Source code for snapchat.research.gbml.preprocessed_metadata_pb2
"""
@generated by mypy-protobuf. Do not edit manually!
isort:skip_file
"""
import builtins
import collections.abc
import google.protobuf.descriptor
import google.protobuf.internal.containers
import google.protobuf.message
import sys
import typing
if sys.version_info >= (3, 8):
import typing as typing_extensions
else:
import typing_extensions
[docs]
class PreprocessedMetadata(google.protobuf.message.Message):
[docs]
class MultiBitQuantizationState(google.protobuf.message.Message):
"""Lower clipping bound; dequantized value for linear code 0."""
"""Upper clipping bound; dequantized value for max linear code."""
"""Quantization level bit-width"""
def __init__(
self,
*,
clip_min: builtins.float = ...,
clip_max: builtins.float = ...,
bits: builtins.int = ...,
) -> None: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["bits", b"bits", "clip_max", b"clip_max", "clip_min", b"clip_min"]) -> None: ...
[docs]
class SingleBitQuantizationState(google.protobuf.message.Message):
"""Mean value for negative features, produced by packed bit/code 0."""
"""Mean value for positive features, produced by packed bit/code 1."""
def __init__(
self,
*,
neg_mean: builtins.float = ...,
pos_mean: builtins.float = ...,
) -> None: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["neg_mean", b"neg_mean", "pos_mean", b"pos_mean"]) -> None: ...
[docs]
class FeatureQuantizationMetadata(google.protobuf.message.Message):
"""Field in output TFRecords that stores packed uint8 features."""
@property
[docs]
def quantized_feature_indices(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.int]:
"""Original feature indices stored in packed_feature_key."""
@property
@property
def __init__(
self,
*,
packed_feature_key: builtins.str = ...,
quantized_feature_indices: collections.abc.Iterable[builtins.int] | None = ...,
multi_bit_state: global___PreprocessedMetadata.MultiBitQuantizationState | None = ...,
single_bit_state: global___PreprocessedMetadata.SingleBitQuantizationState | None = ...,
) -> None: ...
[docs]
def HasField(self, field_name: typing_extensions.Literal["multi_bit_state", b"multi_bit_state", "single_bit_state", b"single_bit_state", "state", b"state"]) -> builtins.bool: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["multi_bit_state", b"multi_bit_state", "packed_feature_key", b"packed_feature_key", "quantized_feature_indices", b"quantized_feature_indices", "single_bit_state", b"single_bit_state", "state", b"state"]) -> None: ...
[docs]
def WhichOneof(self, oneof_group: typing_extensions.Literal["state", b"state"]) -> typing_extensions.Literal["multi_bit_state", "single_bit_state"] | None: ...
[docs]
class NodeMetadataOutput(google.protobuf.message.Message):
"""Houses metadata about node TFTransform output from DataPreprocessor."""
"""The field in output TFRecords which references the node identifier."""
@property
[docs]
def feature_keys(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Fields in output TFRecords which reference features."""
@property
[docs]
def label_keys(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Fields in output TFRecords which reference labels."""
"""GCS prefix which can be used to glob the TFRecord dataset."""
"""GCS path to a schema which can be used to parse the TFRecord dataset."""
"""BQ path to a table which stores the original to enumerated node id association."""
"""BQ path to a table which stores the enumerated node id to node metadata association."""
"""Feature dimension after preprocessing"""
"""Contains categorical feature vocabularies"""
@property
[docs]
def quantized_feature_metadata(self) -> global___PreprocessedMetadata.FeatureQuantizationMetadata:
"""Optional quantized node feature metadata."""
def __init__(
self,
*,
node_id_key: builtins.str = ...,
feature_keys: collections.abc.Iterable[builtins.str] | None = ...,
label_keys: collections.abc.Iterable[builtins.str] | None = ...,
tfrecord_uri_prefix: builtins.str = ...,
schema_uri: builtins.str = ...,
enumerated_node_ids_bq_table: builtins.str = ...,
enumerated_node_data_bq_table: builtins.str = ...,
feature_dim: builtins.int | None = ...,
transform_fn_assets_uri: builtins.str = ...,
quantized_feature_metadata: global___PreprocessedMetadata.FeatureQuantizationMetadata | None = ...,
) -> None: ...
