# Path: google/cloud/datalabeling_v1beta1/types/annotation.py
# class AnnotationSource(proto.Enum):
# class AnnotationSentiment(proto.Enum):
# class AnnotationType(proto.Enum):
# class Annotation(proto.Message):
# class AnnotationValue(proto.Message):
# class ImageClassificationAnnotation(proto.Message):
# class Vertex(proto.Message):
# class NormalizedVertex(proto.Message):
# class BoundingPoly(proto.Message):
# class NormalizedBoundingPoly(proto.Message):
# class ImageBoundingPolyAnnotation(proto.Message):
# class Polyline(proto.Message):
# class NormalizedPolyline(proto.Message):
# class ImagePolylineAnnotation(proto.Message):
# class ImageSegmentationAnnotation(proto.Message):
# class TextClassificationAnnotation(proto.Message):
# class TextEntityExtractionAnnotation(proto.Message):
# class SequentialSegment(proto.Message):
# class TimeSegment(proto.Message):
# class VideoClassificationAnnotation(proto.Message):
# class ObjectTrackingFrame(proto.Message):
# class VideoObjectTrackingAnnotation(proto.Message):
# class VideoEventAnnotation(proto.Message):
# class AnnotationMetadata(proto.Message):
# class OperatorMetadata(proto.Message):
#     ANNOTATION_SOURCE_UNSPECIFIED = 0
#     OPERATOR = 3
#     ANNOTATION_SENTIMENT_UNSPECIFIED = 0
#     NEGATIVE = 1
#     POSITIVE = 2
#     ANNOTATION_TYPE_UNSPECIFIED = 0
#     IMAGE_CLASSIFICATION_ANNOTATION = 1
#     IMAGE_BOUNDING_BOX_ANNOTATION = 2
#     IMAGE_ORIENTED_BOUNDING_BOX_ANNOTATION = 13
#     IMAGE_BOUNDING_POLY_ANNOTATION = 10
#     IMAGE_POLYLINE_ANNOTATION = 11
#     IMAGE_SEGMENTATION_ANNOTATION = 12
#     VIDEO_SHOTS_CLASSIFICATION_ANNOTATION = 3
#     VIDEO_OBJECT_TRACKING_ANNOTATION = 4
#     VIDEO_OBJECT_DETECTION_ANNOTATION = 5
#     VIDEO_EVENT_ANNOTATION = 6
#     TEXT_CLASSIFICATION_ANNOTATION = 8
#     TEXT_ENTITY_EXTRACTION_ANNOTATION = 9
#     GENERAL_CLASSIFICATION_ANNOTATION = 14
# 
# Path: google/cloud/datalabeling_v1beta1/types/data_payloads.py
# class ImagePayload(proto.Message):
# class TextPayload(proto.Message):
# class VideoThumbnail(proto.Message):
# class VideoPayload(proto.Message):
# 
# Path: google/cloud/datalabeling_v1beta1/types/human_annotation_config.py
# class StringAggregationType(proto.Enum):
# class HumanAnnotationConfig(proto.Message):
# class ImageClassificationConfig(proto.Message):
# class BoundingPolyConfig(proto.Message):
# class PolylineConfig(proto.Message):
# class SegmentationConfig(proto.Message):
# class VideoClassificationConfig(proto.Message):
#     class AnnotationSpecSetConfig(proto.Message):
# class ObjectDetectionConfig(proto.Message):
# class ObjectTrackingConfig(proto.Message):
# class EventConfig(proto.Message):
# class TextClassificationConfig(proto.Message):
# class SentimentConfig(proto.Message):
# class TextEntityExtractionConfig(proto.Message):
#     STRING_AGGREGATION_TYPE_UNSPECIFIED = 0
#     MAJORITY_VOTE = 1
#     UNANIMOUS_VOTE = 2
#     NO_AGGREGATION = 3


import proto  # type: ignore
from google.cloud.datalabeling_v1beta1.types import annotation
from google.cloud.datalabeling_v1beta1.types import data_payloads
from google.cloud.datalabeling_v1beta1.types import (
    human_annotation_config as gcd_human_annotation_config,
)
from google.protobuf import timestamp_pb2  # type: ignore
            Output only. Source of the annotation.
        annotation_type (google.cloud.datalabeling_v1beta1.types.AnnotationType):
            Output only. Type of the annotation. It is
            specified when starting labeling task.
        example_count (int):
            Output only. Number of examples in the
            annotated dataset.
        completed_example_count (int):
            Output only. Number of examples that have
            annotation in the annotated dataset.
        label_stats (google.cloud.datalabeling_v1beta1.types.LabelStats):
            Output only. Per label statistics.
        create_time (google.protobuf.timestamp_pb2.Timestamp):
            Output only. Time the AnnotatedDataset was
            created.
        metadata (google.cloud.datalabeling_v1beta1.types.AnnotatedDatasetMetadata):
            Output only. Additional information about
            AnnotatedDataset.
        blocking_resources (Sequence[str]):
            Output only. The names of any related
            resources that are blocking changes to the
            annotated dataset.
    """

    name = proto.Field(proto.STRING, number=1,)
    display_name = proto.Field(proto.STRING, number=2,)
    description = proto.Field(proto.STRING, number=9,)
    annotation_source = proto.Field(
        proto.ENUM, number=3, enum=annotation.AnnotationSource,
    )

