




'''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  A		:




				'''simple docstring'''





				def __init__(self			,  _UpperCAmelCase			,  _UpperCAmelCase=3_6_0_0_0			,  _UpperCAmelCase=1_3			,  _UpperCAmelCase=7			,  _UpperCAmelCase=True			,  _UpperCAmelCase=3_2			,  _UpperCAmelCase=4_2			,  _UpperCAmelCase=5			,  _UpperCAmelCase=2			,  _UpperCAmelCase=4			,  _UpperCAmelCase=2			,  _UpperCAmelCase=3_2			,  _UpperCAmelCase=8_0			,  _UpperCAmelCase=3_3			,  _UpperCAmelCase=0.0			,  _UpperCAmelCase=1E-6			,  _UpperCAmelCase=1_0_0			,  _UpperCAmelCase=0.0			,  )     ->	Optional[Any]:
							__UpperCamelCase				:      List[str]									=       vocab_size
							__UpperCamelCase				:      Dict									=       parent
							__UpperCamelCase				:      Dict									=       batch_size
							__UpperCamelCase				:      List[str]									=       num_contexts
							# For common tests
							__UpperCamelCase				:      Dict									=       self.num_contexts
							__UpperCamelCase				:      Optional[Any]									=       is_training
							__UpperCamelCase				:      str									=       hidden_size
							__UpperCamelCase				:      Any									=       num_ext_layers
							__UpperCamelCase				:      Optional[Any]									=       ext_size
							__UpperCamelCase				:      Optional[Any]									=       num_hidden_layers
							__UpperCamelCase				:      Optional[Any]									=       num_attention_heads
							__UpperCamelCase				:      Optional[Any]									=       num_experts
							__UpperCamelCase				:      Union[str, Any]									=       d_ff
							__UpperCamelCase				:      Tuple									=       d_ext
							__UpperCamelCase				:      List[str]									=       d_spout
							__UpperCamelCase				:      Optional[Any]									=       dropout_rate
							__UpperCamelCase				:      Optional[Any]									=       layer_norm_epsilon
							__UpperCamelCase				:      Any									=       expert_capacity
							__UpperCamelCase				:      List[str]									=       router_jitter_noise
				def 	a_	(self      )     ->	Optional[Any]:
							return GPTSanJapaneseConfig.from_pretrained("Tanrei/GPTSAN-japanese"      )
				def 	a_	(self      )     ->	List[str]:
							__UpperCamelCase				:      Optional[int]									=       ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size      )

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

							return (config, input_ids)
				def 	a_	(self      )     ->	int:
							__UpperCamelCase				:      List[Any]									=       ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size      )

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

							return (config, {"input_ids": input_ids})
				def 	a_	(self      )     ->	Optional[int]:
							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			,  _UpperCAmelCase			,  _UpperCAmelCase			,  )     ->	List[str]:
							__UpperCamelCase				:      str									=       GPTSanJapaneseForConditionalGeneration(config=_UpperCAmelCase      )
							model.to(_UpperCAmelCase      )
							model.eval()
							__UpperCamelCase				:      Tuple									=       model(
							    input_ids=_UpperCAmelCase			,  )
							self.parent.assertIsNotNone(_UpperCAmelCase      )







@require_torch
class  A		(			SCREAMING_SNAKE_CASE__      ,							SCREAMING_SNAKE_CASE__      ,							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 	a_	(self			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase			,  _UpperCAmelCase      )     ->	Optional[Any]:
							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[Any]:
							__UpperCamelCase				:      Optional[Any]									=       GPTSanJapaneseTester(self      )
							__UpperCamelCase				:      Union[str, Any]									=       ConfigTester(self			,  config_class=_UpperCAmelCase			,  d_model=3_7      )
				def 	a_	(self      )     ->	Union[str, Any]:
							GPTSanJapaneseConfig()
				def 	a_	(self      )     ->	List[Any]:
							__UpperCamelCase				:      List[str]									=       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 	a_	(self      )     ->	str:
							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]:
							pass







