""" @generated by mypy-protobuf. Do not edit manually! isort:skip_file """ import builtins import collections.abc import sys import typing import google.protobuf.descriptor import google.protobuf.internal.containers import google.protobuf.internal.enum_type_wrapper import google.protobuf.message import tensorflow.core.framework.tensor_pb2 import tensorflow.tsl.protobuf.histogram_pb2 if sys.version_info >= (3, 10): import typing as typing_extensions else: import typing_extensions from tensorflow.tsl.protobuf.histogram_pb2 import HistogramProto as HistogramProto DESCRIPTOR: google.protobuf.descriptor.FileDescriptor class _DataClass: ValueType = typing.NewType("ValueType", builtins.int) V: typing_extensions.TypeAlias = ValueType class _DataClassEnumTypeWrapper(google.protobuf.internal.enum_type_wrapper._EnumTypeWrapper[_DataClass.ValueType], builtins.type): DESCRIPTOR: google.protobuf.descriptor.EnumDescriptor DATA_CLASS_UNKNOWN: _DataClass.ValueType # 0 """Unknown data class, used (implicitly) for legacy data. Will not be processed by data ingestion pipelines. """ DATA_CLASS_SCALAR: _DataClass.ValueType # 1 """Scalar time series. Each `Value` for the corresponding tag must have `tensor` set to a rank-0 tensor of type `DT_FLOAT` (float32). """ DATA_CLASS_TENSOR: _DataClass.ValueType # 2 """Tensor time series. Each `Value` for the corresponding tag must have `tensor` set. The tensor value is arbitrary, but should be small to accommodate direct storage in database backends: an upper bound of a few kilobytes is a reasonable rule of thumb. """ DATA_CLASS_BLOB_SEQUENCE: _DataClass.ValueType # 3 """Blob sequence time series. Each `Value` for the corresponding tag must have `tensor` set to a rank-1 tensor of bytestring dtype. """ class DataClass(_DataClass, metaclass=_DataClassEnumTypeWrapper): ... DATA_CLASS_UNKNOWN: DataClass.ValueType # 0 """Unknown data class, used (implicitly) for legacy data. Will not be processed by data ingestion pipelines. """ DATA_CLASS_SCALAR: DataClass.ValueType # 1 """Scalar time series. Each `Value` for the corresponding tag must have `tensor` set to a rank-0 tensor of type `DT_FLOAT` (float32). """ DATA_CLASS_TENSOR: DataClass.ValueType # 2 """Tensor time series. Each `Value` for the corresponding tag must have `tensor` set. The tensor value is arbitrary, but should be small to accommodate direct storage in database backends: an upper bound of a few kilobytes is a reasonable rule of thumb. """ DATA_CLASS_BLOB_SEQUENCE: DataClass.ValueType # 3 """Blob sequence time series. Each `Value` for the corresponding tag must have `tensor` set to a rank-1 tensor of bytestring dtype. """ global___DataClass = DataClass @typing_extensions.final class SummaryDescription(google.protobuf.message.Message): """Metadata associated with a series of Summary data""" DESCRIPTOR: google.protobuf.descriptor.Descriptor TYPE_HINT_FIELD_NUMBER: builtins.int type_hint: builtins.str """Hint on how plugins should process the data in this series. Supported values include "scalar", "histogram", "image", "audio" """ def __init__( self, *, type_hint: builtins.str | None = ..., ) -> None: ... def ClearField(self, field_name: typing_extensions.Literal["type_hint", b"type_hint"]) -> None: ... global___SummaryDescription = SummaryDescription @typing_extensions.final class SummaryMetadata(google.protobuf.message.Message): """A SummaryMetadata encapsulates information on which plugins are able to make use of a certain summary value. """ DESCRIPTOR: google.protobuf.descriptor.Descriptor @typing_extensions.final class PluginData(google.protobuf.message.Message): DESCRIPTOR: google.protobuf.descriptor.Descriptor PLUGIN_NAME_FIELD_NUMBER: builtins.int CONTENT_FIELD_NUMBER: builtins.int plugin_name: builtins.str """The name of the plugin this data pertains to.""" content: builtins.bytes """The content to store for the plugin. The best practice is for this to be a binary serialized protocol buffer. """ def __init__( self, *, plugin_name: builtins.str | None = ..., content: builtins.bytes | None = ..., ) -> None: ... def ClearField(self, field_name: typing_extensions.Literal["content", b"content", "plugin_name", b"plugin_name"]) -> None: ... PLUGIN_DATA_FIELD_NUMBER: builtins.int DISPLAY_NAME_FIELD_NUMBER: builtins.int SUMMARY_DESCRIPTION_FIELD_NUMBER: builtins.int DATA_CLASS_FIELD_NUMBER: builtins.int @property def plugin_data(self) -> global___SummaryMetadata.PluginData: """Data that associates a summary with a certain plugin.""" display_name: builtins.str """Display name for viewing in TensorBoard.""" summary_description: builtins.str """Longform readable description of the summary sequence. Markdown supported.""" data_class: global___DataClass.ValueType """Class of data stored in this time series. Required for compatibility with TensorBoard's generic data facilities (`DataProvider`, et al.). This value imposes constraints on the dtype and shape of the corresponding tensor values. See `DataClass` docs for details. """ def __init__( self, *, plugin_data: global___SummaryMetadata.PluginData | None = ..., display_name: builtins.str | None = ..., summary_description: builtins.str | None = ..., data_class: global___DataClass.ValueType | None = ..., ) -> None: ... def HasField(self, field_name: typing_extensions.Literal["plugin_data", b"plugin_data"]) -> builtins.bool: ... def ClearField(self, field_name: typing_extensions.Literal["data_class", b"data_class", "display_name", b"display_name", "plugin_data", b"plugin_data", "summary_description", b"summary_description"]) -> None: ... global___SummaryMetadata = SummaryMetadata @typing_extensions.final class Summary(google.protobuf.message.Message): """A Summary is a set of named values to be displayed by the visualizer. Summaries are produced regularly during training, as controlled by the "summary_interval_secs" attribute of the training operation. Summaries are also produced at the end of an evaluation. """ DESCRIPTOR: google.protobuf.descriptor.Descriptor @typing_extensions.final class Image(google.protobuf.message.Message): DESCRIPTOR: google.protobuf.descriptor.Descriptor HEIGHT_FIELD_NUMBER: builtins.int WIDTH_FIELD_NUMBER: builtins.int COLORSPACE_FIELD_NUMBER: builtins.int ENCODED_IMAGE_STRING_FIELD_NUMBER: builtins.int height: builtins.int """Dimensions of the image.""" width: builtins.int colorspace: builtins.int """Valid colorspace values are 1 - grayscale 2 - grayscale + alpha 3 - RGB 4 - RGBA 5 - DIGITAL_YUV 6 - BGRA """ encoded_image_string: builtins.bytes """Image data in encoded format. All image formats supported by image_codec::CoderUtil can be stored here. """ def __init__( self, *, height: builtins.int | None = ..., width: builtins.int | None = ..., colorspace: builtins.int | None = ..., encoded_image_string: builtins.bytes | None = ..., ) -> None: ... def ClearField(self, field_name: typing_extensions.Literal["colorspace", b"colorspace", "encoded_image_string", b"encoded_image_string", "height", b"height", "width", b"width"]) -> None: ... @typing_extensions.final class Audio(google.protobuf.message.Message): DESCRIPTOR: google.protobuf.descriptor.Descriptor SAMPLE_RATE_FIELD_NUMBER: builtins.int NUM_CHANNELS_FIELD_NUMBER: builtins.int LENGTH_FRAMES_FIELD_NUMBER: builtins.int ENCODED_AUDIO_STRING_FIELD_NUMBER: builtins.int CONTENT_TYPE_FIELD_NUMBER: builtins.int sample_rate: builtins.float """Sample rate of the audio in Hz.""" num_channels: builtins.int """Number of channels of audio.""" length_frames: builtins.int """Length of the audio in frames (samples per channel).""" encoded_audio_string: builtins.bytes """Encoded audio data and its associated RFC 2045 