration_auto import (
     RobertaConfig,
     TransfoXLConfig,
     XLMConfig,
-    XLNetConfig,
     XLMRobertaConfig,
+    XLNetConfig,
+)
+from .file_utils import add_start_docstrings
+from .modeling_albert import (
+    ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+    AlbertForMaskedLM,
+    AlbertForQuestionAnswering,
+    AlbertForSequenceClassification,
+    AlbertModel,
 )
-
 from .modeling_bert import (
-    BertModel,
+    BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
     BertForMaskedLM,
-    BertForSequenceClassification,
     BertForQuestionAnswering,
+    BertForSequenceClassification,
     BertForTokenClassification,
-    BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
-)
-from .modeling_openai import OpenAIGPTModel, OpenAIGPTLMHeadModel, OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP
-from .modeling_gpt2 import GPT2Model, GPT2LMHeadModel, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP
-from .modeling_ctrl import CTRLModel, CTRLLMHeadModel, CTRL_PRETRAINED_MODEL_ARCHIVE_MAP
-from .modeling_transfo_xl import TransfoXLModel, TransfoXLLMHeadModel, TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP
-from .modeling_xlnet import (
-    XLNetModel,
-    XLNetLMHeadModel,
-    XLNetForSequenceClassification,
-    XLNetForQuestionAnswering,
-    XLNetForTokenClassification,
-    XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
-)
-from .modeling_xlm import (
-    XLMModel,
-    XLMWithLMHeadModel,
-    XLMForSequenceClassification,
-    XLMForQuestionAnswering,
-    XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
+    BertModel,
 )
-from .modeling_roberta import (
-    RobertaModel,
-    RobertaForMaskedLM,
-    RobertaForSequenceClassification,
-    RobertaForTokenClassification,
-    ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
+from .modeling_camembert import (
+    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+    CamembertForMaskedLM,
+    CamembertForMultipleChoice,
+    CamembertForSequenceClassification,
+    CamembertForTokenClassification,
+    CamembertModel,
 )
+from .modeling_ctrl import CTRL_PRETRAINED_MODEL_ARCHIVE_MAP, CTRLLMHeadModel, CTRLModel
 from .modeling_distilbert import (
-    DistilBertModel,
-    DistilBertForQuestionAnswering,
+    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
     DistilBertForMaskedLM,
+    DistilBertForQuestionAnswering,
     DistilBertForSequenceClassification,
     DistilBertForTokenClassification,
-    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+    DistilBertModel,
 )
-from .modeling_camembert import (
-    CamembertModel,
-    CamembertForMaskedLM,
-    CamembertForSequenceClassification,
-    CamembertForMultipleChoice,
-    CamembertForTokenClassification,
-    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+from .modeling_gpt2 import GPT2_PRETRAINED_MODEL_ARCHIVE_MAP, GPT2LMHeadModel, GPT2Model
+from .modeling_openai import OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_MAP, OpenAIGPTLMHeadModel, OpenAIGPTModel
+from .modeling_roberta import (
+    ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
+    RobertaForMaskedLM,
+    RobertaForSequenceClassification,
+    RobertaForTokenClassification,
+    RobertaModel,
 )
-from .modeling_albert import (
-    AlbertModel,
-    AlbertForMaskedLM,
-    AlbertForSequenceClassification,
-    AlbertForQuestionAnswering,
-    ALBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+from .modeling_t5 import T5_PRETRAINED_MODEL_ARCHIVE_MAP, T5Model, T5WithLMHeadModel
+from .modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP, TransfoXLLMHeadModel, TransfoXLModel
+from .modeling_utils import PreTrainedModel, SequenceSummary
+from .modeling_xlm import (
+    XLM_PRETRAINED_MODEL_ARCHIVE_MAP,
+    XLMForQuestionAnswering,
+    XLMForSequenceClassification,
+    XLMModel,
+    XLMWithLMHeadModel,
 )
-from .modeling_t5 import T5Model, T5WithLMHeadModel, T5_PRETRAINED_MODEL_ARCHIVE_MAP
 from .modeling_xlm_roberta import (
-    XLMRobertaModel,
+    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
     XLMRobertaForMaskedLM,
-    XLMRobertaForSequenceClassification,
     XLMRobertaForMultipleChoice,
+    XLMRobertaForSequenceClassification,
     XLMRobertaForTokenClassification,
-    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
+    XLMRobertaModel,
+)
+from .modeling_xlnet import (
+    XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
+    XLNetForQuestionAnswering,
+    XLNetForSequenceClassification,
+    XLNetForTokenClassification,
+    XLNetLMHeadModel,
+    XLNetModel,
 )
 
-from .modeling_utils import PreTrainedModel, SequenceSummary
-
-from .file_utils import add_start_docstrings
 
 logger = logging.getLogger(__name__)
 

transformers/modeling_bert.py

@@ -26,9 +26,10 @@ import torch
 from torch import nn
 from torch.nn import CrossEntropyLoss, MSELoss
 
-from .modeling_utils import PreTrainedModel, prune_linear_layer
 from .configuration_bert import BertConfig
 from .file_utils import add_start_docstrings
+from .modeling_utils import PreTrainedModel, prune_linear_layer
+
 
 logger = logging.getLogger(__name__)
 

transformers/modeling_camembert.py

@@ -19,15 +19,16 @@ from __future__ import absolute_import, division, print_function, unicode_litera
 
 import logging
 
+from .configuration_camembert import CamembertConfig
+from .file_utils import add_start_docstrings
 from .modeling_roberta import (
-    RobertaModel,
     RobertaForMaskedLM,
-    RobertaForSequenceClassification,
     RobertaForMultipleChoice,
+    RobertaForSequenceClassification,
     RobertaForTokenClassification,
+    RobertaModel,
 )
-from .configuration_camembert import CamembertConfig
-from .file_utils import add_start_docstrings
+
 
 logger = logging.getLogger(__name__)
 

transformers/modeling_ctrl.py

@@ -24,15 +24,17 @@ import math
 import os
 import sys
 from io import open
+
 import numpy as np
 import torch
 import torch.nn as nn
 from torch.nn import CrossEntropyLoss
 from torch.nn.parameter import Parameter
 
-from .modeling_utils import PreTrainedModel, Conv1D, prune_conv1d_layer, SequenceSummary
 from .configuration_ctrl import CTRLConfig
 from .file_utils import add_start_docstrings
+from .modeling_utils import Conv1D, PreTrainedModel, SequenceSummary, prune_conv1d_layer
+
 
 logger = logging.getLogger(__name__)
 

transformers/modeling_distilbert.py

@@ -18,25 +18,23 @@
 """
 from __future__ import absolute_import, division, print_function, unicode_literals
 
+import copy
+import itertools
 import json
 import logging
 import math
-import copy
 import sys
 from io import open
 
-import itertools
 import numpy as np
-
 import torch
 import torch.nn as nn
 from torch.nn import CrossEntropyLoss
 
-from .modeling_utils import PreTrainedModel, prune_linear_layer
 from .configuration_distilbert import DistilBertConfig
 from .file_utils import add_start_docstrings
+from .modeling_utils import PreTrainedModel, prune_linear_layer
 
-import logging
 
 logger = logging.getLogger(__name__)
 

transformers/modeling_encoder_decoder.py

@@ -26,6 +26,7 @@ from tqdm import trange
 
 from .modeling_auto import AutoModel, AutoModelWithLMHead
 
+
 logger = logging.getLogger(__name__)