Black 20 release

src/transformers/tokenization_utils_fast.py

@@ -139,12 +139,12 @@ class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
         return_length: bool = False,
         verbose: bool = True,
     ) -> Dict[str, Any]:
-        """ Convert the encoding representation (from low-level HuggingFace tokenizer output) to a python Dict.
+        """Convert the encoding representation (from low-level HuggingFace tokenizer output) to a python Dict.
 
-            Overflowing tokens are converted to additional examples (like batches) so the output values of
-            the dict are lists (overflows) of lists (tokens).
+        Overflowing tokens are converted to additional examples (like batches) so the output values of
+        the dict are lists (overflows) of lists (tokens).
 
-            Output shape: (overflows, sequence length)
+        Output shape: (overflows, sequence length)
         """
         if return_token_type_ids is None:
             return_token_type_ids = "token_type_ids" in self.model_input_names

src/transformers/tokenization_xlm.py

@@ -902,7 +902,12 @@ class XLMTokenizer(PreTrainedTokenizer):
                     "You should not supply a second sequence if the provided sequence of "
                     "ids is already formated with special tokens for the model."
                 )
-            return list(map(lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0, token_ids_0,))
+            return list(
+                map(
+                    lambda x: 1 if x in [self.sep_token_id, self.cls_token_id] else 0,
+                    token_ids_0,
+                )
+            )
 
         if token_ids_1 is not None:
             return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]

src/transformers/training_args.py

@@ -141,10 +141,12 @@ class TrainingArguments:
     do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."})
     do_predict: bool = field(default=False, metadata={"help": "Whether to run predictions on the test set."})
     evaluate_during_training: bool = field(
-        default=False, metadata={"help": "Run evaluation during training at each logging step."},
+        default=False,
+        metadata={"help": "Run evaluation during training at each logging step."},
     )
     prediction_loss_only: bool = field(
-        default=False, metadata={"help": "When performing evaluation and predictions, only returns the loss."},
+        default=False,
+        metadata={"help": "When performing evaluation and predictions, only returns the loss."},
     )
 
     per_device_train_batch_size: int = field(

src/transformers/training_args_tf.py

@@ -100,7 +100,8 @@ class TFTrainingArguments(TrainingArguments):
     """
 
     tpu_name: str = field(
-        default=None, metadata={"help": "Name of TPU"},
+        default=None,
+        metadata={"help": "Name of TPU"},
     )
 
     @cached_property

templates/adding_a_new_example_script/utils_xxx.py

@@ -703,10 +703,10 @@ def write_predictions_extended(
     tokenizer,
     verbose_logging,
 ):
-    """ XLNet write prediction logic (more complex than Bert's).
-        Write final predictions to the json file and log-odds of null if needed.
+    """XLNet write prediction logic (more complex than Bert's).
+    Write final predictions to the json file and log-odds of null if needed.
 
-        Requires utils_squad_evaluate.py
+    Requires utils_squad_evaluate.py
     """
     _PrelimPrediction = collections.namedtuple(  # pylint: disable=invalid-name
         "PrelimPrediction", ["feature_index", "start_index", "end_index", "start_log_prob", "end_log_prob"]

templates/adding_a_new_model/configuration_xxx.py

@@ -31,47 +31,47 @@ XXX_PRETRAINED_CONFIG_ARCHIVE_MAP = {
 
 class XxxConfig(PretrainedConfig):
     r"""
-        This is the configuration class to store the configuration of a :class:`~transformers.XXXModel`.
-        It is used to instantiate a XXX model according to the specified arguments, defining the model
-        architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
-        the XXX `xxx-base-uncased <https://huggingface.co/xxx/xxx-base-uncased>`__ architecture.
+    This is the configuration class to store the configuration of a :class:`~transformers.XXXModel`.
+    It is used to instantiate a XXX model according to the specified arguments, defining the model
+    architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
+    the XXX `xxx-base-uncased <https://huggingface.co/xxx/xxx-base-uncased>`__ architecture.
 