content type (e.g. "audio/wav"). """ content_type: builtins.str def __init__( self, *, sample_rate: builtins.float | None = ..., num_channels: builtins.int | None = ..., length_frames: builtins.int | None = ..., encoded_audio_string: builtins.bytes | None = ..., content_type: builtins.str | None = ..., ) -> None: ... def ClearField(self, field_name: typing_extensions.Literal["content_type", b"content_type", "encoded_audio_string", b"encoded_audio_string", "length_frames", b"length_frames", "num_channels", b"num_channels", "sample_rate", b"sample_rate"]) -> None: ... @typing_extensions.final class Value(google.protobuf.message.Message): DESCRIPTOR: google.protobuf.descriptor.Descriptor NODE_NAME_FIELD_NUMBER: builtins.int TAG_FIELD_NUMBER: builtins.int METADATA_FIELD_NUMBER: builtins.int SIMPLE_VALUE_FIELD_NUMBER: builtins.int OBSOLETE_OLD_STYLE_HISTOGRAM_FIELD_NUMBER: builtins.int IMAGE_FIELD_NUMBER: builtins.int HISTO_FIELD_NUMBER: builtins.int AUDIO_FIELD_NUMBER: builtins.int TENSOR_FIELD_NUMBER: builtins.int node_name: builtins.str """This field is deprecated and will not be set.""" tag: builtins.str """Tag name for the data. Used by TensorBoard plugins to organize data. Tags are often organized by scope (which contains slashes to convey hierarchy). For example: foo/bar/0 """ @property def metadata(self) -> global___SummaryMetadata: """Contains metadata on the summary value such as which plugins may use it. Take note that many summary values may lack a metadata field. This is because the FileWriter only keeps a metadata object on the first summary value with a certain tag for each tag. TensorBoard then remembers which tags are associated with which plugins. This saves space. """ simple_value: builtins.float obsolete_old_style_histogram: builtins.bytes @property def image(self) -> global___Summary.Image: ... @property def histo(self) -> tensorflow.tsl.protobuf.histogram_pb2.HistogramProto: ... @property def audio(self) -> global___Summary.Audio: ... @property def tensor(self) -> tensorflow.core.framework.tensor_pb2.TensorProto: ... def __init__( self, *, node_name: builtins.str | None = ..., tag: builtins.str | None = ..., metadata: global___SummaryMetadata | None = ..., simple_value: builtins.float | None = ..., obsolete_old_style_histogram: builtins.bytes | None = ..., image: global___Summary.Image | None = ..., histo: tensorflow.tsl.protobuf.histogram_pb2.HistogramProto | None = ..., audio: global___Summary.Audio | None = ..., tensor: tensorflow.core.framework.tensor_pb2.TensorProto | None = ..., ) -> None: ... def HasField(self, field_name: typing_extensions.Literal["audio", b"audio", "histo", b"histo", "image", b"image", "metadata", b"metadata", "obsolete_old_style_histogram", b"obsolete_old_style_histogram", "simple_value", b"simple_value", "tensor", b"tensor", "value", b"value"]) -> builtins.bool: ... def ClearField(self, field_name: typing_extensions.Literal["audio", b"audio", "histo", b"histo", "image", b"image", "metadata", b"metadata", "node_name", b"node_name", "obsolete_old_style_histogram", b"obsolete_old_style_histogram", "simple_value", b"simple_value", "tag", b"tag", "tensor", b"tensor", "value", b"value"]) -> None: ... def WhichOneof(self, oneof_group: typing_extensions.Literal["value", b"value"]) -> typing_extensions.Literal["simple_value", "obsolete_old_style_histogram", "image", "histo", "audio", "tensor"] | None: ... VALUE_FIELD_NUMBER: builtins.int @property def value(self) -> google.protobuf.internal.containers.RepeatedCompositeFieldContainer[global___Summary.Value]: """Set of values for the summary.""" def __init__( self, *, value: collections.abc.Iterable[global___Summary.Value] | None = ..., ) -> None: ... def ClearField(self, field_name: typing_extensions.Literal["value", b"value"]) -> None: ... global___Summary = Summary