Enforce string-formatting with f-strings (#10980)

* First third * Styling and fix mistake * Quality * All the rest * Treat %s and %d * typo * Missing ) * Apply suggestions from code review Co-authored-by: Lysandre Debut <lysandre@huggingface.co> Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
2021-03-31 10:00:27 -04:00
parent d0b3797a3b
commit acc3bd9d2a
224 changed files with 984 additions and 1312 deletions
--- a/examples/multiple-choice/run_swag.py
+++ b/examples/multiple-choice/run_swag.py
@@ -247,7 +247,7 @@ def main():
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
-    logger.info("Training/evaluation parameters %s", training_args)
+    logger.info(f"Training/evaluation parameters {training_args}")

    # Set seed before initializing model.
    set_seed(training_args.seed)
--- a/examples/multiple-choice/run_tf_multiple_choice.py
+++ b/examples/multiple-choice/run_tf_multiple_choice.py
@@ -116,12 +116,10 @@ def main():
        level=logging.INFO,
    )
    logger.warning(
-        "device: %s, n_replicas: %s, 16-bits training: %s",
-        training_args.device,
-        training_args.n_replicas,
-        training_args.fp16,
+        f"device: {training_args.device}, n_replicas: {training_args.n_replicas}, "
+        f"16-bits training: {training_args.fp16}"
    )
-    logger.info("Training/evaluation parameters %s", training_args)
+    logger.info(f"Training/evaluation parameters {training_args}")

    # Set seed
    set_seed(training_args.seed)
@@ -131,7 +129,7 @@ def main():
        label_list = processor.get_labels()
        num_labels = len(label_list)
    except KeyError:
-        raise ValueError("Task not found: %s" % (data_args.task_name))
+        raise ValueError(f"Task not found: {data_args.task_name}")

    # Load pretrained model and tokenizer
    #
@@ -210,8 +208,8 @@ def main():
        with open(output_eval_file, "w") as writer:
            logger.info("***** Eval results *****")
            for key, value in result.items():
-                logger.info("  %s = %s", key, value)
-                writer.write("%s = %s\n" % (key, value))
+                logger.info(f"  {key} = {value}")
+                writer.write(f"{key} = {value}\n")

            results.update(result)

--- a/examples/multiple-choice/utils_multiple_choice.py
+++ b/examples/multiple-choice/utils_multiple_choice.py
@@ -99,13 +99,7 @@ if is_torch_available():
            processor = processors[task]()

            cached_features_file = os.path.join(
-                data_dir,
-                "cached_{}_{}_{}_{}".format(
-                    mode.value,
-                    tokenizer.__class__.__name__,
-                    str(max_seq_length),
-                    task,
-                ),
+                data_dir, f"cached_{mode.value}_{tokenizer.__class__.__name__}_{max_seq_length}_{task}"
            )

            # Make sure only the first process in distributed training processes the dataset,
@@ -125,14 +119,14 @@ if is_torch_available():
                        examples = processor.get_test_examples(data_dir)
                    else:
                        examples = processor.get_train_examples(data_dir)
-                    logger.info("Training examples: %s", len(examples))
+                    logger.info(f"Training examples: {len(examples)}")
                    self.features = convert_examples_to_features(
                        examples,
                        label_list,
                        max_seq_length,
                        tokenizer,
                    )
-                    logger.info("Saving features into cached file %s", cached_features_file)
+                    logger.info(f"Saving features into cached file {cached_features_file}")
                    torch.save(self.features, cached_features_file)

        def __len__(self):
@@ -172,7 +166,7 @@ if is_tf_available():
                examples = processor.get_test_examples(data_dir)
            else:
                examples = processor.get_train_examples(data_dir)
-            logger.info("Training examples: %s", len(examples))
+            logger.info(f"Training examples: {len(examples)}")

            self.features = convert_examples_to_features(
                examples,
@@ -184,7 +178,7 @@ if is_tf_available():
            def gen():
                for (ex_index, ex) in tqdm.tqdm(enumerate(self.features), desc="convert examples to features"):
                    if ex_index % 10000 == 0:
-                        logger.info("Writing example %d of %d" % (ex_index, len(examples)))
+                        logger.info(f"Writing example {ex_index} of {len(examples)}")

                    yield (
                        {
@@ -255,7 +249,7 @@ class RaceProcessor(DataProcessor):

    def get_train_examples(self, data_dir):
        """See base class."""
-        logger.info("LOOKING AT {} train".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} train")
        high = os.path.join(data_dir, "train/high")
        middle = os.path.join(data_dir, "train/middle")
        high = self._read_txt(high)
@@ -264,7 +258,7 @@ class RaceProcessor(DataProcessor):

