code: 
  '''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  lowerCAmelCase__   :
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    def __init__(self  : str	, lowerCAmelCase_  : List[str]	, lowerCAmelCase_  : Union[str, Any]=3_60_00	, lowerCAmelCase_  : Union[str, Any]=13	, lowerCAmelCase_  : int=7	, lowerCAmelCase_  : int=True	, lowerCAmelCase_  : str=32	, lowerCAmelCase_  : List[Any]=42	, lowerCAmelCase_  : Any=5	, lowerCAmelCase_  : List[Any]=2	, lowerCAmelCase_  : int=4	, lowerCAmelCase_  : Tuple=2	, lowerCAmelCase_  : Any=32	, lowerCAmelCase_  : Tuple=80	, lowerCAmelCase_  : Union[str, Any]=33	, lowerCAmelCase_  : str=0.0	, lowerCAmelCase_  : Optional[Any]=1E-6	, lowerCAmelCase_  : List[Any]=1_00	, lowerCAmelCase_  : Optional[Any]=0.0	, )   ->       Union[str, Any]:
          snake_case_						       =							vocab_size
          snake_case_						       =							parent
          snake_case_						       =							batch_size
          snake_case_						       =							num_contexts
          # For common tests
          snake_case_						       =							self.num_contexts
          snake_case_						       =							is_training
          snake_case_						       =							hidden_size
          snake_case_						       =							num_ext_layers
          snake_case_						       =							ext_size
          snake_case_						       =							num_hidden_layers
          snake_case_						       =							num_attention_heads
          snake_case_						       =							num_experts
          snake_case_						       =							d_ff
          snake_case_						       =							d_ext
          snake_case_						       =							d_spout
          snake_case_						       =							dropout_rate
          snake_case_						       =							layer_norm_epsilon
          snake_case_						       =							expert_capacity
          snake_case_						       =							router_jitter_noise
  
    def 		_lowerCamelCase	(self  : Union[str, Any]    )   ->       Dict:
          return GPTSanJapaneseConfig.from_pretrained("Tanrei/GPTSAN-japanese"    )
  
    def 		_lowerCamelCase	(self  : Optional[Any]    )   ->       List[str]:
          snake_case_						       =							ids_tensor([self.batch_size, self.seq_length]	, self.vocab_size    )
  
          snake_case_						       =							self.get_config()
  
          return (config, input_ids)
  
    def 		_lowerCamelCase	(self  : Optional[int]    )   ->       Union[str, Any]:
          snake_case_						       =							ids_tensor([self.batch_size, self.seq_length]	, self.vocab_size    )
  
          snake_case_						       =							self.get_config()
  
          return (config, {"input_ids": input_ids})
  
    def 		_lowerCamelCase	(self  : Dict    )   ->       Optional[int]:
          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 		_lowerCamelCase	(self  : Optional[int]	, lowerCAmelCase_  : int	, lowerCAmelCase_  : Dict	, )   ->       List[str]:
          snake_case_						       =							GPTSanJapaneseForConditionalGeneration(config=lowerCAmelCase_    )
          model.to(lowerCAmelCase_    )
          model.eval()
          snake_case_						       =							model(
              input_ids=lowerCAmelCase_	, )
          self.parent.assertIsNotNone(lowerCAmelCase_    )
  
  
  
  @require_torch
  class  lowerCAmelCase__   (       UpperCamelCase					,							UpperCamelCase					,							unittest.TestCase       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    _UpperCamelCase						= (GPTSanJapaneseModel,) if is_torch_available() else ()
    _UpperCamelCase						= (
        {
            'conversational': GPTSanJapaneseForConditionalGeneration,
            'feature-extraction': GPTSanJapaneseForConditionalGeneration,
            'summarization': GPTSanJapaneseForConditionalGeneration,
            'text2text-generation': GPTSanJapaneseForConditionalGeneration,
            'translation': GPTSanJapaneseForConditionalGeneration,
        }
        if is_torch_available()
        else {}
    )
    _UpperCamelCase						= False
    _UpperCamelCase						= False
    _UpperCamelCase						= False
    _UpperCamelCase						= False
    _UpperCamelCase						= False
    _UpperCamelCase						= False
    _UpperCamelCase						= False
    _UpperCamelCase						= False
    # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
    _UpperCamelCase						= [0.8, 0.9]
  
    def 		_lowerCamelCase	(self  : Optional[Any]	, lowerCAmelCase_  : Any	, lowerCAmelCase_  : Tuple	, lowerCAmelCase_  : List[str]	, lowerCAmelCase_  : Dict	, lowerCAmelCase_  : int    )   ->       List[Any]:
          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 		_lowerCamelCase	(self  : Tuple    )   ->       Dict:
          snake_case_						       =							GPTSanJapaneseTester(self    )
          snake_case_						       =							ConfigTester(self	, config_class=lowerCAmelCase_	, d_model=37    )
  
    def 		_lowerCamelCase	(self  : Optional[int]    )   ->       Tuple:
          GPTSanJapaneseConfig()
  
    def 		_lowerCamelCase	(self  : Union[str, Any]    )   ->       Any:
          snake_case_						       =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCAmelCase_    )
  
