Merge pull request #16459 from code-review-doctor:fix-probably-meant-…

…fstring

PiperOrigin-RevId: 444340251

keras/applications/mobilenet.py

@@ -213,7 +213,7 @@ def MobileNet(input_shape=None,
     if depth_multiplier != 1:
       raise ValueError('If imagenet weights are being loaded, '
                        'depth multiplier must be 1.  '
-                       'Received depth_multiplier={depth_multiplier}')
+                       f'Received depth_multiplier={depth_multiplier}')
 
     if alpha not in [0.25, 0.50, 0.75, 1.0]:
       raise ValueError('If imagenet weights are being loaded, '

keras/benchmarks/keras_examples_benchmarks/text_classification_transformer_benchmark_test.py

@@ -147,8 +147,8 @@ def __init__(self, embed_dim, num_heads=8):
     self.embed_dim = embed_dim
     self.num_heads = num_heads
     if embed_dim % num_heads != 0:
-      raise ValueError('embedding dimension = {embed_dim} should be divisible'
-                       'by number of heads = {num_heads}')
+      raise ValueError(f'embedding dimension = {embed_dim} should be divisible'
+                       f'by number of heads = {num_heads}')
     self.projection_dim = embed_dim // num_heads
     self.query_dense = tf.keras.layers.Dense(embed_dim)
     self.key_dense = tf.keras.layers.Dense(embed_dim)

keras/dtensor/layout_map.py

@@ -113,7 +113,7 @@ def __setitem__(self, key, layout):
                        'not use duplicated keys.')
     if not isinstance(layout, dtensor.Layout):
       raise ValueError(f'{layout} should be a dtensor.Layout type, '
-                       'got {type(layout)}')
+                       f'got {type(layout)}')
 
     self._layout_map[key] = layout
 
@@ -435,8 +435,8 @@ def _set_object_by_path(object_to_set, path, value):
     if i == len(path) - 1:
       # We found the actual attribute to set
       if isinstance(attr_name, int):
-      # This means we are trying to set an element in the array, make sure the
-      # instance is array like object.
+        # This means we are trying to set an element in the array, make sure the
+        # instance is array like object.
         object_to_set[attr_name] = value
       else:
         setattr(object_to_set, attr_name, value)

keras/dtensor/lazy_variable.py

@@ -112,7 +112,7 @@ def __init__(
         initial_value, "graph") and initial_value.graph.building_function:
       raise ValueError(f"Argument `initial_value` ({initial_value}) could not "
                        "be lifted out of a `tf.function`. "
-                       "(Tried to create variable with name='{name}'). "
+                       f"(Tried to create variable with name='{name}'). "
                        "To avoid this error, when constructing `tf.Variable`s "
                        "inside of `tf.function` you can create the "
                        "`initial_value` tensor in a "
@@ -217,4 +217,3 @@ def disable_init_variable_creator():
     yield
   finally:
     _DISABLE_LAZY_VARIABLE_INIT.disabled = existing_value
-

keras/engine/base_layer.py

@@ -2677,7 +2677,7 @@ def _get_node_attribute_at_index(self, node_index, attr, attr_name):
     """
     if not self._inbound_nodes:
       raise RuntimeError(f'The layer {self.name} has never been called '
-                         'and thus has no defined {attr_name}.')
+                         f'and thus has no defined {attr_name}.')
     if not len(self._inbound_nodes) > node_index:
       raise ValueError(f'Asked to get {attr_name} at node '
                        f'{node_index}, but the layer has only '

keras/layers/rnn/cell_wrappers.py

@@ -231,9 +231,8 @@ def tensor_and_const_value(v):
         tensor_prob, const_prob = tensor_and_const_value(prob)
         if const_prob is not None:
           if const_prob < 0 or const_prob > 1:
-            raise ValueError(
-                f"Parameter {attr} must be between 0 and 1. "
-                "Received {const_prob}")
+            raise ValueError(f"Parameter {attr} must be between 0 and 1. "
+                             f"Received {const_prob}")
           setattr(self, "_%s" % attr, float(const_prob))
         else:
           setattr(self, "_%s" % attr, tensor_prob)