[docs]
def HasField(self, field_name: typing_extensions.Literal["_feature_dim", b"_feature_dim", "feature_dim", b"feature_dim", "quantized_feature_metadata", b"quantized_feature_metadata"]) -> builtins.bool: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["_feature_dim", b"_feature_dim", "enumerated_node_data_bq_table", b"enumerated_node_data_bq_table", "enumerated_node_ids_bq_table", b"enumerated_node_ids_bq_table", "feature_dim", b"feature_dim", "feature_keys", b"feature_keys", "label_keys", b"label_keys", "node_id_key", b"node_id_key", "quantized_feature_metadata", b"quantized_feature_metadata", "schema_uri", b"schema_uri", "tfrecord_uri_prefix", b"tfrecord_uri_prefix", "transform_fn_assets_uri", b"transform_fn_assets_uri"]) -> None: ...
[docs]
def WhichOneof(self, oneof_group: typing_extensions.Literal["_feature_dim", b"_feature_dim"]) -> typing_extensions.Literal["feature_dim"] | None: ...
[docs]
class EdgeMetadataInfo(google.protobuf.message.Message):
"""Houses metadata of edge features output from DataPreprocessor"""
@property
[docs]
def feature_keys(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Fields in output TFRecords which reference features."""
@property
[docs]
def label_keys(self) -> google.protobuf.internal.containers.RepeatedScalarFieldContainer[builtins.str]:
"""Fields in output TFRecords which reference labels."""
"""GCS prefix which can be used to glob the TFRecord dataset."""
"""GCS path to a schema which can be used to parse the TFRecord dataset."""
"""BQ path to a table which stores the enumerated node id to node metadata association."""
"""Feature dimension after preprocessing"""
"""Contains categorical feature vocabularies"""
@property
[docs]
def quantized_feature_metadata(self) -> global___PreprocessedMetadata.FeatureQuantizationMetadata:
"""Optional quantized main-edge feature metadata."""
def __init__(
self,
*,
feature_keys: collections.abc.Iterable[builtins.str] | None = ...,
label_keys: collections.abc.Iterable[builtins.str] | None = ...,
tfrecord_uri_prefix: builtins.str = ...,
schema_uri: builtins.str = ...,
enumerated_edge_data_bq_table: builtins.str = ...,
feature_dim: builtins.int | None = ...,
transform_fn_assets_uri: builtins.str = ...,
quantized_feature_metadata: global___PreprocessedMetadata.FeatureQuantizationMetadata | None = ...,
) -> None: ...
[docs]
def HasField(self, field_name: typing_extensions.Literal["_feature_dim", b"_feature_dim", "feature_dim", b"feature_dim", "quantized_feature_metadata", b"quantized_feature_metadata"]) -> builtins.bool: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["_feature_dim", b"_feature_dim", "enumerated_edge_data_bq_table", b"enumerated_edge_data_bq_table", "feature_dim", b"feature_dim", "feature_keys", b"feature_keys", "label_keys", b"label_keys", "quantized_feature_metadata", b"quantized_feature_metadata", "schema_uri", b"schema_uri", "tfrecord_uri_prefix", b"tfrecord_uri_prefix", "transform_fn_assets_uri", b"transform_fn_assets_uri"]) -> None: ...
[docs]
def WhichOneof(self, oneof_group: typing_extensions.Literal["_feature_dim", b"_feature_dim"]) -> typing_extensions.Literal["feature_dim"] | None: ...
[docs]
class EdgeMetadataOutput(google.protobuf.message.Message):
"""Houses metadata about edge TFTransform output from DataPreprocessor."""
"""The field in output TFRecords which references the source node identifier."""
"""The field in output TFRecords which references the destination node identifier."""
