make style (#11442)

src/transformers/models/flaubert/modeling_tf_flaubert.py

@@ -342,11 +342,11 @@ class TFFlaubertMultiHeadAttention(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/fsmt/tokenization_fsmt.py

@@ -374,7 +374,7 @@ class FSMTTokenizer(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):
@@ -382,7 +382,7 @@ class FSMTTokenizer(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."""
 
         # remove BPE
         tokens = [t.replace(" ", "").replace("</w>", " ") for t in tokens]

src/transformers/models/funnel/modeling_funnel.py

@@ -188,7 +188,7 @@ class FunnelAttentionStructure(nn.Module):
         self.pooling_mult = None
 
     def init_attention_inputs(self, inputs_embeds, attention_mask=None, token_type_ids=None):
-        """ Returns the attention inputs associated to the inputs of the model. """
+        """Returns the attention inputs associated to the inputs of the model."""
         # inputs_embeds has shape batch_size x seq_len x d_model
         # attention_mask and token_type_ids have shape batch_size x seq_len
         self.pooling_mult = 1
@@ -383,7 +383,7 @@ class FunnelAttentionStructure(nn.Module):
         return tensor
 
     def pre_attention_pooling(self, output, attention_inputs):
-        """ Pool `output` and the proper parts of `attention_inputs` before the attention layer. """
+        """Pool `output` and the proper parts of `attention_inputs` before the attention layer."""
         position_embeds, token_type_mat, attention_mask, cls_mask = attention_inputs
         if self.config.pool_q_only:
             if self.config.attention_type == "factorized":
@@ -403,7 +403,7 @@ class FunnelAttentionStructure(nn.Module):
         return output, attention_inputs
 
     def post_attention_pooling(self, attention_inputs):
-        """ Pool the proper parts of `attention_inputs` after the attention layer. """
+        """Pool the proper parts of `attention_inputs` after the attention layer."""
         position_embeds, token_type_mat, attention_mask, cls_mask = attention_inputs
         if self.config.pool_q_only:
             self.pooling_mult *= 2
@@ -457,7 +457,7 @@ class FunnelRelMultiheadAttention(nn.Module):
         self.scale = 1.0 / (d_head ** 0.5)
 
     def relative_positional_attention(self, position_embeds, q_head, context_len, cls_mask=None):
-        """ Relative attention score for the positional encodings """
+        """Relative attention score for the positional encodings"""
         # q_head has shape batch_size x sea_len x n_head x d_head
         if self.config.attention_type == "factorized":
             # Notations from the paper, appending A.2.2, final formula (https://arxiv.org/abs/2006.03236)
@@ -499,7 +499,7 @@ class FunnelRelMultiheadAttention(nn.Module):
         return positional_attn
 
     def relative_token_type_attention(self, token_type_mat, q_head, cls_mask=None):
-        """ Relative attention score for the token_type_ids """
+        """Relative attention score for the token_type_ids"""
         if token_type_mat is None:
             return 0
         batch_size, seq_len, context_len = token_type_mat.shape

src/transformers/models/funnel/modeling_tf_funnel.py

@@ -139,7 +139,7 @@ class TFFunnelAttentionStructure:
         self.pooling_mult = None
 
     def init_attention_inputs(self, inputs_embeds, attention_mask=None, token_type_ids=None, training=False):
-        """ Returns the attention inputs associated to the inputs of the model. """
+        """Returns the attention inputs associated to the inputs of the model."""
         # inputs_embeds has shape batch_size x seq_len x d_model
         # attention_mask and token_type_ids have shape batch_size x seq_len
         self.pooling_mult = 1
@@ -328,7 +328,7 @@ class TFFunnelAttentionStructure:
         return tf.squeeze(tensor, 2) if ndim == 2 else tensor
 
     def pre_attention_pooling(self, output, attention_inputs):
-        """ Pool `output` and the proper parts of `attention_inputs` before the attention layer. """
+        """Pool `output` and the proper parts of `attention_inputs` before the attention layer."""
         position_embeds, token_type_mat, attention_mask, cls_mask = attention_inputs
         if self.pool_q_only:
             if self.attention_type == "factorized":
@@ -348,7 +348,7 @@ class TFFunnelAttentionStructure:
         return output, attention_inputs
 
