Fix quality

pyproject.toml

@@ -4,7 +4,7 @@ target-version = ['py37']
 
 [tool.ruff]
 # Never enforce `E501` (line length violations).
-ignore = ["E501", "E741", "W605"]
+ignore = ["C901", "E501", "E741", "W605"]
 select = ["C", "E", "F", "I", "W"]
 line-length = 119
 

src/transformers/models/efficientnet/convert_efficientnet_to_pytorch.py

@@ -160,7 +160,7 @@ def convert_image_processor(model_name):
 # here we list all keys to be renamed (original name on the left, our name on the right)
 def rename_keys(original_param_names):
     block_names = [v.split("_")[0].split("block")[1] for v in original_param_names if v.startswith("block")]
-    block_names = sorted(list(set(block_names)))
+    block_names = sorted(set(block_names))
     num_blocks = len(block_names)
     block_name_mapping = {b: str(i) for b, i in zip(block_names, range(num_blocks))}
 
@@ -267,7 +267,7 @@ def convert_efficientnet_checkpoint(model_name, pytorch_dump_folder_path, save_m
     tf_params = {param.name: param.numpy() for param in tf_params}
     for param in tf_non_train_params:
         tf_params[param.name] = param.numpy()
-    tf_param_names = [k for k in tf_params.keys()]
+    tf_param_names = list(tf_params.keys())
 
     # Load HuggingFace model
     config = get_efficientnet_config(model_name)

src/transformers/models/gptsan_japanese/modeling_gptsan_japanese.py

@@ -1029,7 +1029,7 @@ class GPTSanJapaneseModel(GPTSanJapanesePreTrainedModel):
             )  # n_layer x batch x n_heads x N x N
 
         # outputs
-        present_key_value_states = tuple() if self.config.use_cache or use_cache else None
+        present_key_value_states = () if self.config.use_cache or use_cache else None
         all_hidden_states = () if self.config.output_hidden_states or output_hidden_states else None
         all_attentions = () if self.config.output_attentions or output_attentions else None
         all_router_probs = () if self.config.output_router_logits or output_router_logits else None