Changed redundant Union(X, None) to Optional(X) in related files.

official/nlp/modeling/layers/transformer_encoder_block.py

@@ -44,7 +44,7 @@ def __init__(
     self.epsilon = epsilon
 
   def build(self, 
-            input_shape: Union[tf.TensorShape, Sequence[Union[int, None]]]):
+            input_shape: Union[tf.TensorShape, Sequence[Optional[int]]]):
     input_shape = tf.TensorShape(input_shape)
     scale_shape = [1] * input_shape.rank
     for dim in self.axis:

official/recommendation/uplift/layers/uplift_networks/base_uplift_networks.py

@@ -15,7 +15,7 @@
 """Defines base abstract uplift network layers."""
 
 import abc
-from typing import Union
+from typing import Union, Optional
 
 import tensorflow as tf, tf_keras
 
@@ -34,7 +34,7 @@ class BaseTwoTowerUpliftNetwork(tf_keras.layers.Layer, metaclass=abc.ABCMeta):
   def call(
       self,
       inputs: types.DictOfTensors,
-      training: Union[bool, None] = None,
-      mask: Union[tf.Tensor, None] = None,
+      training: Optional[bool] = None,
+      mask: Optional[tf.Tensor] = None,
   ) -> types.TwoTowerTrainingOutputs:
     raise NotImplementedError()

official/recommendation/uplift/metrics/label_mean.py

@@ -14,7 +14,7 @@
 
 """Keras metric for computing the label mean sliced by treatment group."""
 
-from typing import Union
+from typing import Union, Optional
 
 import tensorflow as tf, tf_keras
 
@@ -73,7 +73,7 @@ def update_state(
       self,
       y_true: tf.Tensor,
       y_pred: types.TwoTowerTrainingOutputs,
-      sample_weight: Union[tf.Tensor, None] = None,
+      sample_weight: Optional[tf.Tensor] = None,
   ):
     """Updates the overall, control and treatment label means.
 

official/recommendation/uplift/metrics/label_variance.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 """Keras metric for computing the label variance sliced by treatment group."""
-from typing import Union
+from typing import Optional
 
 import tensorflow as tf, tf_keras
 
@@ -73,7 +73,7 @@ def update_state(
       self,
       y_true: tf.Tensor,
       y_pred: types.TwoTowerTrainingOutputs,
-      sample_weight: Union[tf.Tensor, None] = None,
+      sample_weight: Optional[tf.Tensor] = None,
   ):
     """Updates the overall, control and treatment label variances.
 

official/recommendation/uplift/metrics/metric_configs.py

@@ -16,7 +16,7 @@
 
 from collections.abc import Mapping
 import dataclasses
-from typing import Any, Union
+from typing import Any, Optional
 
 from official.core.config_definitions import base_config
 
@@ -33,9 +33,9 @@ class SlicedMetricConfig(base_config.Config):
       values to slice on.
   """
 
-  slicing_feature: Union[str, None] = None
-  slicing_spec: Union[Mapping[str, int], None] = None
-  slicing_feature_dtype: Union[str, None] = None
+  slicing_feature: Optional[str] = None
+  slicing_spec: Optional[Mapping[str, int]] = None
+  slicing_feature_dtype: Optional[str] = None
 
   def __post_init__(
       self, default_params: dict[str, Any], restrictions: list[str]

official/recommendation/uplift/metrics/sliced_metric.py

@@ -15,7 +15,7 @@
 """Keras metric for reporting metrics sliced by a feature."""
 
 import copy
-from typing import Union
+from typing import Union, Optional
 
 import tensorflow as tf, tf_keras
 
@@ -68,8 +68,8 @@ def __init__(
       self,
       metric: tf_keras.metrics.Metric,
       slicing_spec: Union[dict[str, str], dict[str, int]],
-      slicing_feature_dtype: Union[tf.DType, None] = None,
-      name: Union[str, None] = None,
+      slicing_feature_dtype: Optional[tf.DType] = None,
+      name: Optional[str] = None,
   ):
     """Initializes the instance.
 
@@ -124,7 +124,7 @@ def __init__(
   def update_state(
       self,
       *args: tf.Tensor,
-      sample_weight: Union[tf.Tensor, None] = None,
+      sample_weight: Optional[tf.Tensor] = None,
       slicing_feature: tf.Tensor,
       **kwargs,
   ):

official/recommendation/uplift/metrics/treatment_fraction.py

@@ -18,7 +18,7 @@
 
 from official.recommendation.uplift import types
 
-from typing import Union
+from typing import Optional
 
 @tf_keras.utils.register_keras_serializable(package="Uplift")
 class TreatmentFraction(tf_keras.metrics.Metric):
@@ -58,7 +58,7 @@ def update_state(
       self,
       y_true: tf.Tensor,
       y_pred: types.TwoTowerTrainingOutputs,
-      sample_weight: Union[tf.Tensor, None] = None,
+      sample_weight: Optional[tf.Tensor] = None,
   ) -> None:
     """Updates the treatment fraction.
 

official/recommendation/uplift/metrics/uplift_mean.py

@@ -14,7 +14,7 @@
 
 """Keras metric for computing the mean uplift sliced by treatment group."""
 
