Revert "Replace RandomGenerator with SeedGenerator (keras-team#2150…

…)"

This reverts commit 365a675.

keras_cv/backend/random.py

@@ -18,3 +18,119 @@
     from keras.random import *  # noqa: F403, F401
 else:
     from keras_core.random import *  # noqa: F403, F401
+class SeedGenerator:
+    def __init__(self, seed=None, **kwargs):
+        if keras_3():
+            self._seed_generator = keras.random.SeedGenerator(
+                seed=seed, **kwargs
+            )
+        else:
+            self._current_seed = [0, seed]
+
+    def next(self, ordered=True):
+        if keras_3():
+            return self._seed_generator.next(ordered=ordered)
+        else:
+            self._current_seed[0] += 1
+            return self._current_seed[:]
+
+
+def normal(shape, mean=0.0, stddev=1.0, dtype=None, seed=None):
+    if isinstance(seed, SeedGenerator):
+        seed = seed.next()
+        init_seed = seed[0] + seed[1]
+    else:
+        init_seed = seed
+
+    kwargs = {}
+    if dtype:
+        kwargs["dtype"] = dtype
+    if keras_3():
+        return keras.random.normal(
+            shape,
+            mean=mean,
+            stddev=stddev,
+            seed=init_seed,
+            **kwargs,
+        )
+    else:
+        import tensorflow as tf
+
+        return tf.random.normal(
+            shape,
+            mean=mean,
+            stddev=stddev,
+            seed=init_seed,
+            **kwargs,
+        )
+
+
+def uniform(shape, minval=0.0, maxval=1.0, dtype=None, seed=None):
+    if isinstance(seed, SeedGenerator):
+        seed = seed.next()
+        init_seed = seed[0] + seed[1]
+    else:
+        init_seed = seed
+    kwargs = {}
+    if dtype:
+        kwargs["dtype"] = dtype
+    if keras_3():
+        return keras.random.uniform(
+            shape,
+            minval=minval,
+            maxval=maxval,
+            seed=init_seed,
+            **kwargs,
+        )
+    else:
+        import tensorflow as tf
+
+        return tf.random.uniform(
+            shape,
+            minval=minval,
+            maxval=maxval,
+            seed=init_seed,
+            **kwargs,
+        )
+
+
+def shuffle(x, axis=0, seed=None):
+    if isinstance(seed, SeedGenerator):
+        seed = seed.next()
+        init_seed = seed[0] + seed[1]
+    else:
+        init_seed = seed
+
+    if keras_3():
+        return keras.random.shuffle(x=x, axis=axis, seed=init_seed)
+    else:
+        import tensorflow as tf
+
+        return tf.random.shuffle(x=x, axis=axis, seed=init_seed)
+
+
+def categorical(logits, num_samples, dtype=None, seed=None):
+    if isinstance(seed, SeedGenerator):
+        seed = seed.next()
+        init_seed = seed[0] + seed[1]
+    else:
+        init_seed = seed
+    kwargs = {}
+    if dtype:
+        kwargs["dtype"] = dtype
+    if keras_3():
+        return keras.random.categorical(
+            logits=logits,
+            num_samples=num_samples,
+            seed=init_seed,
+            **kwargs,
+        )
+    else:
+        import tensorflow as tf
+
+        return tf.random.categorical(
+            logits=logits,
+            num_samples=num_samples,
+            seed=init_seed,
+            **kwargs,
+        )

keras_cv/layers/preprocessing/base_image_augmentation_layer.py

@@ -22,10 +22,8 @@
 
 from keras_cv import bounding_box
 from keras_cv.api_export import keras_cv_export
-from keras_cv.backend import keras
-from keras_cv.backend import scope
+from keras_cv.backend import keras, scope
 from keras_cv.backend.config import multi_backend
-from keras_cv.backend.random import RandomGenerator
 from keras_cv.utils import preprocessing
 
 # In order to support both unbatched and batched inputs, the horizontal
@@ -132,7 +130,7 @@ def augment_image(self, image, transformation):
 
     def __init__(self, seed=None, **kwargs):
         force_generator = kwargs.pop("force_generator", False)
-        self._random_generator = RandomGenerator(
+        self._random_generator = keras_backend.RandomGenerator(
             seed=seed, force_generator=force_generator
         )
         super().__init__(**kwargs)

keras_cv/layers/preprocessing/vectorized_base_image_augmentation_layer.py

@@ -22,10 +22,8 @@
 
 from keras_cv import bounding_box
 from keras_cv.api_export import keras_cv_export
-from keras_cv.backend import keras
-from keras_cv.backend import scope
+from keras_cv.backend import keras, scope
 from keras_cv.backend.config import multi_backend
-from keras_cv.backend.random import RandomGenerator
 from keras_cv.utils import preprocessing
 
 H_AXIS = -3
@@ -111,7 +109,7 @@ def __init__(self):
 
     def __init__(self, seed=None, **kwargs):
         force_generator = kwargs.pop("force_generator", False)
-        self._random_generator = RandomGenerator(
+        self._random_generator = keras_backend.RandomGenerator(
             seed=seed, force_generator=force_generator
         )
         super().__init__(**kwargs)