    def get_dev_examples(self, data_dir):
        """See base class."""
-        logger.info("LOOKING AT {} dev".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} dev")
        high = os.path.join(data_dir, "dev/high")
        middle = os.path.join(data_dir, "dev/middle")
        high = self._read_txt(high)
@@ -273,7 +267,7 @@ class RaceProcessor(DataProcessor):

    def get_test_examples(self, data_dir):
        """See base class."""
-        logger.info("LOOKING AT {} test".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} test")
        high = os.path.join(data_dir, "test/high")
        middle = os.path.join(data_dir, "test/middle")
        high = self._read_txt(high)
@@ -298,7 +292,7 @@ class RaceProcessor(DataProcessor):
        """Creates examples for the training and dev sets."""
        examples = []
        for (_, data_raw) in enumerate(lines):
-            race_id = "%s-%s" % (set_type, data_raw["race_id"])
+            race_id = f"{set_type}-{data_raw['race_id']}"
            article = data_raw["article"]
            for i in range(len(data_raw["answers"])):
                truth = str(ord(data_raw["answers"][i]) - ord("A"))
@@ -322,17 +316,17 @@ class SynonymProcessor(DataProcessor):

    def get_train_examples(self, data_dir):
        """See base class."""
-        logger.info("LOOKING AT {} train".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} train")
        return self._create_examples(self._read_csv(os.path.join(data_dir, "mctrain.csv")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
-        logger.info("LOOKING AT {} dev".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} dev")
        return self._create_examples(self._read_csv(os.path.join(data_dir, "mchp.csv")), "dev")

    def get_test_examples(self, data_dir):
        """See base class."""
-        logger.info("LOOKING AT {} dev".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} dev")

        return self._create_examples(self._read_csv(os.path.join(data_dir, "mctest.csv")), "test")

@@ -368,17 +362,17 @@ class SwagProcessor(DataProcessor):

    def get_train_examples(self, data_dir):
        """See base class."""
-        logger.info("LOOKING AT {} train".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} train")
        return self._create_examples(self._read_csv(os.path.join(data_dir, "train.csv")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
-        logger.info("LOOKING AT {} dev".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} dev")
        return self._create_examples(self._read_csv(os.path.join(data_dir, "val.csv")), "dev")

    def get_test_examples(self, data_dir):
        """See base class."""
-        logger.info("LOOKING AT {} dev".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} dev")
        raise ValueError(
            "For swag testing, the input file does not contain a label column. It can not be tested in current code"
            "setting!"
@@ -419,16 +413,16 @@ class ArcProcessor(DataProcessor):

    def get_train_examples(self, data_dir):
        """See base class."""
-        logger.info("LOOKING AT {} train".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} train")
        return self._create_examples(self._read_json(os.path.join(data_dir, "train.jsonl")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
-        logger.info("LOOKING AT {} dev".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} dev")
        return self._create_examples(self._read_json(os.path.join(data_dir, "dev.jsonl")), "dev")

    def get_test_examples(self, data_dir):
-        logger.info("LOOKING AT {} test".format(data_dir))
+        logger.info(f"LOOKING AT {data_dir} test")
        return self._create_examples(self._read_json(os.path.join(data_dir, "test.jsonl")), "test")

    def get_labels(self):
@@ -450,7 +444,7 @@ class ArcProcessor(DataProcessor):
            elif truth in "1234":
                return int(truth) - 1
            else:
-                logger.info("truth ERROR! %s", str(truth))
+                logger.info(f"truth ERROR! {truth}")
                return None

        examples = []
@@ -496,11 +490,11 @@ class ArcProcessor(DataProcessor):
        if type == "train":
            assert len(examples) > 1
            assert examples[0].label is not None
-        logger.info("len examples: %s}", str(len(examples)))
-        logger.info("Three choices: %s", str(three_choice))
-        logger.info("Five choices: %s", str(five_choice))
-        logger.info("Other choices: %s", str(other_choices))
-        logger.info("four choices: %s", str(four_choice))
+        logger.info(f"len examples: {len(examples)}")
+        logger.info(f"Three choices: {three_choice}")
+        logger.info(f"Five choices: {five_choice}")
+        logger.info(f"Other choices: {other_choices}")
+        logger.info(f"four choices: {four_choice}")

        return examples

@@ -520,7 +514,7 @@ def convert_examples_to_features(
    features = []
    for (ex_index, example) in tqdm.tqdm(enumerate(examples), desc="convert examples to features"):
        if ex_index % 10000 == 0:
-            logger.info("Writing example %d of %d" % (ex_index, len(examples)))
+            logger.info(f"Writing example {ex_index} of {len(examples)}")
        choices_inputs = []
        for ending_idx, (context, ending) in enumerate(zip(example.contexts, example.endings)):
            text_a = context
@@ -570,7 +564,7 @@ def convert_examples_to_features(

    for f in features[:2]:
        logger.info("*** Example ***")
-        logger.info("feature: %s" % f)
+        logger.info("feature: {f}")

    return features