Fix ruff warnings

benchmark_v2/framework/benchmark_runner.py

@@ -420,7 +420,7 @@ def save_results(self, model_name: str, results: dict, timestamp: str = "", summ
         os.makedirs(model_dir, exist_ok=True)
 
         # Create filename with timestamp
-        timestamp = timestamp if timestamp else datetime.now().strftime("%Y%m%d_%H%M%S")
+        timestamp = timestamp or datetime.now().strftime("%Y%m%d_%H%M%S")
         filename = f"{model_name}_benchmark_{timestamp}.json"
         filepath = os.path.join(model_dir, filename)
 
@@ -471,7 +471,7 @@ def push_results_to_hub(self, dataset_id: str, results: dict[Any, Any], timestam
                     f.write("\n".join(json_lines))
 
                 # NOTE: we expect the repository to already exist
-                timestamp = timestamp if timestamp else datetime.now().strftime("%Y%m%d_%H%M%S")
+                timestamp = timestamp or datetime.now().strftime("%Y%m%d_%H%M%S")
                 file_name = file_name + "/" + f"benchmark_run_{timestamp}.jsonl"
                 api.upload_file(
                     path_or_fileobj=jsonl_path,

examples/modular-transformers/modeling_new_task_model.py

@@ -173,10 +173,8 @@ def create_causal_mask_mapping(
     # from `forward` call. If users run a `forward` call, we have no option to infer `is_first_iteration` because users may be
     # running generation with custom loop. Thus we need to infer it in a `non-perfect` way
     # NOTE: Determining prefill in that case requires checking data values, which is not compile-compatible.
-    is_first_iteration = (
-        is_first_iteration
-        if is_first_iteration
-        else (past_key_values is None or not past_key_values.is_initialized or pixel_values is not None)
+    is_first_iteration = is_first_iteration or (
+        past_key_values is None or not past_key_values.is_initialized or pixel_values is not None
     )
 
     if is_first_iteration or not kwargs.get("use_cache", True):

examples/pytorch/image-pretraining/run_mae.py

@@ -112,7 +112,7 @@ def __post_init__(self):
             data_files["train"] = self.train_dir
         if self.validation_dir is not None:
             data_files["val"] = self.validation_dir
-        self.data_files = data_files if data_files else None
+        self.data_files = data_files or None
 
 
 @dataclass

examples/pytorch/image-pretraining/run_mim.py

@@ -115,7 +115,7 @@ def __post_init__(self):
             data_files["train"] = self.train_dir
         if self.validation_dir is not None:
             data_files["val"] = self.validation_dir
-        self.data_files = data_files if data_files else None
+        self.data_files = data_files or None
 
 
 @dataclass

examples/pytorch/image-pretraining/run_mim_no_trainer.py

@@ -333,7 +333,7 @@ def parse_args():
         data_files["train"] = args.train_dir
     if args.validation_dir is not None:
         data_files["val"] = args.validation_dir
-    args.data_files = data_files if data_files else None
+    args.data_files = data_files or None
 
     if args.push_to_hub:
         assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."

examples/pytorch/multiple-choice/run_swag.py

@@ -254,14 +254,14 @@ def main():
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     config = AutoConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        model_args.config_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
         trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
-        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        model_args.tokenizer_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,

examples/pytorch/question-answering/run_qa.py

@@ -308,14 +308,14 @@ def main():
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     config = AutoConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        model_args.config_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
         trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
-        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        model_args.tokenizer_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=True,
         revision=model_args.model_revision,

examples/pytorch/question-answering/run_qa_beam_search.py

@@ -306,13 +306,13 @@ def main():
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     config = XLNetConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        model_args.config_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
     )
     tokenizer = XLNetTokenizerFast.from_pretrained(
-        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        model_args.tokenizer_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,

examples/pytorch/question-answering/run_seq2seq_qa.py

@@ -354,14 +354,14 @@ def main():
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     config = AutoConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        model_args.config_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
         trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
-        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        model_args.tokenizer_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,

examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py

@@ -370,7 +370,7 @@ def main():
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     config = AutoConfig.from_pretrained(
-        (model_args.config_name if model_args.config_name else model_args.model_name_or_path),
+        (model_args.config_name or model_args.model_name_or_path),
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,

examples/pytorch/summarization/run_summarization.py

@@ -420,14 +420,14 @@ def main():
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     config = AutoConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        model_args.config_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
         trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
-        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        model_args.tokenizer_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,

