






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__       :

	def __init__(							self						,      __UpperCAmelCase						,      __UpperCAmelCase=36000						,      __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=100						,      __UpperCAmelCase=0.0						,      )->   Optional[int]:







							'''simple docstring'''





							lowerCAmelCase__	 =  vocab_size
							lowerCAmelCase__	 =  parent
							lowerCAmelCase__	 =  batch_size
							lowerCAmelCase__	 =  num_contexts
							# For common tests
							lowerCAmelCase__	 =  self.num_contexts
							lowerCAmelCase__	 =  is_training
							lowerCAmelCase__	 =  hidden_size
							lowerCAmelCase__	 =  num_ext_layers
							lowerCAmelCase__	 =  ext_size
							lowerCAmelCase__	 =  num_hidden_layers
							lowerCAmelCase__	 =  num_attention_heads
							lowerCAmelCase__	 =  num_experts
							lowerCAmelCase__	 =  d_ff
							lowerCAmelCase__	 =  d_ext
							lowerCAmelCase__	 =  d_spout
							lowerCAmelCase__	 =  dropout_rate
							lowerCAmelCase__	 =  layer_norm_epsilon
							lowerCAmelCase__	 =  expert_capacity
							lowerCAmelCase__	 =  router_jitter_noise

	def 						UpperCAmelCase					(							self    )->   Any:







							'''simple docstring'''





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

	def 						UpperCAmelCase					(							self    )->   Dict:







							'''simple docstring'''





							lowerCAmelCase__	 =  ids_tensor([self.batch_size, self.seq_length]						,      self.vocab_size    )

							lowerCAmelCase__	 =  self.get_config()

							return (config, input_ids)

	def 						UpperCAmelCase					(							self    )->   str:







							'''simple docstring'''





							lowerCAmelCase__	 =  ids_tensor([self.batch_size, self.seq_length]						,      self.vocab_size    )

							lowerCAmelCase__	 =  self.get_config()

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

	def 						UpperCAmelCase					(							self    )->   Dict:







							'''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						,      __UpperCAmelCase						,      __UpperCAmelCase						,      )->   str:







							'''simple docstring'''





							lowerCAmelCase__	 =  GPTSanJapaneseForConditionalGeneration(config=__UpperCAmelCase    )
							model.to(__UpperCAmelCase    )
							model.eval()
							lowerCAmelCase__	 =  model(
							    input_ids=__UpperCAmelCase						,      )
							self.parent.assertIsNotNone(__UpperCAmelCase    )





@require_torch
class        lowercase__       (     _UpperCAmelCase,   _UpperCAmelCase,   unittest.TestCase		):
	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 						UpperCAmelCase					(							self						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase						,      __UpperCAmelCase    )->   Optional[int]:







							'''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    )->   str:







							'''simple docstring'''





							lowerCAmelCase__	 =  GPTSanJapaneseTester(self    )
							lowerCAmelCase__	 =  ConfigTester(self						,      config_class=__UpperCAmelCase						,      d_model=37    )

	def 						UpperCAmelCase					(							self    )->   Optional[Any]:







							'''simple docstring'''





							GPTSanJapaneseConfig()

	def 						UpperCAmelCase					(							self    )->   List[str]:







							'''simple docstring'''





							lowerCAmelCase__	 =  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 						UpperCAmelCase					(							self    )->   List[str]:







							'''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





@require_torch
class        lowercase__       (     _UpperCAmelCase,   _UpperCAmelCase,   unittest.TestCase		):
	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 						UpperCAmelCase					(							self    )->   List[str]:







							'''simple docstring'''





							lowerCAmelCase__	 =  GPTSanJapaneseTester(self    )
							lowerCAmelCase__	 =  ConfigTester(self						,      config_class=__UpperCAmelCase						,      d_model=37    )

	def 						UpperCAmelCase					(							self    )->   Any:







							'''simple docstring'''





							GPTSanJapaneseConfig()

	def 						UpperCAmelCase					(							self    )->   str:







							'''simple docstring'''





							lowerCAmelCase__	 =  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 						UpperCAmelCase					(							self    )->   Union[str, 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[Any]:







							'''simple docstring'''





							pass

	@slow
	def 						UpperCAmelCase					(							self    )->   Union[str, Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"    )
							lowerCAmelCase__	 =  GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"    )
							lowerCAmelCase__	 =  tokenizer.encode("武田信玄は"						,      return_tensors="pt"    )
							lowerCAmelCase__	 =  model(__UpperCAmelCase    )
							lowerCAmelCase__	 =  outputs.logits.detach().cpu().numpy()
							# Output of original model created with mesh-tensoflow
							lowerCAmelCase__	 =  [
							    # 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__	 =  np.array(__UpperCAmelCase    ).flatten()
							lowerCAmelCase__	 =  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 						UpperCAmelCase					(							self    )->   Optional[Any]:







							'''simple docstring'''





							lowerCAmelCase__	 =  GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"    )
							lowerCAmelCase__	 =  GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"    )
							model.to(__UpperCAmelCase    )

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

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

							lowerCAmelCase__	 =  "left"
							lowerCAmelCase__	 =  tokenizer(__UpperCAmelCase						,      return_tensors="pt"						,      padding=__UpperCAmelCase    )
							lowerCAmelCase__	 =  inputs["input_ids"].to(__UpperCAmelCase    )

