





"""simple docstring"""
import unittest

import numpy as np

from transformers import (
    GPTSanJapaneseConfig,
    GPTSanJapaneseForConditionalGeneration,
    GPTSanJapaneseModel,
    GPTSanJapaneseTokenizer,
    is_torch_available,
)
from transformers.generation import GenerationConfig
from transformers.testing_utils import require_torch, slow, tooslow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin







class 	a				:



 def __init__(			self							,    _snake_case							,    _snake_case=3_60_00							,    _snake_case=13							,    _snake_case=7							,    _snake_case=True							,    _snake_case=32							,    _snake_case=42							,    _snake_case=5							,    _snake_case=2							,    _snake_case=4							,    _snake_case=2							,    _snake_case=32							,    _snake_case=80							,    _snake_case=33							,    _snake_case=0.0							,    _snake_case=1E-6							,    _snake_case=1_00							,    _snake_case=0.0							,    ):







  """simple docstring"""






  lowerCAmelCase  							=      vocab_size
  lowerCAmelCase  							=      parent
  lowerCAmelCase  							=      batch_size
  lowerCAmelCase  							=      num_contexts
  # For common tests
  lowerCAmelCase  							=      self.num_contexts
  lowerCAmelCase  							=      is_training
  lowerCAmelCase  							=      hidden_size
  lowerCAmelCase  							=      num_ext_layers
  lowerCAmelCase  							=      ext_size
  lowerCAmelCase  							=      num_hidden_layers
  lowerCAmelCase  							=      num_attention_heads
  lowerCAmelCase  							=      num_experts
  lowerCAmelCase  							=      d_ff
  lowerCAmelCase  							=      d_ext
  lowerCAmelCase  							=      d_spout
  lowerCAmelCase  							=      dropout_rate
  lowerCAmelCase  							=      layer_norm_epsilon
  lowerCAmelCase  							=      expert_capacity
  lowerCAmelCase  							=      router_jitter_noise



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  return GPTSanJapaneseConfig.from_pretrained('Tanrei/GPTSAN-japanese'			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size			)

  lowerCAmelCase  							=      self.get_config()

  return (config, input_ids)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size			)

  lowerCAmelCase  							=      self.get_config()

  return (config, {"input_ids": input_ids})



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  return GPTSanJapaneseConfig(
      vocab_size=self.vocab_size							,    num_contexts=self.seq_length							,    d_model=self.hidden_size							,    d_ff=self.d_ff							,    d_ext=self.d_ext							,    d_spout=self.d_spout							,    num_switch_layers=self.num_hidden_layers - self.num_ext_layers							,    num_ext_layers=self.num_ext_layers							,    num_heads=self.num_attention_heads							,    num_experts=self.num_experts							,    expert_capacity=self.expert_capacity							,    dropout_rate=self.dropout_rate							,    layer_norm_epsilon=self.layer_norm_epsilon							,    router_jitter_noise=self.router_jitter_noise							,    )



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    ):







  """simple docstring"""






  lowerCAmelCase  							=      GPTSanJapaneseForConditionalGeneration(config=_snake_case			)
  model.to(_snake_case			)
  model.eval()
  lowerCAmelCase  							=      model(
      input_ids=_snake_case							,    )
  self.parent.assertIsNotNone(_snake_case			)







@require_torch
class 	a				(					a__					,      a__					,      unittest.TestCase   ):
 snake_case__    	=    (GPTSanJapaneseModel,) if is_torch_available() else ()
 snake_case__    	=    (
     {
         '''conversational''': GPTSanJapaneseForConditionalGeneration,
         '''feature-extraction''': GPTSanJapaneseForConditionalGeneration,
         '''summarization''': GPTSanJapaneseForConditionalGeneration,
         '''text2text-generation''': GPTSanJapaneseForConditionalGeneration,
         '''translation''': GPTSanJapaneseForConditionalGeneration,
     }
     if is_torch_available()
     else {}
 )
 snake_case__    	=    False
 snake_case__    	=    False
 snake_case__    	=    False
 snake_case__    	=    False
 snake_case__    	=    False
 snake_case__    	=    False
 snake_case__    	=    False
 snake_case__    	=    False
 # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
 snake_case__    	=    [0.8, 0.9]



