Fix F401 flake8 warning (x28).

Do manually what autoflake couldn't manage.

examples/run_bertology.py

@@ -31,18 +31,6 @@ from torch.utils.data.distributed import DistributedSampler
 from tqdm import tqdm
 
 from run_glue import ALL_MODELS, MODEL_CLASSES, load_and_cache_examples, set_seed
-from transformers import (
-    WEIGHTS_NAME,
-    BertConfig,
-    BertForSequenceClassification,
-    BertTokenizer,
-    XLMConfig,
-    XLMForSequenceClassification,
-    XLMTokenizer,
-    XLNetConfig,
-    XLNetForSequenceClassification,
-    XLNetTokenizer,
-)
 from transformers import glue_compute_metrics as compute_metrics
 from transformers import glue_output_modes as output_modes
 from transformers import glue_processors as processors

templates/adding_a_new_model/tests/modeling_tf_xxx_test.py

@@ -30,7 +30,6 @@ if is_tf_available():
         TFXxxForSequenceClassification,
         TFXxxForTokenClassification,
         TFXxxForQuestionAnswering,
-        TF_XXX_PRETRAINED_MODEL_ARCHIVE_MAP,
     )
 
 

templates/adding_a_new_model/tests/modeling_xxx_test.py

@@ -28,12 +28,9 @@ if is_torch_available():
         XxxConfig,
         XxxModel,
         XxxForMaskedLM,
-        XxxForNextSentencePrediction,
-        XxxForPreTraining,
         XxxForQuestionAnswering,
         XxxForSequenceClassification,
         XxxForTokenClassification,
-        XxxForMultipleChoice,
     )
     from transformers.modeling_xxx import XXX_PRETRAINED_MODEL_ARCHIVE_MAP
 

transformers/convert_pytorch_checkpoint_to_tf2.py

@@ -47,7 +47,6 @@ from transformers import (
     TFCTRLLMHeadModel,
     TFDistilBertForMaskedLM,
     TFDistilBertForQuestionAnswering,
-    TFDistilBertForSequenceClassification,
     TFGPT2LMHeadModel,
     TFOpenAIGPTLMHeadModel,
     TFRobertaForMaskedLM,

transformers/convert_roberta_original_pytorch_checkpoint_to_pytorch.py

@@ -28,20 +28,13 @@ from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
 from fairseq.modules import TransformerSentenceEncoderLayer
 from transformers.modeling_bert import (
     BertConfig,
-    BertEncoder,
     BertIntermediate,
     BertLayer,
-    BertModel,
     BertOutput,
     BertSelfAttention,
     BertSelfOutput,
 )
-from transformers.modeling_roberta import (
+from transformers.modeling_roberta import RobertaForMaskedLM, RobertaForSequenceClassification
-    RobertaEmbeddings,
-    RobertaForMaskedLM,
-    RobertaForSequenceClassification,
-    RobertaModel,
-)
 
 
 if version.parse(fairseq.__version__) < version.parse("0.9.0"):

transformers/modeling_auto.py

@@ -50,7 +50,6 @@ from .modeling_bert import (
 from .modeling_camembert import (
     CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_MAP,
     CamembertForMaskedLM,
-    CamembertForMultipleChoice,
     CamembertForSequenceClassification,
     CamembertForTokenClassification,
     CamembertModel,
@@ -85,7 +84,6 @@ from .modeling_xlm import (
 from .modeling_xlm_roberta import (
     XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP,
     XLMRobertaForMaskedLM,
-    XLMRobertaForMultipleChoice,
     XLMRobertaForSequenceClassification,
     XLMRobertaForTokenClassification,
     XLMRobertaModel,

transformers/modeling_tf_utils.py

@@ -25,15 +25,7 @@ import tensorflow as tf
 from tensorflow.python.keras.saving import hdf5_format
 
 from .configuration_utils import PretrainedConfig
-from .file_utils import (
+from .file_utils import DUMMY_INPUTS, TF2_WEIGHTS_NAME, WEIGHTS_NAME, cached_path, hf_bucket_url, is_remote_url
-    DUMMY_INPUTS,
-    TF2_WEIGHTS_NAME,
-    TF_WEIGHTS_NAME,
-    WEIGHTS_NAME,
-    cached_path,
-    hf_bucket_url,
-    is_remote_url,
-)
 from .modeling_tf_pytorch_utils import load_pytorch_checkpoint_in_tf2_model
 
 

transformers/modeling_tf_xlm.py

@@ -25,14 +25,7 @@ import tensorflow as tf
 
 from .configuration_xlm import XLMConfig
 from .file_utils import add_start_docstrings
-from .modeling_tf_utils import (
+from .modeling_tf_utils import TFPreTrainedModel, TFSequenceSummary, TFSharedEmbeddings, get_initializer, shape_list
-    DUMMY_INPUTS,
-    TFPreTrainedModel,
-    TFSequenceSummary,
-    TFSharedEmbeddings,
-    get_initializer,
-    shape_list,
-)
 
 
 logger = logging.getLogger(__name__)

transformers/modeling_xlnet.py

@@ -28,14 +28,7 @@ from torch.nn import functional as F
 
 from .configuration_xlnet import XLNetConfig
 from .file_utils import add_start_docstrings
-from .modeling_utils import (
+from .modeling_utils import PoolerAnswerClass, PoolerEndLogits, PoolerStartLogits, PreTrainedModel, SequenceSummary
-    PoolerAnswerClass,
-    PoolerEndLogits,
-    PoolerStartLogits,
-    PreTrainedModel,
-    SequenceSummary,
-    prune_linear_layer,
-)
 
 
 logger = logging.getLogger(__name__)

transformers/tests/modeling_common_test.py

@@ -41,9 +41,6 @@ if is_torch_available():
         BertModel,
         BertConfig,
         BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
-        GPT2LMHeadModel,
-        GPT2Config,
-        GPT2_PRETRAINED_MODEL_ARCHIVE_MAP,
     )
 
 if sys.version_info[0] == 2:

transformers/tests/modeling_tf_bert_test.py

@@ -34,7 +34,6 @@ if is_tf_available():
         TFBertForMultipleChoice,
         TFBertForTokenClassification,
         TFBertForQuestionAnswering,
-        TF_BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
     )