Some routine cleanup while writing some new tools for checkpoint admin

- Remove broken test in preset_utils we don't ever run
- Move load_serialized_object to our preset loading class
  (for consistency)
- Move all admin related tooling to a dedicated folder in tools/
- Remove some no longer used scripts.

Author: mattdangerw

keras_hub/src/utils/preset_utils.py

@@ -454,16 +454,6 @@ def load_json(preset, config_file=CONFIG_FILE):
     return config
 
 
-def load_serialized_object(config, **kwargs):
-    # `dtype` in config might be a serialized `DTypePolicy` or `DTypePolicyMap`.
-    # Ensure that `dtype` is properly configured.
-    dtype = kwargs.pop("dtype", None)
-    config = set_dtype_in_config(config, dtype)
-
-    config["config"] = {**config["config"], **kwargs}
-    return keras.saving.deserialize_keras_object(config)
-
-
 def check_config_class(config):
     """Validate a preset is being loaded on the correct class."""
     registered_name = config["registered_name"]
@@ -631,26 +621,26 @@ def check_backbone_class(self):
         return check_config_class(self.config)
 
     def load_backbone(self, cls, load_weights, **kwargs):
-        backbone = load_serialized_object(self.config, **kwargs)
+        backbone = self._load_serialized_object(self.config, **kwargs)
         if load_weights:
             jax_memory_cleanup(backbone)
             backbone.load_weights(get_file(self.preset, MODEL_WEIGHTS_FILE))
         return backbone
 
     def load_tokenizer(self, cls, config_file=TOKENIZER_CONFIG_FILE, **kwargs):
         tokenizer_config = load_json(self.preset, config_file)
-        tokenizer = load_serialized_object(tokenizer_config, **kwargs)
+        tokenizer = self._load_serialized_object(tokenizer_config, **kwargs)
         if hasattr(tokenizer, "load_preset_assets"):
             tokenizer.load_preset_assets(self.preset)
         return tokenizer
 
     def load_audio_converter(self, cls, **kwargs):
         converter_config = load_json(self.preset, AUDIO_CONVERTER_CONFIG_FILE)
-        return load_serialized_object(converter_config, **kwargs)
+        return self._load_serialized_object(converter_config, **kwargs)
 
     def load_image_converter(self, cls, **kwargs):
         converter_config = load_json(self.preset, IMAGE_CONVERTER_CONFIG_FILE)
-        return load_serialized_object(converter_config, **kwargs)
+        return self._load_serialized_object(converter_config, **kwargs)
 
     def load_task(self, cls, load_weights, load_task_weights, **kwargs):
         # If there is no `task.json` or it's for the wrong class delegate to the
@@ -671,7 +661,7 @@ def load_task(self, cls, load_weights, load_task_weights, **kwargs):
             backbone_config = task_config["config"]["backbone"]["config"]
             backbone_config = {**backbone_config, **backbone_kwargs}
             task_config["config"]["backbone"]["config"] = backbone_config
-        task = load_serialized_object(task_config, **kwargs)
+        task = self._load_serialized_object(task_config, **kwargs)
         if task.preprocessor and hasattr(
             task.preprocessor, "load_preset_assets"
         ):
@@ -699,11 +689,20 @@ def load_preprocessor(
         if not issubclass(check_config_class(preprocessor_json), cls):
             return super().load_preprocessor(cls, **kwargs)
         # We found a `preprocessing.json` with a complete config for our class.
-        preprocessor = load_serialized_object(preprocessor_json, **kwargs)
+        preprocessor = self._load_serialized_object(preprocessor_json, **kwargs)
         if hasattr(preprocessor, "load_preset_assets"):
             preprocessor.load_preset_assets(self.preset)
         return preprocessor
 
+    def _load_serialized_object(self, config, **kwargs):
+        # `dtype` in config might be a serialized `DTypePolicy` or
+        # `DTypePolicyMap`. Ensure that `dtype` is properly configured.
+        dtype = kwargs.pop("dtype", None)
+        config = set_dtype_in_config(config, dtype)
+
+        config["config"] = {**config["config"], **kwargs}
+        return keras.saving.deserialize_keras_object(config)
+
 
 class KerasPresetSaver:
     def __init__(self, preset_dir):

keras_hub/src/utils/preset_utils_test.py

@@ -11,9 +11,7 @@
 from keras_hub.src.models.bert.bert_backbone import BertBackbone
 from keras_hub.src.models.bert.bert_tokenizer import BertTokenizer
 from keras_hub.src.tests.test_case import TestCase
-from keras_hub.src.utils.keras_utils import has_quantization_support
 from keras_hub.src.utils.preset_utils import CONFIG_FILE
-from keras_hub.src.utils.preset_utils import load_serialized_object
 from keras_hub.src.utils.preset_utils import upload_preset
 
 
@@ -88,18 +86,3 @@ def test_upload_with_invalid_json(self):
         # Verify error handling.
         with self.assertRaisesRegex(ValueError, "is an invalid json"):
             upload_preset("kaggle://test/test/test", local_preset_dir)
-
-    @parameterized.named_parameters(
-        ("gemma2_2b_en", "gemma2_2b_en", "bfloat16", False),
-        ("llama2_7b_en_int8", "llama2_7b_en_int8", "bfloat16", True),
-    )
-    @pytest.mark.extra_large
-    def test_load_serialized_object(self, preset, dtype, is_quantized):
-        if is_quantized and not has_quantization_support():
-            self.skipTest("This version of Keras doesn't support quantization.")
-
-        model = load_serialized_object(preset, dtype=dtype)
-        if is_quantized:
-            self.assertEqual(model.dtype_policy.name, "map_bfloat16")
-        else:
-            self.assertEqual(model.dtype_policy.name, "bfloat16")