code: 
  
  
  
  
  
  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 	UpperCamelCase_     :
  
  
  
  					def __init__(		self    :    Union[str, Any]					,			lowerCamelCase    :    List[Any]					,			lowerCamelCase    :    Any=3_60_00					,			lowerCamelCase    :    Any=13					,			lowerCamelCase    :    Union[str, Any]=7					,			lowerCamelCase    :    List[Any]=True					,			lowerCamelCase    :    Tuple=32					,			lowerCamelCase    :    Optional[Any]=42					,			lowerCamelCase    :    Dict=5					,			lowerCamelCase    :    Tuple=2					,			lowerCamelCase    :    str=4					,			lowerCamelCase    :    int=2					,			lowerCamelCase    :    Optional[Any]=32					,			lowerCamelCase    :    Dict=80					,			lowerCamelCase    :    Union[str, Any]=33					,			lowerCamelCase    :    Dict=0.0					,			lowerCamelCase    :    List[str]=1E-6					,			lowerCamelCase    :    Union[str, Any]=1_00					,			lowerCamelCase    :    Optional[int]=0.0					,			):
  							lowerCamelCase_  :    Tuple   	=			vocab_size
  							lowerCamelCase_  :    List[Any]   	=			parent
  							lowerCamelCase_  :    List[Any]   	=			batch_size
  							lowerCamelCase_  :    List[Any]   	=			num_contexts
  							# For common tests
  							lowerCamelCase_  :    int   	=			self.num_contexts
  							lowerCamelCase_  :    Dict   	=			is_training
  							lowerCamelCase_  :    Optional[int]   	=			hidden_size
  							lowerCamelCase_  :    str   	=			num_ext_layers
  							lowerCamelCase_  :    Optional[int]   	=			ext_size
  							lowerCamelCase_  :    List[Any]   	=			num_hidden_layers
  							lowerCamelCase_  :    Tuple   	=			num_attention_heads
  							lowerCamelCase_  :    Optional[Any]   	=			num_experts
  							lowerCamelCase_  :    Any   	=			d_ff
  							lowerCamelCase_  :    Union[str, Any]   	=			d_ext
  							lowerCamelCase_  :    List[Any]   	=			d_spout
  							lowerCamelCase_  :    int   	=			dropout_rate
  							lowerCamelCase_  :    str   	=			layer_norm_epsilon
  							lowerCamelCase_  :    Dict   	=			expert_capacity
  							lowerCamelCase_  :    List[str]   	=			router_jitter_noise
  
  
  
  					def      __a    (		self    :    Union[str, Any]	):
  							return GPTSanJapaneseConfig.from_pretrained('Tanrei/GPTSAN-japanese'	)
  
  
  
  					def      __a    (		self    :    Union[str, Any]	):
  							lowerCamelCase_  :    List[str]   	=			ids_tensor([self.batch_size, self.seq_length]					,			self.vocab_size	)
  
  							lowerCamelCase_  :    Optional[int]   	=			self.get_config()
  
  							return (config, input_ids)
  
  
  
  					def      __a    (		self    :    Optional[Any]	):
  							lowerCamelCase_  :    Optional[Any]   	=			ids_tensor([self.batch_size, self.seq_length]					,			self.vocab_size	)
  
  							lowerCamelCase_  :    str   	=			self.get_config()
  
  							return (config, {"input_ids": input_ids})
  
  
  
  					def      __a    (		self    :    Optional[Any]	):
  							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      __a    (		self    :    List[str]					,			lowerCamelCase    :    List[Any]					,			lowerCamelCase    :    int					,			):
  							lowerCamelCase_  :    Tuple   	=			GPTSanJapaneseForConditionalGeneration(config=lowerCamelCase	)
  							model.to(lowerCamelCase	)
  							model.eval()
  							lowerCamelCase_  :    str   	=			model(
  							    input_ids=lowerCamelCase					,			)
  							self.parent.assertIsNotNone(lowerCamelCase	)
  
  
  
  
  
  
  @require_torch
  class 	UpperCamelCase_     (     snake_case__     ,    snake_case__     ,    unittest.TestCase       ):
  					_a			:	List[str]							=				(GPTSanJapaneseModel,) if is_torch_available() else ()
  					_a			:	Optional[int]							=				(
  					    {
  					        'conversational': GPTSanJapaneseForConditionalGeneration,
  					        'feature-extraction': GPTSanJapaneseForConditionalGeneration,
  					        'summarization': GPTSanJapaneseForConditionalGeneration,
  					        'text2text-generation': GPTSanJapaneseForConditionalGeneration,
  					        'translation': GPTSanJapaneseForConditionalGeneration,
  					    }
  					    if is_torch_available()
  					    else {}
  					)
  					_a			:	Any							=				False
  					_a			:	Optional[int]							=				False
  					_a			:	Union[str, Any]							=				False
  					_a			:	Optional[int]							=				False
  					_a			:	Union[str, Any]							=				False
  					_a			:	List[str]							=				False
  					_a			:	int							=				False
  					_a			:	Dict							=				False
  					# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
  					_a			:	int							=				[0.8, 0.9]
  
  
  
  					def      __a    (		self    :    Optional[Any]					,			lowerCamelCase    :    Dict					,			lowerCamelCase    :    Optional[Any]					,			lowerCamelCase    :    Union[str, Any]					,			lowerCamelCase    :    List[str]					,			lowerCamelCase    :    str	):
  							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      __a    (		self    :    List[str]	):
  							lowerCamelCase_  :    List[str]   	=			GPTSanJapaneseTester(self	)
  							lowerCamelCase_  :    Tuple   	=			ConfigTester(self					,			config_class=lowerCamelCase					,			d_model=37	)
  
  
  
  					def      __a    (		self    :    Dict	):
  							GPTSanJapaneseConfig()
  
  
  
  					def      __a    (		self    :    Tuple	):
  							lowerCamelCase_  :    Tuple   	=			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      __a    (		self    :    Any	):
  							super().test_model_parallelism()
  
  
  
  
  
  					@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
  					def      __a    (		self    :    Optional[int]	):
  							pass
  
  
  
  
  
  
  @require_torch
  class 	UpperCamelCase_     (     snake_case__     ,    snake_case__     ,    unittest.TestCase       ):
  					_a			:	Optional[int]							=				(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
  					_a			:	List[Any]							=				False
  					_a			:	Tuple							=				False
  					_a			:	Optional[Any]							=				False
  					_a			:	Optional[Any]							=				False
  					_a			:	Any							=				False
  					_a			:	Dict							=				False
  					# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
  					_a			:	Dict							=				[0.8, 0.9]
  
  
  
  					def      __a    (		self    :    str	):
  							lowerCamelCase_  :    Union[str, Any]   	=			GPTSanJapaneseTester(self	)
  							lowerCamelCase_  :    Tuple   	=			ConfigTester(self					,			config_class=lowerCamelCase					,			d_model=37	)
  
  
  
  					def      __a    (		self    :    int	):
  							GPTSanJapaneseConfig()
  
  
  
  					def      __a    (		self    :    List[Any]	):
  							lowerCamelCase_  :    int   	=			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      __a    (		self    :    Any	):
  							super().test_model_parallelism()
  
  
  
  					@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'	)
  					def      __a    (		self    :    Tuple	):
  							pass
  
  
  
  					@slow
  					def      __a    (		self    :    str	):
  							lowerCamelCase_  :    Dict   	=			GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'	)
  							lowerCamelCase_  :    Tuple   	=			GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'	)
  							lowerCamelCase_  :    str   	=			tokenizer.encode('武田信玄は'					,			return_tensors='pt'	)
  							lowerCamelCase_  :    Any   	=			model(lowerCamelCase	)
  							lowerCamelCase_  :    List[Any]   	=			outputs.logits.detach().cpu().numpy()
  							# Output of original model created with mesh-tensoflow
  							lowerCamelCase_  :    Any   	=			[
  							    # 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_  :    Dict   	=			np.array(lowerCamelCase	).flatten()
  							lowerCamelCase_  :    Any   	=			output_logits[0, :, :20].flatten()
  
  							def check(lowerCamelCase    :    Tuple					,			lowerCamelCase    :    Dict					,			lowerCamelCase    :    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      __a    (		self    :    int	):
  							lowerCamelCase_  :    Tuple   	=			GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'	)
  							lowerCamelCase_  :    Dict   	=			GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'	)
  							model.to(lowerCamelCase	)
  
  							# set deterministically
  							lowerCamelCase_  :    List[Any]   	=			GenerationConfig.from_pretrained('Tanrei/GPTSAN-japanese'	)
  							lowerCamelCase_  :    Dict   	=			1
  
  							# use different length sentences to test batching
  							lowerCamelCase_  :    str   	=			[
  							    '甲斐なら武田と言うほど',
  							    '織田信長は、',
  							]
  
  							lowerCamelCase_  :    Tuple   	=			'left'
  							lowerCamelCase_  :    Optional[int]   	=			tokenizer(lowerCamelCase					,			return_tensors='pt'					,			padding=lowerCamelCase	)
  							lowerCamelCase_  :    List[str]   	=			inputs['input_ids'].to(lowerCamelCase	)
  
  							self.assertNotEqual(inputs['attention_mask'][0].numpy().tolist()					,			inputs['attention_mask'][1].numpy().tolist()	)
  
  							lowerCamelCase_  :    int   	=			model.generate(
  							    input_ids=lowerCamelCase					,			attention_mask=inputs['attention_mask'].to(lowerCamelCase	)					,			max_new_tokens=3					,			generation_config=lowerCamelCase					,			)
  
  							lowerCamelCase_  :    Any   	=			tokenizer(sentences[0]					,			return_tensors='pt'	).input_ids.to(lowerCamelCase	)
  							lowerCamelCase_  :    Tuple   	=			model.generate(
  							    input_ids=lowerCamelCase					,			max_new_tokens=3					,			generation_config=lowerCamelCase	)
  
  							lowerCamelCase_  :    Any   	=			tokenizer(sentences[1]					,			return_tensors='pt'	).input_ids.to(lowerCamelCase	)
  							lowerCamelCase_  :    Optional[Any]   	=			model.generate(input_ids=lowerCamelCase					,			max_new_tokens=3					,			generation_config=lowerCamelCase	)
  
  							self.assertNotEqual(inputs_non_padded.shape					,			inputs_padded.shape	)
  
  							lowerCamelCase_  :    List[str]   	=			tokenizer.batch_decode(lowerCamelCase					,			skip_special_tokens=lowerCamelCase	)
  							lowerCamelCase_  :    Optional[int]   	=			tokenizer.decode(output_non_padded[0]					,			skip_special_tokens=lowerCamelCase	)
  							lowerCamelCase_  :    str   	=			tokenizer.decode(output_padded[0]					,			skip_special_tokens=lowerCamelCase	)
  
  							lowerCamelCase_  :    Optional[Any]   	=			[
  							    '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      __a    (		self    :    Union[str, Any]	):
  							lowerCamelCase_  :    List[str]   	=			GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'	)
  							lowerCamelCase_  :    Optional[int]   	=			GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'	)
  							# Output of original model created with mesh-tensoflow
  							lowerCamelCase_  :    Dict   	=			[
  							    ('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_  :    Dict   	=			tokenizer.encode(lowerCamelCase					,			return_tensors='pt'	)
  									lowerCamelCase_  :    Any   	=			model(lowerCamelCase	)
  									lowerCamelCase_  :    int   	=			outputs.logits.detach().cpu().numpy()[0]
  									lowerCamelCase_  :    int   	=			np.argmax(output_logits[-1]	)
  									self.assertEqual(lowerCamelCase					,			lowerCamelCase	)
  
  
  
  					@slow
  					def      __a    (		self    :    Dict	):
  							lowerCamelCase_  :    Optional[Any]   	=			GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'	)
  							lowerCamelCase_  :    List[str]   	=			GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'	)
  							model.to(lowerCamelCase	)
  
  							# set deterministically
  							lowerCamelCase_  :    Optional[Any]   	=			GenerationConfig.from_pretrained('Tanrei/GPTSAN-japanese'	)
  							lowerCamelCase_  :    Tuple   	=			1
  
  							lowerCamelCase_  :    int   	=			'武田信玄は、'
  							lowerCamelCase_  :    Dict   	=			tokenizer(lowerCamelCase					,			return_tensors='pt'	).input_ids.to(lowerCamelCase	)
  							lowerCamelCase_  :    Any   	=			tokenizer([input_text, input_text]					,			return_tensors='pt'	).input_ids.to(lowerCamelCase	)
  
  							# spout from uniform and one-hot
  							lowerCamelCase_  :    str   	=			[
  							    # 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_  :    str   	=			model.generate(
  							    input_ids=lowerCamelCase					,			spout=spouts[0]					,			max_new_tokens=20					,			generation_config=lowerCamelCase					,			)
  
  							lowerCamelCase_  :    Union[str, Any]   	=			model.generate(
  							    input_ids=lowerCamelCase					,			spout=spouts[1]					,			max_new_tokens=20					,			generation_config=lowerCamelCase					,			)
  
  							lowerCamelCase_  :    Any   	=			model.generate(
  							    input_ids=lowerCamelCase					,			spout=lowerCamelCase					,			max_new_tokens=20					,			generation_config=lowerCamelCase					,			)
  
  							lowerCamelCase_  :    List[str]   	=			tokenizer.decode(outputa[0]	)
  							lowerCamelCase_  :    Dict   	=			tokenizer.decode(outputa[0]	)
  							lowerCamelCase_  :    Tuple   	=			tokenizer.batch_decode(lowerCamelCase	)
  
  							lowerCamelCase_  :    str   	=			[
  							    '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      __a    (		self    :    List[Any]	):
  							lowerCamelCase_  :    Tuple   	=			GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'	)
  							lowerCamelCase_  :    Optional[int]   	=			GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'	)
  							model.to(lowerCamelCase	)
  
  							# set deterministically
  							lowerCamelCase_  :    Tuple   	=			GenerationConfig.from_pretrained('Tanrei/GPTSAN-japanese'	)
  							lowerCamelCase_  :    Union[str, Any]   	=			1
  
  							lowerCamelCase_  :    List[Any]   	=			'武田信玄'
  							lowerCamelCase_  :    Dict   	=			'織田信長'
  							lowerCamelCase_  :    List[Any]   	=			'は、'
  							lowerCamelCase_  :    List[Any]   	=			'が、'
  							lowerCamelCase_  :    List[str]   	=			tokenizer(lowerCamelCase					,			prefix_text=lowerCamelCase					,			return_tensors='pt'	)
  							lowerCamelCase_  :    str   	=			tokenizer(lowerCamelCase					,			prefix_text=lowerCamelCase					,			return_tensors='pt'	)
  							lowerCamelCase_  :    Optional[Any]   	=			tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]					,			return_tensors='pt'	)
  
  							lowerCamelCase_  :    List[Any]   	=			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					,			)
  
  							lowerCamelCase_  :    Any   	=			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					,			)
  
  							lowerCamelCase_  :    Any   	=			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					,			)
  
  							lowerCamelCase_  :    str   	=			tokenizer.decode(outputa[0]	)
  							lowerCamelCase_  :    str   	=			tokenizer.decode(outputa[0]	)
  							lowerCamelCase_  :    Tuple   	=			tokenizer.batch_decode(lowerCamelCase	)
  
  							lowerCamelCase_  :    List[str]   	=			[
  							    '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: 
  
  
  
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__       ,lowerCAmelCase__	):
  		return (input_a, input_a).count(0	) % 2
  
  
  def 		_SCREAMING_SNAKE_CASE    (   ):
  		assert xor_gate(0       ,0	) == 0
  		assert xor_gate(0       ,1	) == 1
  		assert xor_gate(1       ,0	) == 1
  		assert xor_gate(1       ,1	) == 0
  
  
  if __name__ == "__main__":
  		print(xor_gate(0, 0))
  		print(xor_gate(0, 1))
  