     def post_attention_pooling(self, attention_inputs):
-        """ Pool the proper parts of `attention_inputs` after the attention layer. """
+        """Pool the proper parts of `attention_inputs` after the attention layer."""
         position_embeds, token_type_mat, attention_mask, cls_mask = attention_inputs
         if self.pool_q_only:
             self.pooling_mult *= 2
@@ -424,7 +424,7 @@ class TFFunnelRelMultiheadAttention(tf.keras.layers.Layer):
         super().build(input_shape)
 
     def relative_positional_attention(self, position_embeds, q_head, context_len, cls_mask=None):
-        """ Relative attention score for the positional encodings """
+        """Relative attention score for the positional encodings"""
         # q_head has shape batch_size x sea_len x n_head x d_head
         if self.attention_type == "factorized":
             # Notations from the paper, appending A.2.2, final formula (https://arxiv.org/abs/2006.03236)
@@ -470,7 +470,7 @@ class TFFunnelRelMultiheadAttention(tf.keras.layers.Layer):
         return positional_attn
 
     def relative_token_type_attention(self, token_type_mat, q_head, cls_mask=None):
-        """ Relative attention score for the token_type_ids """
+        """Relative attention score for the token_type_ids"""
         if token_type_mat is None:
             return 0
         batch_size, seq_len, context_len = shape_list(token_type_mat)
@@ -723,7 +723,7 @@ class TFFunnelDecoder(tf.keras.layers.Layer):
 
 @keras_serializable
 class TFFunnelBaseLayer(tf.keras.layers.Layer):
-    """ Base model without decoder """
+    """Base model without decoder"""
 
     config_class = FunnelConfig
 
@@ -807,7 +807,7 @@ class TFFunnelBaseLayer(tf.keras.layers.Layer):
 
 @keras_serializable
 class TFFunnelMainLayer(tf.keras.layers.Layer):
-    """ Base model with decoder """
+    """Base model with decoder"""
 
     config_class = FunnelConfig
 

src/transformers/models/gpt2/tokenization_gpt2.py

@@ -242,7 +242,7 @@ class GPT2Tokenizer(PreTrainedTokenizer):
         return word
 
     def _tokenize(self, text):
-        """ Tokenize a string. """
+        """Tokenize a string."""
         bpe_tokens = []
         for token in re.findall(self.pat, text):
             token = "".join(
@@ -252,7 +252,7 @@ class GPT2Tokenizer(PreTrainedTokenizer):
         return bpe_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):
@@ -260,7 +260,7 @@ class GPT2Tokenizer(PreTrainedTokenizer):
         return self.decoder.get(index)
 
     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."""
         text = "".join(tokens)
         text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
         return text

src/transformers/models/ibert/modeling_ibert.py

@@ -645,7 +645,7 @@ class IBertPreTrainedModel(PreTrainedModel):
     base_model_prefix = "ibert"
 
     def _init_weights(self, module):
-        """ Initialize the weights """
+        """Initialize the weights"""
         if isinstance(module, (QuantLinear, 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/layoutlm/modeling_layoutlm.py

@@ -611,7 +611,7 @@ class LayoutLMPreTrainedModel(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/led/modeling_led.py

@@ -509,7 +509,7 @@ class LEDEncoderSelfAttention(nn.Module):
 
     @staticmethod
     def _get_global_attn_indices(is_index_global_attn):
-        """ compute global attn indices required throughout forward pass """
+        """compute global attn indices required throughout forward pass"""
         # helper variable
         num_global_attn_indices = is_index_global_attn.long().sum(dim=1)
 

src/transformers/models/led/modeling_tf_led.py

@@ -670,7 +670,7 @@ class TFLEDEncoderSelfAttention(tf.keras.layers.Layer):
 
