# Path: google/cloud/datalabeling_v1beta1/types/annotation_spec_set.py
# class AnnotationSpecSet(proto.Message):
# class AnnotationSpec(proto.Message):
# 
# Path: google/cloud/datalabeling_v1beta1/types/dataset.py
# class DataType(proto.Enum):
# class Dataset(proto.Message):
# class InputConfig(proto.Message):
# class TextMetadata(proto.Message):
# class ClassificationMetadata(proto.Message):
# class GcsSource(proto.Message):
# class BigQuerySource(proto.Message):
# class OutputConfig(proto.Message):
# class GcsDestination(proto.Message):
# class GcsFolderDestination(proto.Message):
# class DataItem(proto.Message):
# class AnnotatedDataset(proto.Message):
# class LabelStats(proto.Message):
# class AnnotatedDatasetMetadata(proto.Message):
# class Example(proto.Message):
#     DATA_TYPE_UNSPECIFIED = 0
#     IMAGE = 1
#     VIDEO = 2
#     TEXT = 4
#     GENERAL_DATA = 6
# 
# Path: google/cloud/datalabeling_v1beta1/types/evaluation.py
# class Evaluation(proto.Message):
# class EvaluationConfig(proto.Message):
# class BoundingBoxEvaluationOptions(proto.Message):
# class EvaluationMetrics(proto.Message):
# class ClassificationMetrics(proto.Message):
# class ObjectDetectionMetrics(proto.Message):
# class PrCurve(proto.Message):
#     class ConfidenceMetricsEntry(proto.Message):
# class ConfusionMatrix(proto.Message):
#     class ConfusionMatrixEntry(proto.Message):
#     class Row(proto.Message):
# 
# Path: google/cloud/datalabeling_v1beta1/types/evaluation_job.py
# class EvaluationJob(proto.Message):
#     class State(proto.Enum):
# class EvaluationJobConfig(proto.Message):
# class EvaluationJobAlertConfig(proto.Message):
# class Attempt(proto.Message):
#         STATE_UNSPECIFIED = 0
#         SCHEDULED = 1
#         RUNNING = 2
#         PAUSED = 3
#         STOPPED = 4
# 
# 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/instruction.py
# class Instruction(proto.Message):
# class CsvInstruction(proto.Message):
# class PdfInstruction(proto.Message):


import proto  # type: ignore
from google.cloud.datalabeling_v1beta1.types import (
    annotation_spec_set as gcd_annotation_spec_set,
)
from google.cloud.datalabeling_v1beta1.types import dataset as gcd_dataset
from google.cloud.datalabeling_v1beta1.types import evaluation
from google.cloud.datalabeling_v1beta1.types import evaluation_job as gcd_evaluation_job
from google.cloud.datalabeling_v1beta1.types import human_annotation_config
from google.cloud.datalabeling_v1beta1.types import instruction as gcd_instruction
from google.protobuf import field_mask_pb2  # type: ignore
            value is 100.
        page_token (str):
            Optional. A token identifying a page of results for the
            server to return. Typically obtained by
            [ListDataItemsResponse.next_page_token][google.cloud.datalabeling.v1beta1.ListDataItemsResponse.next_page_token]
            of the previous [DataLabelingService.ListDataItems] call.
            Return first page if empty.
    """

    parent = proto.Field(proto.STRING, number=1,)
    filter = proto.Field(proto.STRING, number=2,)
    page_size = proto.Field(proto.INT32, number=3,)
    page_token = proto.Field(proto.STRING, number=4,)


class ListDataItemsResponse(proto.Message):
    r"""Results of listing data items in a dataset.

