Merge pull request #2255 from aaugustin/implement-best-practices

Implement some Python best practices

hubconf.py

 from transformers import (
-    AutoTokenizer, AutoConfig, AutoModel, AutoModelWithLMHead, AutoModelForSequenceClassification, AutoModelForQuestionAnswering
+    AutoConfig,
+    AutoModel,
+    AutoModelForQuestionAnswering,
+    AutoModelForSequenceClassification,
+    AutoModelWithLMHead,
+    AutoTokenizer,
 )
 from transformers.file_utils import add_start_docstrings
 
-dependencies = ['torch', 'tqdm', 'boto3', 'requests', 'regex', 'sentencepiece', 'sacremoses']
+
+dependencies = ["torch", "tqdm", "boto3", "requests", "regex", "sentencepiece", "sacremoses"]
+
 
 @add_start_docstrings(AutoConfig.__doc__)
 def config(*args, **kwargs):
-    r""" 
+    r"""
                 # Using torch.hub !
                 import torch
 
@@ -27,7 +34,7 @@ def config(*args, **kwargs):
 
 @add_start_docstrings(AutoTokenizer.__doc__)
 def tokenizer(*args, **kwargs):
-    r""" 
+    r"""
         # Using torch.hub !
         import torch
 
@@ -57,6 +64,7 @@ def model(*args, **kwargs):
 
     return AutoModel.from_pretrained(*args, **kwargs)
 
+
 @add_start_docstrings(AutoModelWithLMHead.__doc__)
 def modelWithLMHead(*args, **kwargs):
     r"""

transformers/commands/__init__.py

 from abc import ABC, abstractmethod
 from argparse import ArgumentParser
 
+
 class BaseTransformersCLICommand(ABC):
     @staticmethod
     @abstractmethod