commplying with isort

examples/movement-pruning/emmental/__init__.py

@@ -1,11 +1,9 @@
-from .modules import *
-
 from .configuration_bert_masked import MaskedBertConfig
-
 from .modeling_bert_masked import (
-    MaskedBertModel,
+    MaskedBertForMultipleChoice,
     MaskedBertForQuestionAnswering,
     MaskedBertForSequenceClassification,
     MaskedBertForTokenClassification,
-    MaskedBertForMultipleChoice,
+    MaskedBertModel,
 )
+from .modules import *

examples/movement-pruning/emmental/configuration_bert_masked.py

@@ -19,8 +19,9 @@ and adapts it to the specificities of MaskedBert (`pruning_method`, `mask_init`
 
 import logging
 
-from transformers.configuration_utils import PretrainedConfig
 from transformers.configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP
+from transformers.configuration_utils import PretrainedConfig
+
 
 logger = logging.getLogger(__name__)
 

examples/movement-pruning/emmental/modeling_bert_masked.py

@@ -26,13 +26,16 @@ import torch
 from torch import nn
 from torch.nn import CrossEntropyLoss, MSELoss
 
+from emmental import MaskedBertConfig, MaskedLinear
 from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_callable
+from transformers.modeling_bert import (
+    ACT2FN,
+    BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
+    BertLayerNorm,
+    load_tf_weights_in_bert,
+)
 from transformers.modeling_utils import PreTrainedModel, prune_linear_layer
-from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
-from transformers.modeling_bert import load_tf_weights_in_bert, ACT2FN, BertLayerNorm
 
-from emmental import MaskedLinear
-from emmental import MaskedBertConfig
 
 logger = logging.getLogger(__name__)
 

examples/movement-pruning/emmental/modules/__init__.py

@@ -1,2 +1,2 @@
-from .binarizer import ThresholdBinarizer, TopKBinarizer, MagnitudeBinarizer
+from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
 from .masked_nn import MaskedLinear

examples/movement-pruning/emmental/modules/masked_nn.py

@@ -19,14 +19,14 @@ the weight matrix to prune a portion of the weights.
 The pruned weight matrix is then multiplied against the inputs (and if necessary, the bias is added).
 """
 
+import math
+
 import torch
 from torch import nn
 from torch.nn import functional as F
 from torch.nn import init
 
-import math
-
-from .binarizer import ThresholdBinarizer, TopKBinarizer, MagnitudeBinarizer
+from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
 
 
 class MaskedLinear(nn.Linear):