





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  :

      """simple docstring"""


      def __init__(						self:	Union[str, Any]					,  __lowerCamelCase:	int					,  __lowerCamelCase:	int=3_6000					,  __lowerCamelCase:	List[str]=13					,  __lowerCamelCase:	Tuple=7					,  __lowerCamelCase:	Dict=True					,  __lowerCamelCase:	int=32					,  __lowerCamelCase:	List[str]=42					,  __lowerCamelCase:	List[str]=5					,  __lowerCamelCase:	Optional[int]=2					,  __lowerCamelCase:	Tuple=4					,  __lowerCamelCase:	Any=2					,  __lowerCamelCase:	List[str]=32					,  __lowerCamelCase:	List[Any]=80					,  __lowerCamelCase:	List[str]=33					,  __lowerCamelCase:	Optional[int]=0.0					,  __lowerCamelCase:	Any=1e-6					,  __lowerCamelCase:	Optional[int]=100					,  __lowerCamelCase:	Optional[Any]=0.0					,  ):
          '''simple docstring'''


          UpperCamelCase__:					Union[str, Any]												=      vocab_size
          UpperCamelCase__:					int												=      parent
          UpperCamelCase__:					int												=      batch_size
          UpperCamelCase__:					Dict												=      num_contexts
          # For common tests
          UpperCamelCase__:					Dict												=      self.num_contexts
          UpperCamelCase__:					List[Any]												=      is_training
          UpperCamelCase__:					int												=      hidden_size
          UpperCamelCase__:					Any												=      num_ext_layers
          UpperCamelCase__:					Tuple												=      ext_size
          UpperCamelCase__:					List[Any]												=      num_hidden_layers
          UpperCamelCase__:					Any												=      num_attention_heads
          UpperCamelCase__:					Tuple												=      num_experts
          UpperCamelCase__:					int												=      d_ff
          UpperCamelCase__:					Dict												=      d_ext
          UpperCamelCase__:					Any												=      d_spout
          UpperCamelCase__:					List[Any]												=      dropout_rate
          UpperCamelCase__:					int												=      layer_norm_epsilon
          UpperCamelCase__:					Dict												=      expert_capacity
          UpperCamelCase__:					Dict												=      router_jitter_noise






      def 	UpperCAmelCase_   (						self:	List[Any]			):
          '''simple docstring'''


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






      def 	UpperCAmelCase_   (						self:	Tuple			):
          '''simple docstring'''


          UpperCamelCase__:					Optional[Any]												=      ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size			)

          UpperCamelCase__:					Optional[Any]												=      self.get_config()

          return (config, input_ids)






      def 	UpperCAmelCase_   (						self:	str			):
          '''simple docstring'''


          UpperCamelCase__:					Optional[Any]												=      ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size			)

          UpperCamelCase__:					Union[str, Any]												=      self.get_config()

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






      def 	UpperCAmelCase_   (						self:	str			):
          '''simple docstring'''


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






      def 	UpperCAmelCase_   (						self:	List[Any]					,  __lowerCamelCase:	Optional[Any]					,  __lowerCamelCase:	Dict					,  ):
          '''simple docstring'''


          UpperCamelCase__:					List[str]												=      GPTSanJapaneseForConditionalGeneration(config=__lowerCamelCase			)
          model.to(__lowerCamelCase			)
          model.eval()
          UpperCamelCase__:					List[str]												=      model(
              input_ids=__lowerCamelCase					,  )
          self.parent.assertIsNotNone(__lowerCamelCase			)




@require_torch
class    _a  (  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 	UpperCAmelCase_   (						self:	int					,  __lowerCamelCase:	Dict					,  __lowerCamelCase:	Dict					,  __lowerCamelCase:	List[Any]					,  __lowerCamelCase:	List[str]					,  __lowerCamelCase:	str			):
          '''simple docstring'''


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

          return False






      def 	UpperCAmelCase_   (						self:	Optional[int]			):
          '''simple docstring'''


          UpperCamelCase__:					List[str]												=      GPTSanJapaneseTester(self			)
          UpperCamelCase__:					str												=      ConfigTester(self					,  config_class=__lowerCamelCase					,  d_model=37			)






      def 	UpperCAmelCase_   (						self:	Optional[int]			):
          '''simple docstring'''


