19/libtextclassifier3/VocabModel.py

# automatically generated by the FlatBuffers compiler, do not modify

# namespace: libtextclassifier3

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

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

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

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

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

    # VocabModel
    def VocabTrie(self, j):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
        if o != 0:
            a = self._tab.Vector(o)
            return self._tab.Get(flatbuffers.number_types.Uint8Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1))
        return 0

    # VocabModel
    def VocabTrieAsNumpy(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
        if o != 0:
            return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint8Flags, o)
        return 0

    # VocabModel
    def VocabTrieLength(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
        if o != 0:
            return self._tab.VectorLen(o)
        return 0

    # VocabModel
    def VocabTrieIsNone(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4))
        return o == 0

    # VocabModel
    def BeginnerLevel(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
        if o != 0:
            x = self._tab.Indirect(o + self._tab.Pos)
            from libtextclassifier3.BitVectorData import BitVectorData
            obj = BitVectorData()
            obj.Init(self._tab.Bytes, x)
            return obj
        return None

    # VocabModel
    def DoNotTriggerInUpperCase(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
        if o != 0:
            x = self._tab.Indirect(o + self._tab.Pos)
            from libtextclassifier3.BitVectorData import BitVectorData
            obj = BitVectorData()
            obj.Init(self._tab.Bytes, x)
            return obj
        return None

    # VocabModel
    def TriggeringLocales(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(10))
        if o != 0:
            return self._tab.String(o + self._tab.Pos)
        return None

    # VocabModel
    def TargetClassificationScore(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
        if o != 0:
            return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
        return 1.0

    # VocabModel
    def PriorityScore(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(14))
        if o != 0:
            return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
        return 0.0

def VocabModelStart(builder): builder.StartObject(6)
def VocabModelAddVocabTrie(builder, vocabTrie): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(vocabTrie), 0)
def VocabModelStartVocabTrieVector(builder, numElems): return builder.StartVector(1, numElems, 1)
def VocabModelAddBeginnerLevel(builder, beginnerLevel): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(beginnerLevel), 0)
def VocabModelAddDoNotTriggerInUpperCase(builder, doNotTriggerInUpperCase): builder.PrependUOffsetTRelativeSlot(2, flatbuffers.number_types.UOffsetTFlags.py_type(doNotTriggerInUpperCase), 0)
def VocabModelAddTriggeringLocales(builder, triggeringLocales): builder.PrependUOffsetTRelativeSlot(3, flatbuffers.number_types.UOffsetTFlags.py_type(triggeringLocales), 0)
def VocabModelAddTargetClassificationScore(builder, targetClassificationScore): builder.PrependFloat32Slot(4, targetClassificationScore, 1.0)
def VocabModelAddPriorityScore(builder, priorityScore): builder.PrependFloat32Slot(5, priorityScore, 0.0)
def VocabModelEnd(builder): return builder.EndObject()