# automatically generated by the FlatBuffers compiler, do not modify

# namespace: libtextclassifier3

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

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

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

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

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

    # PodNerModel
    def TfliteModel(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

    # PodNerModel
    def TfliteModelAsNumpy(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

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

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

    # PodNerModel
    def WordPieceVocab(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.Uint8Flags, a + flatbuffers.number_types.UOffsetTFlags.py_type(j * 1))
        return 0

    # PodNerModel
    def WordPieceVocabAsNumpy(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(6))
        if o != 0:
            return self._tab.GetVectorAsNumpy(flatbuffers.number_types.Uint8Flags, o)
        return 0

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

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

    # PodNerModel
    def LowercaseInput(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(8))
        if o != 0:
            return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
        return True

    # PodNerModel
    def LogitsIndexInOutputTensor(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

    # PodNerModel
    def AppendFinalPeriod(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(12))
        if o != 0:
            return bool(self._tab.Get(flatbuffers.number_types.BoolFlags, o + self._tab.Pos))
        return False

    # PodNerModel
    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

    # PodNerModel
    def MaxNumWordpieces(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(16))
        if o != 0:
            return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
        return 128

    # PodNerModel
    def SlidingWindowNumWordpiecesOverlap(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(18))
        if o != 0:
            return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
        return 20

    # PodNerModel
    def Labels(self, j):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(22))
        if o != 0:
            x = self._tab.Vector(o)
            x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4
            x = self._tab.Indirect(x)
            from libtextclassifier3.PodNerModel_.Label import Label
            obj = Label()
            obj.Init(self._tab.Bytes, x)
            return obj
        return None

    # PodNerModel
    def LabelsLength(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(22))
        if o != 0:
            return self._tab.VectorLen(o)
        return 0

    # PodNerModel
    def LabelsIsNone(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(22))
        return o == 0

    # PodNerModel
    def MaxRatioUnknownWordpieces(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(24))
        if o != 0:
            return self._tab.Get(flatbuffers.number_types.Float32Flags, o + self._tab.Pos)
        return 0.1

    # PodNerModel
    def Collections(self, j):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(26))
        if o != 0:
            x = self._tab.Vector(o)
            x += flatbuffers.number_types.UOffsetTFlags.py_type(j) * 4
            x = self._tab.Indirect(x)
            from libtextclassifier3.PodNerModel_.Collection import Collection
            obj = Collection()
            obj.Init(self._tab.Bytes, x)
            return obj
        return None

    # PodNerModel
    def CollectionsLength(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(26))
        if o != 0:
            return self._tab.VectorLen(o)
        return 0

    # PodNerModel
    def CollectionsIsNone(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(26))
        return o == 0

    # PodNerModel
    def MinNumberOfTokens(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(28))
        if o != 0:
            return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
        return 1

    # PodNerModel
    def MinNumberOfWordpieces(self):
        o = flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(30))
        if o != 0:
            return self._tab.Get(flatbuffers.number_types.Int32Flags, o + self._tab.Pos)
        return 1

def PodNerModelStart(builder): builder.StartObject(14)
def PodNerModelAddTfliteModel(builder, tfliteModel): builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(tfliteModel), 0)
def PodNerModelStartTfliteModelVector(builder, numElems): return builder.StartVector(1, numElems, 1)
def PodNerModelAddWordPieceVocab(builder, wordPieceVocab): builder.PrependUOffsetTRelativeSlot(1, flatbuffers.number_types.UOffsetTFlags.py_type(wordPieceVocab), 0)
def PodNerModelStartWordPieceVocabVector(builder, numElems): return builder.StartVector(1, numElems, 1)
def PodNerModelAddLowercaseInput(builder, lowercaseInput): builder.PrependBoolSlot(2, lowercaseInput, 1)
def PodNerModelAddLogitsIndexInOutputTensor(builder, logitsIndexInOutputTensor): builder.PrependInt32Slot(3, logitsIndexInOutputTensor, 0)
def PodNerModelAddAppendFinalPeriod(builder, appendFinalPeriod): builder.PrependBoolSlot(4, appendFinalPeriod, 0)
def PodNerModelAddPriorityScore(builder, priorityScore): builder.PrependFloat32Slot(5, priorityScore, 0.0)
def PodNerModelAddMaxNumWordpieces(builder, maxNumWordpieces): builder.PrependInt32Slot(6, maxNumWordpieces, 128)
def PodNerModelAddSlidingWindowNumWordpiecesOverlap(builder, slidingWindowNumWordpiecesOverlap): builder.PrependInt32Slot(7, slidingWindowNumWordpiecesOverlap, 20)
def PodNerModelAddLabels(builder, labels): builder.PrependUOffsetTRelativeSlot(9, flatbuffers.number_types.UOffsetTFlags.py_type(labels), 0)
def PodNerModelStartLabelsVector(builder, numElems): return builder.StartVector(4, numElems, 4)
def PodNerModelAddMaxRatioUnknownWordpieces(builder, maxRatioUnknownWordpieces): builder.PrependFloat32Slot(10, maxRatioUnknownWordpieces, 0.1)
def PodNerModelAddCollections(builder, collections): builder.PrependUOffsetTRelativeSlot(11, flatbuffers.number_types.UOffsetTFlags.py_type(collections), 0)
def PodNerModelStartCollectionsVector(builder, numElems): return builder.StartVector(4, numElems, 4)
def PodNerModelAddMinNumberOfTokens(builder, minNumberOfTokens): builder.PrependInt32Slot(12, minNumberOfTokens, 1)
def PodNerModelAddMinNumberOfWordpieces(builder, minNumberOfWordpieces): builder.PrependInt32Slot(13, minNumberOfWordpieces, 1)
def PodNerModelEnd(builder): return builder.EndObject()