19/libtextclassifier3/Model_/EmbeddingPruningMask.py

# automatically generated by the FlatBuffers compiler, do not modify

# namespace: Model_

import flatbuffers
from flatbuffers.compat import import_numpy
np = import_numpy()

class EmbeddingPruningMask(object):
    __slots__ = ['_tab']

    @classmethod
    def GetRootAsEmbeddingPruningMask(cls, buf, offset):
        n = flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
        x = EmbeddingPruningMask()
        x.Init(buf, n + offset)
        return x

    @classmethod
    def EmbeddingPruningMaskBufferHasIdentifier(cls, buf, offset, size_prefixed=False):
        return flatbuffers.util.BufferHasIdentifier(buf, offset, b"\x54\x43\x32\x20", size_prefixed=size_prefixed)

    # EmbeddingPruningMask
    def Init(self, buf, pos):
        self._tab = flatbuffers.table.Table(buf, pos)

    # EmbeddingPruningMask
    def Enabled(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
        if o != 0:
            return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
        return False

    # EmbeddingPruningMask
    def PruningMask(self, j):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
        if o != 0:
            a = self._tab.Vector(o)
            return self._tab.Get(flatbuffers.number_types.Uint64Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 8))
        return 0

    # EmbeddingPruningMask
    def PruningMaskAsNumpy(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
        if o != 0:
            return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint64Flags, o)
        return 0

    # EmbeddingPruningMask
    def PruningMaskLength(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
        if o != 0:
            return self._tab.VectorLen(o)
        return 0

    # EmbeddingPruningMask
    def PruningMaskIsNone(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
        return o == 0

    # EmbeddingPruningMask
    def FullNumBuckets(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
        if o != 0:
            return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
        return 0

    # EmbeddingPruningMask
    def PrunedRowBucketId(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
        if o != 0:
            return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
        return 0

def EmbeddingPruningMaskStart(builder): builder.StartObject(4)
def EmbeddingPruningMaskAddEnabled(builder, enabled): builder.PrependBoolSlot(0, enabled, 0)
def EmbeddingPruningMaskAddPruningMask(builder, pruningMask): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(pruningMask), 0)
def EmbeddingPruningMaskStartPruningMaskVector(builder, numElems): return builder.StartVector(8, numElems, 8)
def EmbeddingPruningMaskAddFullNumBuckets(builder, fullNumBuckets): builder.PrependInt32Slot(2, fullNumBuckets, 0)
def EmbeddingPruningMaskAddPrunedRowBucketId(builder, prunedRowBucketId): builder.PrependInt32Slot(3, prunedRowBucketId, 0)
def EmbeddingPruningMaskEnd(builder): return builder.EndObject()