keras/layers/rnn/legacy_cell_wrappers.py

@@ -266,9 +266,8 @@ def tensor_and_const_value(v):
         tensor_prob, const_prob = tensor_and_const_value(prob)
         if const_prob is not None:
           if const_prob < 0 or const_prob > 1:
-            raise ValueError(
-                f"Parameter {attr} must be between 0 and 1. "
-                "Received {const_prob}")
+            raise ValueError(f"Parameter {attr} must be between 0 and 1. "
+                             f"Received {const_prob}")
           setattr(self, "_%s" % attr, float(const_prob))
         else:
           setattr(self, "_%s" % attr, tensor_prob)

keras/layers/rnn/legacy_cells.py

@@ -473,11 +473,9 @@ class GRUCell(LayerRNNCell):
       the first input). Required when `build` is called before `call`.
     **kwargs: Dict, keyword named properties for common layer attributes, like
       `trainable` etc when constructing the cell from configs of get_config().
-
-      References:
-    Learning Phrase Representations using RNN Encoder Decoder for Statistical
-    Machine Translation:
-      [Cho et al., 2014]
+      References: Learning Phrase Representations using RNN Encoder Decoder for
+        Statistical
+    Machine Translation: [Cho et al., 2014]
       (https://aclanthology.coli.uni-saarland.de/papers/D14-1179/d14-1179)
       ([pdf](http://emnlp2014.org/papers/pdf/EMNLP2014179.pdf))
   """
@@ -938,9 +936,8 @@ def output_size(self):
   @tf_utils.shape_type_conversion
   def build(self, inputs_shape):
     if inputs_shape[-1] is None:
-      raise ValueError(
-          "Expected inputs.shape[-1] to be known, "
-          "received shape: {inputs_shape}")
+      raise ValueError("Expected inputs.shape[-1] to be known, "
+                       f"received shape: {inputs_shape}")
     _check_supported_dtypes(self.dtype)
     input_depth = inputs_shape[-1]
     h_depth = self._num_units if self._num_proj is None else self._num_proj

keras/optimizers/optimizer_v2/optimizer_v2.py

@@ -1187,7 +1187,7 @@ def set_weights(self, weights):
     params = self.weights
     if len(params) != len(weights):
       raise ValueError(
-          "You called `set_weights(weights)` on optimizer {self._name} "
+          f"You called `set_weights(weights)` on optimizer {self._name} "
           f"with a  weight list of length {str(len(weights))}, "
           f"but the optimizer was expecting {str(len(params))} "
           f"weights. Provided weights: {str(weights)[:50]}...")

keras/saving/utils_v1/signature_def_utils.py

@@ -58,7 +58,7 @@ def _supervised_signature_def(
     ValueError: If inputs or outputs is `None`.
   """
   if inputs is None or not inputs:
-    raise ValueError('f{method_name} `inputs` cannot be None or empty.')
+    raise ValueError(f'{method_name} `inputs` cannot be None or empty.')
 
   signature_inputs = {key: tf.compat.v1.saved_model.build_tensor_info(tensor)
                       for key, tensor in inputs.items()}

keras/utils/audio_dataset.py

@@ -150,16 +150,12 @@ def audio_dataset_from_directory(
 
   if sampling_rate is not None:
     if not isinstance(sampling_rate, int):
-      raise ValueError(
-          '`sampling_rate` should have an integer value. '
-          'Received: sampling_rate={sampling_rate}'
-      )
+      raise ValueError('`sampling_rate` should have an integer value. '
+                       f'Received: sampling_rate={sampling_rate}')
 
     if sampling_rate <= 0:
-      raise ValueError(
-          f'`sampling_rate` should be higher than 0. '
-          'Received: sampling_rate={sampling_rate}'
-      )
+      raise ValueError(f'`sampling_rate` should be higher than 0. '
+                       f'Received: sampling_rate={sampling_rate}')
 
     if tfio is None:
       raise ImportError(