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 				A    :
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_=3_6000     ,  lowercase_=13     ,  lowercase_=7     ,  lowercase_=True     ,  lowercase_=32     ,  lowercase_=42     ,  lowercase_=5     ,  lowercase_=2     ,  lowercase_=4     ,  lowercase_=2     ,  lowercase_=32     ,  lowercase_=80     ,  lowercase_=33     ,  lowercase_=0.0     ,  lowercase_=1E-6     ,  lowercase_=100     ,  lowercase_=0.0     ,  )      ->   Any:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							str     				=						vocab_size
               _snake_case      :							Optional[int]     				=						parent
               _snake_case      :							Tuple     				=						batch_size
               _snake_case      :							str     				=						num_contexts
               # For common tests
               _snake_case      :							str     				=						self.num_contexts
               _snake_case      :							Tuple     				=						is_training
               _snake_case      :							Any     				=						hidden_size
               _snake_case      :							List[Any]     				=						num_ext_layers
               _snake_case      :							Tuple     				=						ext_size
               _snake_case      :							List[Any]     				=						num_hidden_layers
               _snake_case      :							Any     				=						num_attention_heads
               _snake_case      :							int     				=						num_experts
               _snake_case      :							Optional[int]     				=						d_ff
               _snake_case      :							List[str]     				=						d_ext
               _snake_case      :							Any     				=						d_spout
               _snake_case      :							Tuple     				=						dropout_rate
               _snake_case      :							Optional[Any]     				=						layer_norm_epsilon
               _snake_case      :							str     				=						expert_capacity
               _snake_case      :							str     				=						router_jitter_noise
  
  
  
        def    __a (					self					)      ->   int:
  
  
  
               '''simple docstring'''
  
               return GPTSanJapaneseConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
  
  
  
        def    __a (					self					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Dict     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)
  
               _snake_case      :							Union[str, Any]     				=						self.get_config()
  
               return (config, input_ids)
  
  
  
        def    __a (					self					)      ->   str:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Any     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)
  
               _snake_case      :							int     				=						self.get_config()
  
               return (config, {"input_ids": input_ids})
  
  
  
        def    __a (					self					)      ->   Union[str, Any]:
  
  
  
               '''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    __a (					self     ,  lowercase_     ,  lowercase_     ,  )      ->   str:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							int     				=						GPTSanJapaneseForConditionalGeneration(config=lowercase_					)
               model.to(lowercase_					)
               model.eval()
               _snake_case      :							str     				=						model(
                   input_ids=lowercase_     ,  )
               self.parent.assertIsNotNone(lowercase_					)
  
  
  @require_torch
  class 				A    (__UpperCAmelCase       ,__UpperCAmelCase       ,unittest.TestCase				):
        _SCREAMING_SNAKE_CASE					=							(GPTSanJapaneseModel,) if is_torch_available() else ()
        _SCREAMING_SNAKE_CASE					=							(
            {
                """conversational""": GPTSanJapaneseForConditionalGeneration,
                """feature-extraction""": GPTSanJapaneseForConditionalGeneration,
                """summarization""": GPTSanJapaneseForConditionalGeneration,
                """text2text-generation""": GPTSanJapaneseForConditionalGeneration,
                """translation""": GPTSanJapaneseForConditionalGeneration,
            }
            if is_torch_available()
            else {}
        )
        _SCREAMING_SNAKE_CASE					=							False
        _SCREAMING_SNAKE_CASE					=							False
        _SCREAMING_SNAKE_CASE					=							False
        _SCREAMING_SNAKE_CASE					=							False
        _SCREAMING_SNAKE_CASE					=							False
        _SCREAMING_SNAKE_CASE					=							False
        _SCREAMING_SNAKE_CASE					=							False
        _SCREAMING_SNAKE_CASE					=							False
        # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
        _SCREAMING_SNAKE_CASE					=							[0.8, 0.9]
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   List[Any]:
  
  
  
               '''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    __a (					self					)      ->   List[str]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[str]     				=						GPTSanJapaneseTester(self					)
               _snake_case      :							str     				=						ConfigTester(self     ,  config_class=lowercase_     ,  d_model=37					)
  
  
  
        def    __a (					self					)      ->   int:
  
  
  
               '''simple docstring'''
  
               GPTSanJapaneseConfig()
  
  
  
        def    __a (					self					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							int     				=						self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_model(*lowercase_					)
  
  
  
        @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					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               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]:
  
  
  
               '''simple docstring'''
  
               pass
  
  
  @require_torch
  class 				A    (__UpperCAmelCase       ,__UpperCAmelCase       ,unittest.TestCase				):
        _SCREAMING_SNAKE_CASE					=							(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
        _SCREAMING_SNAKE_CASE					=							False
        _SCREAMING_SNAKE_CASE					=							False
        _SCREAMING_SNAKE_CASE					=							False
        _SCREAMING_SNAKE_CASE					=							False
        _SCREAMING_SNAKE_CASE					=							False
        _SCREAMING_SNAKE_CASE					=							False
        # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
        _SCREAMING_SNAKE_CASE					=							[0.8, 0.9]
  
  
  
        def    __a (					self					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[str]     				=						GPTSanJapaneseTester(self					)
               _snake_case      :							str     				=						ConfigTester(self     ,  config_class=lowercase_     ,  d_model=37					)
  
  
  
        def    __a (					self					)      ->   str:
  
  
  
               '''simple docstring'''
  
               GPTSanJapaneseConfig()
  
  
  
        def    __a (					self					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_model(*lowercase_					)
  
  
  
        @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					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               super().test_model_parallelism()
  
  
  
        @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''					)
        def    __a (					self					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               pass
  
  
  
        @slow
        def    __a (					self					)      ->   Union[str, Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Union[str, Any]     				=						GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
               _snake_case      :							List[str]     				=						GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
               _snake_case      :							Optional[int]     				=						tokenizer.encode('''武田信玄は'''     ,  return_tensors='''pt'''					)
               _snake_case      :							List[str]     				=						model(lowercase_					)
               _snake_case      :							List[Any]     				=						outputs.logits.detach().cpu().numpy()
               # Output of original model created with mesh-tensoflow
               _snake_case      :							List[str]     				=						[
                   # fmt: off
                   [-12.037_8398_8952_6367, -12.433_0615_9973_1445, -14.333_8403_7017_8223, -12.450_3459_9304_1992, -11.1_6613_7695_3125,
                   -11.930_1376_3427_7344, -10.659_7404_4799_8047, -12.909_5745_0866_6992, -13.241_0430_9082_0312, -13.398_5795_9747_3145,
                   -11.107_5248_7182_6172, -12.3_6859_4169_6167, -22.97_9431_1523_4375, -10.481_0676_5747_0703, -12.484_0307_2357_1777,
                   -12.807_3606_4910_8887, -14.769_7000_5035_4004, -12.233_5796_3562_0117, -13.428_1454_0863_0371, -22.624_1779_3273_9258],
                   [-7.511_1498_8327_0264, -8.281_8517_6849_3652, -7.943_1271_5530_3955, -7.55_0213_3369_4458, -6.49_8699_6650_6958,
                   -7.586_7962_8372_1924, -6.978_0859_9472_0459, -7.839_1451_8356_3232, -8.21_9641_6854_8584, -8.695_0912_4755_8594,
                   -6.706_9106_1019_8975, -6.6585_7982_6354_9805, -19.565_6986_2365_7227, -5.353_8427_3529_0527, -8.350_6860_7330_3223,
                   -8.039_3886_5661_6211, -10.856_5692_9016_1133, -7.75_1544_4755_5542, -8.819_0221_7864_9902, -19.51_5327_4536_1328],
                   [-9.73_0662_3458_8623, -10.223_9227_2949_2188, -9.932_9814_9108_8867, -11.857_8367_2332_7637, -7.662_6262_6647_9492,
                   -11.13_5291_0995_4834, -7.765_0976_1810_3027, -11.472_9232_7880_8594, -9.543_1499_4812_0117, -11.905_6339_2639_1602,
                   -9.366_1642_0745_8496, -11.5_7342_8153_9917, -23.699_0032_1960_4492, -9.429_5902_2521_9727, -10.42_8392_4102_7832,
                   -10.585_2403_6407_4707, -10.94_7719_5739_7461, -11.095_4160_6903_0762, -10.390_2406_6925_0488, -23.769_3729_4006_3477],
                   [-9.728_2657_6232_9102, -9.859_7126_0070_8008, -10.09_7292_9000_8545, -9.678_5221_0998_5352, -6.879_5199_3942_2607,
                   -9.68_4875_4882_8125, -4.2803_4257_8887_9395, -10.018_9142_2271_7285, -9.308_4459_3048_0957, -10.63_3945_4650_8789,
                   -8.083_6467_7429_1992, -9.06_3014_9841_3086, -21.904_2663_5742_1875, -8.90_1608_4671_0205, -8.841_8760_2996_8262,
                   -11.856_7199_7070_3125, -12.079_3981_5521_2402, -11.233_7532_0434_5703, -10.177_3386_0015_8691, -21.87_2566_2231_4453],
                   [-9.669_7645_1873_7793, -9.614_1986_8469_2383, -9.814_5103_4545_8984, -9.996_5019_2260_7422, -11.375_6904_6020_5078,
                   -10.113_4052_2766_1133, -10.546_8673_7060_5469, -10.04_3690_6814_5752, -10.907_8092_5750_7324, -10.504_2161_9415_2832,
                   -11.129_1990_2801_5137, -10.151_1240_0054_9316, -21.96_5867_9962_1582, -9.086_3494_8730_4688, -11.730_3390_5029_2969,
                   -10.460_6676_1016_8457, -10.298_0499_2675_7812, -10.784_1482_1624_7559, -10.840_6934_7381_5918, -22.03_1528_4729_0039],
                   # fmt: on
               ]
               _snake_case      :							List[str]     				=						np.array(lowercase_					).flatten()
               _snake_case      :							List[Any]     				=						output_logits[0, :, :20].flatten()
  
               def check(lowercase_     ,  lowercase_     ,  lowercase_=5E-4					):
                      return abs(a - b					) < epsilon * max(abs(lowercase_					)     ,  abs(lowercase_					)					)
  
               self.assertTrue(np.all([check(target[i]     ,  predict[i]					) for i in range(len(lowercase_					)					)]					)					)
  
  
  
        @slow
        def    __a (					self					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Any     				=						GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
               _snake_case      :							List[Any]     				=						GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
               model.to(lowercase_					)
  
               # set deterministically
               _snake_case      :							List[Any]     				=						GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
               _snake_case      :							List[Any]     				=						1
  
               # use different length sentences to test batching
               _snake_case      :							int     				=						[
                   '''甲斐なら武田と言うほど''',
                   '''織田信長は、''',
               ]
  
               _snake_case      :							str     				=						'''left'''
               _snake_case      :							Optional[int]     				=						tokenizer(lowercase_     ,  return_tensors='''pt'''     ,  padding=lowercase_					)
               _snake_case      :							Optional[int]     				=						inputs['''input_ids'''].to(lowercase_					)
  
               self.assertNotEqual(inputs['''attention_mask'''][0].numpy().tolist()     ,  inputs['''attention_mask'''][1].numpy().tolist()					)
  
               _snake_case      :							Optional[Any]     				=						model.generate(
                   input_ids=lowercase_     ,  attention_mask=inputs['''attention_mask'''].to(lowercase_					)     ,  max_new_tokens=3     ,  generation_config=lowercase_     ,  )
  
               _snake_case      :							Dict     				=						tokenizer(sentences[0]     ,  return_tensors='''pt'''					).input_ids.to(lowercase_					)
               _snake_case      :							List[str]     				=						model.generate(
                   input_ids=lowercase_     ,  max_new_tokens=3     ,  generation_config=lowercase_					)
  
               _snake_case      :							Optional[int]     				=						tokenizer(sentences[1]     ,  return_tensors='''pt'''					).input_ids.to(lowercase_					)
               _snake_case      :							int     				=						model.generate(input_ids=lowercase_     ,  max_new_tokens=3     ,  generation_config=lowercase_					)
  
               self.assertNotEqual(inputs_non_padded.shape     ,  inputs_padded.shape					)
  
               _snake_case      :							List[str]     				=						tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)
               _snake_case      :							Any     				=						tokenizer.decode(output_non_padded[0]     ,  skip_special_tokens=lowercase_					)
               _snake_case      :							Union[str, Any]     				=						tokenizer.decode(output_padded[0]     ,  skip_special_tokens=lowercase_					)
  
               _snake_case      :							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(lowercase_     ,  lowercase_					)
               self.assertListEqual(lowercase_     ,  [non_padded_sentence, padded_sentence]					)
  
  
  
        @tooslow
        def    __a (					self					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Any     				=						GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
               _snake_case      :							Optional[int]     				=						GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
               # Output of original model created with mesh-tensoflow
               _snake_case      :							Optional[Any]     				=						[
                   ('''武田信玄は''', 3_5675),
                   ('''武田信玄は、''', 45),
                   ('''武田信玄は、この''', 29),
                   ('''武田信玄は、このよう''', 3_0642),
                   ('''武田信玄は、このような''', 3_5680),
                   ('''武田信玄は、このような「''', 8640),
                   ('''武田信玄は、このような「武田''', 3_1617),
                   ('''武田信玄は、このような「武田家''', 3_0646),
                   ('''武田信玄は、このような「武田家の''', 3_1617),
                   ('''武田信玄は、このような「武田家の家''', 3_1381),
               ]
               for input, output in target:
                      _snake_case      :							Any     				=						tokenizer.encode(lowercase_     ,  return_tensors='''pt'''					)
                      _snake_case      :							Dict     				=						model(lowercase_					)
                      _snake_case      :							Dict     				=						outputs.logits.detach().cpu().numpy()[0]
                      _snake_case      :							int     				=						np.argmax(output_logits[-1]					)
                      self.assertEqual(lowercase_     ,  lowercase_					)
  
  
  
        @slow
        def    __a (					self					)      ->   Dict:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[str]     				=						GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
               _snake_case      :							Any     				=						GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
               model.to(lowercase_					)
  
               # set deterministically
               _snake_case      :							List[str]     				=						GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
               _snake_case      :							List[Any]     				=						1
  
               _snake_case      :							List[str]     				=						'''武田信玄は、'''
               _snake_case      :							int     				=						tokenizer(lowercase_     ,  return_tensors='''pt'''					).input_ids.to(lowercase_					)
               _snake_case      :							Optional[Any]     				=						tokenizer([input_text, input_text]     ,  return_tensors='''pt'''					).input_ids.to(lowercase_					)
  
               # spout from uniform and one-hot
               _snake_case      :							List[str]     				=						[
                   # fmt: off
                   [0.8788_2208, 0.3842_6396, 0.3322_0248, 0.4389_0406, 0.1656_2252,
                   0.0480_3985, 0.21_1572  , 0.2318_8473, 0.3715_3068, 0.783_6377 ,
                   0.0216_0172, 0.3876_1719, 0.7529_0772, 0.9019_8857, 0.3436_5777,
                   0.6416_8169, 0.4431_8471, 0.1457_5746, 0.9256_2881, 0.4081_2148,
                   0.2901_9122, 0.8886_1599, 0.6552_4846, 0.4356_3456, 0.3817_7187,
                   0.7083_2965, 0.8152_7892, 0.6883_2812, 0.3883_3192, 0.456_1522 ,
                   0.1482_8817, 0.4724_8213, 0.5435_7335, 0.8200_9566, 0.133_8884 ,
                   0.0275_5417, 0.1976_4677, 0.242_2084 , 0.0475_7674, 0.6540_9606,
                   0.082_4589 , 0.0330_4383, 0.9438_7689, 0.9876_4509, 0.8243_3901,
                   0.2764_6741, 0.6490_7493, 0.7600_9406, 0.3008_7915, 0.1790_4689,
                   0.4160_1714, 0.6704_6398, 0.1042_2822, 0.0844_7374, 0.0735_4344,
                   0.6142_3565, 0.7028_4866, 0.753_2333 , 0.197_2038 , 0.2957_5659,
                   0.9058_3886, 0.2926_5307, 0.5000_0175, 0.7040_7655, 0.88_9363  ,
                   0.8190_4418, 0.6682_9128, 0.6446_8815, 0.5656_3723, 0.8560_1875,
                   0.9492_4672, 0.0016_6762, 0.2522_0643, 0.7454_0219, 0.6799_3247,
                   0.154_9675 , 0.3938_5352, 0.9215_3607, 0.6374_5931, 0.2775_9043,
                   0.8470_2295, 0.6590_4271, 0.5867_6614, 0.866_6936 , 0.3960_7438,
                   0.7995_4983, 0.4222_0697, 0.3965_0381, 0.784_9864 , 0.5615_0201,
                   0.1567_8925, 0.1474_6032, 0.3454_2114, 0.4702_6783, 0.1195_6489,
                   0.2542_1435, 0.3378_8901, 0.6893_4842, 0.3642_4685, 0.7173_7898,
                   0.3898_3449, 0.9439_3779, 0.3957_5588, 0.3661_6553, 0.8710_4665,
                   0.6463_0203, 0.2251_6905, 0.8827_0804, 0.1503_1338, 0.7514_4345,
                   0.4645_9025, 0.8539_6454, 0.8635_5643, 0.6513_9851, 0.7026_6061,
                   0.3024_1389, 0.8105_6497, 0.8886_5969, 0.3877_3807, 0.7063_5849,
                   0.9071_8459, 0.4324_5789, 0.2800_0654, 0.4593_5562, 0.0877_3519,
                   0.955_2151 , 0.9390_1511, 0.2248_9288], # 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      :							Dict     				=						model.generate(
                   input_ids=lowercase_     ,  spout=spouts[0]     ,  max_new_tokens=20     ,  generation_config=lowercase_     ,  )
  
               _snake_case      :							Tuple     				=						model.generate(
                   input_ids=lowercase_     ,  spout=spouts[1]     ,  max_new_tokens=20     ,  generation_config=lowercase_     ,  )
  
               _snake_case      :							str     				=						model.generate(
                   input_ids=lowercase_     ,  spout=lowercase_     ,  max_new_tokens=20     ,  generation_config=lowercase_     ,  )
  
               _snake_case      :							Dict     				=						tokenizer.decode(outputa[0]					)
               _snake_case      :							Tuple     				=						tokenizer.decode(outputa[0]					)
               _snake_case      :							Optional[Any]     				=						tokenizer.batch_decode(lowercase_					)
  
               _snake_case      :							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(lowercase_     ,  lowercase_					)
               self.assertListEqual(lowercase_     ,  [outa_sentence, outa_sentence]					)
  
  
  
        @slow
        def    __a (					self					)      ->   int:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Any     				=						GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
               _snake_case      :							Optional[int]     				=						GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
               model.to(lowercase_					)
  
               # set deterministically
               _snake_case      :							List[Any]     				=						GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
               _snake_case      :							Dict     				=						1
  
               _snake_case      :							Any     				=						'''武田信玄'''
               _snake_case      :							int     				=						'''織田信長'''
               _snake_case      :							str     				=						'''は、'''
               _snake_case      :							Dict     				=						'''が、'''
               _snake_case      :							List[str]     				=						tokenizer(lowercase_     ,  prefix_text=lowercase_     ,  return_tensors='''pt'''					)
               _snake_case      :							Dict     				=						tokenizer(lowercase_     ,  prefix_text=lowercase_     ,  return_tensors='''pt'''					)
               _snake_case      :							Optional[Any]     				=						tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]     ,  return_tensors='''pt'''					)
  
               _snake_case      :							Tuple     				=						model.generate(
                   input_ids=input_tok_a.input_ids.to(lowercase_					)     ,  token_type_ids=input_tok_a.token_type_ids.to(lowercase_					)     ,  max_new_tokens=20     ,  generation_config=lowercase_     ,  )
  
               _snake_case      :							List[Any]     				=						model.generate(
                   input_ids=input_tok_a.input_ids.to(lowercase_					)     ,  token_type_ids=input_tok_a.token_type_ids.to(lowercase_					)     ,  max_new_tokens=20     ,  generation_config=lowercase_     ,  )
  
               _snake_case      :							List[str]     				=						model.generate(
                   input_ids=input_tok_a.input_ids.to(lowercase_					)     ,  token_type_ids=input_tok_a.token_type_ids.to(lowercase_					)     ,  attention_mask=input_tok_a.attention_mask.to(lowercase_					)     ,  max_new_tokens=20     ,  generation_config=lowercase_     ,  )
  
               _snake_case      :							Optional[int]     				=						tokenizer.decode(outputa[0]					)
               _snake_case      :							Tuple     				=						tokenizer.decode(outputa[0]					)
               _snake_case      :							List[str]     				=						tokenizer.batch_decode(lowercase_					)
  
               _snake_case      :							int     				=						[
                   '''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(lowercase_     ,  lowercase_					)
               self.assertListEqual(lowercase_     ,  [outa_sentence, outa_sentence]					)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  from __future__ import annotations
  
  class 				A    :
  
  
  
        def __init__(					self     ,  lowercase_					)      ->   None:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						vertices
               _snake_case      :							int     				=						[[0] * vertices for _ in range(lowercase_					)]
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   None:
  
  
  
               '''simple docstring'''
  
               print('''Vertex \t Distance from Source'''					)
               for vertex in range(self.vertices					):
                      print(lowercase_     ,  '''\t\t'''     ,  distances_from_source[vertex]					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_					)      ->   int:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						1E7
               _snake_case      :							Tuple     				=						0
  
               # Search not nearest vertex not in the shortest path tree
               for vertex in range(self.vertices					):
                      if distances_from_source[vertex] < minimum and visited[vertex] is False:
                             _snake_case      :							Dict     				=						distances_from_source[vertex]
                             _snake_case      :							int     				=						vertex
               return min_index
  
  
  
        def    __a (					self     ,  lowercase_					)      ->   None:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						[int(1E7					)] * self.vertices  # distances from the source
               _snake_case      :							List[Any]     				=						0
               _snake_case      :							List[str]     				=						[False] * self.vertices
  
               for _ in range(self.vertices					):
                      _snake_case      :							List[Any]     				=						self.minimum_distance(lowercase_     ,  lowercase_					)
                      _snake_case      :							List[str]     				=						True
  
                      # Update dist value of the adjacent vertices
                      # of the picked vertex only if the current
                      # distance is greater than new distance and
                      # the vertex in not in the shortest path tree
                      for v in range(self.vertices					):
                             if (
                                 self.graph[u][v] > 0
                                 and visited[v] is False
                                 and distances[v] > distances[u] + self.graph[u][v]
                             ):
                                    _snake_case      :							Any     				=						distances[u] + self.graph[u][v]
  
               self.print_solution(lowercase_					)
  
  
  if __name__ == "__main__":
     lowerCAmelCase_   =   Graph(9)
     lowerCAmelCase_   =   [
         [0, 4, 0, 0, 0, 0, 0, 8, 0],
         [4, 0, 8, 0, 0, 0, 0, 1_1, 0],
         [0, 8, 0, 7, 0, 4, 0, 0, 2],
         [0, 0, 7, 0, 9, 1_4, 0, 0, 0],
         [0, 0, 0, 9, 0, 1_0, 0, 0, 0],
         [0, 0, 4, 1_4, 1_0, 0, 2, 0, 0],
         [0, 0, 0, 0, 0, 2, 0, 1, 6],
         [8, 1_1, 0, 0, 0, 0, 1, 0, 7],
         [0, 0, 2, 0, 0, 0, 6, 7, 0],
     ]
     graph.dijkstra(0)
  
  
  
  
  
  
  