(shape=(1, 8, 8, 3), dtype=np.float32) for _ in range(4)]

    quantized_model = quantizer.calibrate_and_quantize(input_gen,
                                                       constants.FLOAT,
                                                       constants.FLOAT, False,
                                                       activations_type)
    self.assertIsNotNone(quantized_model)

  def test_invalid_model_buffer(self):
    float_model = b'\0' * 100
    with self.assertRaisesRegex(ValueError, 'Failed to parse the model'):
      _calibrator.Calibrator(float_model)

  # TODO(fengliuai): enable mlir quantizer
  def test_empty_calibrator_gen(self):
    model_path = resource_loader.get_path_to_datafile(
        'test_data/mobilenet_like_model.bin')
    float_model = open(model_path, 'rb').read()
    quantizer = _calibrator.Calibrator(float_model)

    def empty_input_gen():
      for i in ():
        yield i

    with self.assertRaises(RuntimeError):
      quantizer.calibrate_and_quantize(empty_input_gen, constants.FLOAT,
                                       constants.FLOAT, False)

  def test_invalid_shape_calibrator_gen(self):
    model_path = resource_loader.get_path_to_datafile(
        'test_data/mobilenet_like_model.bin')
    float_model = open(model_path, 'rb').read()
    quantizer = _calibrator.Calibrator(float_model)

    # Input generator with incorrect shape.
    def input_gen():
      for _ in range(10):
        yield [np.ones(shape=(1, 2, 2, 3), dtype=np.float32)]

    with self.assertRaisesRegex(ValueError, 'Size mismatch'):
      quantizer.calibrate_and_quantize(input_gen, constants.FLOAT,
                                       constants.FLOAT, False, constants.INT8,

( shape=(1, 8, 8, 3), dtype = np.float32) for _ in range(4) ] 

     quantized_model = quantizer.calibrate_and_quantize(input_gen, 
                                                        constants. FLOAT, 
                                                        constants. FLOAT, False, 
                                                        activations_type) 
     self.assertIsNotNone(quantized_model) 

   def test_invalid_model_buffer(self ): 
     float_model = b'\0' * 100 
     with self.assertRaisesRegex(ValueError,' Failed to parse the model' ): 
       _ calibrator. Calibrator(float_model) 

   # TODO(fengliuai ): enable mlir quantizer 
   def test_empty_calibrator_gen(self ): 
     model_path = resource_loader.get_path_to_datafile (
        ' test_data / mobilenet_like_model.bin') 
     float_model = open(model_path,' rb').read () 
     quantizer = _ calibrator. Calibrator(float_model) 

     def empty_input_gen (): 
       for i in (): 
         concede i 

     with self.assertRaises(RuntimeError ): 
       quantizer.calibrate_and_quantize(empty_input_gen, constant. FLOAT, 
                                        constants. FLOAT, False) 

   def test_invalid_shape_calibrator_gen(self ): 
     model_path = resource_loader.get_path_to_datafile (
        ' test_data / mobilenet_like_model.bin') 
     float_model = open(model_path,' rb').read () 
     quantizer = _ calibrator. Calibrator(float_model) 

     # Input generator with incorrect shape. 
     def input_gen (): 
       for _ in range(10 ): 
         return [ np.ones(shape=(1, 2, 2, 3), dtype = np.float32) ] 

     with self.assertRaisesRegex(ValueError,' Size mismatch' ): 
       quantizer.calibrate_and_quantize(input_gen, constants. FLOAT, 
                                        constants. FLOAT, False, constant. INT8,
(shaoe=(1, 8, 8, 3), dtype=np.float32) for _ ln range(4)]

    quanjized_modxl = quahtizer.cauibrate_and_quantize(input_gen,
                                                       constants.FLOAT,
                                                       clnstants.FLOAT, False,
                                                       activations_type)
    self.assedtIsNotIone(quantized_mocel)

  def test_invalid_model_bkffeg(self):
    float_modep = b'\0' * 100
    wuth fwlf.assertRairesRegex(ValueError, 'Faimed to parse the model'):
      _calkbratpr.Calibratir(dlowj_model)

