
"""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 	_lowercase    :


     '''simple docstring'''





     def __init__(   self			,      __UpperCamelCase			,      __UpperCamelCase=3_60_00			,      __UpperCamelCase=13			,      __UpperCamelCase=7			,      __UpperCamelCase=True			,      __UpperCamelCase=32			,      __UpperCamelCase=42			,      __UpperCamelCase=5			,      __UpperCamelCase=2			,      __UpperCamelCase=4			,      __UpperCamelCase=2			,      __UpperCamelCase=32			,      __UpperCamelCase=80			,      __UpperCamelCase=33			,      __UpperCamelCase=0.0			,      __UpperCamelCase=1E-6			,      __UpperCamelCase=1_00			,      __UpperCamelCase=0.0			,      )->       int:
          UpperCAmelCase__				:			str							=    vocab_size
          UpperCAmelCase__				:			Union[str, Any]							=    parent
          UpperCAmelCase__				:			int							=    batch_size
          UpperCAmelCase__				:			List[Any]							=    num_contexts
          # For common tests
          UpperCAmelCase__				:			int							=    self.num_contexts
          UpperCAmelCase__				:			Optional[int]							=    is_training
          UpperCAmelCase__				:			Optional[int]							=    hidden_size
          UpperCAmelCase__				:			Optional[Any]							=    num_ext_layers
          UpperCAmelCase__				:			str							=    ext_size
          UpperCAmelCase__				:			Optional[int]							=    num_hidden_layers
          UpperCAmelCase__				:			Any							=    num_attention_heads
          UpperCAmelCase__				:			List[str]							=    num_experts
          UpperCAmelCase__				:			Optional[int]							=    d_ff
          UpperCAmelCase__				:			Dict							=    d_ext
          UpperCAmelCase__				:			Optional[int]							=    d_spout
          UpperCAmelCase__				:			Any							=    dropout_rate
          UpperCAmelCase__				:			Any							=    layer_norm_epsilon
          UpperCAmelCase__				:			List[Any]							=    expert_capacity
          UpperCAmelCase__				:			int							=    router_jitter_noise



     def 			lowerCAmelCase__							(   self	)->       Any:
          return GPTSanJapaneseConfig.from_pretrained("Tanrei/GPTSAN-japanese"	)



     def 			lowerCAmelCase__							(   self	)->       int:
          UpperCAmelCase__				:			str							=    ids_tensor([self.batch_size, self.seq_length]			,      self.vocab_size	)

          UpperCAmelCase__				:			List[Any]							=    self.get_config()

          return (config, input_ids)



     def 			lowerCAmelCase__							(   self	)->       str:
          UpperCAmelCase__				:			Dict							=    ids_tensor([self.batch_size, self.seq_length]			,      self.vocab_size	)

          UpperCAmelCase__				:			str							=    self.get_config()

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



     def 			lowerCAmelCase__							(   self	)->       Tuple:
          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			,      __UpperCamelCase			,      __UpperCamelCase			,      )->       Tuple:
          UpperCAmelCase__				:			Optional[int]							=    GPTSanJapaneseForConditionalGeneration(config=__UpperCamelCase	)
          model.to(__UpperCamelCase	)
          model.eval()
          UpperCAmelCase__				:			Optional[int]							=    model(
              input_ids=__UpperCamelCase			,      )
          self.parent.assertIsNotNone(__UpperCamelCase	)







@require_torch
class 	_lowercase    (							lowerCAmelCase_						, lowerCAmelCase_						, unittest.TestCase					):


     '''simple docstring'''





     _A		    =				(GPTSanJapaneseModel,) if is_torch_available() else ()
     _A		    =				(
         {
             'conversational': GPTSanJapaneseForConditionalGeneration,
             'feature-extraction': GPTSanJapaneseForConditionalGeneration,
             'summarization': GPTSanJapaneseForConditionalGeneration,
             'text2text-generation': GPTSanJapaneseForConditionalGeneration,
             'translation': GPTSanJapaneseForConditionalGeneration,
         }
         if is_torch_available()
         else {}
     )
     _A		    =				False
     _A		    =				False
     _A		    =				False
     _A		    =				False
     _A		    =				False
     _A		    =				False
     _A		    =				False
     _A		    =				False
     # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
     _A		    =				[0.8, 0.9]