    @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 		_lowerCamelCase	(self  : List[str]    )   ->       List[Any]:
          super().test_model_parallelism()
  
  
    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."    )
    def 		_lowerCamelCase	(self  : Any    )   ->       int:
          pass
  
  
  
  @require_torch
  class  lowerCAmelCase__   (       UpperCamelCase					,							UpperCamelCase					,							unittest.TestCase       ):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    _UpperCamelCase						= (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
    _UpperCamelCase						= False
    _UpperCamelCase						= False
    _UpperCamelCase						= False
    _UpperCamelCase						= False
    _UpperCamelCase						= False
    _UpperCamelCase						= False
    # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
    _UpperCamelCase						= [0.8, 0.9]
  
    def 		_lowerCamelCase	(self  : str    )   ->       Union[str, Any]:
          snake_case_						       =							GPTSanJapaneseTester(self    )
          snake_case_						       =							ConfigTester(self	, config_class=lowerCAmelCase_	, d_model=37    )
  
    def 		_lowerCamelCase	(self  : Optional[int]    )   ->       Tuple:
          GPTSanJapaneseConfig()
  
    def 		_lowerCamelCase	(self  : Optional[Any]    )   ->       List[Any]:
          snake_case_						       =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCAmelCase_    )
  
    @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 		_lowerCamelCase	(self  : Tuple    )   ->       Dict:
          super().test_model_parallelism()
  
    @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."    )
    def 		_lowerCamelCase	(self  : Tuple    )   ->       Dict:
          pass
  
    @slow
    def 		_lowerCamelCase	(self  : Optional[Any]    )   ->       Tuple:
          snake_case_						       =							GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"    )
          snake_case_						       =							GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"    )
          snake_case_						       =							tokenizer.encode("武田信玄は"	, return_tensors="pt"    )
          snake_case_						       =							model(lowerCAmelCase_    )
          snake_case_						       =							outputs.logits.detach().cpu().numpy()
          # Output of original model created with mesh-tensoflow
          snake_case_						       =							[
              # 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
          ]
          snake_case_						       =							np.array(lowerCAmelCase_    ).flatten()
          snake_case_						       =							output_logits[0, :, :20].flatten()
  
          def check(lowerCAmelCase_  : str	, lowerCAmelCase_  : Any	, lowerCAmelCase_  : Optional[Any]=5E-4    ):
                return abs(a - b    ) < epsilon * max(abs(lowerCAmelCase_    )	, abs(lowerCAmelCase_    )    )
  
          self.assertTrue(np.all([check(target[i]	, predict[i]    ) for i in range(len(lowerCAmelCase_    )    )]    )    )
  
    @slow
    def 		_lowerCamelCase	(self  : str    )   ->       Dict:
          snake_case_						       =							GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"    )
          snake_case_						       =							GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"    )
          model.to(lowerCAmelCase_    )
  
          # set deterministically
          snake_case_						       =							GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"    )
          snake_case_						       =							1
  
          # use different length sentences to test batching
          snake_case_						       =							[
              "甲斐なら武田と言うほど",
              "織田信長は、",
          ]
  
          snake_case_						       =							"left"
          snake_case_						       =							tokenizer(lowerCAmelCase_	, return_tensors="pt"	, padding=lowerCAmelCase_    )
          snake_case_						       =							inputs["input_ids"].to(lowerCAmelCase_    )
  
          self.assertNotEqual(inputs["attention_mask"][0].numpy().tolist()	, inputs["attention_mask"][1].numpy().tolist()    )
  
          snake_case_						       =							model.generate(
              input_ids=lowerCAmelCase_	, attention_mask=inputs["attention_mask"].to(lowerCAmelCase_    )	, max_new_tokens=3	, generation_config=lowerCAmelCase_	, )
  
          snake_case_						       =							tokenizer(sentences[0]	, return_tensors="pt"    ).input_ids.to(lowerCAmelCase_    )
          snake_case_						       =							model.generate(
              input_ids=lowerCAmelCase_	, max_new_tokens=3	, generation_config=lowerCAmelCase_    )
  
          snake_case_						       =							tokenizer(sentences[1]	, return_tensors="pt"    ).input_ids.to(lowerCAmelCase_    )
          snake_case_						       =							model.generate(input_ids=lowerCAmelCase_	, max_new_tokens=3	, generation_config=lowerCAmelCase_    )
  
