make style (#11442)

src/transformers/models/albert/modeling_albert.py

@@ -71,7 +71,7 @@ ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
 
 
 def load_tf_weights_in_albert(model, config, tf_checkpoint_path):
-    """ Load tf checkpoints in a pytorch model."""
+    """Load tf checkpoints in a pytorch model."""
     try:
         import re
 

src/transformers/models/albert/modeling_tf_albert.py

@@ -189,7 +189,7 @@ class TFAlbertEmbeddings(tf.keras.layers.Layer):
 
 
 class TFAlbertAttention(tf.keras.layers.Layer):
-    """ Contains the complete attention sublayer, including both dropouts and layer norm. """
+    """Contains the complete attention sublayer, including both dropouts and layer norm."""
 
     def __init__(self, config: AlbertConfig, **kwargs):
         super().__init__(**kwargs)

src/transformers/models/albert/tokenization_albert.py

@@ -187,7 +187,7 @@ class AlbertTokenizer(PreTrainedTokenizer):
         return outputs
 
     def _tokenize(self, text, sample=False):
-        """ Tokenize a string. """
+        """Tokenize a string."""
         text = self.preprocess_text(text)
 
         if not sample:
@@ -211,7 +211,7 @@ class AlbertTokenizer(PreTrainedTokenizer):
         return new_pieces
 
     def _convert_token_to_id(self, token):
-        """ Converts a token (str) in an id using the vocab. """
+        """Converts a token (str) in an id using the vocab."""
         return self.sp_model.PieceToId(token)
 
     def _convert_id_to_token(self, index):

src/transformers/models/barthez/tokenization_barthez.py

@@ -223,7 +223,7 @@ class BarthezTokenizer(PreTrainedTokenizer):
         return self.sp_model.EncodeAsPieces(text)
 
     def _convert_token_to_id(self, token):
-        """ Converts a token (str) in an id using the vocab. """
+        """Converts a token (str) in an id using the vocab."""
         if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
         spm_id = self.sp_model.PieceToId(token)

src/transformers/models/bert/modeling_bert.py

@@ -703,7 +703,7 @@ class BertPreTrainedModel(PreTrainedModel):
     _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def _init_weights(self, module):
-        """ Initialize the weights """
+        """Initialize the weights"""
         if isinstance(module, nn.Linear):
             # Slightly different from the TF version which uses truncated_normal for initialization
             # cf https://github.com/pytorch/pytorch/pull/5617

src/transformers/models/bert/tokenization_bert.py

@@ -233,7 +233,7 @@ class BertTokenizer(PreTrainedTokenizer):
         return split_tokens
 
     def _convert_token_to_id(self, token):
-        """ Converts a token (str) in an id using the vocab. """
+        """Converts a token (str) in an id using the vocab."""
         return self.vocab.get(token, self.vocab.get(self.unk_token))
 
     def _convert_id_to_token(self, index):
@@ -241,7 +241,7 @@ class BertTokenizer(PreTrainedTokenizer):
         return self.ids_to_tokens.get(index, self.unk_token)
 
     def convert_tokens_to_string(self, tokens):
-        """ Converts a sequence of tokens (string) in a single string. """
+        """Converts a sequence of tokens (string) in a single string."""
         out_string = " ".join(tokens).replace(" ##", "").strip()
         return out_string
 

src/transformers/models/bert_generation/modeling_bert_generation.py

@@ -177,7 +177,7 @@ class BertGenerationPreTrainedModel(PreTrainedModel):
     _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def _init_weights(self, module):
-        """ Initialize the weights """
+        """Initialize the weights"""
         if isinstance(module, nn.Linear):
             # Slightly different from the TF version which uses truncated_normal for initialization
             # cf https://github.com/pytorch/pytorch/pull/5617

src/transformers/models/bert_generation/tokenization_bert_generation.py

@@ -119,7 +119,7 @@ class BertGenerationTokenizer(PreTrainedTokenizer):
         return pieces
 
     def _convert_token_to_id(self, token):
-        """ Converts a token (str) in an id using the vocab. """
+        """Converts a token (str) in an id using the vocab."""
         return self.sp_model.piece_to_id(token)
 
     def _convert_id_to_token(self, index):
@@ -128,7 +128,7 @@ class BertGenerationTokenizer(PreTrainedTokenizer):
         return token
 
     def convert_tokens_to_string(self, tokens):
-        """ Converts a sequence of tokens (string) in a single string. """
+        """Converts a sequence of tokens (string) in a single string."""
         out_string = self.sp_model.decode_pieces(tokens)
         return out_string
 

src/transformers/models/bertweet/tokenization_bertweet.py

@@ -368,7 +368,7 @@ class BertweetTokenizer(PreTrainedTokenizer):
             return token
 
