Avoid hard erroring if tf is not installed (#2028)

Instead we only want to fail on features that need it (mainly
tokenization).

keras_hub/src/utils/preset_utils.py

@@ -7,20 +7,10 @@
 
 import keras
 from absl import logging
-from packaging.version import parse
 
 from keras_hub.src.api_export import keras_hub_export
 from keras_hub.src.utils.keras_utils import print_msg
 
-try:
-    import tensorflow as tf
-except ImportError:
-    raise ImportError(
-        "To use `keras_hub`, please install Tensorflow: "
-        "`pip install tensorflow`. The TensorFlow package is required for data "
-        "preprocessing with any backend."
-    )
-
 try:
     import kagglehub
     from kagglehub.exceptions import KaggleApiHTTPError
@@ -173,22 +163,8 @@ def get_file(preset, path):
                 )
             else:
                 raise ValueError(message)
-
-    elif scheme in tf.io.gfile.get_registered_schemes():
-        url = os.path.join(preset, path)
-        subdir = preset.replace("://", "_").replace("-", "_").replace("/", "_")
-        filename = os.path.basename(path)
-        subdir = os.path.join(subdir, os.path.dirname(path))
-        try:
-            return copy_gfile_to_cache(
-                filename,
-                url,
-                cache_subdir=os.path.join("models", subdir),
-            )
-        except (tf.errors.PermissionDeniedError, tf.errors.NotFoundError) as e:
-            raise FileNotFoundError(
-                f"`{path}` doesn't exist in preset directory `{preset}`.",
-            ) from e
+    elif scheme in tf_registered_schemes():
+        return tf_copy_gfile_to_cache(preset, path)
     elif scheme == HF_SCHEME:
         if huggingface_hub is None:
             raise ImportError(
@@ -237,29 +213,48 @@ def get_file(preset, path):
         )
 
 
-def copy_gfile_to_cache(filename, url, cache_subdir):
+def tf_registered_schemes():
+    try:
+        import tensorflow as tf
+
+        return tf.io.gfile.get_registered_schemes()
+    except ImportError:
+        return []
+
+
+def tf_copy_gfile_to_cache(preset, path):
     """Much of this is adapted from get_file of keras core."""
     if "KERAS_HOME" in os.environ:
-        cachdir_base = os.environ.get("KERAS_HOME")
+        base_dir = os.environ.get("KERAS_HOME")
     else:
-        cachdir_base = os.path.expanduser(os.path.join("~", ".keras"))
-    if not os.access(cachdir_base, os.W_OK):
-        cachdir_base = os.path.join("/tmp", ".keras")
-    cachedir = os.path.join(cachdir_base, cache_subdir)
-    os.makedirs(cachedir, exist_ok=True)
-
-    fpath = os.path.join(cachedir, filename)
-    if not os.path.exists(fpath):
+        base_dir = os.path.expanduser(os.path.join("~", ".keras"))
+    if not os.access(base_dir, os.W_OK):
+        base_dir = os.path.join("/tmp", ".keras")
+
+    url = os.path.join(preset, path)
+    model_dir = preset.replace("://", "_").replace("-", "_").replace("/", "_")
+    local_path = os.path.join(base_dir, "models", model_dir, path)
+
+    if not os.path.exists(local_path):
         print_msg(f"Downloading data from {url}")
         try:
-            tf.io.gfile.copy(url, fpath)
+            import tensorflow as tf
+
+            os.make_dirs(os.path.dirname(local_path), exist_ok=True)
+            tf.io.gfile.copy(url, local_path)
         except Exception as e:
             # gfile.copy will leave an empty file after an error.
             # Work around this bug.
-            os.remove(fpath)
+            os.remove(local_path)
+            if isinstance(
+                e, tf.errors.PermissionDeniedError, tf.errors.NotFoundError
+            ):
+                raise FileNotFoundError(
+                    f"`{path}` doesn't exist in preset directory `{preset}`.",
+                ) from e
             raise e
 
-    return fpath
+    return local_path
 
 
 def check_file_exists(preset, path):
@@ -394,12 +389,6 @@ def upload_preset(
                 "Uploading a model to Kaggle Hub requires the `kagglehub` "
                 "package. Please install with `pip install kagglehub`."
             )
-        if parse(kagglehub.__version__) < parse("0.2.4"):
-            raise ImportError(
-                "Uploading a model to Kaggle Hub requires the `kagglehub` "
-                "package version `0.2.4` or higher. Please upgrade with "
-                "`pip install --upgrade kagglehub`."
-            )
         kaggle_handle = uri.removeprefix(KAGGLE_PREFIX)
         kagglehub.model_upload(kaggle_handle, preset)
     elif uri.startswith(HF_PREFIX):