-from typing import Union
+from typing import Optional
 
 import tensorflow as tf, tf_keras
 
@@ -70,7 +70,7 @@ def update_state(
       self,
       y_true: tf.Tensor,
       y_pred: types.TwoTowerTrainingOutputs,
-      sample_weight: Union[tf.Tensor, None] = None,
+      sample_weight: Optional[tf.Tensor] = None,
   ) -> None:
     """Updates the overall, control and treatment uplift means.
 

official/vision/configs/retinanet.py

@@ -53,7 +53,7 @@ class Parser(hyperparams.Config):
   match_threshold: float = 0.5
   unmatched_threshold: float = 0.5
   aug_rand_hflip: bool = False
-  aug_rand_jpeg: Union[common.RandJpegQuality, None] = None
+  aug_rand_jpeg: Optional[common.RandJpegQuality] = None
   aug_scale_min: float = 1.0
   aug_scale_max: float = 1.0
   skip_crowd_during_training: bool = True

official/vision/dataloaders/retinanet_input.py

@@ -18,7 +18,7 @@
 into (image, labels) tuple for RetinaNet.
 """
 
-from typing import Optional, Union, List
+from typing import Optional, List
 
 # Import libraries
 
@@ -40,16 +40,16 @@ class Parser(parser.Parser):
   def __init__(self,
                output_size,
                max_level,
-               min_level: Union[int, None] = None,
-               num_scales: Union[int, None] = None,
-               aspect_ratios: Union[List[float], None] = None,
-               anchor_size: Union[float, None] = None,
+               min_level: Optional[int] = None,
+               num_scales: Optional[int] = None,
+               aspect_ratios: Optional[List[float]] = None,
+               anchor_size: Optional[float] = None,
                match_threshold=0.5,
                unmatched_threshold=0.5,
                box_coder_weights=None,
                aug_type=None,
                aug_rand_hflip=False,
-               aug_rand_jpeg: Union[cfg.RandJpegQuality, None] = None,
+               aug_rand_jpeg: Optional[cfg.RandJpegQuality] = None,
                aug_scale_min=1.0,
                aug_scale_max=1.0,
                use_autoaugment=False,

official/vision/modeling/backbones/mobilenet.py

@@ -15,7 +15,7 @@
 """Contains definitions of MobileNet Networks."""
 
 import dataclasses
-from typing import Any, Union
+from typing import Any, Optional
 
 # Import libraries
 
@@ -92,9 +92,9 @@ class BlockSpec(hyperparams.Config):
   use_normalization: bool = True
   activation: str = 'relu6'
   # Used for block type InvertedResConv.
-  expand_ratio: Union[float, None] = 6.0
+  expand_ratio: Optional[float] = 6.0
   # Used for block type InvertedResConv with SE.
-  se_ratio: Union[float, None] = None
+  se_ratio: Optional[float] = None
   use_depthwise: bool = True
   use_residual: bool = True
   is_output: bool = True
@@ -145,8 +145,8 @@ def __init__(
       use_explicit_padding: bool = False,
       activation: str = 'relu6',
       kernel_initializer: str = 'VarianceScaling',
-      kernel_regularizer: Union[tf_keras.regularizers.Regularizer, None] = None,
-      bias_regularizer: Union[tf_keras.regularizers.Regularizer, None] = None,
+      kernel_regularizer: Optional[tf_keras.regularizers.Regularizer] = None,
+      bias_regularizer: Optional[tf_keras.regularizers.Regularizer] = None,
       use_normalization: bool = True,
       use_sync_bn: bool = False,
       norm_momentum: float = 0.99,
@@ -1230,10 +1230,10 @@ def __init__(
       norm_momentum: float = 0.99,
       norm_epsilon: float = 0.001,
       kernel_initializer: str = 'VarianceScaling',
-      kernel_regularizer: Union[tf_keras.regularizers.Regularizer, None] = None,
-      bias_regularizer: Union[tf_keras.regularizers.Regularizer, None] = None,
+      kernel_regularizer: Optional[tf_keras.regularizers.Regularizer] = None,
+      bias_regularizer: Optional[tf_keras.regularizers.Regularizer] = None,
       # The followings should be kept the same most of the times.
-      output_stride: Union[int, None] = None,
+      output_stride: Optional[int] = None,
       min_depth: int = 8,
       # divisible is not used in MobileNetV1.
       divisible_by: int = 8,
@@ -1595,7 +1595,7 @@ def build_mobilenet(
     input_specs: tf_keras.layers.InputSpec,
     backbone_config: hyperparams.Config,
     norm_activation_config: hyperparams.Config,
-    l2_regularizer: Union[tf_keras.regularizers.Regularizer, None] = None,
+    l2_regularizer: Optional[tf_keras.regularizers.Regularizer] = None,
 ) -> tf_keras.Model:
   """Builds MobileNet backbone from a config."""
   backbone_type = backbone_config.type

