make style (#11442)

src/transformers/models/pegasus/tokenization_pegasus.py

@@ -175,7 +175,7 @@ class PegasusTokenizer(PreTrainedTokenizer):
         return pieces
 
     def _convert_token_to_id(self, token: str) -> int:
-        """ Converts a token (str) to an id using the vocab. """
+        """Converts a token (str) to an id using the vocab."""
         if token in self.decoder:
             return self.decoder[token]
         elif token in self.added_tokens_decoder:
@@ -194,7 +194,7 @@ class PegasusTokenizer(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/phobert/tokenization_phobert.py

@@ -295,7 +295,7 @@ class PhobertTokenizer(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):
@@ -303,7 +303,7 @@ class PhobertTokenizer(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/prophetnet/tokenization_prophetnet.py

@@ -172,7 +172,7 @@ class ProphetNetTokenizer(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):
@@ -180,7 +180,7 @@ class ProphetNetTokenizer(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/reformer/modeling_reformer.py

@@ -1779,7 +1779,7 @@ class ReformerPreTrainedModel(PreTrainedModel):
         return dummy_inputs
 
     def _init_weights(self, module):
-        """ Initialize the weights """
+        """Initialize the weights"""
         if isinstance(module, AxialPositionEmbeddings):
             for weight in module.weights:
                 torch.nn.init.normal_(weight, std=self.config.axial_norm_std)

src/transformers/models/reformer/tokenization_reformer.py

@@ -115,7 +115,7 @@ class ReformerTokenizer(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):
@@ -125,7 +125,7 @@ class ReformerTokenizer(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/retribert/modeling_retribert.py

@@ -50,7 +50,7 @@ class RetriBertPreTrainedModel(PreTrainedModel):
     base_model_prefix = "retribert"
 
     def _init_weights(self, module):
-        """ Initialize the weights """
+        """Initialize the weights"""
         if isinstance(module, nn.Linear):
             module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
             if module.bias is not None:

src/transformers/models/roberta/modeling_roberta.py

@@ -574,7 +574,7 @@ class RobertaPreTrainedModel(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

src/transformers/models/squeezebert/modeling_squeezebert.py

@@ -431,7 +431,7 @@ class SqueezeBertPreTrainedModel(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, nn.Conv1d)):
             # Slightly different from the TF version which uses truncated_normal for initialization
             # cf https://github.com/pytorch/pytorch/pull/5617

src/transformers/models/t5/modeling_t5.py

@@ -389,7 +389,7 @@ class T5Attention(nn.Module):
         return relative_buckets
 
     def compute_bias(self, query_length, key_length):
-        """ Compute binned relative position bias """
+        """Compute binned relative position bias"""
         context_position = torch.arange(query_length, dtype=torch.long)[:, None]
         memory_position = torch.arange(key_length, dtype=torch.long)[None, :]
         relative_position = memory_position - context_position  # shape (query_length, key_length)
@@ -436,15 +436,15 @@ class T5Attention(nn.Module):
         key_length = real_seq_length if key_value_states is None else key_value_states.shape[1]
 
         def shape(states):
-            """  projection """
+            """projection"""
             return states.view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
 
         def unshape(states):
-            """  reshape """
+            """reshape"""
             return states.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
 
         def project(hidden_states, proj_layer, key_value_states, past_key_value):
-            """ projects hidden states correctly to key/query states """
+            """projects hidden states correctly to key/query states"""
             if key_value_states is None:
                 # self-attn
                 # (batch_size, n_heads, seq_length, dim_per_head)
@@ -718,7 +718,7 @@ class T5PreTrainedModel(PreTrainedModel):
         return dummy_inputs
 
     def _init_weights(self, module):
-        """ Initialize the weights """
+        """Initialize the weights"""
         factor = self.config.initializer_factor  # Used for testing weights initialization
         if isinstance(module, T5LayerNorm):
             module.weight.data.fill_(factor * 1.0)

src/transformers/models/t5/modeling_tf_t5.py

@@ -80,7 +80,7 @@ class TFT5LayerNorm(tf.keras.layers.Layer):
         self.variance_epsilon = epsilon
 
     def build(self, input_shape):
-        """Build shared word embedding layer """
+        """Build shared word embedding layer"""
         self.weight = self.add_weight("weight", shape=(input_shape[-1],), initializer="ones")
         super().build(input_shape)
 
