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>

templates/adding_a_new_example_script/{{cookiecutter.directory_name}}/run_{{cookiecutter.example_shortcut}}.py

@@ -236,7 +236,7 @@ def main():
     # Set the verbosity to info of the Transformers logger (on main process only):
     if is_main_process(training_args.local_rank):
         transformers.utils.logging.set_verbosity_info()
-    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)

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_tf_{{cookiecutter.lowercase_modelname}}.py

@@ -357,7 +357,7 @@ class TF{{cookiecutter.camelcase_modelname}}Encoder(tf.keras.layers.Layer):
     def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs):
         super().__init__(**kwargs)
 
-        self.layer = [TF{{cookiecutter.camelcase_modelname}}Layer(config, name="layer_._{}".format(i)) for i in range(config.num_hidden_layers)]
+        self.layer = [TF{{cookiecutter.camelcase_modelname}}Layer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]
 
     def call(
         self,

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/modeling_{{cookiecutter.lowercase_modelname}}.py

@@ -78,13 +78,13 @@ def load_tf_weights_in_{{cookiecutter.lowercase_modelname}}(model, config, tf_ch
         )
         raise
     tf_path = os.path.abspath(tf_checkpoint_path)
-    logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
+    logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
     # Load weights from TF model
     init_vars = tf.train.list_variables(tf_path)
     names = []
     arrays = []
     for name, shape in init_vars:
-        logger.info("Loading TF weight {} with shape {}".format(name, shape))
+        logger.info(f"Loading TF weight {name} with shape {shape}")
         array = tf.train.load_variable(tf_path, name)
         names.append(name)
         arrays.append(array)
@@ -97,7 +97,7 @@ def load_tf_weights_in_{{cookiecutter.lowercase_modelname}}(model, config, tf_ch
             n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
             for n in name
         ):
-            logger.info("Skipping {}".format("/".join(name)))
+            logger.info(f"Skipping {'/'.join(name)}")
             continue
         pointer = model
         for m_name in name:
@@ -117,7 +117,7 @@ def load_tf_weights_in_{{cookiecutter.lowercase_modelname}}(model, config, tf_ch
                 try:
                     pointer = getattr(pointer, scope_names[0])
                 except AttributeError:
-                    logger.info("Skipping {}".format("/".join(name)))
+                    logger.info(f"Skipping {'/'.join(name)}")
                     continue
             if len(scope_names) >= 2:
                 num = int(scope_names[1])
@@ -133,7 +133,7 @@ def load_tf_weights_in_{{cookiecutter.lowercase_modelname}}(model, config, tf_ch
         except AssertionError as e:
             e.args += (pointer.shape, array.shape)
             raise
-        logger.info("Initialize PyTorch weight {}".format(name))
+        logger.info(f"Initialize PyTorch weight {name}")
         pointer.data = torch.from_numpy(array)
     return model
 
@@ -196,8 +196,8 @@ class {{cookiecutter.camelcase_modelname}}SelfAttention(nn.Module):
         super().__init__()
         if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
             raise ValueError(
-                "The hidden size (%d) is not a multiple of the number of attention "
-                "heads (%d)" % (config.hidden_size, config.num_attention_heads)
+                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
+                f"heads ({config.num_attention_heads})"
             )
 
         self.num_attention_heads = config.num_attention_heads

templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/test_modeling_tf_{{cookiecutter.lowercase_modelname}}.py

@@ -585,10 +585,9 @@ def _assert_tensors_equal(a, b, atol=1e-12, prefix=""):
             return True
         raise
     except Exception:
-        msg = "{} != {}".format(a, b)
-        if prefix:
-            msg = prefix + ": " + msg
-        raise AssertionError(msg)
+        if len(prefix) > 0:
+            prefix = f"{prefix}: "
+        raise AssertionError(f"{prefix}{a} != {b}")
 
 
 def _long_tensor(tok_lst):