          GPTSanJapaneseConfig()






      def 	UpperCAmelCase_   (						self:	List[str]			):
          '''simple docstring'''


          UpperCamelCase__:					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 	UpperCAmelCase_   (						self:	List[Any]			):
          '''simple docstring'''


          super().test_model_parallelism()






      @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."			)
      def 	UpperCAmelCase_   (						self:	List[str]			):
          '''simple docstring'''


          pass




@require_torch
class    _a  (  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 	UpperCAmelCase_   (						self:	Any			):
          '''simple docstring'''


          UpperCamelCase__:					str												=      GPTSanJapaneseTester(self			)
          UpperCamelCase__:					Dict												=      ConfigTester(self					,  config_class=__lowerCamelCase					,  d_model=37			)






      def 	UpperCAmelCase_   (						self:	Any			):
          '''simple docstring'''


          GPTSanJapaneseConfig()






      def 	UpperCAmelCase_   (						self:	Tuple			):
          '''simple docstring'''


          UpperCamelCase__:					List[str]												=      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 	UpperCAmelCase_   (						self:	Optional[Any]			):
          '''simple docstring'''


          super().test_model_parallelism()






      @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."			)
      def 	UpperCAmelCase_   (						self:	Optional[int]			):
          '''simple docstring'''


          pass






      @slow
      def 	UpperCAmelCase_   (						self:	Optional[int]			):
          '''simple docstring'''


          UpperCamelCase__:					str												=      GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"			)
          UpperCamelCase__:					Tuple												=      GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"			)
          UpperCamelCase__:					List[Any]												=      tokenizer.encode("武田信玄は"					,  return_tensors="pt"			)
          UpperCamelCase__:					Dict												=      model(__lowerCamelCase			)
          UpperCamelCase__:					List[Any]												=      outputs.logits.detach().cpu().numpy()
          # Output of original model created with mesh-tensoflow
          UpperCamelCase__:					Optional[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
          ]
          UpperCamelCase__:					Optional[int]												=      np.array(__lowerCamelCase			).flatten()
          UpperCamelCase__:					List[str]												=      output_logits[0, :, :20].flatten()

          def check(__lowerCamelCase:	Optional[int]					,  __lowerCamelCase:	Optional[int]					,  __lowerCamelCase:	Union[str, 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 	UpperCAmelCase_   (						self:	List[str]			):
          '''simple docstring'''


          UpperCamelCase__:					Any												=      GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"			)
          UpperCamelCase__:					Tuple												=      GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"			)
          model.to(__lowerCamelCase			)

          # set deterministically
          UpperCamelCase__:					List[str]												=      GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"			)
          UpperCamelCase__:					Tuple												=      1

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

          UpperCamelCase__:					Tuple												=      "left"
          UpperCamelCase__:					Union[str, Any]												=      tokenizer(__lowerCamelCase					,  return_tensors="pt"					,  padding=__lowerCamelCase			)
          UpperCamelCase__:					Union[str, Any]												=      inputs["input_ids"].to(__lowerCamelCase			)

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

          UpperCamelCase__:					Dict												=      model.generate(
              input_ids=__lowerCamelCase					,  attention_mask=inputs["attention_mask"].to(__lowerCamelCase			)					,  max_new_tokens=3					,  generation_config=__lowerCamelCase					,  )

          UpperCamelCase__:					List[str]												=      tokenizer(sentences[0]					,  return_tensors="pt"			).input_ids.to(__lowerCamelCase			)
          UpperCamelCase__:					Dict												=      model.generate(
              input_ids=__lowerCamelCase					,  max_new_tokens=3					,  generation_config=__lowerCamelCase			)

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

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

          UpperCamelCase__:					Optional[int]												=      tokenizer.batch_decode(__lowerCamelCase					,  skip_special_tokens=__lowerCamelCase			)
          UpperCamelCase__:					Optional[int]												=      tokenizer.decode(output_non_padded[0]					,  skip_special_tokens=__lowerCamelCase			)
          UpperCamelCase__:					List[Any]												=      tokenizer.decode(output_padded[0]					,  skip_special_tokens=__lowerCamelCase			)

