feat: publish updated protos for cloud/automl/v1 service (#318)

* fix: proto field markdown comment for the display_name field in annotation_payload.proto to point the correct public v1/ version fix: Add back java_multiple_files option to the text_sentiment.proto to match with the previous published version of text_sentiment proto

PiperOrigin-RevId: 421849336

Source-Link: googleapis/googleapis@5c24921

Source-Link: https://github.com/googleapis/googleapis-gen/commit/0195e8e45f41225dde15b3a24fe89ed27686cb64
Copy-Tag: eyJwIjoiLmdpdGh1Yi8uT3dsQm90LnlhbWwiLCJoIjoiMDE5NWU4ZTQ1ZjQxMjI1ZGRlMTViM2EyNGZlODllZDI3Njg2Y2I2NCJ9

* 🦉 Updates from OwlBot

See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md

Co-authored-by: Owl Bot <gcf-owl-bot[bot]@users.noreply.github.com>
Co-authored-by: Anthonios Partheniou <[email protected]>

Author: gcf-owl-bot[bot]

google/cloud/automl_v1/services/auto_ml/async_client.py

@@ -73,7 +73,7 @@ class AutoMlAsyncClient:
     Currently the only supported ``location_id`` is "us-central1".
 
     On any input that is documented to expect a string parameter in
-    snake_case or kebab-case, either of those cases is accepted.
+    snake_case or dash-case, either of those cases is accepted.
     """
 
     _client: AutoMlClient
@@ -1411,7 +1411,6 @@ async def deploy_model(
         r"""Deploys a model. If a model is already deployed, deploying it
         with the same parameters has no effect. Deploying with different
         parametrs (as e.g. changing
-
         [node_number][google.cloud.automl.v1p1beta.ImageObjectDetectionModelDeploymentMetadata.node_number])
         will reset the deployment state without pausing the model's
         availability.

google/cloud/automl_v1/services/auto_ml/client.py

@@ -107,7 +107,7 @@ class AutoMlClient(metaclass=AutoMlClientMeta):
     Currently the only supported ``location_id`` is "us-central1".
 
     On any input that is documented to expect a string parameter in
-    snake_case or kebab-case, either of those cases is accepted.
+    snake_case or dash-case, either of those cases is accepted.
     """
 
     @staticmethod
@@ -1593,7 +1593,6 @@ def deploy_model(
         r"""Deploys a model. If a model is already deployed, deploying it
         with the same parameters has no effect. Deploying with different
         parametrs (as e.g. changing
-
         [node_number][google.cloud.automl.v1p1beta.ImageObjectDetectionModelDeploymentMetadata.node_number])
         will reset the deployment state without pausing the model's
         availability.

google/cloud/automl_v1/services/auto_ml/transports/grpc.py

@@ -53,7 +53,7 @@ class AutoMlGrpcTransport(AutoMlTransport):
     Currently the only supported ``location_id`` is "us-central1".
 
     On any input that is documented to expect a string parameter in
-    snake_case or kebab-case, either of those cases is accepted.
+    snake_case or dash-case, either of those cases is accepted.
 
     This class defines the same methods as the primary client, so the
     primary client can load the underlying transport implementation
@@ -626,7 +626,6 @@ def deploy_model(
         Deploys a model. If a model is already deployed, deploying it
         with the same parameters has no effect. Deploying with different
         parametrs (as e.g. changing
-
         [node_number][google.cloud.automl.v1p1beta.ImageObjectDetectionModelDeploymentMetadata.node_number])
         will reset the deployment state without pausing the model's
         availability.

google/cloud/automl_v1/services/auto_ml/transports/grpc_asyncio.py

@@ -54,7 +54,7 @@ class AutoMlGrpcAsyncIOTransport(AutoMlTransport):
     Currently the only supported ``location_id`` is "us-central1".
 
     On any input that is documented to expect a string parameter in
-    snake_case or kebab-case, either of those cases is accepted.
+    snake_case or dash-case, either of those cases is accepted.
 