     def _convert_token_to_id(self, token):
-        """ Converts a token (str) in an id using the vocab. """
+        """Converts a token (str) in an id using the vocab."""
         return self.encoder.get(token, self.encoder.get(self.unk_token))
 
     def _convert_id_to_token(self, index):
@@ -376,7 +376,7 @@ class BertweetTokenizer(PreTrainedTokenizer):
         return self.decoder.get(index, self.unk_token)
 
     def convert_tokens_to_string(self, tokens):
-        """ Converts a sequence of tokens (string) in a single string. """
+        """Converts a sequence of tokens (string) in a single string."""
         out_string = " ".join(tokens).replace("@@ ", "").strip()
         return out_string
 

src/transformers/models/big_bird/modeling_big_bird.py

@@ -484,7 +484,7 @@ class BigBirdBlockSparseAttention(nn.Module):
 
     @staticmethod
     def torch_bmm_nd(inp_1, inp_2, ndim=None):
-        """ Fast nd matrix multiplication """
+        """Fast nd matrix multiplication"""
         # faster replacement of torch.einsum ("bhqk,bhkd->bhqd")
         return torch.bmm(inp_1.reshape((-1,) + inp_1.shape[-2:]), inp_2.reshape((-1,) + inp_2.shape[-2:])).view(
             inp_1.shape[: ndim - 2] + (inp_1.shape[ndim - 2], inp_2.shape[ndim - 1])
@@ -492,7 +492,7 @@ class BigBirdBlockSparseAttention(nn.Module):
 
     @staticmethod
     def torch_bmm_nd_transpose(inp_1, inp_2, ndim=None):
-        """ Fast nd matrix multiplication with transpose """
+        """Fast nd matrix multiplication with transpose"""
         # faster replacement of torch.einsum (bhqd,bhkd->bhqk)
         return torch.bmm(
             inp_1.reshape((-1,) + inp_1.shape[-2:]), inp_2.reshape((-1,) + inp_2.shape[-2:]).transpose(1, 2)
@@ -1743,7 +1743,7 @@ class BigBirdPreTrainedModel(PreTrainedModel):
     _keys_to_ignore_on_load_missing = [r"position_ids"]
 
     def _init_weights(self, module):
-        """ Initialize the weights """
+        """Initialize the weights"""
         if isinstance(module, nn.Linear):
             # Slightly different from the TF version which uses truncated_normal for initialization
             # cf https://github.com/pytorch/pytorch/pull/5617

src/transformers/models/big_bird/tokenization_big_bird.py

@@ -149,7 +149,7 @@ class BigBirdTokenizer(PreTrainedTokenizer):
         return pieces
 
     def _convert_token_to_id(self, token):
-        """ Converts a token (str) in an id using the vocab. """
+        """Converts a token (str) in an id using the vocab."""
         return self.sp_model.piece_to_id(token)
 
     def _convert_id_to_token(self, index):
@@ -158,7 +158,7 @@ class BigBirdTokenizer(PreTrainedTokenizer):
         return token
 
     def convert_tokens_to_string(self, tokens):
-        """ Converts a sequence of tokens (string) in a single string. """
+        """Converts a sequence of tokens (string) in a single string."""
         out_string = self.sp_model.decode_pieces(tokens)
         return out_string
 

src/transformers/models/blenderbot_small/tokenization_blenderbot_small.py

@@ -183,7 +183,7 @@ class BlenderbotSmallTokenizer(PreTrainedTokenizer):
         return " ".join(words)
 
     def _tokenize(self, text: str) -> List[str]:
-        """ Split a string into tokens using BPE."""
+        """Split a string into tokens using BPE."""
         split_tokens = []
 
         words = re.findall(r"\S+\n?", text)
@@ -193,7 +193,7 @@ class BlenderbotSmallTokenizer(PreTrainedTokenizer):
         return split_tokens
 
     def _convert_token_to_id(self, token: str) -> int:
-        """ Converts a token to an id using the vocab. """
+        """Converts a token to an id using the vocab."""
         token = token.lower()
         return self.encoder.get(token, self.encoder.get(self.unk_token))
 
@@ -202,7 +202,7 @@ class BlenderbotSmallTokenizer(PreTrainedTokenizer):
         return self.decoder.get(index, self.unk_token)
 
     def convert_tokens_to_string(self, tokens: List[str]) -> str:
-        """ Converts a sequence of tokens  in a single string. """
+        """Converts a sequence of tokens  in a single string."""
         out_string = " ".join(tokens).replace("@@ ", "").strip()
         return out_string
 

src/transformers/models/camembert/tokenization_camembert.py

@@ -222,7 +222,7 @@ class CamembertTokenizer(PreTrainedTokenizer):
         return self.sp_model.EncodeAsPieces(text)
 
