Fix E741 flake8 warning (x14).

examples/contrib/run_swag.py

@@ -76,7 +76,7 @@ class SwagExample(object):
         return self.__repr__()
 
     def __repr__(self):
-        l = [
+        attributes = [
             "swag_id: {}".format(self.swag_id),
             "context_sentence: {}".format(self.context_sentence),
             "start_ending: {}".format(self.start_ending),
@@ -87,9 +87,9 @@ class SwagExample(object):
         ]
 
         if self.label is not None:
-            l.append("label: {}".format(self.label))
+            attributes.append("label: {}".format(self.label))
 
-        return ", ".join(l)
+        return ", ".join(attributes)
 
 
 class InputFeatures(object):

templates/adding_a_new_model/modeling_xxx.py

@@ -89,25 +89,25 @@ def load_tf_weights_in_xxx(model, config, tf_checkpoint_path):
         pointer = model
         for m_name in name:
             if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
-                l = re.split(r"_(\d+)", m_name)
+                scope_names = re.split(r"_(\d+)", m_name)
             else:
-                l = [m_name]
-            if l[0] == "kernel" or l[0] == "gamma":
+                scope_names = [m_name]
+            if scope_names[0] == "kernel" or scope_names[0] == "gamma":
                 pointer = getattr(pointer, "weight")
-            elif l[0] == "output_bias" or l[0] == "beta":
+            elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
                 pointer = getattr(pointer, "bias")
-            elif l[0] == "output_weights":
+            elif scope_names[0] == "output_weights":
                 pointer = getattr(pointer, "weight")
-            elif l[0] == "squad":
+            elif scope_names[0] == "squad":
                 pointer = getattr(pointer, "classifier")
             else:
                 try:
-                    pointer = getattr(pointer, l[0])
+                    pointer = getattr(pointer, scope_names[0])
                 except AttributeError:
                     logger.info("Skipping {}".format("/".join(name)))
                     continue
-            if len(l) >= 2:
-                num = int(l[1])
+            if len(scope_names) >= 2:
+                num = int(scope_names[1])
                 pointer = pointer[num]
         if m_name[-11:] == "_embeddings":
             pointer = getattr(pointer, "weight")

transformers/modeling_albert.py

@@ -124,26 +124,26 @@ def load_tf_weights_in_albert(model, config, tf_checkpoint_path):
         pointer = model
         for m_name in name:
             if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
-                l = re.split(r"_(\d+)", m_name)
+                scope_names = re.split(r"_(\d+)", m_name)
             else:
-                l = [m_name]
+                scope_names = [m_name]
 
-            if l[0] == "kernel" or l[0] == "gamma":
+            if scope_names[0] == "kernel" or scope_names[0] == "gamma":
                 pointer = getattr(pointer, "weight")
-            elif l[0] == "output_bias" or l[0] == "beta":
+            elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
                 pointer = getattr(pointer, "bias")
-            elif l[0] == "output_weights":
+            elif scope_names[0] == "output_weights":
                 pointer = getattr(pointer, "weight")
-            elif l[0] == "squad":
+            elif scope_names[0] == "squad":
                 pointer = getattr(pointer, "classifier")
             else:
                 try:
-                    pointer = getattr(pointer, l[0])
+                    pointer = getattr(pointer, scope_names[0])
                 except AttributeError:
                     logger.info("Skipping {}".format("/".join(name)))
                     continue
-            if len(l) >= 2:
-                num = int(l[1])
+            if len(scope_names) >= 2:
+                num = int(scope_names[1])
                 pointer = pointer[num]
 
         if m_name[-11:] == "_embeddings":

transformers/modeling_bert.py

@@ -93,25 +93,25 @@ def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
         pointer = model
         for m_name in name:
             if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
-                l = re.split(r"_(\d+)", m_name)
+                scope_names = re.split(r"_(\d+)", m_name)
             else:
-                l = [m_name]
-            if l[0] == "kernel" or l[0] == "gamma":
+                scope_names = [m_name]
+            if scope_names[0] == "kernel" or scope_names[0] == "gamma":
                 pointer = getattr(pointer, "weight")
-            elif l[0] == "output_bias" or l[0] == "beta":
+            elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
                 pointer = getattr(pointer, "bias")
-            elif l[0] == "output_weights":
+            elif scope_names[0] == "output_weights":
                 pointer = getattr(pointer, "weight")
-            elif l[0] == "squad":
+            elif scope_names[0] == "squad":
                 pointer = getattr(pointer, "classifier")
             else:
                 try:
-                    pointer = getattr(pointer, l[0])
+                    pointer = getattr(pointer, scope_names[0])
                 except AttributeError:
                     logger.info("Skipping {}".format("/".join(name)))
                     continue
-            if len(l) >= 2:
-                num = int(l[1])
+            if len(scope_names) >= 2:
+                num = int(scope_names[1])
                 pointer = pointer[num]
         if m_name[-11:] == "_embeddings":
             pointer = getattr(pointer, "weight")