@@ -230,7 +230,7 @@ class TFT5Attention(tf.keras.layers.Layer):
         return relative_buckets
 
     def compute_bias(self, query_length, key_length):
-        """ Compute binned relative position bias """
+        """Compute binned relative position bias"""
         context_position = tf.range(query_length)[:, None]
         memory_position = tf.range(key_length)[None, :]
         relative_position = memory_position - context_position  # shape (query_length, key_length)
@@ -279,17 +279,17 @@ class TFT5Attention(tf.keras.layers.Layer):
         key_length = real_seq_length if key_value_states is None else shape_list(key_value_states)[1]
 
         def shape(hidden_states):
-            """  projection """
+            """projection"""
             return tf.transpose(
                 tf.reshape(hidden_states, (batch_size, -1, self.n_heads, self.key_value_proj_dim)), perm=(0, 2, 1, 3)
             )
 
         def unshape(hidden_states):
-            """  compute context """
+            """compute context"""
             return tf.reshape(tf.transpose(hidden_states, perm=(0, 2, 1, 3)), (batch_size, -1, self.inner_dim))
 
         def project(hidden_states, proj_layer, key_value_states, past_key_value):
-            """ projects hidden states correctly to key/query states """
+            """projects hidden states correctly to key/query states"""
             if key_value_states is None:
                 # self-attn
                 # (batch_size, n_heads, seq_length, dim_per_head)

src/transformers/models/t5/tokenization_t5.py

@@ -243,7 +243,7 @@ class T5Tokenizer(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."""
         if token.startswith("<extra_id_"):
             match = re.match(r"<extra_id_(\d+)>", token)
             num = int(match.group(1))
@@ -259,7 +259,7 @@ class T5Tokenizer(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."""
         current_sub_tokens = []
         out_string = ""
         for token in tokens:

src/transformers/models/tapas/modeling_tapas.py

@@ -699,7 +699,7 @@ class TapasPreTrainedModel(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

src/transformers/models/tapas/tokenization_tapas.py

@@ -374,7 +374,7 @@ class TapasTokenizer(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):
@@ -382,7 +382,7 @@ class TapasTokenizer(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/transfo_xl/tokenization_transfo_xl.py

@@ -434,7 +434,7 @@ class TransfoXLTokenizer(PreTrainedTokenizer):
         return self.idx2sym[idx]
 
     def _convert_token_to_id(self, sym):
-        """ Converts a token (str) in an id using the vocab. """
+        """Converts a token (str) in an id using the vocab."""
         if sym in self.sym2idx:
             return self.sym2idx[sym]
         else:

src/transformers/models/vit/modeling_vit.py

@@ -372,7 +372,7 @@ class ViTPreTrainedModel(PreTrainedModel):
     base_model_prefix = "vit"
 
     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/wav2vec2/modeling_wav2vec2.py

@@ -680,7 +680,7 @@ class Wav2Vec2PreTrainedModel(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/xlm/modeling_tf_xlm.py

@@ -151,11 +151,11 @@ class TFXLMMultiHeadAttention(tf.keras.layers.Layer):
         mask_reshape = (bs, 1, qlen, klen) if len(shape_list(mask)) == 3 else (bs, 1, 1, klen)
 
         def shape(x):
-            """  projection """
+            """projection"""
             return tf.transpose(tf.reshape(x, (bs, -1, self.n_heads, dim_per_head)), perm=(0, 2, 1, 3))
 
         def unshape(x):
-            """  compute context """
+            """compute context"""
             return tf.reshape(tf.transpose(x, perm=(0, 2, 1, 3)), (bs, -1, self.n_heads * dim_per_head))
 
         q = shape(self.q_lin(input))  # (bs, n_heads, qlen, dim_per_head)

src/transformers/models/xlm/modeling_xlm.py

@@ -159,11 +159,11 @@ class MultiHeadAttention(nn.Module):
         mask_reshape = (bs, 1, qlen, klen) if mask.dim() == 3 else (bs, 1, 1, klen)
 
         def shape(x):
-            """  projection """
+            """projection"""
             return x.view(bs, -1, self.n_heads, dim_per_head).transpose(1, 2)
 
         def unshape(x):
-            """  compute context """
+            """compute context"""
             return x.transpose(1, 2).contiguous().view(bs, -1, self.n_heads * dim_per_head)
 
         q = shape(self.q_lin(input))  # (bs, n_heads, qlen, dim_per_head)
@@ -251,7 +251,7 @@ class XLMPreTrainedModel(PreTrainedModel):
         return {"input_ids": inputs_list, "attention_mask": attns_list, "langs": langs_list}
 
     def _init_weights(self, module):
-        """ Initialize the weights. """
+        """Initialize the weights."""
         if isinstance(module, nn.Embedding):
             if self.config is not None and self.config.embed_init_std is not None:
                 nn.init.normal_(module.weight, mean=0, std=self.config.embed_init_std)

src/transformers/models/xlm/tokenization_xlm.py

@@ -847,7 +847,7 @@ class XLMTokenizer(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):
@@ -855,7 +855,7 @@ class XLMTokenizer(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("</w>", " ").strip()
         return out_string
 

src/transformers/models/xlm_prophetnet/tokenization_xlm_prophetnet.py

@@ -245,7 +245,7 @@ class XLMProphetNetTokenizer(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)