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  if pipeline_test_casse_name == "SummarizationPipelineTests":
   # TODO: fix `_reorder_cache` is not implemented for this model
   return True
  elif pipeline_test_casse_name == "Text2TextGenerationPipelineTests":
   # TODO: check this.
   return True

  return False



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      GPTSanJapaneseTester(self			)
  lowerCAmelCase  							=      ConfigTester(self							,    config_class=_snake_case							,    d_model=37			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  GPTSanJapaneseConfig()



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*_snake_case			)



 @unittest.skip(
     reason='skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`'			)
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  super().test_model_parallelism()



 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'			)
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  pass







@require_torch
class 	a				(					a__					,      a__					,      unittest.TestCase   ):
 snake_case__    	=    (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
 snake_case__    	=    False
 snake_case__    	=    False
 snake_case__    	=    False
 snake_case__    	=    False
 snake_case__    	=    False
 snake_case__    	=    False
 # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
 snake_case__    	=    [0.8, 0.9]



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      GPTSanJapaneseTester(self			)
  lowerCAmelCase  							=      ConfigTester(self							,    config_class=_snake_case							,    d_model=37			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  GPTSanJapaneseConfig()



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*_snake_case			)



 @unittest.skip(
     reason='skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`'			)
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  super().test_model_parallelism()



 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'			)
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  pass



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'			)
  lowerCAmelCase  							=      GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'			)
  lowerCAmelCase  							=      tokenizer.encode('武田信玄は'							,    return_tensors='pt'			)
  lowerCAmelCase  							=      model(_snake_case			)
  lowerCAmelCase  							=      outputs.logits.detach().cpu().numpy()
  # Output of original model created with mesh-tensoflow
  lowerCAmelCase  							=      [
      # fmt: off
      [-12.037_839_889_526_367, -12.433_061_599_731_445, -14.333_840_370_178_223, -12.450_345_993_041_992, -11.1_661_376_953_125,
      -11.930_137_634_277_344, -10.659_740_447_998_047, -12.909_574_508_666_992, -13.241_043_090_820_312, -13.398_579_597_473_145,
      -11.107_524_871_826_172, -12.3_685_941_696_167, -22.97_943_115_234_375, -10.481_067_657_470_703, -12.484_030_723_571_777,
      -12.807_360_649_108_887, -14.769_700_050_354_004, -12.233_579_635_620_117, -13.428_145_408_630_371, -22.624_177_932_739_258],
      [-7.511_149_883_270_264, -8.281_851_768_493_652, -7.943_127_155_303_955, -7.55_021_333_694_458, -6.49_869_966_506_958,
      -7.586_796_283_721_924, -6.978_085_994_720_459, -7.839_145_183_563_232, -8.21_964_168_548_584, -8.695_091_247_558_594,
      -6.706_910_610_198_975, -6.6_585_798_263_549_805, -19.565_698_623_657_227, -5.353_842_735_290_527, -8.350_686_073_303_223,
      -8.039_388_656_616_211, -10.856_569_290_161_133, -7.75_154_447_555_542, -8.819_022_178_649_902, -19.51_532_745_361_328],
      [-9.73_066_234_588_623, -10.223_922_729_492_188, -9.932_981_491_088_867, -11.857_836_723_327_637, -7.662_626_266_479_492,
      -11.13_529_109_954_834, -7.765_097_618_103_027, -11.472_923_278_808_594, -9.543_149_948_120_117, -11.905_633_926_391_602,
      -9.366_164_207_458_496, -11.5_734_281_539_917, -23.699_003_219_604_492, -9.429_590_225_219_727, -10.42_839_241_027_832,
      -10.585_240_364_074_707, -10.94_771_957_397_461, -11.095_416_069_030_762, -10.390_240_669_250_488, -23.769_372_940_063_477],
      [-9.728_265_762_329_102, -9.859_712_600_708_008, -10.09_729_290_008_545, -9.678_522_109_985_352, -6.879_519_939_422_607,
      -9.68_487_548_828_125, -4.2_803_425_788_879_395, -10.018_914_222_717_285, -9.308_445_930_480_957, -10.63_394_546_508_789,
      -8.083_646_774_291_992, -9.06_301_498_413_086, -21.904_266_357_421_875, -8.90_160_846_710_205, -8.841_876_029_968_262,
      -11.856_719_970_703_125, -12.079_398_155_212_402, -11.233_753_204_345_703, -10.177_338_600_158_691, -21.87_256_622_314_453],
      [-9.669_764_518_737_793, -9.614_198_684_692_383, -9.814_510_345_458_984, -9.996_501_922_607_422, -11.375_690_460_205_078,
      -10.113_405_227_661_133, -10.546_867_370_605_469, -10.04_369_068_145_752, -10.907_809_257_507_324, -10.504_216_194_152_832,
      -11.129_199_028_015_137, -10.151_124_000_549_316, -21.96_586_799_621_582, -9.086_349_487_304_688, -11.730_339_050_292_969,
      -10.460_667_610_168_457, -10.298_049_926_757_812, -10.784_148_216_247_559, -10.840_693_473_815_918, -22.03_152_847_290_039],
      # fmt: on
  ]
  lowerCAmelCase  							=      np.array(_snake_case			).flatten()
  lowerCAmelCase  							=      output_logits[0, :, :20].flatten()