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

							lowerCAmelCase__	 =  model.generate(
							    input_ids=__UpperCAmelCase						,      attention_mask=inputs["attention_mask"].to(__UpperCAmelCase    )						,      max_new_tokens=3						,      generation_config=__UpperCAmelCase						,      )

							lowerCAmelCase__	 =  tokenizer(sentences[0]						,      return_tensors="pt"    ).input_ids.to(__UpperCAmelCase    )
							lowerCAmelCase__	 =  model.generate(
							    input_ids=__UpperCAmelCase						,      max_new_tokens=3						,      generation_config=__UpperCAmelCase    )

							lowerCAmelCase__	 =  tokenizer(sentences[1]						,      return_tensors="pt"    ).input_ids.to(__UpperCAmelCase    )
							lowerCAmelCase__	 =  model.generate(input_ids=__UpperCAmelCase						,      max_new_tokens=3						,      generation_config=__UpperCAmelCase    )

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

							lowerCAmelCase__	 =  tokenizer.batch_decode(__UpperCAmelCase						,      skip_special_tokens=__UpperCAmelCase    )
							lowerCAmelCase__	 =  tokenizer.decode(output_non_padded[0]						,      skip_special_tokens=__UpperCAmelCase    )
							lowerCAmelCase__	 =  tokenizer.decode(output_padded[0]						,      skip_special_tokens=__UpperCAmelCase    )

							lowerCAmelCase__	 =  [
							    "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 						UpperCAmelCase					(							self    )->   List[str]:







							'''simple docstring'''





							lowerCAmelCase__	 =  GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"    )
							lowerCAmelCase__	 =  GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"    )
							# Output of original model created with mesh-tensoflow
							lowerCAmelCase__	 =  [
							    ("武田信玄は", 35675),
							    ("武田信玄は、", 45),
							    ("武田信玄は、この", 29),
							    ("武田信玄は、このよう", 30642),
							    ("武田信玄は、このような", 35680),
							    ("武田信玄は、このような「", 8640),
							    ("武田信玄は、このような「武田", 31617),
							    ("武田信玄は、このような「武田家", 30646),
							    ("武田信玄は、このような「武田家の", 31617),
							    ("武田信玄は、このような「武田家の家", 31381),
							]
							for input, output in target:
													lowerCAmelCase__	 =  tokenizer.encode(__UpperCAmelCase						,      return_tensors="pt"    )
													lowerCAmelCase__	 =  model(__UpperCAmelCase    )
													lowerCAmelCase__	 =  outputs.logits.detach().cpu().numpy()[0]
													lowerCAmelCase__	 =  np.argmax(output_logits[-1]    )
													self.assertEqual(__UpperCAmelCase						,      __UpperCAmelCase    )

	@slow
	def 						UpperCAmelCase					(							self    )->   Dict:







							'''simple docstring'''





							lowerCAmelCase__	 =  GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"    )
							lowerCAmelCase__	 =  GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"    )
							model.to(__UpperCAmelCase    )

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

							lowerCAmelCase__	 =  "武田信玄は、"
							lowerCAmelCase__	 =  tokenizer(__UpperCAmelCase						,      return_tensors="pt"    ).input_ids.to(__UpperCAmelCase    )
							lowerCAmelCase__	 =  tokenizer([input_text, input_text]						,      return_tensors="pt"    ).input_ids.to(__UpperCAmelCase    )

							# spout from uniform and one-hot
							lowerCAmelCase__	 =  [
							    # 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__	 =  model.generate(
							    input_ids=__UpperCAmelCase						,      spout=spouts[0]						,      max_new_tokens=20						,      generation_config=__UpperCAmelCase						,      )

							lowerCAmelCase__	 =  model.generate(
							    input_ids=__UpperCAmelCase						,      spout=spouts[1]						,      max_new_tokens=20						,      generation_config=__UpperCAmelCase						,      )

							lowerCAmelCase__	 =  model.generate(
							    input_ids=__UpperCAmelCase						,      spout=__UpperCAmelCase						,      max_new_tokens=20						,      generation_config=__UpperCAmelCase						,      )

							lowerCAmelCase__	 =  tokenizer.decode(outputa[0]    )
							lowerCAmelCase__	 =  tokenizer.decode(outputa[0]    )
							lowerCAmelCase__	 =  tokenizer.batch_decode(__UpperCAmelCase    )

							lowerCAmelCase__	 =  [
							    "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 						UpperCAmelCase					(							self    )->   List[str]:







							'''simple docstring'''





							lowerCAmelCase__	 =  GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"    )
							lowerCAmelCase__	 =  GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"    )
							model.to(__UpperCAmelCase    )

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

							lowerCAmelCase__	 =  "武田信玄"
							lowerCAmelCase__	 =  "織田信長"
							lowerCAmelCase__	 =  "は、"
							lowerCAmelCase__	 =  "が、"
							lowerCAmelCase__	 =  tokenizer(__UpperCAmelCase						,      prefix_text=__UpperCAmelCase						,      return_tensors="pt"    )
							lowerCAmelCase__	 =  tokenizer(__UpperCAmelCase						,      prefix_text=__UpperCAmelCase						,      return_tensors="pt"    )
							lowerCAmelCase__	 =  tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]						,      return_tensors="pt"    )

							lowerCAmelCase__	 =  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						,      )

							lowerCAmelCase__	 =  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						,      )

							lowerCAmelCase__	 =  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						,      )

							lowerCAmelCase__	 =  tokenizer.decode(outputa[0]    )
							lowerCAmelCase__	 =  tokenizer.decode(outputa[0]    )
							lowerCAmelCase__	 =  tokenizer.batch_decode(__UpperCAmelCase    )

							lowerCAmelCase__	 =  [
							    "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]    )














def     _a					(       UpperCamelCase_   :			int = 100				)	->				int:
						"""simple docstring"""







						lowerCAmelCase__	 =  1
						lowerCAmelCase__	 =  0
						for i in range(1      ,				num + 1				):
												fact *= i

						for j in str(UpperCamelCase_				):
												result += int(UpperCamelCase_				)

						return result


if __name__ == "__main__":
	print(solution(int(input('''Enter the Number: ''').strip())))