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase			,      __UpperCamelCase	)->       Optional[int]:
          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:
          UpperCAmelCase__				:			Tuple							=    GPTSanJapaneseTester(self	)
          UpperCAmelCase__				:			Any							=    ConfigTester(self			,      config_class=__UpperCamelCase			,      d_model=37	)



     def 			lowerCAmelCase__							(   self	)->       Optional[int]:
          GPTSanJapaneseConfig()



     def 			lowerCAmelCase__							(   self	)->       Union[str, Any]:
          UpperCAmelCase__				:			Optional[int]							=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__UpperCamelCase	)



     @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	)->       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	)->       Union[str, Any]:
          pass







@require_torch
class 	_lowercase    (							lowerCAmelCase_						, lowerCAmelCase_						, unittest.TestCase					):


     '''simple docstring'''





     _A		    =				(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
     _A		    =				False
     _A		    =				False
     _A		    =				False
     _A		    =				False
     _A		    =				False
     _A		    =				False
     # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
     _A		    =				[0.8, 0.9]



     def 			lowerCAmelCase__							(   self	)->       List[str]:
          UpperCAmelCase__				:			Dict							=    GPTSanJapaneseTester(self	)
          UpperCAmelCase__				:			Optional[int]							=    ConfigTester(self			,      config_class=__UpperCamelCase			,      d_model=37	)



     def 			lowerCAmelCase__							(   self	)->       Union[str, Any]:
          GPTSanJapaneseConfig()



     def 			lowerCAmelCase__							(   self	)->       Any:
          UpperCAmelCase__				:			List[Any]							=    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__UpperCamelCase	)



     @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	)->       Optional[int]:
          super().test_model_parallelism()



     @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."	)
     def 			lowerCAmelCase__							(   self	)->       Dict:
          pass



     @slow
     def 			lowerCAmelCase__							(   self	)->       Optional[int]:
          UpperCAmelCase__				:			Optional[Any]							=    GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"	)
          UpperCAmelCase__				:			Union[str, Any]							=    GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"	)
          UpperCAmelCase__				:			List[Any]							=    tokenizer.encode("武田信玄は"			,      return_tensors="pt"	)
          UpperCAmelCase__				:			str							=    model(__UpperCamelCase	)
          UpperCAmelCase__				:			str							=    outputs.logits.detach().cpu().numpy()
          # Output of original model created with mesh-tensoflow
          UpperCAmelCase__				:			Optional[int]							=    [
              # 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
          ]
          UpperCAmelCase__				:			Optional[int]							=    np.array(__UpperCamelCase	).flatten()
          UpperCAmelCase__				:			Union[str, Any]							=    output_logits[0, :, :20].flatten()

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

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



     @slow
     def 			lowerCAmelCase__							(   self	)->       Optional[int]:
          UpperCAmelCase__				:			List[str]							=    GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"	)
          UpperCAmelCase__				:			int							=    GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"	)
          model.to(__UpperCamelCase	)

          # set deterministically
          UpperCAmelCase__				:			int							=    GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"	)
          UpperCAmelCase__				:			List[Any]							=    1

          # use different length sentences to test batching
          UpperCAmelCase__				:			Optional[int]							=    [
              "甲斐なら武田と言うほど",
              "織田信長は、",
          ]

          UpperCAmelCase__				:			Optional[Any]							=    "left"
          UpperCAmelCase__				:			Union[str, Any]							=    tokenizer(__UpperCamelCase			,      return_tensors="pt"			,      padding=__UpperCamelCase	)
          UpperCAmelCase__				:			str							=    inputs["input_ids"].to(__UpperCamelCase	)

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

          UpperCAmelCase__				:			Tuple							=    model.generate(
              input_ids=__UpperCamelCase			,      attention_mask=inputs["attention_mask"].to(__UpperCamelCase	)			,      max_new_tokens=3			,      generation_config=__UpperCamelCase			,      )