@require_torch
class  A		(			SCREAMING_SNAKE_CASE__      ,							SCREAMING_SNAKE_CASE__      ,							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 	a_	(self      )     ->	Optional[int]:
							__UpperCamelCase				:      List[str]									=       GPTSanJapaneseTester(self      )
							__UpperCamelCase				:      Optional[int]									=       ConfigTester(self			,  config_class=_UpperCAmelCase			,  d_model=3_7      )
				def 	a_	(self      )     ->	str:
							GPTSanJapaneseConfig()
				def 	a_	(self      )     ->	str:
							__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 	a_	(self      )     ->	Any:
							super().test_model_parallelism()
				@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."      )
				def 	a_	(self      )     ->	Optional[Any]:
							pass
				@slow
				def 	a_	(self      )     ->	List[str]:
							__UpperCamelCase				:      Any									=       GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"      )
							__UpperCamelCase				:      List[str]									=       GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"      )
							__UpperCamelCase				:      Union[str, Any]									=       tokenizer.encode("武田信玄は"			,  return_tensors="pt"      )
							__UpperCamelCase				:      Optional[int]									=       model(_UpperCAmelCase      )
							__UpperCamelCase				:      int									=       outputs.logits.detach().cpu().numpy()
							# Output of original model created with mesh-tensoflow
							__UpperCamelCase				:      str									=       [
							    # 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				:      Dict									=       np.array(_UpperCAmelCase      ).flatten()
							__UpperCamelCase				:      List[str]									=       output_logits[0, :, :2_0].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 	a_	(self      )     ->	Union[str, Any]:
							__UpperCamelCase				:      Optional[Any]									=       GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"      )
							__UpperCamelCase				:      Dict									=       GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"      )
							model.to(_UpperCAmelCase      )

							# set deterministically
							__UpperCamelCase				:      Any									=       GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"      )
							__UpperCamelCase				:      List[Any]									=       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				:      Optional[Any]									=       "left"
							__UpperCamelCase				:      List[Any]									=       tokenizer(_UpperCAmelCase			,  return_tensors="pt"			,  padding=_UpperCAmelCase      )
							__UpperCamelCase				:      Optional[int]									=       inputs["input_ids"].to(_UpperCAmelCase      )

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

							__UpperCamelCase				:      Optional[Any]									=       model.generate(
							    input_ids=_UpperCAmelCase			,  attention_mask=inputs["attention_mask"].to(_UpperCAmelCase      )			,  max_new_tokens=3			,  generation_config=_UpperCAmelCase			,  )

							__UpperCamelCase				:      int									=       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				:      Optional[int]									=       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				:      Dict									=       tokenizer.batch_decode(_UpperCAmelCase			,  skip_special_tokens=_UpperCAmelCase      )
							__UpperCamelCase				:      Any									=       tokenizer.decode(output_non_padded[0]			,  skip_special_tokens=_UpperCAmelCase      )
							__UpperCamelCase				:      Optional[int]									=       tokenizer.decode(output_padded[0]			,  skip_special_tokens=_UpperCAmelCase      )

							__UpperCamelCase				:      int									=       [
							    "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 	a_	(self      )     ->	Optional[int]:
							__UpperCamelCase				:      List[Any]									=       GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"      )
							__UpperCamelCase				:      Union[str, Any]									=       GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"      )
							# Output of original model created with mesh-tensoflow
							__UpperCamelCase				:      List[str]									=       [
							    ("武田信玄は", 3_5_6_7_5),
							    ("武田信玄は、", 4_5),
							    ("武田信玄は、この", 2_9),
							    ("武田信玄は、このよう", 3_0_6_4_2),
							    ("武田信玄は、このような", 3_5_6_8_0),
							    ("武田信玄は、このような「", 8_6_4_0),
							    ("武田信玄は、このような「武田", 3_1_6_1_7),
							    ("武田信玄は、このような「武田家", 3_0_6_4_6),
							    ("武田信玄は、このような「武田家の", 3_1_6_1_7),
							    ("武田信玄は、このような「武田家の家", 3_1_3_8_1),
							]
							for input, output in target:
										__UpperCamelCase				:      Union[str, Any]									=       tokenizer.encode(_UpperCAmelCase			,  return_tensors="pt"      )
										__UpperCamelCase				:      Any									=       model(_UpperCAmelCase      )
										__UpperCamelCase				:      str									=       outputs.logits.detach().cpu().numpy()[0]
										__UpperCamelCase				:      str									=       np.argmax(output_logits[-1]      )
										self.assertEqual(_UpperCAmelCase			,  _UpperCAmelCase      )
				@slow
				def 	a_	(self      )     ->	Tuple:
							__UpperCamelCase				:      Optional[int]									=       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				:      Dict									=       1