examples/pytorch/text-classification/run_classification.py

@@ -471,7 +471,7 @@ def main():
     # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     config = AutoConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        model_args.config_name or model_args.model_name_or_path,
         num_labels=num_labels,
         finetuning_task="text-classification",
         cache_dir=model_args.cache_dir,
@@ -491,7 +491,7 @@ def main():
         logger.info("setting problem type to single label classification")
 
     tokenizer = AutoTokenizer.from_pretrained(
-        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        model_args.tokenizer_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,

examples/pytorch/text-classification/run_glue.py

@@ -361,7 +361,7 @@ def main():
     # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     config = AutoConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        model_args.config_name or model_args.model_name_or_path,
         num_labels=num_labels,
         finetuning_task=data_args.task_name,
         cache_dir=model_args.cache_dir,
@@ -370,7 +370,7 @@ def main():
         trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
-        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        model_args.tokenizer_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,

examples/pytorch/text-classification/run_xnli.py

@@ -274,7 +274,7 @@ def main():
     # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     config = AutoConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        model_args.config_name or model_args.model_name_or_path,
         num_labels=num_labels,
         id2label={str(i): label for i, label in enumerate(label_list)},
         label2id={label: i for i, label in enumerate(label_list)},
@@ -285,7 +285,7 @@ def main():
         trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
-        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        model_args.tokenizer_name or model_args.model_name_or_path,
         do_lower_case=model_args.do_lower_case,
         cache_dir=model_args.cache_dir,
         use_fast=model_args.use_fast_tokenizer,

examples/pytorch/text-generation/run_generation.py

@@ -133,13 +133,13 @@ def prepare_xlm_input(args, model, tokenizer, prompt_text):
 
 
 def prepare_xlnet_input(args, _, tokenizer, prompt_text):
-    prefix = args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
+    prefix = args.prefix or (args.padding_text or PREFIX)
     prompt_text = prefix + prompt_text
     return prompt_text
 
 
 def prepare_transfoxl_input(args, _, tokenizer, prompt_text):
-    prefix = args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
+    prefix = args.prefix or (args.padding_text or PREFIX)
     prompt_text = prefix + prompt_text
     return prompt_text
 
@@ -366,7 +366,7 @@ def main():
     args.length = adjust_length_to_model(args.length, max_sequence_length=max_seq_length)
     logger.info(args)
 
-    prompt_text = args.prompt if args.prompt else input("Model prompt >>> ")
+    prompt_text = args.prompt or input("Model prompt >>> ")
 
     # Different models need different input formatting and/or extra arguments
     requires_preprocessing = args.model_type in PREPROCESSING_FUNCTIONS
@@ -380,7 +380,7 @@ def main():
             preprocessed_prompt_text, add_special_tokens=False, return_tensors="pt", **tokenizer_kwargs
         )
     else:
-        prefix = args.prefix if args.prefix else args.padding_text
+        prefix = args.prefix or args.padding_text
         encoded_prompt = tokenizer.encode(prefix + prompt_text, add_special_tokens=False, return_tensors="pt")
     encoded_prompt = encoded_prompt.to(distributed_state.device)
 

examples/pytorch/token-classification/run_ner.py

@@ -345,7 +345,7 @@ def get_label_list(labels):
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     config = AutoConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        model_args.config_name or model_args.model_name_or_path,
         num_labels=num_labels,
         finetuning_task=data_args.task_name,
         cache_dir=model_args.cache_dir,
@@ -354,7 +354,7 @@ def get_label_list(labels):
         trust_remote_code=model_args.trust_remote_code,
     )
 
-    tokenizer_name_or_path = model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path
+    tokenizer_name_or_path = model_args.tokenizer_name or model_args.model_name_or_path
     if config.model_type in {"bloom", "gpt2", "roberta", "deberta"}:
         tokenizer = AutoTokenizer.from_pretrained(
             tokenizer_name_or_path,

examples/pytorch/token-classification/run_ner_no_trainer.py

@@ -419,7 +419,7 @@ def get_label_list(labels):
         config = CONFIG_MAPPING[args.model_type]()
         logger.warning("You are instantiating a new config instance from scratch.")
 
