Sort imports with isort.

This is the result of:

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

transformers/configuration_distilbert.py

@@ -15,13 +15,14 @@
 """ DistilBERT model configuration """
 from __future__ import absolute_import, division, print_function, unicode_literals
 
-import sys
 import json
 import logging
+import sys
 from io import open
 
 from .configuration_utils import PretrainedConfig
 
+
 logger = logging.getLogger(__name__)
 
 DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {

transformers/configuration_gpt2.py

@@ -24,6 +24,7 @@ from io import open
 
 from .configuration_utils import PretrainedConfig
 
+
 logger = logging.getLogger(__name__)
 
 GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP = {

transformers/configuration_mmbt.py

@@ -19,6 +19,7 @@ from __future__ import absolute_import, division, print_function, unicode_litera
 
 import logging
 
+
 logger = logging.getLogger(__name__)
 
 

transformers/configuration_openai.py

@@ -24,6 +24,7 @@ from io import open
 
 from .configuration_utils import PretrainedConfig
 
+
 logger = logging.getLogger(__name__)
 
 OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP = {

transformers/configuration_roberta.py

@@ -21,6 +21,7 @@ import logging
 
 from .configuration_bert import BertConfig
 
+
 logger = logging.getLogger(__name__)
 
 ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP = {

transformers/configuration_t5.py

@@ -19,11 +19,13 @@ from __future__ import absolute_import, division, print_function, unicode_litera
 import json
 import logging
 import sys
-import six
 from io import open
 
+import six
+
 from .configuration_utils import PretrainedConfig
 
+
 logger = logging.getLogger(__name__)
 
 T5_PRETRAINED_CONFIG_ARCHIVE_MAP = {

transformers/configuration_transfo_xl.py

@@ -24,6 +24,7 @@ from io import open
 
 from .configuration_utils import PretrainedConfig
 
+
 logger = logging.getLogger(__name__)
 
 TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP = {

transformers/configuration_utils.py

@@ -23,7 +23,8 @@ import logging
 import os
 from io import open
 
-from .file_utils import CONFIG_NAME, cached_path, is_remote_url, hf_bucket_url
+from .file_utils import CONFIG_NAME, cached_path, hf_bucket_url, is_remote_url
+
 
 logger = logging.getLogger(__name__)
 

transformers/configuration_xlm.py

@@ -22,6 +22,7 @@ from io import open
 
 from .configuration_utils import PretrainedConfig
 
+
 logger = logging.getLogger(__name__)
 
 XLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {

transformers/configuration_xlm_roberta.py

@@ -21,6 +21,7 @@ import logging
 
 from .configuration_roberta import RobertaConfig
 
+
 logger = logging.getLogger(__name__)
 
 XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP = {

transformers/configuration_xlnet.py

@@ -23,6 +23,7 @@ from io import open
 
 from .configuration_utils import PretrainedConfig
 
+
 logger = logging.getLogger(__name__)
 
 XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {

transformers/convert_albert_original_tf_checkpoint_to_pytorch.py

@@ -14,16 +14,15 @@
 # limitations under the License.
 """Convert ALBERT checkpoint."""
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
+from __future__ import absolute_import, division, print_function
 
 import argparse
+import logging
+
 import torch
 
 from transformers import AlbertConfig, AlbertForMaskedLM, load_tf_weights_in_albert
 
-import logging
 
 logging.basicConfig(level=logging.INFO)
 

transformers/convert_bert_original_tf_checkpoint_to_pytorch.py

@@ -14,16 +14,15 @@
 # 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 argparse
+import logging
+
 import torch
 
 from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
 
-import logging
 
 logging.basicConfig(level=logging.INFO)
 

transformers/convert_bert_pytorch_checkpoint_to_original_tf.py

@@ -15,11 +15,13 @@
 
 """Convert Huggingface Pytorch checkpoint to Tensorflow checkpoint."""
 
-import os
 import argparse
-import torch
+import os
+
 import numpy as np
 import tensorflow as tf
+import torch
+
 from transformers import BertModel
 
 

transformers/convert_gpt2_original_tf_checkpoint_to_pytorch.py

@@ -17,13 +17,13 @@
 from __future__ import absolute_import, division, print_function
 
 import argparse
+import logging
 from io import open
 
 import torch
 
 from transformers import CONFIG_NAME, WEIGHTS_NAME, GPT2Config, GPT2Model, load_tf_weights_in_gpt2
 
-import logging
 
 logging.basicConfig(level=logging.INFO)
 

transformers/convert_openai_original_tf_checkpoint_to_pytorch.py

@@ -17,13 +17,13 @@
 from __future__ import absolute_import, division, print_function
 
 import argparse
+import logging
 from io import open
 
 import torch
 
 from transformers import CONFIG_NAME, WEIGHTS_NAME, OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
 
