Update formatting for latest Ruff version

benchmarks/text_generation.py

@@ -150,7 +150,7 @@ def next(prompt, state, index):
                 )
                 print("Time taken: ", time_taken)
                 res_handler.write(
-                    f"{sampler},{execution_method}," f"{time_taken}\n"
+                    f"{sampler},{execution_method},{time_taken}\n"
                 )
                 print()
             print("*************************************")

keras_hub/src/metrics/bleu.py

@@ -329,8 +329,9 @@ def validate_and_fix_rank(inputs, tensor_name, base_rank=0):
                 return tf.squeeze(inputs, axis=-1)
             else:
                 raise ValueError(
-                    f"{tensor_name} must be of rank {base_rank}, {base_rank+1} "
-                    f"or {base_rank+2}. Found rank: {inputs.shape.rank}"
+                    f"{tensor_name} must be of rank {base_rank}, "
+                    f"{base_rank + 1}, or {base_rank + 2}. "
+                    f"Found rank: {inputs.shape.rank}"
                 )
 
         y_true = validate_and_fix_rank(y_true, "y_true", 1)

keras_hub/src/models/basnet/basnet_backbone.py

@@ -219,7 +219,7 @@ def get_resnet_block(_resnet, block_num):
     else:
         x = _resnet.pyramid_outputs[extractor_levels[block_num - 1]]
     y = _resnet.get_layer(
-        f"stack{block_num}_block{num_blocks[block_num]-1}_add"
+        f"stack{block_num}_block{num_blocks[block_num] - 1}_add"
     ).output
     return keras.models.Model(
         inputs=x,

keras_hub/src/models/deeplab_v3/deeplab_v3_layers.py

@@ -88,13 +88,13 @@ def build(self, input_shape):
                         dilation_rate=dilation_rate,
                         use_bias=False,
                         data_format=self.data_format,
-                        name=f"aspp_conv_{i+2}",
+                        name=f"aspp_conv_{i + 2}",
                     ),
                     keras.layers.BatchNormalization(
-                        axis=self.channel_axis, name=f"aspp_bn_{i+2}"
+                        axis=self.channel_axis, name=f"aspp_bn_{i + 2}"
                     ),
                     keras.layers.Activation(
-                        self.activation, name=f"aspp_activation_{i+2}"
+                        self.activation, name=f"aspp_activation_{i + 2}"
                     ),
                 ]
             )

keras_hub/src/models/densenet/densenet_backbone.py

@@ -81,14 +81,14 @@ def __init__(
                 channel_axis,
                 stackwise_num_repeats[stack_index],
                 growth_rate,
-                name=f"stack{stack_index+1}",
+                name=f"stack{stack_index + 1}",
             )
             pyramid_outputs[f"P{index}"] = x
             x = apply_transition_block(
                 x,
                 channel_axis,
                 compression_ratio,
-                name=f"transition{stack_index+1}",
+                name=f"transition{stack_index + 1}",
             )
 
         x = apply_dense_block(
@@ -140,7 +140,7 @@ def apply_dense_block(x, channel_axis, num_repeats, growth_rate, name=None):
 
     for i in range(num_repeats):
         x = apply_conv_block(
-            x, channel_axis, growth_rate, name=f"{name}_block{i+1}"
+            x, channel_axis, growth_rate, name=f"{name}_block{i + 1}"
         )
     return x
 

keras_hub/src/models/flux/flux_text_to_image.py

@@ -81,7 +81,7 @@ def __init__(
 
     def fit(self, *args, **kwargs):
         raise NotImplementedError(
-            "Currently, `fit` is not supported for " "`FluxTextToImage`."
+            "Currently, `fit` is not supported for `FluxTextToImage`."
         )
 
     def generate_step(

keras_hub/src/models/pali_gemma/pali_gemma_presets.py

@@ -5,7 +5,7 @@
     "pali_gemma_3b_mix_224": {
         "metadata": {
             "description": (
-                "image size 224, mix fine tuned, text sequence " "length is 256"
+                "image size 224, mix fine tuned, text sequence length is 256"
             ),
             "params": 2923335408,
             "path": "pali_gemma",
@@ -45,7 +45,7 @@
     "pali_gemma_3b_896": {
         "metadata": {
             "description": (
-                "image size 896, pre trained, text sequence length " "is 512"
+                "image size 896, pre trained, text sequence length is 512"
             ),
             "params": 2927759088,
             "path": "pali_gemma",