@property
[docs]
def main_edge_info(self) -> global___PreprocessedMetadata.EdgeMetadataInfo:
"""Detailed metadata for message-passing edges"""
@property
[docs]
def positive_edge_info(self) -> global___PreprocessedMetadata.EdgeMetadataInfo:
"""Detailed metadata for user-defined positive edges"""
@property
[docs]
def negative_edge_info(self) -> global___PreprocessedMetadata.EdgeMetadataInfo:
"""Detailed metadata for user-defined negative edges"""
def __init__(
self,
*,
src_node_id_key: builtins.str = ...,
dst_node_id_key: builtins.str = ...,
main_edge_info: global___PreprocessedMetadata.EdgeMetadataInfo | None = ...,
positive_edge_info: global___PreprocessedMetadata.EdgeMetadataInfo | None = ...,
negative_edge_info: global___PreprocessedMetadata.EdgeMetadataInfo | None = ...,
) -> None: ...
[docs]
def HasField(self, field_name: typing_extensions.Literal["_negative_edge_info", b"_negative_edge_info", "_positive_edge_info", b"_positive_edge_info", "main_edge_info", b"main_edge_info", "negative_edge_info", b"negative_edge_info", "positive_edge_info", b"positive_edge_info"]) -> builtins.bool: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["_negative_edge_info", b"_negative_edge_info", "_positive_edge_info", b"_positive_edge_info", "dst_node_id_key", b"dst_node_id_key", "main_edge_info", b"main_edge_info", "negative_edge_info", b"negative_edge_info", "positive_edge_info", b"positive_edge_info", "src_node_id_key", b"src_node_id_key"]) -> None: ...
@typing.overload
[docs]
def WhichOneof(self, oneof_group: typing_extensions.Literal["_negative_edge_info", b"_negative_edge_info"]) -> typing_extensions.Literal["negative_edge_info"] | None: ...
@typing.overload
def WhichOneof(self, oneof_group: typing_extensions.Literal["_positive_edge_info", b"_positive_edge_info"]) -> typing_extensions.Literal["positive_edge_info"] | None: ...
[docs]
class CondensedNodeTypeToPreprocessedMetadataEntry(google.protobuf.message.Message):
@property
def __init__(
self,
*,
key: builtins.int = ...,
value: global___PreprocessedMetadata.NodeMetadataOutput | None = ...,
) -> None: ...
[docs]
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
[docs]
class CondensedEdgeTypeToPreprocessedMetadataEntry(google.protobuf.message.Message):
@property
def __init__(
self,
*,
key: builtins.int = ...,
value: global___PreprocessedMetadata.EdgeMetadataOutput | None = ...,
) -> None: ...
[docs]
def HasField(self, field_name: typing_extensions.Literal["value", b"value"]) -> builtins.bool: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["key", b"key", "value", b"value"]) -> None: ...
@property
[docs]
def condensed_node_type_to_preprocessed_metadata(self) -> google.protobuf.internal.containers.MessageMap[builtins.int, global___PreprocessedMetadata.NodeMetadataOutput]:
"""Maps condensed node types to their respective post-TFTransform outputs."""
@property
[docs]
def condensed_edge_type_to_preprocessed_metadata(self) -> google.protobuf.internal.containers.MessageMap[builtins.int, global___PreprocessedMetadata.EdgeMetadataOutput]:
"""Maps condensed edge types to their respective post-TFTransform outputs."""
def __init__(
self,
*,
condensed_node_type_to_preprocessed_metadata: collections.abc.Mapping[builtins.int, global___PreprocessedMetadata.NodeMetadataOutput] | None = ...,
condensed_edge_type_to_preprocessed_metadata: collections.abc.Mapping[builtins.int, global___PreprocessedMetadata.EdgeMetadataOutput] | None = ...,
) -> None: ...
[docs]
def ClearField(self, field_name: typing_extensions.Literal["condensed_edge_type_to_preprocessed_metadata", b"condensed_edge_type_to_preprocessed_metadata", "condensed_node_type_to_preprocessed_metadata", b"condensed_node_type_to_preprocessed_metadata"]) -> None: ...