# Path: google/cloud/datalabeling_v1beta1/types/annotation.py
# class AnnotationSource(proto.Enum):
# class AnnotationSentiment(proto.Enum):
# class AnnotationType(proto.Enum):
# class Annotation(proto.Message):
# class AnnotationValue(proto.Message):
# class ImageClassificationAnnotation(proto.Message):
# class Vertex(proto.Message):
# class NormalizedVertex(proto.Message):
# class BoundingPoly(proto.Message):
# class NormalizedBoundingPoly(proto.Message):
# class ImageBoundingPolyAnnotation(proto.Message):
# class Polyline(proto.Message):
# class NormalizedPolyline(proto.Message):
# class ImagePolylineAnnotation(proto.Message):
# class ImageSegmentationAnnotation(proto.Message):
# class TextClassificationAnnotation(proto.Message):
# class TextEntityExtractionAnnotation(proto.Message):
# class SequentialSegment(proto.Message):
# class TimeSegment(proto.Message):
# class VideoClassificationAnnotation(proto.Message):
# class ObjectTrackingFrame(proto.Message):
# class VideoObjectTrackingAnnotation(proto.Message):
# class VideoEventAnnotation(proto.Message):
# class AnnotationMetadata(proto.Message):
# class OperatorMetadata(proto.Message):
#     ANNOTATION_SOURCE_UNSPECIFIED = 0
#     OPERATOR = 3
#     ANNOTATION_SENTIMENT_UNSPECIFIED = 0
#     NEGATIVE = 1
#     POSITIVE = 2
#     ANNOTATION_TYPE_UNSPECIFIED = 0
#     IMAGE_CLASSIFICATION_ANNOTATION = 1
#     IMAGE_BOUNDING_BOX_ANNOTATION = 2
#     IMAGE_ORIENTED_BOUNDING_BOX_ANNOTATION = 13
#     IMAGE_BOUNDING_POLY_ANNOTATION = 10
#     IMAGE_POLYLINE_ANNOTATION = 11
#     IMAGE_SEGMENTATION_ANNOTATION = 12
#     VIDEO_SHOTS_CLASSIFICATION_ANNOTATION = 3
#     VIDEO_OBJECT_TRACKING_ANNOTATION = 4
#     VIDEO_OBJECT_DETECTION_ANNOTATION = 5
#     VIDEO_EVENT_ANNOTATION = 6
#     TEXT_CLASSIFICATION_ANNOTATION = 8
#     TEXT_ENTITY_EXTRACTION_ANNOTATION = 9
#     GENERAL_CLASSIFICATION_ANNOTATION = 14
# 
# Path: google/cloud/datalabeling_v1beta1/types/data_payloads.py
# class ImagePayload(proto.Message):
# class TextPayload(proto.Message):
# class VideoThumbnail(proto.Message):
# class VideoPayload(proto.Message):
# 
# Path: google/cloud/datalabeling_v1beta1/types/human_annotation_config.py
# class StringAggregationType(proto.Enum):
# class HumanAnnotationConfig(proto.Message):
# class ImageClassificationConfig(proto.Message):
# class BoundingPolyConfig(proto.Message):
# class PolylineConfig(proto.Message):
# class SegmentationConfig(proto.Message):
# class VideoClassificationConfig(proto.Message):
#     class AnnotationSpecSetConfig(proto.Message):
# class ObjectDetectionConfig(proto.Message):
# class ObjectTrackingConfig(proto.Message):
# class EventConfig(proto.Message):
# class TextClassificationConfig(proto.Message):
# class SentimentConfig(proto.Message):
# class TextEntityExtractionConfig(proto.Message):
#     STRING_AGGREGATION_TYPE_UNSPECIFIED = 0
#     MAJORITY_VOTE = 1
#     UNANIMOUS_VOTE = 2
#     NO_AGGREGATION = 3


# Path: google/cloud/datalabeling_v1beta1/types/dataset.py
import proto  # type: ignore
from google.cloud.datalabeling_v1beta1.types import annotation
from google.cloud.datalabeling_v1beta1.types import data_payloads
from google.cloud.datalabeling_v1beta1.types import (
    human_annotation_config as gcd_human_annotation_config,
)
from google.protobuf import timestamp_pb2  # type: ignore

            Output only. Source of the annotation.
        annotation_type (google.cloud.datalabeling_v1beta1.types.AnnotationType):
            Output only. Type of the annotation. It is
            specified when starting labeling task.
        example_count (int):
            Output only. Number of examples in the
            annotated dataset.
        completed_example_count (int):
            Output only. Number of examples that have
            annotation in the annotated dataset.
        label_stats (google.cloud.datalabeling_v1beta1.types.LabelStats):
            Output only. Per label statistics.
        create_time (google.protobuf.timestamp_pb2.Timestamp):
            Output only. Time the AnnotatedDataset was
            created.
        metadata (google.cloud.datalabeling_v1beta1.types.AnnotatedDatasetMetadata):
            Output only. Additional information about
            AnnotatedDataset.
        blocking_resources (Sequence[str]):
            Output only. The names of any related
            resources that are blocking changes to the
            annotated dataset.
    """

    name = proto.Field(proto.STRING, number=1,)
    display_name = proto.Field(proto.STRING, number=2,)
    description = proto.Field(proto.STRING, number=9,)
    annotation_source = proto.Field(
        proto.ENUM, number=3, enum=annotation.AnnotationSource,
    )

    annotation_type = proto.Field(proto.ENUM, number=8, enum=annotation.AnnotationType,)