     This class defines the same methods as the primary client, so the
     primary client can load the underlying transport implementation
@@ -638,7 +638,6 @@ def deploy_model(
         Deploys a model. If a model is already deployed, deploying it
         with the same parameters has no effect. Deploying with different
         parametrs (as e.g. changing
-
         [node_number][google.cloud.automl.v1p1beta.ImageObjectDetectionModelDeploymentMetadata.node_number])
         will reset the deployment state without pausing the model's
         availability.

google/cloud/automl_v1/services/prediction_service/async_client.py

@@ -47,7 +47,7 @@ class PredictionServiceAsyncClient:
     """AutoML Prediction API.
 
     On any input that is documented to expect a string parameter in
-    snake_case or kebab-case, either of those cases is accepted.
+    snake_case or dash-case, either of those cases is accepted.
     """
 
     _client: PredictionServiceClient
@@ -270,7 +270,6 @@ async def predict(
                 AutoML Tables
 
                 ``feature_importance`` : (boolean) Whether
-
                 [feature_importance][google.cloud.automl.v1.TablesModelColumnInfo.feature_importance]
                 is populated in the returned list of
                 [TablesAnnotation][google.cloud.automl.v1.TablesAnnotation]

google/cloud/automl_v1/services/prediction_service/client.py

@@ -85,7 +85,7 @@ class PredictionServiceClient(metaclass=PredictionServiceClientMeta):
     """AutoML Prediction API.
 
     On any input that is documented to expect a string parameter in
-    snake_case or kebab-case, either of those cases is accepted.
+    snake_case or dash-case, either of those cases is accepted.
     """
 
     @staticmethod
@@ -464,7 +464,6 @@ def predict(
                 AutoML Tables
 
                 ``feature_importance`` : (boolean) Whether
-
                 [feature_importance][google.cloud.automl.v1.TablesModelColumnInfo.feature_importance]
                 is populated in the returned list of
                 [TablesAnnotation][google.cloud.automl.v1.TablesAnnotation]

google/cloud/automl_v1/services/prediction_service/transports/grpc.py

@@ -36,7 +36,7 @@ class PredictionServiceGrpcTransport(PredictionServiceTransport):
     AutoML Prediction API.
 
     On any input that is documented to expect a string parameter in
-    snake_case or kebab-case, either of those cases is accepted.
+    snake_case or dash-case, either of those cases is accepted.
 
     This class defines the same methods as the primary client, so the
     primary client can load the underlying transport implementation

google/cloud/automl_v1/services/prediction_service/transports/grpc_asyncio.py

@@ -37,7 +37,7 @@ class PredictionServiceGrpcAsyncIOTransport(PredictionServiceTransport):
     AutoML Prediction API.
 
     On any input that is documented to expect a string parameter in
-    snake_case or kebab-case, either of those cases is accepted.
+    snake_case or dash-case, either of those cases is accepted.
 
     This class defines the same methods as the primary client, so the
     primary client can load the underlying transport implementation

google/cloud/automl_v1/types/annotation_spec.py

@@ -27,7 +27,6 @@ class AnnotationSpec(proto.Message):
     Attributes:
         name (str):
             Output only. Resource name of the annotation spec. Form:
-
             'projects/{project_id}/locations/{location_id}/datasets/{dataset_id}/annotationSpecs/{annotation_spec_id}'
         display_name (str):
             Required. The name of the annotation spec to show in the

google/cloud/automl_v1/types/classification.py

@@ -169,15 +169,13 @@ class ConfusionMatrix(proto.Message):
             annotation_spec_id (Sequence[str]):
                 Output only. IDs of the annotation specs used in the
                 confusion matrix. For Tables CLASSIFICATION
-
                 [prediction_type][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type]
                 only list of [annotation_spec_display_name-s][] is
                 populated.
             display_name (Sequence[str]):
                 Output only. Display name of the annotation specs used in
                 the confusion matrix, as they were at the moment of the
                 evaluation. For Tables CLASSIFICATION
-
                 [prediction_type-s][google.cloud.automl.v1p1beta.TablesModelMetadata.prediction_type],
                 distinct values of the target column at the moment of the
                 model evaluation are populated here.