-        Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
-        to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
-        for more information.
+    Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
+    to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
+    for more information.
 
 
-        Args:
-            vocab_size (:obj:`int`, optional, defaults to 30522):
-                Vocabulary size of the XXX model. Defines the different tokens that
-                can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.XXXModel`.
-            hidden_size (:obj:`int`, optional, defaults to 768):
-                Dimensionality of the encoder layers and the pooler layer.
-            num_hidden_layers (:obj:`int`, optional, defaults to 12):
-                Number of hidden layers in the Transformer encoder.
-            num_attention_heads (:obj:`int`, optional, defaults to 12):
-                Number of attention heads for each attention layer in the Transformer encoder.
-            hidden_act (:obj:`str` or :obj:`function`, optional, defaults to :obj:`"gelu"`):
-                The non-linear activation function (function or string) in the encoder and pooler.
+    Args:
+        vocab_size (:obj:`int`, optional, defaults to 30522):
+            Vocabulary size of the XXX model. Defines the different tokens that
+            can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.XXXModel`.
+        hidden_size (:obj:`int`, optional, defaults to 768):
+            Dimensionality of the encoder layers and the pooler layer.
+        num_hidden_layers (:obj:`int`, optional, defaults to 12):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (:obj:`int`, optional, defaults to 12):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        hidden_act (:obj:`str` or :obj:`function`, optional, defaults to :obj:`"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler.
 
-                If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
-            hidden_dropout_prob (:obj:`float`, optional, defaults to 0.1):
-                The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
-            attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1):
-                The dropout ratio for the attention probabilities.
-            max_position_embeddings (:obj:`int`, optional, defaults to 512):
-                The maximum sequence length that this model might ever be used with.
-                Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
-            type_vocab_size (:obj:`int`, optional, defaults to 2):
-                The vocabulary size of the `token_type_ids` passed into :class:`~transformers.BertModel`.
-            initializer_range (:obj:`float`, optional, defaults to 0.02):
-                The standard deviation of the :obj:`truncated_normal_initializer` for initializing all weight matrices.
-            layer_norm_eps (:obj:`float`, optional, defaults to 1e-5):
-                The epsilon used by the layer normalization layers.
-            gradient_checkpointing (:obj:`bool`, optional, defaults to :obj:`False`):
-                If :obj:`True`, use gradient checkpointing to save memory at the expense of slower backward pass.
-            kwargs:
-                Additional arguments for common configurations, passed to :class:`~transformers.PretrainedConfig`.
+            If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
+        hidden_dropout_prob (:obj:`float`, optional, defaults to 0.1):
+            The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
+        attention_probs_dropout_prob (:obj:`float`, optional, defaults to 0.1):
+            The dropout ratio for the attention probabilities.
+        max_position_embeddings (:obj:`int`, optional, defaults to 512):
+            The maximum sequence length that this model might ever be used with.
+            Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
+        type_vocab_size (:obj:`int`, optional, defaults to 2):
+            The vocabulary size of the `token_type_ids` passed into :class:`~transformers.BertModel`.
+        initializer_range (:obj:`float`, optional, defaults to 0.02):
+            The standard deviation of the :obj:`truncated_normal_initializer` for initializing all weight matrices.
+        layer_norm_eps (:obj:`float`, optional, defaults to 1e-5):
+            The epsilon used by the layer normalization layers.
+        gradient_checkpointing (:obj:`bool`, optional, defaults to :obj:`False`):
+            If :obj:`True`, use gradient checkpointing to save memory at the expense of slower backward pass.
+        kwargs:
+            Additional arguments for common configurations, passed to :class:`~transformers.PretrainedConfig`.
     """
     model_type = "xxx"
 

templates/adding_a_new_model/modeling_tf_xxx.py

@@ -223,7 +223,10 @@ class TFXxxMainLayer(tf.keras.layers.Layer):
         pooled_output = self.pooler(sequence_output)
 