transformers/modeling_gpt2.py

@@ -77,20 +77,20 @@ def load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path):
         pointer = model
         for m_name in name:
             if re.fullmatch(r"[A-Za-z]+\d+", m_name):
-                l = re.split(r"(\d+)", m_name)
+                scope_names = re.split(r"(\d+)", m_name)
             else:
-                l = [m_name]
-            if l[0] == "w" or l[0] == "g":
+                scope_names = [m_name]
+            if scope_names[0] == "w" or scope_names[0] == "g":
                 pointer = getattr(pointer, "weight")
-            elif l[0] == "b":
+            elif scope_names[0] == "b":
                 pointer = getattr(pointer, "bias")
-            elif l[0] == "wpe" or l[0] == "wte":
-                pointer = getattr(pointer, l[0])
+            elif scope_names[0] == "wpe" or scope_names[0] == "wte":
+                pointer = getattr(pointer, scope_names[0])
                 pointer = getattr(pointer, "weight")
             else:
-                pointer = getattr(pointer, l[0])
-            if len(l) >= 2:
-                num = int(l[1])
+                pointer = getattr(pointer, scope_names[0])
+            if len(scope_names) >= 2:
+                num = int(scope_names[1])
                 pointer = pointer[num]
         try:
             assert pointer.shape == array.shape

transformers/modeling_openai.py

@@ -90,19 +90,19 @@ def load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path):
         pointer = model
         for m_name in name:
             if re.fullmatch(r"[A-Za-z]+\d+", m_name):
-                l = re.split(r"(\d+)", m_name)
+                scope_names = re.split(r"(\d+)", m_name)
             else:
-                l = [m_name]
-            if l[0] == "g":
+                scope_names = [m_name]
+            if scope_names[0] == "g":
                 pointer = getattr(pointer, "weight")
-            elif l[0] == "b":
+            elif scope_names[0] == "b":
                 pointer = getattr(pointer, "bias")
-            elif l[0] == "w":
+            elif scope_names[0] == "w":
                 pointer = getattr(pointer, "weight")
             else:
-                pointer = getattr(pointer, l[0])
-            if len(l) >= 2:
-                num = int(l[1])
+                pointer = getattr(pointer, scope_names[0])
+            if len(scope_names) >= 2:
+                num = int(scope_names[1])
                 pointer = pointer[num]
         try:
             assert pointer.shape == array.shape

transformers/modeling_t5.py

@@ -95,29 +95,29 @@ def load_tf_weights_in_t5(model, config, tf_checkpoint_path):
         array = tf_weights[txt_name]
         for m_name in name:
             if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
-                l = re.split(r"_(\d+)", m_name)
+                scope_names = re.split(r"_(\d+)", m_name)
             else:
-                l = [m_name]
-            if l[0] in ["kernel", "scale", "embedding"]:
+                scope_names = [m_name]
+            if scope_names[0] in ["kernel", "scale", "embedding"]:
                 pointer = getattr(pointer, "weight")
-            # elif l[0] == 'scale':
+            # elif scope_names[0] == 'scale':
             #     pointer = getattr(pointer, 'weight')
-            # elif l[0] == 'output_bias' or l[0] == 'beta':
+            # elif scope_names[0] == 'output_bias' or scope_names[0] == 'beta':
             #     pointer = getattr(pointer, 'bias')
-            # elif l[0] == 'squad':
+            # elif scope_names[0] == 'squad':
             #     pointer = getattr(pointer, 'classifier')
             else:
                 try:
-                    pointer = getattr(pointer, l[0])
+                    pointer = getattr(pointer, scope_names[0])
                 except AttributeError:
                     logger.info("Skipping {}".format("/".join(name)))
                     continue
-            if len(l) >= 2:
-                num = int(l[1])
+            if len(scope_names) >= 2:
+                num = int(scope_names[1])
                 pointer = pointer[num]
-        if l[0] not in ["kernel", "scale", "embedding"]:
+        if scope_names[0] not in ["kernel", "scale", "embedding"]:
             pointer = getattr(pointer, "weight")
-        if l[0] != "embedding":
+        if scope_names[0] != "embedding":
             logger.info("Transposing numpy weight of shape {} for {}".format(array.shape, name))
             array = np.transpose(array)
         try:

transformers/tokenization_t5.py

@@ -160,8 +160,8 @@ class T5Tokenizer(PreTrainedTokenizer):
     def _convert_token_to_id(self, token):
         """ Converts a token (str/unicode) in an id using the vocab. """
         if token.startswith("<extra_id_"):
-            l = re.match(r"<extra_id_(\d+)>", token)
-            num = int(l.group(1))
+            match = re.match(r"<extra_id_(\d+)>", token)
+            num = int(match.group(1))
             return self.vocab_size - num - 1
         return self.sp_model.piece_to_id(token)