  def check(_snake_case							,    _snake_case							,    _snake_case=5E-4			):
   return abs(a - b			) < epsilon * max(abs(_snake_case			)							,    abs(_snake_case			)			)

  self.assertTrue(np.all([check(target[i]							,    predict[i]			) for i in range(len(_snake_case			)			)]			)			)



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'			)
  lowerCAmelCase  							=      GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'			)
  model.to(_snake_case			)

  # set deterministically
  lowerCAmelCase  							=      GenerationConfig.from_pretrained('Tanrei/GPTSAN-japanese'			)
  lowerCAmelCase  							=      1

  # use different length sentences to test batching
  lowerCAmelCase  							=      [
      '甲斐なら武田と言うほど',
      '織田信長は、',
  ]

  lowerCAmelCase  							=      'left'
  lowerCAmelCase  							=      tokenizer(_snake_case							,    return_tensors='pt'							,    padding=_snake_case			)
  lowerCAmelCase  							=      inputs['input_ids'].to(_snake_case			)

  self.assertNotEqual(inputs['attention_mask'][0].numpy().tolist()							,    inputs['attention_mask'][1].numpy().tolist()			)

  lowerCAmelCase  							=      model.generate(
      input_ids=_snake_case							,    attention_mask=inputs['attention_mask'].to(_snake_case			)							,    max_new_tokens=3							,    generation_config=_snake_case							,    )

  lowerCAmelCase  							=      tokenizer(sentences[0]							,    return_tensors='pt'			).input_ids.to(_snake_case			)
  lowerCAmelCase  							=      model.generate(
      input_ids=_snake_case							,    max_new_tokens=3							,    generation_config=_snake_case			)

  lowerCAmelCase  							=      tokenizer(sentences[1]							,    return_tensors='pt'			).input_ids.to(_snake_case			)
  lowerCAmelCase  							=      model.generate(input_ids=_snake_case							,    max_new_tokens=3							,    generation_config=_snake_case			)

  self.assertNotEqual(inputs_non_padded.shape							,    inputs_padded.shape			)

