Fix F401 flake8 warning (x88 / 116).

This change is mostly autogenerated with:

    $ python -m autoflake --in-place --recursive --remove-all-unused-imports --ignore-init-module-imports examples templates transformers utils hubconf.py setup.py

I made minor changes in the generated diff.

transformers/tests/modeling_tf_auto_test.py

@@ -35,10 +35,6 @@ if is_tf_available():
         TFAutoModelForQuestionAnswering,
         TFBertForQuestionAnswering,
     )
-    from transformers.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP
-
-    from .modeling_common_test import CommonTestCases, ids_tensor
-    from .configuration_common_test import ConfigTester
 
 
 @require_tf

transformers/tests/modeling_tf_common_test.py

@@ -24,13 +24,12 @@ import unittest
 
 from transformers import is_tf_available, is_torch_available
 
-from .utils import require_tf, slow
+from .utils import require_tf
 
 
 if is_tf_available():
     import tensorflow as tf
     import numpy as np
-    from transformers import TFPreTrainedModel
 
     # from transformers.modeling_bert import BertModel, BertConfig, BERT_PRETRAINED_MODEL_ARCHIVE_MAP
 

transformers/tests/modeling_tf_ctrl_test.py

@@ -24,7 +24,6 @@ from .utils import CACHE_DIR, require_tf, slow
 
 
 if is_tf_available():
-    import tensorflow as tf
     from transformers.modeling_tf_ctrl import TFCTRLModel, TFCTRLLMHeadModel, TF_CTRL_PRETRAINED_MODEL_ARCHIVE_MAP
 
 

transformers/tests/modeling_tf_distilbert_test.py

@@ -20,11 +20,10 @@ from transformers import DistilBertConfig, is_tf_available
 
 from .configuration_common_test import ConfigTester
 from .modeling_tf_common_test import TFCommonTestCases, ids_tensor
-from .utils import CACHE_DIR, require_tf, slow
+from .utils import require_tf
 
 
 if is_tf_available():
-    import tensorflow as tf
     from transformers.modeling_tf_distilbert import (
         TFDistilBertModel,
         TFDistilBertForMaskedLM,

transformers/tests/modeling_tf_t5_test.py

@@ -24,8 +24,7 @@ from .utils import CACHE_DIR, require_tf, slow
 
 
 if is_tf_available():
-    import tensorflow as tf
-    from transformers.modeling_tf_t5 import TFT5Model, TFT5WithLMHeadModel, TF_T5_PRETRAINED_MODEL_ARCHIVE_MAP
+    from transformers.modeling_tf_t5 import TFT5Model, TFT5WithLMHeadModel
 
 
 @require_tf

transformers/tests/tokenization_albert_test.py

@@ -17,7 +17,7 @@ from __future__ import absolute_import, division, print_function, unicode_litera
 import os
 import unittest
 
-from transformers.tokenization_albert import SPIECE_UNDERLINE, AlbertTokenizer
+from transformers.tokenization_albert import AlbertTokenizer
 
 from .tokenization_tests_commons import CommonTestCases
 

transformers/tests/tokenization_distilbert_test.py

@@ -19,7 +19,6 @@ import unittest
 from transformers.tokenization_distilbert import DistilBertTokenizer
 
 from .tokenization_bert_test import BertTokenizationTest
-from .tokenization_tests_commons import CommonTestCases
 from .utils import slow
 
 

transformers/tests/tokenization_transfo_xl_test.py

@@ -25,7 +25,6 @@ from .utils import require_torch
 
 
 if is_torch_available():
-    import torch
     from transformers.tokenization_transfo_xl import TransfoXLTokenizer, VOCAB_FILES_NAMES
 
 

transformers/tokenization_bert_japanese.py

@@ -24,7 +24,6 @@ import unicodedata
 import six
 
 from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer, load_vocab
-from .tokenization_utils import PreTrainedTokenizer
 
 
 logger = logging.getLogger(__name__)

transformers/tokenization_distilbert.py

@@ -17,7 +17,6 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
 
 import logging
-import unicodedata
 
 from .tokenization_bert import BertTokenizer
 

transformers/tokenization_roberta.py

@@ -17,8 +17,6 @@ from __future__ import absolute_import, division, print_function, unicode_litera
 
 import logging
 
-import regex as re
-
 from .tokenization_gpt2 import GPT2Tokenizer
 
 

transformers/tokenization_xlm.py

@@ -25,7 +25,6 @@ from io import open
 
 import sacremoses as sm
 
-from .tokenization_bert import BasicTokenizer
 from .tokenization_utils import PreTrainedTokenizer