          self.assertNotEqual(inputs_non_padded.shape	, inputs_padded.shape    )
  
          snake_case_						       =							tokenizer.batch_decode(lowerCAmelCase_	, skip_special_tokens=lowerCAmelCase_    )
          snake_case_						       =							tokenizer.decode(output_non_padded[0]	, skip_special_tokens=lowerCAmelCase_    )
          snake_case_						       =							tokenizer.decode(output_padded[0]	, skip_special_tokens=lowerCAmelCase_    )
  
          snake_case_						       =							[
              "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(lowerCAmelCase_	, lowerCAmelCase_    )
          self.assertListEqual(lowerCAmelCase_	, [non_padded_sentence, padded_sentence]    )
  
    @tooslow
    def 		_lowerCamelCase	(self  : List[str]    )   ->       int:
          snake_case_						       =							GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"    )
          snake_case_						       =							GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"    )
          # Output of original model created with mesh-tensoflow
          snake_case_						       =							[
              ("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:
                snake_case_						       =							tokenizer.encode(lowerCAmelCase_	, return_tensors="pt"    )
                snake_case_						       =							model(lowerCAmelCase_    )
                snake_case_						       =							outputs.logits.detach().cpu().numpy()[0]
                snake_case_						       =							np.argmax(output_logits[-1]    )
                self.assertEqual(lowerCAmelCase_	, lowerCAmelCase_    )
  
    @slow
    def 		_lowerCamelCase	(self  : int    )   ->       Tuple:
          snake_case_						       =							GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"    )
          snake_case_						       =							GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"    )
          model.to(lowerCAmelCase_    )
  
          # set deterministically
          snake_case_						       =							GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"    )
          snake_case_						       =							1
  
          snake_case_						       =							"武田信玄は、"
          snake_case_						       =							tokenizer(lowerCAmelCase_	, return_tensors="pt"    ).input_ids.to(lowerCAmelCase_    )
          snake_case_						       =							tokenizer([input_text, input_text]	, return_tensors="pt"    ).input_ids.to(lowerCAmelCase_    )
  
          # spout from uniform and one-hot
          snake_case_						       =							[
              # 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
          ]
  
          snake_case_						       =							model.generate(
              input_ids=lowerCAmelCase_	, spout=spouts[0]	, max_new_tokens=20	, generation_config=lowerCAmelCase_	, )
  
          snake_case_						       =							model.generate(
              input_ids=lowerCAmelCase_	, spout=spouts[1]	, max_new_tokens=20	, generation_config=lowerCAmelCase_	, )
  
          snake_case_						       =							model.generate(
              input_ids=lowerCAmelCase_	, spout=lowerCAmelCase_	, max_new_tokens=20	, generation_config=lowerCAmelCase_	, )
  
          snake_case_						       =							tokenizer.decode(outputa[0]    )
          snake_case_						       =							tokenizer.decode(outputa[0]    )
          snake_case_						       =							tokenizer.batch_decode(lowerCAmelCase_    )
  
          snake_case_						       =							[
              "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(lowerCAmelCase_	, lowerCAmelCase_    )
          self.assertListEqual(lowerCAmelCase_	, [outa_sentence, outa_sentence]    )
  
  
    @slow
    def 		_lowerCamelCase	(self  : Tuple    )   ->       List[str]:
          snake_case_						       =							GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"    )
          snake_case_						       =							GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"    )
          model.to(lowerCAmelCase_    )
  
          # set deterministically
          snake_case_						       =							GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"    )
          snake_case_						       =							1
  
          snake_case_						       =							"武田信玄"
          snake_case_						       =							"織田信長"
          snake_case_						       =							"は、"
          snake_case_						       =							"が、"
          snake_case_						       =							tokenizer(lowerCAmelCase_	, prefix_text=lowerCAmelCase_	, return_tensors="pt"    )
          snake_case_						       =							tokenizer(lowerCAmelCase_	, prefix_text=lowerCAmelCase_	, return_tensors="pt"    )
          snake_case_						       =							tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]	, return_tensors="pt"    )
  
          snake_case_						       =							model.generate(
              input_ids=input_tok_a.input_ids.to(lowerCAmelCase_    )	, token_type_ids=input_tok_a.token_type_ids.to(lowerCAmelCase_    )	, max_new_tokens=20	, generation_config=lowerCAmelCase_	, )
  
          snake_case_						       =							model.generate(
              input_ids=input_tok_a.input_ids.to(lowerCAmelCase_    )	, token_type_ids=input_tok_a.token_type_ids.to(lowerCAmelCase_    )	, max_new_tokens=20	, generation_config=lowerCAmelCase_	, )
  