  lowerCAmelCase  							=      tokenizer.batch_decode(_snake_case							,    skip_special_tokens=_snake_case			)
  lowerCAmelCase  							=      tokenizer.decode(output_non_padded[0]							,    skip_special_tokens=_snake_case			)
  lowerCAmelCase  							=      tokenizer.decode(output_padded[0]							,    skip_special_tokens=_snake_case			)

  lowerCAmelCase  							=      [
      '甲斐なら武田と言うほど甲斐の武田',
      '織田信長は、このような',
  ]
  self.assertListEqual(_snake_case							,    _snake_case			)
  self.assertListEqual(_snake_case							,    [non_padded_sentence, padded_sentence]			)



 @tooslow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'			)
  lowerCAmelCase  							=      GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'			)
  # Output of original model created with mesh-tensoflow
  lowerCAmelCase  							=      [
      ('武田信玄は', 3_56_75),
      ('武田信玄は、', 45),
      ('武田信玄は、この', 29),
      ('武田信玄は、このよう', 3_06_42),
      ('武田信玄は、このような', 3_56_80),
      ('武田信玄は、このような「', 86_40),
      ('武田信玄は、このような「武田', 3_16_17),
      ('武田信玄は、このような「武田家', 3_06_46),
      ('武田信玄は、このような「武田家の', 3_16_17),
      ('武田信玄は、このような「武田家の家', 3_13_81),
  ]
  for input, output in target:
   lowerCAmelCase  							=      tokenizer.encode(_snake_case							,    return_tensors='pt'			)
   lowerCAmelCase  							=      model(_snake_case			)
   lowerCAmelCase  							=      outputs.logits.detach().cpu().numpy()[0]
   lowerCAmelCase  							=      np.argmax(output_logits[-1]			)
   self.assertEqual(_snake_case							,    _snake_case			)



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'			)
  lowerCAmelCase  							=      GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'			)
  model.to(_snake_case			)

  # set deterministically
  lowerCAmelCase  							=      GenerationConfig.from_pretrained('Tanrei/GPTSAN-japanese'			)
  lowerCAmelCase  							=      1

  lowerCAmelCase  							=      '武田信玄は、'
  lowerCAmelCase  							=      tokenizer(_snake_case							,    return_tensors='pt'			).input_ids.to(_snake_case			)
  lowerCAmelCase  							=      tokenizer([input_text, input_text]							,    return_tensors='pt'			).input_ids.to(_snake_case			)