google/cloud/automl_v1/types/data_items.py

@@ -139,7 +139,6 @@ class Layout(proto.Message):
                 The position of the
                 [text_segment][google.cloud.automl.v1.Document.Layout.text_segment]
                 in the page. Contains exactly 4
-
                 [normalized_vertices][google.cloud.automl.v1p1beta.BoundingPoly.normalized_vertices]
                 and they are connected by edges in the order provided, which
                 will represent a rectangle parallel to the frame. The

google/cloud/automl_v1/types/image.py

@@ -61,13 +61,13 @@ class ImageClassificationModelMetadata(proto.Message):
             ``location`` as the new model to create, and have the same
             ``model_type``.
         train_budget_milli_node_hours (int):
-            The train budget of creating this model, expressed in milli
-            node hours i.e. 1,000 value in this field means 1 node hour.
-            The actual ``train_cost`` will be equal or less than this
-            value. If further model training ceases to provide any
-            improvements, it will stop without using full budget and the
-            stop_reason will be ``MODEL_CONVERGED``. Note, node_hour =
-            actual_hour \* number_of_nodes_invovled. For model type
+            Optional. The train budget of creating this model, expressed
+            in milli node hours i.e. 1,000 value in this field means 1
+            node hour. The actual ``train_cost`` will be equal or less
+            than this value. If further model training ceases to provide
+            any improvements, it will stop without using full budget and
+            the stop_reason will be ``MODEL_CONVERGED``. Note, node_hour
+            = actual_hour \* number_of_nodes_invovled. For model type
             ``cloud``\ (default), the train budget must be between 8,000
             and 800,000 milli node hours, inclusive. The default value
             is 192, 000 which represents one day in wall time. For model
@@ -199,13 +199,13 @@ class ImageObjectDetectionModelMetadata(proto.Message):
             Output only. The reason that this create model operation
             stopped, e.g. ``BUDGET_REACHED``, ``MODEL_CONVERGED``.
         train_budget_milli_node_hours (int):
-            The train budget of creating this model, expressed in milli
-            node hours i.e. 1,000 value in this field means 1 node hour.
-            The actual ``train_cost`` will be equal or less than this
-            value. If further model training ceases to provide any
-            improvements, it will stop without using full budget and the
-            stop_reason will be ``MODEL_CONVERGED``. Note, node_hour =
-            actual_hour \* number_of_nodes_invovled. For model type
+            Optional. The train budget of creating this model, expressed
+            in milli node hours i.e. 1,000 value in this field means 1
+            node hour. The actual ``train_cost`` will be equal or less
+            than this value. If further model training ceases to provide
+            any improvements, it will stop without using full budget and
+            the stop_reason will be ``MODEL_CONVERGED``. Note, node_hour
+            = actual_hour \* number_of_nodes_invovled. For model type
             ``cloud-high-accuracy-1``\ (default) and
             ``cloud-low-latency-1``, the train budget must be between
             20,000 and 900,000 milli node hours, inclusive. The default
@@ -242,7 +242,6 @@ class ImageClassificationModelDeploymentMetadata(proto.Message):
             Input only. The number of nodes to deploy the model on. A
             node is an abstraction of a machine resource, which can
             handle online prediction QPS as given in the model's
-
             [node_qps][google.cloud.automl.v1.ImageClassificationModelMetadata.node_qps].
             Must be between 1 and 100, inclusive on both ends.
     """
@@ -258,7 +257,6 @@ class ImageObjectDetectionModelDeploymentMetadata(proto.Message):
             Input only. The number of nodes to deploy the model on. A
             node is an abstraction of a machine resource, which can
             handle online prediction QPS as given in the model's
-
             [qps_per_node][google.cloud.automl.v1.ImageObjectDetectionModelMetadata.qps_per_node].
             Must be between 1 and 100, inclusive on both ends.
     """