official/vision/modeling/factory.py

@@ -264,7 +264,7 @@ def build_retinanet(
     backbone: Optional[tf_keras.Model] = None,
     decoder: Optional[tf_keras.Model] = None,
     num_anchors_per_location: Union[int, Dict[str, int], None] = None,
-    anchor_boxes: Union[Mapping[str, tf.Tensor], None] = None,
+    anchor_boxes: Optional[Mapping[str, tf.Tensor]] = None,
 ) -> tf_keras.Model:
   """Builds a RetinaNet model.
 

official/vision/modeling/layers/detection_generator.py

@@ -14,7 +14,7 @@
 
 """Contains definitions of generators to generate the final detections."""
 import contextlib
-from typing import Any, Dict, List, Optional, Mapping, Sequence, Tuple, Union
+from typing import Any, Dict, List, Optional, Mapping, Sequence, Tuple
 
 # Import libraries
 
@@ -794,7 +794,7 @@ def _generate_detections_tflite(
     raw_scores: Mapping[str, tf.Tensor],
     anchor_boxes: Mapping[str, tf.Tensor],
     config: Dict[str, Any],
-    box_coder_weights: Union[List[float], None] = None,
+    box_coder_weights: Optional[List[float]] = None,
 ) -> Sequence[Any]:
   """Generate detections for conversion to TFLite.
 

official/vision/modeling/layers/nn_blocks.py

@@ -788,12 +788,12 @@ def __init__(
       start_dw_kernel_size: int = 0,
       middle_dw_kernel_size: int = 3,
       end_dw_kernel_size: int = 0,
-      stochastic_depth_drop_rate: Union[float, None] = None,
+      stochastic_depth_drop_rate: Optional[float] = None,
       kernel_initializer: str = 'VarianceScaling',
-      kernel_regularizer: Union[tf_keras.regularizers.Regularizer, None] = None,
-      bias_regularizer: Union[tf_keras.regularizers.Regularizer, None] = None,
+      kernel_regularizer: Optional[tf_keras.regularizers.Regularizer] = None,
+      bias_regularizer: Optional[tf_keras.regularizers.Regularizer] = None,
       activation: str = 'relu',
-      depthwise_activation: Union[str, None] = None,
+      depthwise_activation: Optional[str] = None,
       use_sync_bn: bool = False,
       dilation_rate: int = 1,
       divisible_by: int = 1,

official/vision/modeling/retinanet_model.py

@@ -31,7 +31,7 @@ def __init__(self,
                decoder: tf_keras.Model,
                head: tf_keras.layers.Layer,
                detection_generator: tf_keras.layers.Layer,
-               anchor_boxes: Union[Mapping[str, tf.Tensor], None] = None,
+               anchor_boxes: Optional[Mapping[str, tf.Tensor]] = None,
                min_level: Optional[int] = None,
                max_level: Optional[int] = None,
                num_scales: Optional[int] = None,
@@ -85,7 +85,7 @@ def __init__(self,
   def call(self,
            images: Union[tf.Tensor, Sequence[tf.Tensor]],
            image_shape: Optional[tf.Tensor] = None,
-           anchor_boxes: Union[Mapping[str, tf.Tensor], None] = None,
+           anchor_boxes: Optional[Mapping[str, tf.Tensor]] = None,
            output_intermediate_features: bool = False,
            training: bool = None) -> Mapping[str, tf.Tensor]:
     """Forward pass of the RetinaNet model.
@@ -240,7 +240,7 @@ def detection_generator(self) -> tf_keras.layers.Layer:
     return self._detection_generator
 
   @property
-  def anchor_boxes(self) -> Union[Mapping[str, tf.Tensor], None]:
+  def anchor_boxes(self) -> Optional[Mapping[str, tf.Tensor]]:
     return self._anchor_boxes
 
   def get_config(self) -> Mapping[str, Any]:

official/vision/serving/export_saved_model_lib.py

@@ -36,7 +36,7 @@ def export_inference_graph(
     input_image_size: List[int],
     params: cfg.ExperimentConfig,
     export_dir: str,
-    checkpoint_path: Union[str, None] = None,
+    checkpoint_path: Optional[str] = None,
     num_channels: Optional[int] = 3,
     export_module: Optional[export_base.ExportModule] = None,
     export_checkpoint_subdir: Optional[str] = None,