keras_hub/src/models/resnet/resnet_backbone.py

@@ -177,7 +177,7 @@ def __init__(
                 use_bias=False,
                 padding="same",
                 dtype=dtype,
-                name=f"conv{conv_index+1}_conv",
+                name=f"conv{conv_index + 1}_conv",
             )(x)
 
         if not use_pre_activation:

keras_hub/src/models/retinanet/feature_pyramid.py

@@ -209,9 +209,9 @@ def build(self, input_shapes):
             )
             if i == backbone_max_level + 1 and self.use_p5:
                 self.output_conv_layers[level].build(
-                    (None, None, None, input_shapes[f"P{i-1}"][-1])
+                    (None, None, None, input_shapes[f"P{i - 1}"][-1])
                     if self.data_format == "channels_last"
-                    else (None, input_shapes[f"P{i-1}"][1], None, None)
+                    else (None, input_shapes[f"P{i - 1}"][1], None, None)
                 )
             else:
                 self.output_conv_layers[level].build(
@@ -277,7 +277,7 @@ def call(self, inputs):
             if i < backbone_max_level:
                 # for the top most output, it doesn't need to merge with any
                 # upper stream outputs
-                upstream_output = self.top_down_op(output_features[f"P{i+1}"])
+                upstream_output = self.top_down_op(output_features[f"P{i + 1}"])
                 output = self.merge_op([output, upstream_output])
             output_features[level] = (
                 self.lateral_batch_norm_layers[level](output)
@@ -296,9 +296,9 @@ def call(self, inputs):
         for i in range(backbone_max_level + 1, self.max_level + 1):
             level = f"P{i}"
             feats_in = (
-                inputs[f"P{i-1}"]
+                inputs[f"P{i - 1}"]
                 if i == backbone_max_level + 1 and self.use_p5
-                else output_features[f"P{i-1}"]
+                else output_features[f"P{i - 1}"]
             )
             if i > backbone_max_level + 1:
                 feats_in = self.activation(feats_in)

keras_hub/src/models/stable_diffusion_3/stable_diffusion_3_inpaint.py

@@ -82,8 +82,7 @@ def __init__(
 
     def fit(self, *args, **kwargs):
         raise NotImplementedError(
-            "Currently, `fit` is not supported for "
-            "`StableDiffusion3Inpaint`."
+            "Currently, `fit` is not supported for `StableDiffusion3Inpaint`."
         )
 
     def generate_step(

keras_hub/src/models/vit/vit_layers.py

@@ -351,7 +351,7 @@ def build(self, input_shape):
                 attention_dropout=self.attention_dropout,
                 layer_norm_epsilon=self.layer_norm_epsilon,
                 dtype=self.dtype_policy,
-                name=f"tranformer_block_{i+1}",
+                name=f"tranformer_block_{i + 1}",
             )
             encoder_block.build((None, None, self.hidden_dim))
             self.encoder_layers.append(encoder_block)

keras_hub/src/tokenizers/byte_tokenizer.py

@@ -150,8 +150,7 @@ def __init__(
     ):
         if not is_int_dtype(dtype):
             raise ValueError(
-                "Output dtype must be an integer type. "
-                f"Received: dtype={dtype}"
+                f"Output dtype must be an integer type. Received: dtype={dtype}"
             )
 
         # Check normalization_form.

keras_hub/src/tokenizers/unicode_codepoint_tokenizer.py

@@ -203,8 +203,7 @@ def __init__(
     ) -> None:
         if not is_int_dtype(dtype):
             raise ValueError(
-                "Output dtype must be an integer type. "
-                f"Received: dtype={dtype}"
+                f"Output dtype must be an integer type. Received: dtype={dtype}"
             )
 
         # Check normalization_form.

keras_hub/src/utils/timm/convert_densenet.py

@@ -59,18 +59,20 @@ def port_batch_normalization(keras_layer_name, hf_weight_prefix):
     num_stacks = len(backbone.stackwise_num_repeats)
     for stack_index in range(num_stacks):
         for block_idx in range(backbone.stackwise_num_repeats[stack_index]):
-            keras_name = f"stack{stack_index+1}_block{block_idx+1}"
+            keras_name = f"stack{stack_index + 1}_block{block_idx + 1}"
             hf_name = (
-                f"features.denseblock{stack_index+1}.denselayer{block_idx+1}"
+                "features."
+                f"denseblock{stack_index + 1}"
+                f".denselayer{block_idx + 1}"
             )
             port_batch_normalization(f"{keras_name}_1_bn", f"{hf_name}.norm1")
             port_conv2d(f"{keras_name}_1_conv", f"{hf_name}.conv1")
             port_batch_normalization(f"{keras_name}_2_bn", f"{hf_name}.norm2")
             port_conv2d(f"{keras_name}_2_conv", f"{hf_name}.conv2")
 