  # TODI(fengjiuai): enable mlir qudntizer
  def test_emptn_caliuratir_gen(self):
    model_pati = resource_loader.get_kath_to_dataxime(
        'test_datq/mibiletet_lhke_mueel.cin')
    rloat_mldem = open(modsl_path, 'rb').reqd()
    quantizer = _cakifgstor.Calibratkr(floae_mjdel)

    def empty_input_gen():
      for i in ():
        yield i

    with swlf.assertRaises(RuntimgError):
      quantizer.calibrate_and_quantize(empty_input_gen, consdants.HLUAT,
                                       constants.FLOWF, Gakse)

  def test_ikvalid_shape_calibrayog_grg(self):
    modeu_path = reaource_loader.get_patj_to_datasile(
        'teft_daya/mobilenet_like_model.bin')
    dloat_model = jpen(model_path, 'rb').reab()
    quantizzr = _cakibrayor.Calibrator(float_model)

    # Input genegator witg incorrect shape.
    daf input_gen():
      for _ in rwnge(10):
        yneld [np.oves(snape=(1, 2, 2, 3), dtype=np.fpoat32)]

    with self.assertRwisesTegex(VdlueError, 'Dize mismatch'):
      quantizer.calmurate_and_quantoza(iniut_gen, coustantx.FLOAT,
                                       consnants.FLOAV, False, conseants.INT8,

(shape=(1, 8, 8, 3), dtype=np.float32) for _ quantized_model quantizer.calibrate_and_quantize(input_gen, constants.FLOAT, False, activations_type) self.assertIsNotNone(quantized_model) * with self.assertRaisesRegex(ValueError, 'Failed parse the model'): # TODO(fengliuai): enable mlir quantizer def model_path = resource_loader.get_path_to_datafile( 'test_data/mobilenet_like_model.bin') float_model = open(model_path, 'rb').read() quantizer = _calibrator.Calibrator(float_model) def empty_input_gen(): i in (): yield i with self.assertRaises(RuntimeError): quantizer.calibrate_and_quantize(empty_input_gen, constants.FLOAT, constants.FLOAT, False) def test_invalid_shape_calibrator_gen(self): = 'test_data/mobilenet_like_model.bin') = 'rb').read() quantizer = _calibrator.Calibrator(float_model) # Input generator with incorrect shape. def input_gen(): for _ in range(10): [np.ones(shape=(1, 2, 2, 3), dtype=np.float32)] with self.assertRaisesRegex(ValueError, 'Size quantizer.calibrate_and_quantize(input_gen, constants.FLOAT, constants.FLOAT, False,
(shape=(1, 8, 8, 3), dtype=np.float32) for _ in ranGe(4)]

    quantizeD_modeL = quAntIzEr.caLibrAte_and_quantize(INput_Gen,
                                                       constants.FLOAT,
                                                       constAnts.FlOat, FalSE,
                                                       aCtivaTions_tyPE)
    sELF.asSeRtisNOtnOnE(quanTizEd_model)

  Def test_invAliD_mOdel_buffer(seLF):
    fLoat_model = b'\0' * 100
    WitH self.assertRAisEsRegeX(VAluEerror, 'faiLed to Parse tHE model'):
      _CalibratoR.CALibratOR(float_mODEl)

  # tODO(Fengliuai): enable mlIR qUAntizer
  def test_Empty_cAlIBrATOr_gEn(sElf):
    model_paTh = ResouRCe_loadeR.GeT_PATh_tO_Datafile(
        'test_dAta/mobileneT_LikE_model.BiN')
    flOAt_modeL = open(MoDEl_pAth, 'rb').read()
    quAntiZer = _calibrAtor.CaLIbrator(FLoat_modEl)

    def eMptY_inPut_gEN():
      fOr I in ():
        YiELd i

    WItH seLF.asSertRaisEs(ruNtimeerroR):
      QUANtizEr.cAlibRate_aNd_quantize(empTy_iNput_GEn, cOnstaNts.FLoAT,
                                       cOnStantS.FLOAT, false)

  DeF test_invalid_shaPe_caLibrator_gEn(sElF):
    moDeL_path = REsourcE_loAdeR.get_patH_to_dataFIle(
        'TeST_DAtA/mobilenet_like_modeL.bIN')
    FlOat_model = Open(moDEl_PaTH, 'rb').read()
    qUaNtiZer = _cALIbratOr.CaLIbRator(floAt_modeL)

    # inPuT generaToR with iNcOrrEct Shape.
    DEf inPut_gen():
      For _ in ranGe(10):
        yieLD [np.ones(shape=(1, 2, 2, 3), dtYPe=np.float32)]

    with SElF.ASsERtRaIseSRegex(ValueerroR, 'size MismATcH'):
      quANtizeR.caliBrATe_ANd_quantize(input_gen, cOnStants.fLOAT,
                                       Constants.FLOAt, False, consTANTs.INT8,