         if not return_dict:
-            return (sequence_output, pooled_output,) + encoder_outputs[1:]
+            return (
+                sequence_output,
+                pooled_output,
+            ) + encoder_outputs[1:]
 
         return TFBaseModelOutputWithPooling(
             last_hidden_state=sequence_output,
@@ -241,8 +244,8 @@ class TFXxxMainLayer(tf.keras.layers.Layer):
 # pointers for your model.
 ####################################################
 class TFXxxPreTrainedModel(TFPreTrainedModel):
-    """ An abstract class to handle weights initialization and
-        a simple interface for downloading and loading pretrained models.
+    """An abstract class to handle weights initialization and
+    a simple interface for downloading and loading pretrained models.
     """
 
     config_class = XxxConfig
@@ -422,7 +425,10 @@ class TFXxxForMaskedLM(TFXxxPreTrainedModel, TFMaskedLanguageModelingLoss):
             return ((loss,) + output) if loss is not None else output
 
         return TFMaskedLMOutput(
-            loss=loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions,
+            loss=loss,
+            logits=prediction_scores,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
         )
 
 
@@ -503,7 +509,10 @@ class TFXxxForSequenceClassification(TFXxxPreTrainedModel, TFSequenceClassificat
             return ((loss,) + output) if loss is not None else output
 
         return TFSequenceClassifierOutput(
-            loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions,
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
         )
 
 
@@ -524,7 +533,7 @@ class TFXxxForMultipleChoice(TFXxxPreTrainedModel, TFMultipleChoiceLoss):
 
     @property
     def dummy_inputs(self):
-        """ Dummy inputs to build the network.
+        """Dummy inputs to build the network.
 
         Returns:
             tf.Tensor with dummy inputs
@@ -631,7 +640,10 @@ class TFXxxForMultipleChoice(TFXxxPreTrainedModel, TFMultipleChoiceLoss):
             return ((loss,) + output) if loss is not None else output
 
         return TFMultipleChoiceModelOutput(
-            loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions,
+            loss=loss,
+            logits=reshaped_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
         )
 
 
@@ -710,7 +722,10 @@ class TFXxxForTokenClassification(TFXxxPreTrainedModel, TFTokenClassificationLos
             return ((loss,) + output) if loss is not None else output
 
         return TFTokenClassifierOutput(
-            loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions,
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
         )
 
 

templates/adding_a_new_model/modeling_xxx.py

@@ -59,8 +59,7 @@ XXX_PRETRAINED_MODEL_ARCHIVE_LIST = [
 # More details: https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28
 ####################################################
 def load_tf_weights_in_xxx(model, config, tf_checkpoint_path):
-    """ Load tf checkpoints in a pytorch model.
-    """
+    """Load tf checkpoints in a pytorch model."""
     try:
         import re
 
@@ -189,8 +188,8 @@ XxxPooler = nn.Module
 
 
 class XxxPreTrainedModel(PreTrainedModel):
-    """ An abstract class to handle weights initialization and
-        a simple interface for downloading and loading pretrained models.
+    """An abstract class to handle weights initialization and
+    a simple interface for downloading and loading pretrained models.
     """
 
     config_class = XxxConfig
@@ -290,9 +289,9 @@ class XxxModel(XxxPreTrainedModel):
         self.embeddings.word_embeddings = new_embeddings
 
     def _prune_heads(self, heads_to_prune):
-        """ Prunes heads of the model.
-            heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
-            See base class PreTrainedModel
+        """Prunes heads of the model.
+        heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
+        See base class PreTrainedModel
         """
         for layer, heads in heads_to_prune.items():
             self.encoder.layer[layer].attention.prune_heads(heads)
@@ -517,7 +516,10 @@ class XxxForSequenceClassification(XxxPreTrainedModel):
             return ((loss,) + output) if loss is not None else output
 
         return SequenceClassifierOutput(
-            loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions,
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
         )
 
 
@@ -603,7 +605,10 @@ class XxxForMultipleChoice(XxxPreTrainedModel):
             return ((loss,) + output) if loss is not None else output
 
         return MultipleChoiceModelOutput(
-            loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions,
+            loss=loss,
+            logits=reshaped_logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
         )
 
 
@@ -686,7 +691,10 @@ class XxxForTokenClassification(XxxPreTrainedModel):
             return ((loss,) + output) if loss is not None else output
 
         return TokenClassifierOutput(
-            loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions,
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
         )
 
 

tests/test_modeling_albert.py

@@ -39,7 +39,8 @@ if is_torch_available():
 
 class AlbertModelTester:
     def __init__(
-        self, parent,
+        self,
+        parent,
     ):
         self.parent = parent
         self.batch_size = 13

tests/test_modeling_bart.py

@@ -54,7 +54,8 @@ PGE_ARTICLE = """ PG&E stated it scheduled the blackouts in response to forecast
 @require_torch
 class ModelTester:
     def __init__(
-        self, parent,
+        self,
+        parent,
     ):
         self.parent = parent
         self.batch_size = 13
@@ -76,7 +77,9 @@ class ModelTester:
         torch.manual_seed(0)
 
     def prepare_config_and_inputs_for_common(self):
-        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(3,)
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(
+            3,
+        )
         input_ids[:, -1] = 2  # Eos Token
 
         config = BartConfig(
@@ -100,7 +103,9 @@ class ModelTester:
 
 
 def prepare_bart_inputs_dict(
-    config, input_ids, attention_mask=None,
+    config,
+    input_ids,
+    attention_mask=None,
 ):
     if attention_mask is None:
         attention_mask = input_ids.ne(config.pad_token_id)
@@ -261,7 +266,11 @@ class BartHeadTests(unittest.TestCase):
         sequence_labels = ids_tensor([batch_size], 2).to(torch_device)
         model = BartForQuestionAnswering(config)
         model.to(torch_device)
-        outputs = model(input_ids=input_ids, start_positions=sequence_labels, end_positions=sequence_labels,)
+        outputs = model(
+            input_ids=input_ids,
+            start_positions=sequence_labels,
+            end_positions=sequence_labels,
+        )
 
         self.assertEqual(outputs["start_logits"].shape, input_ids.shape)
         self.assertEqual(outputs["end_logits"].shape, input_ids.shape)
@@ -491,7 +500,11 @@ class BartModelIntegrationTests(unittest.TestCase):
 
         EXPECTED_SUMMARY = "California's largest power company has begun shutting off electricity to thousands of customers in the state."
         dct = tok.batch_encode_plus(
-            [PGE_ARTICLE], max_length=1024, padding="max_length", truncation=True, return_tensors="pt",
+            [PGE_ARTICLE],
+            max_length=1024,
+            padding="max_length",
+            truncation=True,
+            return_tensors="pt",
         ).to(torch_device)
 
         hypotheses_batch = model.generate(
@@ -506,7 +519,10 @@ class BartModelIntegrationTests(unittest.TestCase):
             decoder_start_token_id=model.config.eos_token_id,
         )
 
-        decoded = tok.batch_decode(hypotheses_batch, skip_special_tokens=True,)
+        decoded = tok.batch_decode(
+            hypotheses_batch,
+            skip_special_tokens=True,
+        )
         self.assertEqual(EXPECTED_SUMMARY, decoded[0])
 
     def test_xsum_config_generation_params(self):

tests/test_modeling_bert.py

@@ -264,7 +264,10 @@ class BertModelTester:
         model.to(torch_device)
         model.eval()
         result = model(
-            input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, next_sentence_label=sequence_labels,
+            input_ids,
+            attention_mask=input_mask,
+            token_type_ids=token_type_ids,
+            next_sentence_label=sequence_labels,
         )
         self.parent.assertEqual(result.logits.shape, (self.batch_size, 2))
 

tests/test_modeling_camembert.py

@@ -33,7 +33,9 @@ class CamembertModelIntegrationTest(unittest.TestCase):
         model.to(torch_device)
 
         input_ids = torch.tensor(
-            [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]], device=torch_device, dtype=torch.long,
+            [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]],
+            device=torch_device,
+            dtype=torch.long,
         )  # J'aime le camembert !
         output = model(input_ids)["last_hidden_state"]
         expected_shape = torch.Size((1, 10, 768))

tests/test_modeling_common.py

@@ -330,7 +330,9 @@ class ModelTesterMixin:
             # Prepare head_mask
             # Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior)
             head_mask = torch.ones(
-                self.model_tester.num_hidden_layers, self.model_tester.num_attention_heads, device=torch_device,
+                self.model_tester.num_hidden_layers,
+                self.model_tester.num_attention_heads,
+                device=torch_device,
             )
             head_mask[0, 0] = 0
             head_mask[-1, :-1] = 0
@@ -370,7 +372,10 @@ class ModelTesterMixin:
             return
 
         for model_class in self.all_model_classes:
-            (config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common()
+            (
+                config,
+                inputs_dict,
+            ) = self.model_tester.prepare_config_and_inputs_for_common()
 
             if "head_mask" in inputs_dict:
                 del inputs_dict["head_mask"]
@@ -399,7 +404,10 @@ class ModelTesterMixin:
             return
 
         for model_class in self.all_model_classes:
-            (config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common()
+            (
+                config,
+                inputs_dict,
+            ) = self.model_tester.prepare_config_and_inputs_for_common()
 
             if "head_mask" in inputs_dict:
                 del inputs_dict["head_mask"]
@@ -432,7 +440,10 @@ class ModelTesterMixin:
             return
 
         for model_class in self.all_model_classes:
-            (config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common()
+            (
+                config,
+                inputs_dict,
+            ) = self.model_tester.prepare_config_and_inputs_for_common()
 
             if "head_mask" in inputs_dict:
                 del inputs_dict["head_mask"]
@@ -463,7 +474,10 @@ class ModelTesterMixin:
             return
 
         for model_class in self.all_model_classes:
-            (config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common()
+            (
+                config,
+                inputs_dict,
+            ) = self.model_tester.prepare_config_and_inputs_for_common()
 
             if "head_mask" in inputs_dict:
                 del inputs_dict["head_mask"]
@@ -534,7 +548,8 @@ class ModelTesterMixin:
                 seq_length = self.model_tester.seq_length
 
             self.assertListEqual(
-                list(hidden_states[0].shape[-2:]), [seq_length, self.model_tester.hidden_size],
+                list(hidden_states[0].shape[-2:]),
+                [seq_length, self.model_tester.hidden_size],
             )
 
         config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
@@ -550,7 +565,10 @@ class ModelTesterMixin:
             check_hidden_states_output(inputs_dict, config, model_class)
 
     def test_feed_forward_chunking(self):
-        (original_config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common()
+        (
+            original_config,
+            inputs_dict,
+        ) = self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes:
             torch.manual_seed(0)
             config = copy.deepcopy(original_config)
@@ -570,7 +588,10 @@ class ModelTesterMixin:
             self.assertTrue(torch.allclose(hidden_states_no_chunk, hidden_states_with_chunk, atol=1e-3))
 
     def test_resize_tokens_embeddings(self):
-        (original_config, inputs_dict,) = self.model_tester.prepare_config_and_inputs_for_common()
+        (
+            original_config,
+            inputs_dict,
+        ) = self.model_tester.prepare_config_and_inputs_for_common()
         if not self.test_resize_embeddings:
             return
 
@@ -844,7 +865,14 @@ class ModelTesterMixin:
                 model.generate(input_ids, do_sample=False, num_return_sequences=3, num_beams=2)
 