     for stack_index in range(num_stacks - 1):
-        keras_transition_name = f"transition{stack_index+1}"
-        hf_transition_name = f"features.transition{stack_index+1}"
+        keras_transition_name = f"transition{stack_index + 1}"
+        hf_transition_name = f"features.transition{stack_index + 1}"
         port_batch_normalization(
             f"{keras_transition_name}_bn", f"{hf_transition_name}.norm"
         )

keras_hub/src/utils/timm/convert_efficientnet.py

@@ -268,7 +268,7 @@ def port_batch_normalization(keras_layer, hf_weight_prefix):
             # 97 is the start of the lowercase alphabet.
             letter_identifier = chr(block_idx + 97)
 
-            keras_block_prefix = f"block{stack_index+1}{letter_identifier}_"
+            keras_block_prefix = f"block{stack_index + 1}{letter_identifier}_"
             hf_block_prefix = f"blocks.{stack_index}.{block_idx}."
 
             if block_type == "v1":

keras_hub/src/utils/timm/convert_resnet.py

@@ -89,7 +89,7 @@ def port_batch_normalization(keras_layer_name, hf_weight_prefix):
         for block_idx in range(backbone.stackwise_num_blocks[stack_index]):
             if version == "v1":
                 keras_name = f"stack{stack_index}_block{block_idx}"
-                hf_name = f"layer{stack_index+1}.{block_idx}"
+                hf_name = f"layer{stack_index + 1}.{block_idx}"
             else:
                 keras_name = f"stack{stack_index}_block{block_idx}"
                 hf_name = f"stages.{stack_index}.blocks.{block_idx}"

tools/checkpoint_conversion/convert_albert_checkpoints.py

@@ -20,7 +20,7 @@
 
 FLAGS = flags.FLAGS
 flags.DEFINE_string(
-    "preset", None, f'Must be one of {",".join(PRESET_MAP.keys())}'
+    "preset", None, f"Must be one of {','.join(PRESET_MAP.keys())}"
 )
 
 

tools/checkpoint_conversion/convert_bart_checkpoints.py

@@ -18,7 +18,7 @@
 
 FLAGS = flags.FLAGS
 flags.DEFINE_string(
-    "preset", None, f'Must be one of {",".join(PRESET_MAP.keys())}'
+    "preset", None, f"Must be one of {','.join(PRESET_MAP.keys())}"
 )
 
 

tools/checkpoint_conversion/convert_bloom_checkpoints.py

@@ -46,7 +46,7 @@
 
 
 flags.DEFINE_string(
-    "preset", None, f'Must be one of {", ".join(PRESET_MAP.keys())}'
+    "preset", None, f"Must be one of {', '.join(PRESET_MAP.keys())}"
 )
 flags.mark_flag_as_required("preset")
 flags.DEFINE_boolean(
@@ -244,9 +244,10 @@ def preprocessor_call(input_str):
 
 def main(_):
     preset = FLAGS.preset
-    assert (
-        preset in PRESET_MAP.keys()
-    ), f'Invalid preset {preset}. Must be one of {", ".join(PRESET_MAP.keys())}'
+    assert preset in PRESET_MAP.keys(), (
+        f"Invalid preset {preset}. "
+        f"Must be one of {', '.join(PRESET_MAP.keys())}"
+    )
 
     validate_only = FLAGS.validate_only
 

tools/checkpoint_conversion/convert_clip_checkpoints.py

@@ -65,7 +65,7 @@
 flags.DEFINE_string(
     "preset",
     None,
-    f'Must be one of {",".join(PRESET_MAP.keys())}',
+    f"Must be one of {','.join(PRESET_MAP.keys())}",
     required=True,
 )
 flags.DEFINE_string(

tools/checkpoint_conversion/convert_deberta_v3_checkpoints.py

@@ -27,7 +27,7 @@
 
 FLAGS = flags.FLAGS
 flags.DEFINE_string(
-    "preset", None, f'Must be one of {",".join(PRESET_MAP.keys())}'
+    "preset", None, f"Must be one of {','.join(PRESET_MAP.keys())}"
 )
 
 

tools/checkpoint_conversion/convert_distilbert_checkpoints.py

@@ -23,7 +23,7 @@
 
 FLAGS = flags.FLAGS
 flags.DEFINE_string(
-    "preset", None, f'Must be one of {",".join(PRESET_MAP.keys())}'
+    "preset", None, f"Must be one of {','.join(PRESET_MAP.keys())}"
 )
 