     @staticmethod
     def _get_global_attn_indices(is_index_global_attn):
-        """ compute global attn indices required throughout forward pass """
+        """compute global attn indices required throughout forward pass"""
         # helper variable
         num_global_attn_indices = tf.math.count_nonzero(is_index_global_attn, axis=1)
         num_global_attn_indices = tf.cast(num_global_attn_indices, dtype=tf.constant(1).dtype)

src/transformers/models/longformer/modeling_longformer.py

@@ -899,7 +899,7 @@ class LongformerSelfAttention(nn.Module):
 
     @staticmethod
     def _get_global_attn_indices(is_index_global_attn):
-        """ compute global attn indices required throughout forward pass """
+        """compute global attn indices required throughout forward pass"""
         # helper variable
         num_global_attn_indices = is_index_global_attn.long().sum(dim=1)
 
@@ -1363,7 +1363,7 @@ class LongformerPreTrainedModel(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/longformer/modeling_tf_longformer.py

@@ -1189,7 +1189,7 @@ class TFLongformerSelfAttention(tf.keras.layers.Layer):
 
     @staticmethod
     def _get_global_attn_indices(is_index_global_attn):
-        """ compute global attn indices required throughout forward pass """
+        """compute global attn indices required throughout forward pass"""
         # helper variable
         num_global_attn_indices = tf.math.count_nonzero(is_index_global_attn, axis=1)
         num_global_attn_indices = tf.cast(num_global_attn_indices, dtype=tf.constant(1).dtype)

src/transformers/models/lxmert/modeling_lxmert.py

@@ -783,7 +783,7 @@ class LxmertPreTrainedModel(PreTrainedModel):
     base_model_prefix = "lxmert"
 
     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/marian/tokenization_marian.py

@@ -227,7 +227,7 @@ class MarianTokenizer(PreTrainedTokenizer):
         return super().decode(token_ids, **kwargs)
 
     def convert_tokens_to_string(self, tokens: List[str]) -> str:
-        """Uses source spm if _decode_use_source_tokenizer is True, and target spm otherwise """
+        """Uses source spm if _decode_use_source_tokenizer is True, and target spm otherwise"""
         if self._decode_use_source_tokenizer:
             return self.spm_source.DecodePieces(tokens)
         else:

src/transformers/models/mbart/tokenization_mbart50.py

@@ -189,7 +189,7 @@ class MBart50Tokenizer(PreTrainedTokenizer):
         return self.sp_model.EncodeAsPieces(text)
 
     def _convert_token_to_id(self, token: str) -> int:
-        """ 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/megatron_bert/modeling_megatron_bert.py

@@ -708,7 +708,7 @@ class MegatronBertPreTrainedModel(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.Embedding)):
             # Slightly different from the TF version which uses truncated_normal for initialization
             # cf https://github.com/pytorch/pytorch/pull/5617

src/transformers/models/mobilebert/modeling_mobilebert.py

@@ -669,7 +669,7 @@ class MobileBertPreTrainedModel(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/mpnet/modeling_mpnet.py

@@ -56,7 +56,7 @@ class MPNetPreTrainedModel(PreTrainedModel):
     base_model_prefix = "mpnet"
 
     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/mpnet/modeling_tf_mpnet.py

@@ -430,7 +430,7 @@ class TFMPNetEncoder(tf.keras.layers.Layer):
         return ret
 
     def compute_position_bias(self, x, position_ids=None):
-        """ Compute binned relative position bias """
+        """Compute binned relative position bias"""
         input_shape = shape_list(x)
         qlen, klen = input_shape[1], input_shape[1]
 

src/transformers/models/mpnet/tokenization_mpnet.py

@@ -210,7 +210,7 @@ class MPNetTokenizer(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):
@@ -218,7 +218,7 @@ class MPNetTokenizer(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/openai/tokenization_openai.py

@@ -176,7 +176,7 @@ class OpenAIGPTTokenizer(PreTrainedTokenizer):
         return word
 
     def _tokenize(self, text):
-        """ Tokenize a string. """
+        """Tokenize a string."""
         split_tokens = []
         if self.fix_text is None:
             # Using BERT's BasicTokenizer
@@ -191,7 +191,7 @@ class OpenAIGPTTokenizer(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):
@@ -199,7 +199,7 @@ class OpenAIGPTTokenizer(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