          UpperCamelCase__:					Dict												=      [
              "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 	UpperCAmelCase_   (						self:	Optional[int]			):
          '''simple docstring'''


          UpperCamelCase__:					str												=      GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"			)
          UpperCamelCase__:					str												=      GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"			)
          # Output of original model created with mesh-tensoflow
          UpperCamelCase__:					int												=      [
              ("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:
              UpperCamelCase__:					Optional[Any]												=      tokenizer.encode(__lowerCamelCase					,  return_tensors="pt"			)
              UpperCamelCase__:					Dict												=      model(__lowerCamelCase			)
              UpperCamelCase__:					Dict												=      outputs.logits.detach().cpu().numpy()[0]
              UpperCamelCase__:					Tuple												=      np.argmax(output_logits[-1]			)
              self.assertEqual(__lowerCamelCase					,  __lowerCamelCase			)






      @slow
      def 	UpperCAmelCase_   (						self:	Tuple			):
          '''simple docstring'''


          UpperCamelCase__:					int												=      GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"			)
          UpperCamelCase__:					Any												=      GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"			)
          model.to(__lowerCamelCase			)

          # set deterministically
          UpperCamelCase__:					str												=      GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"			)
          UpperCamelCase__:					str												=      1

          UpperCamelCase__:					str												=      "武田信玄は、"
          UpperCamelCase__:					Tuple												=      tokenizer(__lowerCamelCase					,  return_tensors="pt"			).input_ids.to(__lowerCamelCase			)
          UpperCamelCase__:					Dict												=      tokenizer([input_text, input_text]					,  return_tensors="pt"			).input_ids.to(__lowerCamelCase			)

          # spout from uniform and one-hot
          UpperCamelCase__:					Any												=      [
              # 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
          ]

          UpperCamelCase__:					List[str]												=      model.generate(
              input_ids=__lowerCamelCase					,  spout=spouts[0]					,  max_new_tokens=20					,  generation_config=__lowerCamelCase					,  )

          UpperCamelCase__:					Dict												=      model.generate(
              input_ids=__lowerCamelCase					,  spout=spouts[1]					,  max_new_tokens=20					,  generation_config=__lowerCamelCase					,  )

          UpperCamelCase__:					Any												=      model.generate(
              input_ids=__lowerCamelCase					,  spout=__lowerCamelCase					,  max_new_tokens=20					,  generation_config=__lowerCamelCase					,  )

          UpperCamelCase__:					Any												=      tokenizer.decode(outputa[0]			)
          UpperCamelCase__:					Any												=      tokenizer.decode(outputa[0]			)
          UpperCamelCase__:					Any												=      tokenizer.batch_decode(__lowerCamelCase			)

          UpperCamelCase__:					Union[str, Any]												=      [
              "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 	UpperCAmelCase_   (						self:	Dict			):
          '''simple docstring'''


          UpperCamelCase__:					Dict												=      GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"			)
          UpperCamelCase__:					Optional[Any]												=      GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"			)
          model.to(__lowerCamelCase			)

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

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

          UpperCamelCase__:					str												=      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					,  )

          UpperCamelCase__:					int												=      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					,  )

          UpperCamelCase__:					Tuple												=      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					,  )

          UpperCamelCase__:					str												=      tokenizer.decode(outputa[0]			)
          UpperCamelCase__:					Union[str, Any]												=      tokenizer.decode(outputa[0]			)
          UpperCamelCase__:					Dict												=      tokenizer.batch_decode(__lowerCamelCase			)

          UpperCamelCase__:					Any												=      [
              "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]			)








def 							lowerCAmelCase_				(    A_  ,A_  ,A_):
    UpperCamelCase__:					Optional[int]												=      [1 / first_term]
    UpperCamelCase__:					int												=      1 / first_term
    for _ in range(number_of_terms - 1):
        first_term += common_difference
        arithmetic_progression.append(A_)
    UpperCamelCase__:					str												=      [1 / step for step in arithmetic_progression]
    return sum(A_)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(sum_of_harmonic_progression(1 / 2, 2, 2))