-import logging
 
 logging.basicConfig(level=logging.INFO)
 

transformers/convert_pytorch_checkpoint_to_tf2.py

@@ -14,58 +14,59 @@
 # limitations under the License.
 """ Convert pytorch checkpoints to TensorFlow """
 
-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 tensorflow as tf
+import logging
+import os
 
-from transformers import is_torch_available, cached_path
+import tensorflow as tf
 
 from transformers import (
-    load_pytorch_checkpoint_in_tf2_model,
-    BertConfig,
-    TFBertForPreTraining,
-    TFBertForQuestionAnswering,
-    TFBertForSequenceClassification,
+    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    GPT2Config,
-    TFGPT2LMHeadModel,
+    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
+    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    XLNetConfig,
-    TFXLNetLMHeadModel,
-    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    XLMConfig,
-    TFXLMWithLMHeadModel,
-    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    TransfoXLConfig,
-    TFTransfoXLLMHeadModel,
-    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    OpenAIGPTConfig,
-    TFOpenAIGPTLMHeadModel,
     OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    RobertaConfig,
-    TFRobertaForMaskedLM,
-    TFRobertaForSequenceClassification,
     ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
+    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
+    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
+    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
+    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
+    AlbertConfig,
+    BertConfig,
+    CTRLConfig,
     DistilBertConfig,
+    GPT2Config,
+    OpenAIGPTConfig,
+    RobertaConfig,
+    T5Config,
+    TFAlbertForMaskedLM,
+    TFBertForPreTraining,
+    TFBertForQuestionAnswering,
+    TFBertForSequenceClassification,
+    TFCTRLLMHeadModel,
     TFDistilBertForMaskedLM,
     TFDistilBertForQuestionAnswering,
     TFDistilBertForSequenceClassification,
-    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    CTRLConfig,
-    TFCTRLLMHeadModel,
-    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    AlbertConfig,
-    TFAlbertForMaskedLM,
-    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
-    T5Config,
+    TFGPT2LMHeadModel,
+    TFOpenAIGPTLMHeadModel,
+    TFRobertaForMaskedLM,
+    TFRobertaForSequenceClassification,
     TFT5WithLMHeadModel,
-    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
+    TFTransfoXLLMHeadModel,
+    TFXLMWithLMHeadModel,
+    TFXLNetLMHeadModel,
+    TransfoXLConfig,
+    XLMConfig,
+    XLNetConfig,
+    cached_path,
+    is_torch_available,
+    load_pytorch_checkpoint_in_tf2_model,
 )
 
+
 if is_torch_available():
     import torch
     import numpy as np
@@ -158,8 +159,6 @@ else:
     )
 
 
-import logging
-
 logging.basicConfig(level=logging.INFO)
 
 MODEL_CLASSES = {

transformers/convert_roberta_original_pytorch_checkpoint_to_pytorch.py

@@ -18,16 +18,13 @@ from __future__ import absolute_import, division, print_function
 
 import argparse
 import logging
-import numpy as np
-import torch
 import pathlib
 
-import fairseq
+import numpy as np
+import torch
 from packaging import version
 
-if version.parse(fairseq.__version__) < version.parse("0.9.0"):
-    raise Exception("requires fairseq >= 0.9.0")
-
+import fairseq
 from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
 from fairseq.modules import TransformerSentenceEncoderLayer
 from transformers.modeling_bert import (
@@ -47,6 +44,11 @@ from transformers.modeling_roberta import (
     RobertaModel,
 )
 
+
+if version.parse(fairseq.__version__) < version.parse("0.9.0"):
+    raise Exception("requires fairseq >= 0.9.0")
+
+
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 

transformers/convert_t5_original_tf_checkpoint_to_pytorch.py

@@ -14,16 +14,15 @@
 # limitations under the License.
 """Convert T5 checkpoint."""
 
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
+from __future__ import absolute_import, division, print_function
 
 import argparse
+import logging
+
 import torch
 
 from transformers import T5Config, T5Model, load_tf_weights_in_t5
 
-import logging
 
 logging.basicConfig(level=logging.INFO)
 

transformers/convert_transfo_xl_original_tf_checkpoint_to_pytorch.py

@@ -17,6 +17,7 @@
 from __future__ import absolute_import, division, print_function
 
 import argparse
+import logging
 import os
 import sys
 from io import open
@@ -24,17 +25,21 @@ from io import open
 import torch
 
 import transformers.tokenization_transfo_xl as data_utils
-
-from transformers import CONFIG_NAME, WEIGHTS_NAME
-from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
+from transformers import (
+    CONFIG_NAME,
+    WEIGHTS_NAME,
+    TransfoXLConfig,
+    TransfoXLLMHeadModel,
+    load_tf_weights_in_transfo_xl,
+)
 from transformers.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
 
+
 if sys.version_info[0] == 2:
     import cPickle as pickle
 else:
     import pickle
 
-import logging
 
 logging.basicConfig(level=logging.INFO)