    Attributes:
        data_items (Sequence[google.cloud.datalabeling_v1beta1.types.DataItem]):
            The list of data items to return.
        next_page_token (str):
            A token to retrieve next page of results.
    """

    @property
    def raw_page(self):
        return self

    data_items = proto.RepeatedField(

# Path: google/cloud/datalabeling_v1beta1/types/annotation_spec_set.py
# class AnnotationSpecSet(proto.Message):
# class AnnotationSpec(proto.Message):
# 
# Path: google/cloud/datalabeling_v1beta1/types/dataset.py
# class DataType(proto.Enum):
# class Dataset(proto.Message):
# class InputConfig(proto.Message):
# class TextMetadata(proto.Message):
# class ClassificationMetadata(proto.Message):
# class GcsSource(proto.Message):
# class BigQuerySource(proto.Message):
# class OutputConfig(proto.Message):
# class GcsDestination(proto.Message):
# class GcsFolderDestination(proto.Message):
# class DataItem(proto.Message):
# class AnnotatedDataset(proto.Message):
# class LabelStats(proto.Message):
# class AnnotatedDatasetMetadata(proto.Message):
# class Example(proto.Message):
#     DATA_TYPE_UNSPECIFIED = 0
#     IMAGE = 1
#     VIDEO = 2
#     TEXT = 4
#     GENERAL_DATA = 6
# 
# Path: google/cloud/datalabeling_v1beta1/types/evaluation.py
# class Evaluation(proto.Message):
# class EvaluationConfig(proto.Message):
# class BoundingBoxEvaluationOptions(proto.Message):
# class EvaluationMetrics(proto.Message):
# class ClassificationMetrics(proto.Message):
# class ObjectDetectionMetrics(proto.Message):
# class PrCurve(proto.Message):
#     class ConfidenceMetricsEntry(proto.Message):
# class ConfusionMatrix(proto.Message):
#     class ConfusionMatrixEntry(proto.Message):
#     class Row(proto.Message):
# 
# Path: google/cloud/datalabeling_v1beta1/types/evaluation_job.py
# class EvaluationJob(proto.Message):
#     class State(proto.Enum):
# class EvaluationJobConfig(proto.Message):
# class EvaluationJobAlertConfig(proto.Message):
# class Attempt(proto.Message):
#         STATE_UNSPECIFIED = 0
#         SCHEDULED = 1
#         RUNNING = 2
#         PAUSED = 3
#         STOPPED = 4
# 
# 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/instruction.py
# class Instruction(proto.Message):
# class CsvInstruction(proto.Message):
# class PdfInstruction(proto.Message):


# Path: google/cloud/datalabeling_v1beta1/types/data_labeling_service.py
import proto  # type: ignore
from google.cloud.datalabeling_v1beta1.types import (
    annotation_spec_set as gcd_annotation_spec_set,
)
from google.cloud.datalabeling_v1beta1.types import dataset as gcd_dataset
from google.cloud.datalabeling_v1beta1.types import evaluation
from google.cloud.datalabeling_v1beta1.types import evaluation_job as gcd_evaluation_job
from google.cloud.datalabeling_v1beta1.types import human_annotation_config
from google.cloud.datalabeling_v1beta1.types import instruction as gcd_instruction
from google.protobuf import field_mask_pb2  # type: ignore

            value is 100.
        page_token (str):
            Optional. A token identifying a page of results for the
            server to return. Typically obtained by
            [ListDataItemsResponse.next_page_token][google.cloud.datalabeling.v1beta1.ListDataItemsResponse.next_page_token]
            of the previous [DataLabelingService.ListDataItems] call.
            Return first page if empty.
    """

    parent = proto.Field(proto.STRING, number=1,)
    filter = proto.Field(proto.STRING, number=2,)
    page_size = proto.Field(proto.INT32, number=3,)
    page_token = proto.Field(proto.STRING, number=4,)


class ListDataItemsResponse(proto.Message):
    r"""Results of listing data items in a dataset.

    Attributes:
        data_items (Sequence[google.cloud.datalabeling_v1beta1.types.DataItem]):
            The list of data items to return.
        next_page_token (str):
            A token to retrieve next page of results.
    """

    @property
    def raw_page(self):
        return self

    data_items = proto.RepeatedField(

        proto.MESSAGE, number=1, message=gcd_dataset.DataItem,