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# 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()
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