do some (light) housekeeping

Several packages were imported but never used, indentation and line
spaces did not follow PEP8.

transformers/modeling_bert.py

@@ -17,12 +17,10 @@
 
 from __future__ import absolute_import, division, print_function, unicode_literals
 
-import json
 import logging
 import math
 import os
 import sys
-from io import open
 
 import torch
 from torch import nn
@@ -50,6 +48,7 @@ BERT_PRETRAINED_MODEL_ARCHIVE_MAP = {
     'bert-base-cased-finetuned-mrpc': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-finetuned-mrpc-pytorch_model.bin",
 }
 
+
 def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
     """ Load tf checkpoints in a pytorch model.
     """
@@ -125,12 +124,14 @@ def gelu(x):
     """
     return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))
 
+
 def gelu_new(x):
     """ Implementation of the gelu activation function currently in Google Bert repo (identical to OpenAI GPT).
         Also see https://arxiv.org/abs/1606.08415
     """
     return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
 
+
 def swish(x):
     return x * torch.sigmoid(x)
 
@@ -140,6 +141,7 @@ ACT2FN = {"gelu": gelu, "relu": torch.nn.functional.relu, "swish": swish, "gelu_
 
 BertLayerNorm = torch.nn.LayerNorm
 
+
 class BertEmbeddings(nn.Module):
     """Construct the embeddings from word, position and token_type embeddings.
     """
@@ -632,7 +634,8 @@ class BertModel(BertPreTrainedModel):
 
 @add_start_docstrings("""Bert Model with two heads on top as done during the pre-training:
                        a `masked language modeling` head and a `next sentence prediction (classification)` head. """,
-    BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
+                      BERT_START_DOCSTRING,
+                      BERT_INPUTS_DOCSTRING)
 class BertForPreTraining(BertPreTrainedModel):
     r"""
         **masked_lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
@@ -711,7 +714,8 @@ class BertForPreTraining(BertPreTrainedModel):
 
 
 @add_start_docstrings("""Bert Model with a `language modeling` head on top. """,
-    BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
+                      BERT_START_DOCSTRING,
+                      BERT_INPUTS_DOCSTRING)
 class BertForMaskedLM(BertPreTrainedModel):
     r"""
         **masked_lm_labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
@@ -780,7 +784,8 @@ class BertForMaskedLM(BertPreTrainedModel):
 
 
 @add_start_docstrings("""Bert Model with a `next sentence prediction (classification)` head on top. """,
-    BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
+                      BERT_START_DOCSTRING,
+                      BERT_INPUTS_DOCSTRING)
 class BertForNextSentencePrediction(BertPreTrainedModel):
     r"""
         **next_sentence_label**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
@@ -843,7 +848,8 @@ class BertForNextSentencePrediction(BertPreTrainedModel):
 
 @add_start_docstrings("""Bert Model transformer with a sequence classification/regression head on top (a linear layer on top of
                       the pooled output) e.g. for GLUE tasks. """,
-    BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
+                      BERT_START_DOCSTRING,
+                      BERT_INPUTS_DOCSTRING)
 class BertForSequenceClassification(BertPreTrainedModel):
     r"""
         **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
@@ -916,7 +922,8 @@ class BertForSequenceClassification(BertPreTrainedModel):
 
 @add_start_docstrings("""Bert Model with a multiple choice classification head on top (a linear layer on top of
                       the pooled output and a softmax) e.g. for RocStories/SWAG tasks. """,
-    BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
+                      BERT_START_DOCSTRING,
+                      BERT_INPUTS_DOCSTRING)
 class BertForMultipleChoice(BertPreTrainedModel):
     r"""
         **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``:
@@ -991,7 +998,8 @@ class BertForMultipleChoice(BertPreTrainedModel):
 
 @add_start_docstrings("""Bert Model with a token classification head on top (a linear layer on top of
                       the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
-    BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
+                      BERT_START_DOCSTRING,
+                      BERT_INPUTS_DOCSTRING)
 class BertForTokenClassification(BertPreTrainedModel):
     r"""
         **labels**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size, sequence_length)``:
@@ -1063,7 +1071,8 @@ class BertForTokenClassification(BertPreTrainedModel):
 
 @add_start_docstrings("""Bert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
                       the hidden-states output to compute `span start logits` and `span end logits`). """,
-    BERT_START_DOCSTRING, BERT_INPUTS_DOCSTRING)
+                      BERT_START_DOCSTRING,
+                      BERT_INPUTS_DOCSTRING)
 class BertForQuestionAnswering(BertPreTrainedModel):
     r"""
         **start_positions**: (`optional`) ``torch.LongTensor`` of shape ``(batch_size,)``: