Merge pull request #2538 from huggingface/py3_super

💄 super

templates/adding_a_new_model/modeling_tf_xxx.py

@@ -69,7 +69,7 @@ TFXxxOutput = tf.keras.layers.Layer
 
 class TFXxxLayer(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
-        super(TFXxxLayer, self).__init__(**kwargs)
+        super().__init__(**kwargs)
         self.attention = TFXxxAttention(config, name="attention")
         self.intermediate = TFXxxIntermediate(config, name="intermediate")
         self.transformer_output = TFXxxOutput(config, name="output")
@@ -91,7 +91,7 @@ class TFXxxLayer(tf.keras.layers.Layer):
 ####################################################
 class TFXxxMainLayer(tf.keras.layers.Layer):
     def __init__(self, config, **kwargs):
-        super(TFXxxMainLayer, self).__init__(**kwargs)
+        super().__init__(**kwargs)
 
     def _resize_token_embeddings(self, new_num_tokens):
         raise NotImplementedError  # Not implemented yet in the library fr TF 2.0 models
@@ -307,7 +307,7 @@ class TFXxxModel(TFXxxPreTrainedModel):
     """
 
     def __init__(self, config, *inputs, **kwargs):
-        super(TFXxxModel, self).__init__(config, *inputs, **kwargs)
+        super().__init__(config, *inputs, **kwargs)
         self.transformer = TFXxxMainLayer(config, name="transformer")
 
     def call(self, inputs, **kwargs):
@@ -348,7 +348,7 @@ class TFXxxForMaskedLM(TFXxxPreTrainedModel):
     """
 
     def __init__(self, config, *inputs, **kwargs):
-        super(TFXxxForMaskedLM, self).__init__(config, *inputs, **kwargs)
+        super().__init__(config, *inputs, **kwargs)
 
         self.transformer = TFXxxMainLayer(config, name="transformer")
         self.mlm = TFXxxMLMHead(config, self.transformer.embeddings, name="mlm")
@@ -397,7 +397,7 @@ class TFXxxForSequenceClassification(TFXxxPreTrainedModel):
     """
 
     def __init__(self, config, *inputs, **kwargs):
-        super(TFXxxForSequenceClassification, self).__init__(config, *inputs, **kwargs)
+        super().__init__(config, *inputs, **kwargs)
         self.num_labels = config.num_labels
 
         self.transformer = TFXxxMainLayer(config, name="transformer")
@@ -452,7 +452,7 @@ class TFXxxForTokenClassification(TFXxxPreTrainedModel):
     """
 
     def __init__(self, config, *inputs, **kwargs):
-        super(TFXxxForTokenClassification, self).__init__(config, *inputs, **kwargs)
+        super().__init__(config, *inputs, **kwargs)
         self.num_labels = config.num_labels
 
         self.transformer = TFXxxMainLayer(config, name="transformer")
@@ -509,7 +509,7 @@ class TFXxxForQuestionAnswering(TFXxxPreTrainedModel):
     """
 
     def __init__(self, config, *inputs, **kwargs):
-        super(TFXxxForQuestionAnswering, self).__init__(config, *inputs, **kwargs)
+        super().__init__(config, *inputs, **kwargs)
         self.num_labels = config.num_labels
 
         self.transformer = TFXxxMainLayer(config, name="transformer")

templates/adding_a_new_model/modeling_xxx.py

@@ -138,7 +138,7 @@ XxxOutput = nn.Module
 
 class XxxLayer(nn.Module):
     def __init__(self, config):
-        super(XxxLayer, self).__init__()
+        super().__init__()
         self.attention = XxxAttention(config)
         self.intermediate = XxxIntermediate(config)
         self.output = XxxOutput(config)
@@ -298,7 +298,7 @@ class XxxModel(XxxPreTrainedModel):
     """
 
     def __init__(self, config):
-        super(XxxModel, self).__init__(config)
+        super().__init__(config)
 
         self.embeddings = XxxEmbeddings(config)
         self.encoder = XxxEncoder(config)
@@ -426,7 +426,7 @@ class XxxForMaskedLM(XxxPreTrainedModel):
     """
 
     def __init__(self, config):
-        super(XxxForMaskedLM, self).__init__(config)
+        super().__init__(config)
 
         self.transformer = XxxModel(config)
         self.lm_head = nn.Linear(config.n_embd, config.vocab_size)
@@ -507,7 +507,7 @@ class XxxForSequenceClassification(XxxPreTrainedModel):
     """
 
     def __init__(self, config):
-        super(XxxForSequenceClassification, self).__init__(config)
+        super().__init__(config)
         self.num_labels = config.num_labels
 
         self.transformer = XxxModel(config)
@@ -593,7 +593,7 @@ class XxxForTokenClassification(XxxPreTrainedModel):
     """
 
     def __init__(self, config):
-        super(XxxForTokenClassification, self).__init__(config)
+        super().__init__(config)
         self.num_labels = config.num_labels
 
         self.transformer = XxxModel(config)
@@ -692,7 +692,7 @@ class XxxForQuestionAnswering(XxxPreTrainedModel):
     """
 
     def __init__(self, config):
-        super(XxxForQuestionAnswering, self).__init__(config)
+        super().__init__(config)
         self.num_labels = config.num_labels
 
         self.transformer = XxxModel(config)

templates/adding_a_new_model/tests/test_tokenization_xxx.py

@@ -27,7 +27,7 @@ class XxxTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
     tokenizer_class = XxxTokenizer
 
     def setUp(self):
-        super(XxxTokenizationTest, self).setUp()
+        super().setUp()
 
         vocab_tokens = [
             "[UNK]",

templates/adding_a_new_model/tokenization_xxx.py

@@ -109,7 +109,7 @@ class XxxTokenizer(PreTrainedTokenizer):
                 Whether to lower case the input
                 Only has an effect when do_basic_tokenize=True
         """
-        super(XxxTokenizer, self).__init__(
+        super().__init__(
             unk_token=unk_token,
             sep_token=sep_token,
             pad_token=pad_token,

tests/test_tokenization_albert.py

@@ -30,7 +30,7 @@ class AlbertTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
     tokenizer_class = AlbertTokenizer
 
     def setUp(self):
-        super(AlbertTokenizationTest, self).setUp()
+        super().setUp()
 
         # We have a SentencePiece fixture for testing
         tokenizer = AlbertTokenizer(SAMPLE_VOCAB)

tests/test_tokenization_bert.py

@@ -38,7 +38,7 @@ class BertTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
     test_rust_tokenizer = True
 
     def setUp(self):
-        super(BertTokenizationTest, self).setUp()
+        super().setUp()
 
         vocab_tokens = [
             "[UNK]",

tests/test_tokenization_bert_japanese.py

@@ -35,7 +35,7 @@ class BertJapaneseTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
     tokenizer_class = BertJapaneseTokenizer
 
     def setUp(self):
-        super(BertJapaneseTokenizationTest, self).setUp()
+        super().setUp()
 
         vocab_tokens = [
             "[UNK]",
@@ -135,7 +135,7 @@ class BertJapaneseCharacterTokenizationTest(TokenizerTesterMixin, unittest.TestC
     tokenizer_class = BertJapaneseTokenizer
 
     def setUp(self):
-        super(BertJapaneseCharacterTokenizationTest, self).setUp()
+        super().setUp()
 
         vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "こ", "ん", "に", "ち", "は", "ば", "世", "界", "、", "。"]
 

tests/test_tokenization_ctrl.py

@@ -27,7 +27,7 @@ class CTRLTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
     tokenizer_class = CTRLTokenizer
 
     def setUp(self):
-        super(CTRLTokenizationTest, self).setUp()
+        super().setUp()
 
         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         vocab = ["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]

tests/test_tokenization_gpt2.py

@@ -29,7 +29,7 @@ class GPT2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
     test_rust_tokenizer = True
 
     def setUp(self):
-        super(GPT2TokenizationTest, self).setUp()
+        super().setUp()
 
         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         vocab = [

tests/test_tokenization_openai.py

@@ -28,7 +28,7 @@ class OpenAIGPTTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
     tokenizer_class = OpenAIGPTTokenizer
 
     def setUp(self):
-        super(OpenAIGPTTokenizationTest, self).setUp()
+        super().setUp()
 
         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         vocab = [

tests/test_tokenization_roberta.py

@@ -28,7 +28,7 @@ class RobertaTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
     tokenizer_class = RobertaTokenizer
 
     def setUp(self):
-        super(RobertaTokenizationTest, self).setUp()
+        super().setUp()
 
         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         vocab = [

tests/test_tokenization_t5.py

@@ -31,7 +31,7 @@ class T5TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
     tokenizer_class = T5Tokenizer
 
     def setUp(self):
-        super(T5TokenizationTest, self).setUp()
+        super().setUp()
 
         # We have a SentencePiece fixture for testing
         tokenizer = T5Tokenizer(SAMPLE_VOCAB)

tests/test_tokenization_transfo_xl.py

@@ -33,7 +33,7 @@ class TransfoXLTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
     tokenizer_class = TransfoXLTokenizer if is_torch_available() else None
 
     def setUp(self):
-        super(TransfoXLTokenizationTest, self).setUp()
+        super().setUp()
 
         vocab_tokens = [
             "<unk>",

tests/test_tokenization_xlm.py

@@ -29,7 +29,7 @@ class XLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
     tokenizer_class = XLMTokenizer
 
     def setUp(self):
-        super(XLMTokenizationTest, self).setUp()
+        super().setUp()
 
         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         vocab = [

tests/test_tokenization_xlnet.py

@@ -31,7 +31,7 @@ class XLNetTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
     tokenizer_class = XLNetTokenizer
 
     def setUp(self):
-        super(XLNetTokenizationTest, self).setUp()
+        super().setUp()
 
         # We have a SentencePiece fixture for testing
         tokenizer = XLNetTokenizer(SAMPLE_VOCAB, keep_accents=True)