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

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* 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>
This commit is contained in:
Sylvain Gugger
2021-03-31 10:00:27 -04:00
committed by GitHub
parent d0b3797a3b
commit acc3bd9d2a
224 changed files with 984 additions and 1312 deletions
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58
examples/multiple-choice/utils_multiple_choice.py
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@@ -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