     def _convert_token_to_id(self, token):
-        """ Converts a token (str) in an id using the vocab. """
+        """Converts a token (str) in an id using the vocab."""
         if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
         elif self.sp_model.PieceToId(token) == 0:

src/transformers/models/convbert/modeling_convbert.py

@@ -238,7 +238,7 @@ class ConvBertPreTrainedModel(PreTrainedModel):
     authorized_unexpected_keys = [r"convbert\.embeddings_project\.weight", r"convbert\.embeddings_project\.bias"]
 
     def _init_weights(self, module):
-        """ Initialize the weights """
+        """Initialize the weights"""
         if isinstance(module, nn.Linear):
             # Slightly different from the TF version which uses truncated_normal for initialization
             # cf https://github.com/pytorch/pytorch/pull/5617

src/transformers/models/ctrl/tokenization_ctrl.py

@@ -212,7 +212,7 @@ class CTRLTokenizer(PreTrainedTokenizer):
         return split_tokens
 
     def _convert_token_to_id(self, token):
-        """ Converts a token (str) in an id using the vocab. """
+        """Converts a token (str) in an id using the vocab."""
         return self.encoder.get(token, self.encoder.get(self.unk_token))
 
     def _convert_id_to_token(self, index):
@@ -220,7 +220,7 @@ class CTRLTokenizer(PreTrainedTokenizer):
         return self.decoder.get(index, self.unk_token)
 
     def convert_tokens_to_string(self, tokens):
-        """ Converts a sequence of tokens (string) in a single string. """
+        """Converts a sequence of tokens (string) in a single string."""
         out_string = " ".join(tokens).replace("@@ ", "").strip()
         return out_string
 

src/transformers/models/deberta_v2/tokenization_deberta_v2.py

@@ -134,7 +134,7 @@ class DebertaV2Tokenizer(PreTrainedTokenizer):
         return self._tokenizer.tokenize(text)
 
     def _convert_token_to_id(self, token):
-        """ Converts a token (str) in an id using the vocab. """
+        """Converts a token (str) in an id using the vocab."""
         return self._tokenizer.spm.PieceToId(token)
 
     def _convert_id_to_token(self, index):
@@ -142,7 +142,7 @@ class DebertaV2Tokenizer(PreTrainedTokenizer):
         return self._tokenizer.spm.IdToPiece(index) if index < self.vocab_size else self.unk_token
 
     def convert_tokens_to_string(self, tokens):
-        """ Converts a sequence of tokens (string) in a single string. """
+        """Converts a sequence of tokens (string) in a single string."""
         return self._tokenizer.decode(tokens)
 
     def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):

src/transformers/models/deit/modeling_deit.py

@@ -386,7 +386,7 @@ class DeiTPreTrainedModel(PreTrainedModel):
     base_model_prefix = "deit"
 
     def _init_weights(self, module):
-        """ Initialize the weights """
+        """Initialize the weights"""
         if isinstance(module, (nn.Linear, nn.Conv2d)):
             # Slightly different from the TF version which uses truncated_normal for initialization
             # cf https://github.com/pytorch/pytorch/pull/5617

src/transformers/models/distilbert/modeling_distilbert.py

@@ -167,11 +167,11 @@ class MultiHeadSelfAttention(nn.Module):
         mask_reshp = (bs, 1, 1, k_length)
 
         def shape(x):
-            """ separate heads """
+            """separate heads"""
             return x.view(bs, -1, self.n_heads, dim_per_head).transpose(1, 2)
 
         def unshape(x):
-            """ group heads """
+            """group heads"""
             return x.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * dim_per_head)
 
         q = shape(self.q_lin(query))  # (bs, n_heads, q_length, dim_per_head)

src/transformers/models/distilbert/modeling_tf_distilbert.py

@@ -175,11 +175,11 @@ class TFMultiHeadSelfAttention(tf.keras.layers.Layer):
         mask_reshape = [bs, 1, 1, k_length]
 
         def shape(x):
-            """ separate heads """
+            """separate heads"""
             return tf.transpose(tf.reshape(x, (bs, -1, self.n_heads, dim_per_head)), perm=(0, 2, 1, 3))
 
         def unshape(x):
-            """ group heads """
+            """group heads"""
             return tf.reshape(tf.transpose(x, perm=(0, 2, 1, 3)), (bs, -1, self.n_heads * dim_per_head))
 
         q = shape(self.q_lin(query))  # (bs, n_heads, q_length, dim_per_head)

src/transformers/models/electra/modeling_electra.py

@@ -653,7 +653,7 @@ class ElectraPreTrainedModel(PreTrainedModel):
 
     # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
     def _init_weights(self, module):
-        """ Initialize the weights """
+        """Initialize the weights"""
         if isinstance(module, nn.Linear):
             # Slightly different from the TF version which uses truncated_normal for initialization
             # cf https://github.com/pytorch/pytorch/pull/5617