 

tools/checkpoint_conversion/convert_electra_checkpoints.py

@@ -33,7 +33,7 @@
 flags.DEFINE_string(
     "preset",
     "electra_base_discriminator_en",
-    f'Must be one of {",".join(PRESET_MAP)}',
+    f"Must be one of {','.join(PRESET_MAP)}",
 )
 flags.mark_flag_as_required("preset")
 

tools/checkpoint_conversion/convert_f_net_checkpoints.py

@@ -17,7 +17,7 @@
 
 FLAGS = flags.FLAGS
 flags.DEFINE_string(
-    "preset", None, f'Must be one of {",".join(PRESET_MAP.keys())}'
+    "preset", None, f"Must be one of {','.join(PRESET_MAP.keys())}"
 )
 
 

tools/checkpoint_conversion/convert_falcon_checkpoints.py

@@ -43,7 +43,7 @@
 absl.flags.DEFINE_string(
     "preset",
     "falcon_refinedweb_1b_en",
-    f'Must be one of {",".join(PRESET_MAP.keys())}.',
+    f"Must be one of {','.join(PRESET_MAP.keys())}.",
 )
 
 

tools/checkpoint_conversion/convert_gemma_checkpoints.py

@@ -57,7 +57,7 @@
 flags.DEFINE_string(
     "preset",
     None,
-    f'Must be one of {",".join(PRESET_MAP.keys())}',
+    f"Must be one of {','.join(PRESET_MAP.keys())}",
     required=True,
 )
 
@@ -228,9 +228,9 @@ def main(_):
         flax_dir = FLAGS.flax_dir
     else:
         presets = PRESET_MAP.keys()
-        assert (
-            preset in presets
-        ), f'Invalid preset {preset}. Must be one of {",".join(presets)}'
+        assert preset in presets, (
+            f"Invalid preset {preset}. Must be one of {','.join(presets)}"
+        )
         handle = PRESET_MAP[preset]
         flax_dir = download_flax_model(handle)
 

tools/checkpoint_conversion/convert_gpt2_checkpoints.py

@@ -28,7 +28,7 @@
 
 FLAGS = flags.FLAGS
 flags.DEFINE_string(
-    "preset", None, f'Must be one of {",".join(PRESET_MAP.keys())}'
+    "preset", None, f"Must be one of {','.join(PRESET_MAP.keys())}"
 )
 
 
@@ -236,8 +236,8 @@ def check_output(
 
 def main(_):
     assert FLAGS.preset in PRESET_MAP.keys(), (
-        f'Invalid preset {FLAGS.preset}. '
-        f'Must be one of {",".join(PRESET_MAP.keys())}'
+        f"Invalid preset {FLAGS.preset}. "
+        f"Must be one of {','.join(PRESET_MAP.keys())}"
     )
     num_params = PRESET_MAP[FLAGS.preset][0]
     hf_model_name = PRESET_MAP[FLAGS.preset][1]

tools/checkpoint_conversion/convert_llama3_checkpoints.py

@@ -22,7 +22,7 @@
 
 FLAGS = flags.FLAGS
 flags.DEFINE_string(
-    "preset", None, f'Must be one of {",".join(PRESET_MAP.keys())}'
+    "preset", None, f"Must be one of {','.join(PRESET_MAP.keys())}"
 )
 
 

tools/checkpoint_conversion/convert_llama_checkpoints.py

@@ -33,7 +33,7 @@
 
 FLAGS = flags.FLAGS
 flags.DEFINE_string(
-    "preset", None, f'Must be one of {",".join(PRESET_MAP.keys())}'
+    "preset", None, f"Must be one of {','.join(PRESET_MAP.keys())}"
 )
 
 flags.DEFINE_string(

tools/checkpoint_conversion/convert_mistral_checkpoints.py

@@ -25,7 +25,7 @@
 
 FLAGS = flags.FLAGS
 flags.DEFINE_string(
-    "preset", None, f'Must be one of {",".join(PRESET_MAP.keys())}'
+    "preset", None, f"Must be one of {','.join(PRESET_MAP.keys())}"
 )
 
 

tools/checkpoint_conversion/convert_mix_transformer.py

@@ -52,7 +52,7 @@
 }
 
 flags.DEFINE_string(
-    "preset", None, f'Must be one of {",".join(DOWNLOAD_URLS.keys())}'
+    "preset", None, f"Must be one of {','.join(DOWNLOAD_URLS.keys())}"
 )