             # num_return_sequences > 1, sample
-            self._check_generated_ids(model.generate(input_ids, do_sample=True, num_beams=2, num_return_sequences=2,))
+            self._check_generated_ids(
+                model.generate(
+                    input_ids,
+                    do_sample=True,
+                    num_beams=2,
+                    num_return_sequences=2,
+                )
+            )
             # num_return_sequences > 1, greedy
             self._check_generated_ids(model.generate(input_ids, do_sample=False, num_beams=2, num_return_sequences=2))
 

tests/test_modeling_ctrl.py

@@ -30,7 +30,8 @@ if is_torch_available():
 
 class CTRLModelTester:
     def __init__(
-        self, parent,
+        self,
+        parent,
     ):
         self.parent = parent
         self.batch_size = 14

tests/test_modeling_distilbert.py

@@ -179,7 +179,9 @@ if is_torch_available():
             multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
             multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
             result = model(
-                multiple_choice_inputs_ids, attention_mask=multiple_choice_input_mask, labels=choice_labels,
+                multiple_choice_inputs_ids,
+                attention_mask=multiple_choice_input_mask,
+                labels=choice_labels,
             )
             self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
 

tests/test_modeling_dpr.py

@@ -149,7 +149,10 @@ class DPRModelTester:
         model = DPRReader(config=config)
         model.to(torch_device)
         model.eval()
-        result = model(input_ids, attention_mask=input_mask,)
+        result = model(
+            input_ids,
+            attention_mask=input_mask,
+        )
 
         self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
         self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
@@ -173,7 +176,15 @@ class DPRModelTester:
 @require_torch
 class DPRModelTest(ModelTesterMixin, unittest.TestCase):
 
-    all_model_classes = (DPRContextEncoder, DPRQuestionEncoder, DPRReader,) if is_torch_available() else ()
+    all_model_classes = (
+        (
+            DPRContextEncoder,
+            DPRQuestionEncoder,
+            DPRReader,
+        )
+        if is_torch_available()
+        else ()
+    )
 
     test_resize_embeddings = False
     test_missing_keys = False  # why?

tests/test_modeling_electra.py

@@ -39,7 +39,8 @@ if is_torch_available():
 
 class ElectraModelTester:
     def __init__(
-        self, parent,
+        self,
+        parent,
     ):
         self.parent = parent
         self.batch_size = 13

tests/test_modeling_encoder_decoder.py

@@ -391,7 +391,11 @@ class EncoderDecoderMixin:
         decoder_input_ids = ids_tensor([13, 1], model_2.config.encoder.vocab_size)
         attention_mask = ids_tensor([13, 5], vocab_size=2)
         with torch.no_grad():
-            outputs = model_2(input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask,)
+            outputs = model_2(
+                input_ids=input_ids,
+                decoder_input_ids=decoder_input_ids,
+                attention_mask=attention_mask,
+            )
             out_2 = outputs[0].cpu().numpy()
             out_2[np.isnan(out_2)] = 0
 
@@ -401,7 +405,9 @@ class EncoderDecoderMixin:
                 model_1.to(torch_device)
 
                 after_outputs = model_1(
-                    input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask,
+                    input_ids=input_ids,
+                    decoder_input_ids=decoder_input_ids,
+                    attention_mask=attention_mask,
                 )
                 out_1 = after_outputs[0].cpu().numpy()
                 out_1[np.isnan(out_1)] = 0

tests/test_modeling_flaubert.py

@@ -39,7 +39,8 @@ if is_torch_available():
 
 class FlaubertModelTester(object):
     def __init__(
-        self, parent,
+        self,
+        parent,
     ):
         self.parent = parent
         self.batch_size = 13

tests/test_modeling_gpt2.py

@@ -244,7 +244,8 @@ class GPT2ModelTester:
         # append to next input_ids and attn_mask
         next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
         attn_mask = torch.cat(
-            [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)], dim=1,
+            [attn_mask, torch.ones((attn_mask.shape[0], 1), dtype=torch.long, device=torch_device)],
+            dim=1,
         )
 
         # get two different outputs