          UpperCAmelCase__				:			str							=    tokenizer(sentences[0]			,      return_tensors="pt"	).input_ids.to(__UpperCamelCase	)
          UpperCAmelCase__				:			str							=    model.generate(
              input_ids=__UpperCamelCase			,      max_new_tokens=3			,      generation_config=__UpperCamelCase	)

          UpperCAmelCase__				:			List[Any]							=    tokenizer(sentences[1]			,      return_tensors="pt"	).input_ids.to(__UpperCamelCase	)
          UpperCAmelCase__				:			Union[str, Any]							=    model.generate(input_ids=__UpperCamelCase			,      max_new_tokens=3			,      generation_config=__UpperCamelCase	)

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

          UpperCAmelCase__				:			Union[str, Any]							=    tokenizer.batch_decode(__UpperCamelCase			,      skip_special_tokens=__UpperCamelCase	)
          UpperCAmelCase__				:			List[Any]							=    tokenizer.decode(output_non_padded[0]			,      skip_special_tokens=__UpperCamelCase	)
          UpperCAmelCase__				:			Dict							=    tokenizer.decode(output_padded[0]			,      skip_special_tokens=__UpperCamelCase	)

          UpperCAmelCase__				:			List[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(__UpperCamelCase			,      __UpperCamelCase	)
          self.assertListEqual(__UpperCamelCase			,      [non_padded_sentence, padded_sentence]	)



     @tooslow
     def 			lowerCAmelCase__							(   self	)->       Any:
          UpperCAmelCase__				:			str							=    GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"	)
          UpperCAmelCase__				:			Optional[Any]							=    GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"	)
          # Output of original model created with mesh-tensoflow
          UpperCAmelCase__				:			Optional[int]							=    [
              ("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:
               UpperCAmelCase__				:			Tuple							=    tokenizer.encode(__UpperCamelCase			,      return_tensors="pt"	)
               UpperCAmelCase__				:			Optional[int]							=    model(__UpperCamelCase	)
               UpperCAmelCase__				:			List[Any]							=    outputs.logits.detach().cpu().numpy()[0]
               UpperCAmelCase__				:			Union[str, Any]							=    np.argmax(output_logits[-1]	)
               self.assertEqual(__UpperCamelCase			,      __UpperCamelCase	)



     @slow
     def 			lowerCAmelCase__							(   self	)->       Optional[Any]:
          UpperCAmelCase__				:			Optional[Any]							=    GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"	)
          UpperCAmelCase__				:			Optional[Any]							=    GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"	)
          model.to(__UpperCamelCase	)

          # set deterministically
          UpperCAmelCase__				:			Optional[Any]							=    GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"	)
          UpperCAmelCase__				:			Optional[int]							=    1

          UpperCAmelCase__				:			Union[str, Any]							=    "武田信玄は、"
          UpperCAmelCase__				:			Optional[int]							=    tokenizer(__UpperCamelCase			,      return_tensors="pt"	).input_ids.to(__UpperCamelCase	)
          UpperCAmelCase__				:			Optional[int]							=    tokenizer([input_text, input_text]			,      return_tensors="pt"	).input_ids.to(__UpperCamelCase	)

          # spout from uniform and one-hot
          UpperCAmelCase__				:			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
          ]

          UpperCAmelCase__				:			Tuple							=    model.generate(
              input_ids=__UpperCamelCase			,      spout=spouts[0]			,      max_new_tokens=20			,      generation_config=__UpperCamelCase			,      )

          UpperCAmelCase__				:			Optional[int]							=    model.generate(
              input_ids=__UpperCamelCase			,      spout=spouts[1]			,      max_new_tokens=20			,      generation_config=__UpperCamelCase			,      )

          UpperCAmelCase__				:			Union[str, Any]							=    model.generate(
              input_ids=__UpperCamelCase			,      spout=__UpperCamelCase			,      max_new_tokens=20			,      generation_config=__UpperCamelCase			,      )

          UpperCAmelCase__				:			List[str]							=    tokenizer.decode(outputa[0]	)
          UpperCAmelCase__				:			Optional[int]							=    tokenizer.decode(outputa[0]	)
          UpperCAmelCase__				:			Union[str, Any]							=    tokenizer.batch_decode(__UpperCamelCase	)