  # spout from uniform and one-hot
  lowerCAmelCase  							=      [
      # fmt: off
      [0.87_882_208, 0.38_426_396, 0.33_220_248, 0.43_890_406, 0.16_562_252,
      0.04_803_985, 0.211_572  , 0.23_188_473, 0.37_153_068, 0.7_836_377 ,
      0.02_160_172, 0.38_761_719, 0.75_290_772, 0.90_198_857, 0.34_365_777,
      0.64_168_169, 0.44_318_471, 0.14_575_746, 0.92_562_881, 0.40_812_148,
      0.29_019_122, 0.88_861_599, 0.65_524_846, 0.43_563_456, 0.38_177_187,
      0.70_832_965, 0.81_527_892, 0.68_832_812, 0.38_833_192, 0.4_561_522 ,
      0.14_828_817, 0.47_248_213, 0.54_357_335, 0.82_009_566, 0.1_338_884 ,
      0.02_755_417, 0.19_764_677, 0.2_422_084 , 0.04_757_674, 0.65_409_606,
      0.0_824_589 , 0.03_304_383, 0.94_387_689, 0.98_764_509, 0.82_433_901,
      0.27_646_741, 0.64_907_493, 0.76_009_406, 0.30_087_915, 0.17_904_689,
      0.41_601_714, 0.67_046_398, 0.10_422_822, 0.08_447_374, 0.07_354_344,
      0.61_423_565, 0.70_284_866, 0.7_532_333 , 0.1_972_038 , 0.29_575_659,
      0.90_583_886, 0.29_265_307, 0.50_000_175, 0.70_407_655, 0.889_363  ,
      0.81_904_418, 0.66_829_128, 0.64_468_815, 0.56_563_723, 0.85_601_875,
      0.94_924_672, 0.00_166_762, 0.25_220_643, 0.74_540_219, 0.67_993_247,
      0.1_549_675 , 0.39_385_352, 0.92_153_607, 0.63_745_931, 0.27_759_043,
      0.84_702_295, 0.65_904_271, 0.58_676_614, 0.8_666_936 , 0.39_607_438,
      0.79_954_983, 0.42_220_697, 0.39_650_381, 0.7_849_864 , 0.56_150_201,
      0.15_678_925, 0.14_746_032, 0.34_542_114, 0.47_026_783, 0.11_956_489,
      0.25_421_435, 0.33_788_901, 0.68_934_842, 0.36_424_685, 0.71_737_898,
      0.38_983_449, 0.94_393_779, 0.39_575_588, 0.36_616_553, 0.87_104_665,
      0.64_630_203, 0.22_516_905, 0.88_270_804, 0.15_031_338, 0.75_144_345,
      0.46_459_025, 0.85_396_454, 0.86_355_643, 0.65_139_851, 0.70_266_061,
      0.30_241_389, 0.81_056_497, 0.88_865_969, 0.38_773_807, 0.70_635_849,
      0.90_718_459, 0.43_245_789, 0.28_000_654, 0.45_935_562, 0.08_773_519,
      0.9_552_151 , 0.93_901_511, 0.22_489_288], # uniform
      [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
       0., 0., 0., 0., 0., 0., 0., 0.],
      # fmt: on
  ]

  lowerCAmelCase  							=      model.generate(
      input_ids=_snake_case							,    spout=spouts[0]							,    max_new_tokens=20							,    generation_config=_snake_case							,    )

  lowerCAmelCase  							=      model.generate(
      input_ids=_snake_case							,    spout=spouts[1]							,    max_new_tokens=20							,    generation_config=_snake_case							,    )

  lowerCAmelCase  							=      model.generate(
      input_ids=_snake_case							,    spout=_snake_case							,    max_new_tokens=20							,    generation_config=_snake_case							,    )

  lowerCAmelCase  							=      tokenizer.decode(outputa[0]			)
  lowerCAmelCase  							=      tokenizer.decode(outputa[0]			)
  lowerCAmelCase  							=      tokenizer.batch_decode(_snake_case			)

  lowerCAmelCase  							=      [
      '武田信玄は、武田氏の滅亡後、武田氏の居城であった甲斐武田氏の居城である',
      '武田信玄は、武田家の滅亡を防ぐため、武田家の家臣である武田信虎を討',
  ]
  self.assertListEqual(_snake_case							,    _snake_case			)
  self.assertListEqual(_snake_case							,    [outa_sentence, outa_sentence]			)



 @slow
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'			)
  lowerCAmelCase  							=      GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'			)
  model.to(_snake_case			)

  # set deterministically
  lowerCAmelCase  							=      GenerationConfig.from_pretrained('Tanrei/GPTSAN-japanese'			)
  lowerCAmelCase  							=      1

  lowerCAmelCase  							=      '武田信玄'
  lowerCAmelCase  							=      '織田信長'
  lowerCAmelCase  							=      'は、'
  lowerCAmelCase  							=      'が、'
  lowerCAmelCase  							=      tokenizer(_snake_case							,    prefix_text=_snake_case							,    return_tensors='pt'			)
  lowerCAmelCase  							=      tokenizer(_snake_case							,    prefix_text=_snake_case							,    return_tensors='pt'			)
  lowerCAmelCase  							=      tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]							,    return_tensors='pt'			)

  lowerCAmelCase  							=      model.generate(
      input_ids=input_tok_a.input_ids.to(_snake_case			)							,    token_type_ids=input_tok_a.token_type_ids.to(_snake_case			)							,    max_new_tokens=20							,    generation_config=_snake_case							,    )