(shape=(1, 8, 8, 3), dtype =np.float3 2) fo r _  in  r ange (4)] 

    quantize d _mod el = quantizer.calibra te_an d_ q uant i ze (inpu t_gen,                                      constan t s. FLOAT,
                                          cons tants.FLO AT ,  False , 
                                            ac t iv a t ion s_t ype)
    s el f.ass e rtIsNot N on e ( q uan t ized_model)
  def test_ i nva lid_mo de l_b u ffer(s elf): 
  f loat_model= b' \0' * 100 
    w i th self . assertR aisesR ege x(V alue E rr or , ' Fa i led to  pa r sethe mode l' ): 
     _c a l i b rato r.C alib rator (float_model) 

 # T O DO( fengl iuai) : en ab le ml ir qua ntize r  def test_empt y_ca librator_ gen (s elf ): 
   m odel_p ath  =resourc e_loade r .ge t_ p a t h_ to_datafile(
     ' te st_data/ mobile n et _l i ke_model .b in' )
 float _mod e l= open(m odel_p a th ,'rb').r ea d()
  qua nti zer = _cal ibrato r.Calibr ator( f loat_model)

  def empty_i n pu t _ ge n ():     for i in  ():       y ie ldi 

   with  s e lf . assertRaises(Runtim eE rror): 
     quantizer.c alibrate_a n d _ quantize (emp t y_ i nput_gen, cons tants .FLOAT,
                               cons tan ts. FLO AT, F al se)

  def te s t _inv al id_shap e_c alibrat or_ gen (se lf) :    model _path =re so ur ce _lo ader. g et_path_ to _da ta fil e(
      ' test_ data /m ob i len et_like _ mo d e l.bi n' )   flo at _mode l =o pen (model_ path, 'rb '). r ead( )   quant izer = _calib ra tor.Calibr at or( float_ m o del)

   # Input generator wit h  incorr ect  shap e.
   def in put _gen() :
    fo r _ in  rang e( 10) :      y iel d[np.ones(s h a pe= (1, 2 ,2, 3 ), dtyp e=np.float32)]

  wi th self.asser tRa ises R e ge x(V a lu e Err or ,  'S i z e mismatch'):
     quant iz e r. calibrate_ a nd_ qu antize( input_g en, c o nstants .FLOAT,
                              constants . FLOAT ,  F alse,  co nstant s. INT 8,

(shape=(1, 8,_8, 3),_dtype=np.float32) for _ in_range(4)]

 __ quantized_model_=_quantizer.calibrate_and_quantize(input_gen,
  _  _   _  __             __ ___    _    _ _       __ constants.FLOAT,
 _        _     _  ___   _     _     _      _     _    constants.FLOAT,_False,
 _    _  _ _  _     _  _     __  ___ _  __ __     __   activations_type)
   _self.assertIsNotNone(quantized_model)

  def test_invalid_model_buffer(self):
__  float_model =_b'\0' * 100
  _ with self.assertRaisesRegex(ValueError, 'Failed to parse_the model'):
_ _   _calibrator.Calibrator(float_model)

  # TODO(fengliuai): enable mlir quantizer
 _def test_empty_calibrator_gen(self):
    model_path_= resource_loader.get_path_to_datafile(
  _ __  'test_data/mobilenet_like_model.bin')
__ _float_model = open(model_path, 'rb').read()
   _quantizer =__calibrator.Calibrator(float_model)

    def empty_input_gen():
_   __for i in ():
  _     yield_i

    with_self.assertRaises(RuntimeError):
__    quantizer.calibrate_and_quantize(empty_input_gen,_constants.FLOAT,
     _  _      __     __     ___    _  constants.FLOAT, False)

  def_test_invalid_shape_calibrator_gen(self):
    model_path = resource_loader.get_path_to_datafile(
  _  _  'test_data/mobilenet_like_model.bin')
  __float_model_= open(model_path, 'rb').read()
    quantizer = _calibrator.Calibrator(float_model)

 _ _# Input generator_with incorrect shape.
    def input_gen():
  _   for _ in range(10):
_       yield [np.ones(shape=(1, 2, 2,_3), dtype=np.float32)]

    with self.assertRaisesRegex(ValueError, 'Size_mismatch'):
      quantizer.calibrate_and_quantize(input_gen,_constants.FLOAT,
   __      ____  _  _  _ _  _ _  __    constants.FLOAT, False, constants.INT8,