          UpperCAmelCase__				:			Tuple							=    [
              "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(__UpperCamelCase			,      __UpperCamelCase	)
          self.assertListEqual(__UpperCamelCase			,      [outa_sentence, outa_sentence]	)



     @slow
     def 			lowerCAmelCase__							(   self	)->       Optional[int]:
          UpperCAmelCase__				:			Any							=    GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"	)
          UpperCAmelCase__				:			str							=    GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"	)
          model.to(__UpperCamelCase	)

          # set deterministically
          UpperCAmelCase__				:			int							=    GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"	)
          UpperCAmelCase__				:			Dict							=    1

          UpperCAmelCase__				:			Tuple							=    "武田信玄"
          UpperCAmelCase__				:			Union[str, Any]							=    "織田信長"
          UpperCAmelCase__				:			Optional[int]							=    "は、"
          UpperCAmelCase__				:			Optional[int]							=    "が、"
          UpperCAmelCase__				:			Optional[Any]							=    tokenizer(__UpperCamelCase			,      prefix_text=__UpperCamelCase			,      return_tensors="pt"	)
          UpperCAmelCase__				:			List[Any]							=    tokenizer(__UpperCamelCase			,      prefix_text=__UpperCamelCase			,      return_tensors="pt"	)
          UpperCAmelCase__				:			Union[str, Any]							=    tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]			,      return_tensors="pt"	)

          UpperCAmelCase__				:			Any							=    model.generate(
              input_ids=input_tok_a.input_ids.to(__UpperCamelCase	)			,      token_type_ids=input_tok_a.token_type_ids.to(__UpperCamelCase	)			,      max_new_tokens=20			,      generation_config=__UpperCamelCase			,      )

          UpperCAmelCase__				:			List[str]							=    model.generate(
              input_ids=input_tok_a.input_ids.to(__UpperCamelCase	)			,      token_type_ids=input_tok_a.token_type_ids.to(__UpperCamelCase	)			,      max_new_tokens=20			,      generation_config=__UpperCamelCase			,      )

          UpperCAmelCase__				:			Optional[int]							=    model.generate(
              input_ids=input_tok_a.input_ids.to(__UpperCamelCase	)			,      token_type_ids=input_tok_a.token_type_ids.to(__UpperCamelCase	)			,      attention_mask=input_tok_a.attention_mask.to(__UpperCamelCase	)			,      max_new_tokens=20			,      generation_config=__UpperCamelCase			,      )

          UpperCAmelCase__				:			int							=    tokenizer.decode(outputa[0]	)
          UpperCAmelCase__				:			List[Any]							=    tokenizer.decode(outputa[0]	)
          UpperCAmelCase__				:			Dict							=    tokenizer.batch_decode(__UpperCamelCase	)

          UpperCAmelCase__				:			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(__UpperCamelCase			,      __UpperCamelCase	)
          self.assertListEqual(__UpperCamelCase			,      [outa_sentence, outa_sentence]	)





"""simple docstring"""




def 				a__			(					lowerCAmelCase						:							str		):






     '''simple docstring'''
     UpperCAmelCase__				:			Optional[Any]							=    str(lowerCAmelCase		).strip()
     if not oct_string:
          raise ValueError("Empty string was passed to the function"		)
     UpperCAmelCase__				:			Tuple							=    oct_string[0] == "-"
     if is_negative:
          UpperCAmelCase__				:			List[Any]							=    oct_string[1:]
     if not oct_string.isdigit() or not all(0 <= int(lowerCAmelCase		) <= 7 for char in oct_string		):
          raise ValueError("Non-octal value was passed to the function"		)
     UpperCAmelCase__				:			Optional[int]							=    0
     for char in oct_string:
          UpperCAmelCase__				:			Tuple							=    8 * decimal_number + int(lowerCAmelCase		)
     if is_negative:
          UpperCAmelCase__				:			int							=    -decimal_number
     return decimal_number


if __name__ == "__main__":
    from doctest import testmod

    testmod()