  lowerCAmelCase  							=      model.generate(
      input_ids=input_tok_a.input_ids.to(_snake_case			)							,    token_type_ids=input_tok_a.token_type_ids.to(_snake_case			)							,    max_new_tokens=20							,    generation_config=_snake_case							,    )

  lowerCAmelCase  							=      model.generate(
      input_ids=input_tok_a.input_ids.to(_snake_case			)							,    token_type_ids=input_tok_a.token_type_ids.to(_snake_case			)							,    attention_mask=input_tok_a.attention_mask.to(_snake_case			)							,    max_new_tokens=20							,    generation_config=_snake_case							,    )

  lowerCAmelCase  							=      tokenizer.decode(outputa[0]			)
  lowerCAmelCase  							=      tokenizer.decode(outputa[0]			)
  lowerCAmelCase  							=      tokenizer.batch_decode(_snake_case			)

  lowerCAmelCase  							=      [
      '武田信玄は、武田氏の祖である武田信虎を、その子・武田信友を擁して',
      '織田信長が、織田信長の妻・お市の方を妻として迎えたという逸話が残',
  ]
  self.assertListEqual(_snake_case							,    _snake_case			)
  self.assertListEqual(_snake_case							,    [outa_sentence, outa_sentence]			)












"""simple docstring"""
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class 	a				(					a__   ):
 snake_case__    	=    '''AutoFeatureExtractor'''
 snake_case__    	=    '''Speech2Text2Tokenizer'''



 def __init__(			self							,    _snake_case							,    _snake_case			):







  """simple docstring"""






  super().__init__(_snake_case							,    _snake_case			)
  lowerCAmelCase  							=      self.feature_extractor
  lowerCAmelCase  							=      False



 def __call__(			self							,    *_snake_case							,    **_snake_case			):







  """simple docstring"""






  if self._in_target_context_manager:
   return self.current_processor(*_snake_case							,    **_snake_case			)

  if "raw_speech" in kwargs:
   warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'			)
   lowerCAmelCase  							=      kwargs.pop('raw_speech'			)
  else:
   lowerCAmelCase  							=      kwargs.pop('audio'							,    _snake_case			)
  lowerCAmelCase  							=      kwargs.pop('sampling_rate'							,    _snake_case			)
  lowerCAmelCase  							=      kwargs.pop('text'							,    _snake_case			)
  if len(_snake_case			) > 0:
   lowerCAmelCase  							=      args[0]
   lowerCAmelCase  							=      args[1:]

  if audio is None and text is None:
   raise ValueError('You need to specify either an `audio` or `text` input to process.'			)

  if audio is not None:
   lowerCAmelCase  							=      self.feature_extractor(_snake_case							,    *_snake_case							,    sampling_rate=_snake_case							,    **_snake_case			)
  if text is not None:
   lowerCAmelCase  							=      self.tokenizer(_snake_case							,    **_snake_case			)

  if text is None:
   return inputs
  elif audio is None:
   return encodings
  else:
   lowerCAmelCase  							=      encodings['input_ids']
   return inputs



 def 					UpperCamelCase__   (			self							,    *_snake_case							,    **_snake_case			):







  """simple docstring"""






  return self.tokenizer.batch_decode(*_snake_case							,    **_snake_case			)



 def 					UpperCamelCase__   (			self							,    *_snake_case							,    **_snake_case			):







  """simple docstring"""






  return self.tokenizer.decode(*_snake_case							,    **_snake_case			)



 @contextmanager
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  warnings.warn(
      '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
      'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
      'your audio inputs, or in a separate call.'			)
  lowerCAmelCase  							=      True
  lowerCAmelCase  							=      self.tokenizer
  yield
  lowerCAmelCase  							=      self.feature_extractor
  lowerCAmelCase  							=      False





