Remove unnecessary list comprehension (#41305)

Remove unnecessary comprehension

Signed-off-by: Yuanyuan Chen <[email protected]>

Author: cyyever

examples/pytorch/contrastive-image-text/run_clip.py

@@ -387,7 +387,7 @@ def _freeze_params(module):
         return
 
     # 6. Get the column names for input/target.
-    dataset_columns = dataset_name_mapping.get(data_args.dataset_name, None)
+    dataset_columns = dataset_name_mapping.get(data_args.dataset_name)
     if data_args.image_column is None:
         image_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
     else:

examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py

@@ -933,7 +933,7 @@ def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
             all_end_top_index.append(accelerator.gather_for_metrics(end_top_index).cpu().numpy())
             all_cls_logits.append(accelerator.gather_for_metrics(cls_logits).cpu().numpy())
 
-    max_len = max([x.shape[1] for x in all_end_top_log_probs])  # Get the max_length of the tensor
+    max_len = max(x.shape[1] for x in all_end_top_log_probs)  # Get the max_length of the tensor
 
     # concatenate all numpy arrays collected above
     start_top_log_probs_concat = create_and_fill_np_array(all_start_top_log_probs, eval_dataset, max_len)
@@ -993,7 +993,7 @@ def create_and_fill_np_array(start_or_end_logits, dataset, max_len):
                 all_end_top_index.append(accelerator.gather_for_metrics(end_top_index).cpu().numpy())
                 all_cls_logits.append(accelerator.gather_for_metrics(cls_logits).cpu().numpy())
 
-        max_len = max([x.shape[1] for x in all_end_top_log_probs])  # Get the max_length of the tensor
+        max_len = max(x.shape[1] for x in all_end_top_log_probs)  # Get the max_length of the tensor
 
         # concatenate all numpy arrays collected above
         start_top_log_probs_concat = create_and_fill_np_array(all_start_top_log_probs, predict_dataset, max_len)

examples/pytorch/question-answering/run_seq2seq_qa.py

@@ -416,7 +416,7 @@ def main():
         return
 
     # Get the column names for input/target.
-    dataset_columns = question_answering_column_name_mapping.get(data_args.dataset_name, None)
+    dataset_columns = question_answering_column_name_mapping.get(data_args.dataset_name)
     if data_args.question_column is None:
         question_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
     else:

examples/pytorch/summarization/run_summarization.py

@@ -531,7 +531,7 @@ def main():
         model.config.forced_bos_token_id = forced_bos_token_id
 
     # Get the column names for input/target.
-    dataset_columns = summarization_name_mapping.get(data_args.dataset_name, None)
+    dataset_columns = summarization_name_mapping.get(data_args.dataset_name)
     if data_args.text_column is None:
         text_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
     else:

examples/pytorch/summarization/run_summarization_no_trainer.py

@@ -476,7 +476,7 @@ def main():
     column_names = raw_datasets["train"].column_names
 
     # Get the column names for input/target.
-    dataset_columns = summarization_name_mapping.get(args.dataset_name, None)
+    dataset_columns = summarization_name_mapping.get(args.dataset_name)
     if args.text_column is None:
         text_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
     else:

src/transformers/models/deepseek_vl_hybrid/image_processing_deepseek_vl_hybrid_fast.py

@@ -65,7 +65,7 @@ def __init__(self, **kwargs: Unpack[DeepseekVLHybridImageProcessorKwargs]):
         if kwargs.get("image_mean") is None:
             background_color = (127, 127, 127)
         else:
-            background_color = tuple([int(x * 255) for x in kwargs.get("image_mean")])
+            background_color = tuple(int(x * 255) for x in kwargs.get("image_mean"))
         if kwargs.get("high_res_image_mean") is None:
             high_res_background_color = (127, 127, 127)
         else:

src/transformers/models/deepseek_vl_hybrid/modular_deepseek_vl_hybrid.py

@@ -764,7 +764,7 @@ def __init__(self, **kwargs: Unpack[DeepseekVLHybridImageProcessorKwargs]):
         if kwargs.get("image_mean") is None:
             background_color = (127, 127, 127)
         else:
-            background_color = tuple([int(x * 255) for x in kwargs.get("image_mean")])
+            background_color = tuple(int(x * 255) for x in kwargs.get("image_mean"))
         if kwargs.get("high_res_image_mean") is None:
             high_res_background_color = (127, 127, 127)
         else:

src/transformers/models/musicgen/modeling_musicgen.py

@@ -551,7 +551,7 @@ def forward(
         past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0
 
         if inputs_embeds is None:
-            inputs_embeds = sum([self.embed_tokens[codebook](input[:, codebook]) for codebook in range(num_codebooks)])
+            inputs_embeds = sum(self.embed_tokens[codebook](input[:, codebook]) for codebook in range(num_codebooks))
 
         attention_mask = self._update_causal_mask(
             attention_mask,

src/transformers/models/oneformer/modeling_oneformer.py

@@ -718,7 +718,7 @@ def get_num_masks(self, class_labels: torch.Tensor, device: torch.device) -> tor
         """
         Computes the average number of target masks across the batch, for normalization purposes.
         """
-        num_masks = sum([len(classes) for classes in class_labels])
+        num_masks = sum(len(classes) for classes in class_labels)
         num_masks = torch.as_tensor([num_masks], dtype=torch.float, device=device)
         world_size = 1
         if is_accelerate_available():

src/transformers/models/ovis2/image_processing_ovis2.py

@@ -184,7 +184,7 @@ def get_min_tile_covering_grid(
     for tile_grid in candidate_tile_grids:
         tile_regions = split_image_into_grid(image_height, image_width, tile_grid)
         tile_covering_ratio = (
-            sum([compute_patch_covering_area(*region, target_patch_size) for region in tile_regions]) / image_area
+            sum(compute_patch_covering_area(*region, target_patch_size) for region in tile_regions) / image_area
         )
 
         evaluated_grids.append((tile_grid, tile_covering_ratio))