-    tokenizer_name_or_path = args.tokenizer_name if args.tokenizer_name else args.model_name_or_path
+    tokenizer_name_or_path = args.tokenizer_name or args.model_name_or_path
     if not tokenizer_name_or_path:
         raise ValueError(
             "You are instantiating a new tokenizer from scratch. This is not supported by this script. "

examples/pytorch/translation/run_translation.py

@@ -372,14 +372,14 @@ def main():
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     config = AutoConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        model_args.config_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         token=model_args.token,
         trust_remote_code=model_args.trust_remote_code,
     )
     tokenizer = AutoTokenizer.from_pretrained(
-        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        model_args.tokenizer_name or model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
         use_fast=model_args.use_fast_tokenizer,
         revision=model_args.model_revision,

src/transformers/data/metrics/squad_metrics.py

@@ -479,7 +479,7 @@ def compute_predictions_logits(
                     end_logit=null_end_logit,
                 )
             )
-        prelim_predictions = sorted(prelim_predictions, key=lambda x: (x.start_logit + x.end_logit), reverse=True)
+        prelim_predictions = sorted(prelim_predictions, key=lambda x: x.start_logit + x.end_logit, reverse=True)
 
         _NbestPrediction = collections.namedtuple(  # pylint: disable=invalid-name
             "NbestPrediction", ["text", "start_logit", "end_logit"]
@@ -681,9 +681,7 @@ def compute_predictions_log_probs(
                         )
                     )
 
-        prelim_predictions = sorted(
-            prelim_predictions, key=lambda x: (x.start_log_prob + x.end_log_prob), reverse=True
-        )
+        prelim_predictions = sorted(prelim_predictions, key=lambda x: x.start_log_prob + x.end_log_prob, reverse=True)
 
         seen_predictions = {}
         nbest = []

src/transformers/integrations/integration_utils.py

@@ -468,7 +468,7 @@ def run_hp_search_wandb(trainer, n_trials: int, direction: str, **kwargs) -> Bes
         sweep_config["name"] = name
 
     def _objective():
-        run = wandb.run if wandb.run else wandb.init()
+        run = wandb.run or wandb.init()
         trainer.state.trial_name = run.name
         run.config.update({"assignments": {}, "metric": metric})
         config = wandb.config

src/transformers/modelcard.py

@@ -559,7 +559,7 @@ def extract_hyperparameters_from_trainer(trainer):
 
     if trainer.args.optim:
         optimizer_name = trainer.args.optim
-        optimizer_args = trainer.args.optim_args if trainer.args.optim_args else "No additional optimizer arguments"
+        optimizer_args = trainer.args.optim_args or "No additional optimizer arguments"
 
         if "adam" in optimizer_name.lower():
             hyperparameters["optimizer"] = (

src/transformers/models/clap/feature_extraction_clap.py

@@ -296,7 +296,7 @@ def __call__(
                 pipeline.
         """
         truncation = truncation if truncation is not None else self.truncation
-        padding = padding if padding else self.padding
+        padding = padding or self.padding
 
         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
@@ -331,7 +331,7 @@ def __call__(
 
         # convert to mel spectrogram, truncate and pad if needed.
         padded_inputs = [
-            self._get_input_mel(waveform, max_length if max_length else self.nb_max_samples, truncation, padding)
+            self._get_input_mel(waveform, max_length or self.nb_max_samples, truncation, padding)
             for waveform in raw_speech
         ]
 

src/transformers/models/depth_anything/convert_distill_any_depth_to_hf.py

@@ -49,8 +49,12 @@
     r"depth_head\.scratch\.output_conv(\d+)(?:\.(\d+))?\.(weight|bias)": lambda m: (
         f"head.conv{int(m[1]) + (int(m[2]) // 2 if m[2] else 0)}.{m[3]}" if m[1] == "2" else f"head.conv{m[1]}.{m[3]}"
     ),
-    r"depth_head\.scratch\.refinenet(\d+)\.out_conv\.(weight|bias)": lambda m: f"neck.fusion_stage.layers.{3 - (int(m[1]) - 1)}.projection.{m[2]}",
-    r"depth_head\.scratch\.refinenet(\d+)\.resConfUnit(\d+)\.conv(\d+)\.(weight|bias)": lambda m: f"neck.fusion_stage.layers.{3 - (int(m[1]) - 1)}.residual_layer{m[2]}.convolution{m[3]}.{m[4]}",
+    r"depth_head\.scratch\.refinenet(\d+)\.out_conv\.(weight|bias)": lambda m: (
+        f"neck.fusion_stage.layers.{3 - (int(m[1]) - 1)}.projection.{m[2]}"
+    ),
+    r"depth_head\.scratch\.refinenet(\d+)\.resConfUnit(\d+)\.conv(\d+)\.(weight|bias)": lambda m: (
+        f"neck.fusion_stage.layers.{3 - (int(m[1]) - 1)}.residual_layer{m[2]}.convolution{m[3]}.{m[4]}"
+    ),
 }