          snake_case_						       =							model.generate(
              input_ids=input_tok_a.input_ids.to(lowerCAmelCase_    )	, token_type_ids=input_tok_a.token_type_ids.to(lowerCAmelCase_    )	, attention_mask=input_tok_a.attention_mask.to(lowerCAmelCase_    )	, max_new_tokens=20	, generation_config=lowerCAmelCase_	, )
  
          snake_case_						       =							tokenizer.decode(outputa[0]    )
          snake_case_						       =							tokenizer.decode(outputa[0]    )
          snake_case_						       =							tokenizer.batch_decode(lowerCAmelCase_    )
  
          snake_case_						       =							[
              "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(lowerCAmelCase_	, lowerCAmelCase_    )
          self.assertListEqual(lowerCAmelCase_	, [outa_sentence, outa_sentence]    )
  
  
style_context: 
  '''simple docstring'''
  
  
  
  
  
  from __future__ import annotations
  
  A           =   [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
  
  
  
  class  lowerCAmelCase__   :
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    def __init__(self  : Optional[int]	, lowerCAmelCase_  : int    )   ->       None:
          snake_case_						       =							-1
          snake_case_						       =							-1
          snake_case_						       =							[-1] * length
          snake_case_						       =							None
          snake_case_						       =							None
  
  
    def __repr__(self  : List[Any]    )   ->       str:
          return f'''Node(min_value={self.minn} max_value={self.maxx})'''
  
  def   snake_case_       (							_a     :							list[int]			):
        snake_case_						       =							Node(len(_a			)			)
        snake_case_       ,	snake_case_						       =							min(_a			), max(_a			)
        # Leaf node case where the node contains only one unique value
        if root.minn == root.maxx:
              return root
        snake_case_						       =							(root.minn + root.maxx) // 2
  
        snake_case_						       =							[]
        snake_case_						       =							[]
  
        for index, num in enumerate(_a			):
              if num <= pivot:
                    left_arr.append(_a			)
              else:
                    right_arr.append(_a			)
              snake_case_						       =							len(_a			)
        snake_case_						       =							build_tree(_a			)
        snake_case_						       =							build_tree(_a			)
        return root
  def   snake_case_       (							_a     :							Node | None ,					_a     :							int ,					_a     :							int			):
        if index < 0 or node is None:
              return 0
        # Leaf node cases
        if node.minn == node.maxx:
              return index + 1 if node.minn == num else 0
        snake_case_						       =							(node.minn + node.maxx) // 2
        if num <= pivot:
              # go the left subtree and map index to the left subtree
              return rank_till_index(node.left ,					_a ,					node.map_left[index] - 1			)
        else:
              # go to the right subtree and map index to the right subtree
              return rank_till_index(node.right ,					_a ,					index - node.map_left[index]			)
  def   snake_case_       (							_a     :							Node | None ,					_a     :							int ,					_a     :							int ,					_a     :							int			):
        if start > end:
              return 0
        snake_case_						       =							rank_till_index(_a ,					_a ,					_a			)
        snake_case_						       =							rank_till_index(_a ,					_a ,					start - 1			)
        return rank_till_end - rank_before_start
  def   snake_case_       (							_a     :							Node | None ,					_a     :							int ,					_a     :							int ,					_a     :							int			):
        if index > (end - start) or start > end or node is None:
              return -1
        # Leaf node case
        if node.minn == node.maxx:
              return node.minn
        # Number of elements in the left subtree in interval [start, end]
        snake_case_						       =							node.map_left[end] - (
            node.map_left[start - 1] if start else 0
        )
        if num_elements_in_left_tree > index:
              return quantile(
                  node.left ,					_a ,					(node.map_left[start - 1] if start else 0) ,					node.map_left[end] - 1 ,					)
        else:
              return quantile(
                  node.right ,					index - num_elements_in_left_tree ,					start - (node.map_left[start - 1] if start else 0) ,					end - node.map_left[end] ,					)
  def   snake_case_       (							_a     :							Node | None ,					_a     :							int ,					_a     :							int ,					_a     :							int ,					_a     :							int			):
        if (
            start > end
            or node is None
            or start_num > end_num
            or node.minn > end_num
            or node.maxx < start_num
        ):
              return 0
        if start_num <= node.minn and node.maxx <= end_num:
              return end - start + 1
        snake_case_						       =							range_counting(
            node.left ,					(node.map_left[start - 1] if start else 0) ,					node.map_left[end] - 1 ,					_a ,					_a ,					)
        snake_case_						       =							range_counting(
            node.right ,					start - (node.map_left[start - 1] if start else 0) ,					end - node.map_left[end] ,					_a ,					_a ,					)
        return left + right
  
  
  if __name__ == "__main__":
       import doctest
  
       doctest.testmod()
  
  