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

							# 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				:      Dict									=       model.generate(
							    input_ids=_UpperCAmelCase			,  spout=spouts[0]			,  max_new_tokens=2_0			,  generation_config=_UpperCAmelCase			,  )

							__UpperCamelCase				:      str									=       model.generate(
							    input_ids=_UpperCAmelCase			,  spout=spouts[1]			,  max_new_tokens=2_0			,  generation_config=_UpperCAmelCase			,  )

							__UpperCamelCase				:      Optional[int]									=       model.generate(
							    input_ids=_UpperCAmelCase			,  spout=_UpperCAmelCase			,  max_new_tokens=2_0			,  generation_config=_UpperCAmelCase			,  )

							__UpperCamelCase				:      Tuple									=       tokenizer.decode(outputa[0]      )
							__UpperCamelCase				:      Optional[int]									=       tokenizer.decode(outputa[0]      )
							__UpperCamelCase				:      Optional[int]									=       tokenizer.batch_decode(_UpperCAmelCase      )

							__UpperCamelCase				:      Optional[int]									=       [
							    "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 	a_	(self      )     ->	List[Any]:
							__UpperCamelCase				:      Tuple									=       GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"      )
							__UpperCamelCase				:      int									=       GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"      )
							model.to(_UpperCAmelCase      )

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

							__UpperCamelCase				:      Tuple									=       "武田信玄"
							__UpperCamelCase				:      str									=       "織田信長"
							__UpperCamelCase				:      str									=       "は、"
							__UpperCamelCase				:      Union[str, Any]									=       "が、"
							__UpperCamelCase				:      Tuple									=       tokenizer(_UpperCAmelCase			,  prefix_text=_UpperCAmelCase			,  return_tensors="pt"      )
							__UpperCamelCase				:      List[str]									=       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=2_0			,  generation_config=_UpperCAmelCase			,  )

							__UpperCamelCase				:      int									=       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=2_0			,  generation_config=_UpperCAmelCase			,  )

							__UpperCamelCase				:      str									=       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=2_0			,  generation_config=_UpperCAmelCase			,  )

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

							__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(_UpperCAmelCase			,  _UpperCAmelCase      )
							self.assertListEqual(_UpperCAmelCase			,  [outa_sentence, outa_sentence]      )









'''simple docstring'''






import math

import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute

def       __lowerCAmelCase (   snake_case__ = 1 ,				snake_case__ = 1						):
			if isinstance(snake_case__ ,				snake_case__						) or isinstance(snake_case__ ,				snake_case__						):
						raise TypeError("inputs must be integers."						)
			if (bit_a < 0) or (bit_a < 0):
						raise ValueError("inputs must be positive."						)
			if (math.floor(snake_case__						) != bit_a) or (math.floor(snake_case__						) != bit_a):
						raise ValueError("inputs must be exact integers."						)
			if (bit_a > 1) or (bit_a > 1):
						raise ValueError("inputs must be less or equal to 1."						)

			# build registers
			__UpperCamelCase				:      Tuple									=       QuantumRegister(2 ,				"qr"						)
			__UpperCamelCase				:      Dict									=       ClassicalRegister(2 ,				"cr"						)

			__UpperCamelCase				:      str									=       QuantumCircuit(snake_case__ ,				snake_case__						)

			# entanglement the qubits
			quantum_circuit.h(1						)
			quantum_circuit.cx(1 ,				0						)

			# send the information
			__UpperCamelCase				:      Optional[Any]									=       str(snake_case__						) + str(snake_case__						)

			if c_information == "11":
						quantum_circuit.x(1						)
						quantum_circuit.z(1						)
			elif c_information == "10":
						quantum_circuit.z(1						)
			elif c_information == "01":
						quantum_circuit.x(1						)
			else:
						quantum_circuit.i(1						)

			# unentangled the circuit
			quantum_circuit.cx(1 ,				0						)
			quantum_circuit.h(1						)

			# measure the circuit
			quantum_circuit.measure(snake_case__ ,				snake_case__						)

			__UpperCamelCase				:      Dict									=       Aer.get_backend("aer_simulator"						)
			__UpperCamelCase				:      Tuple									=       execute(snake_case__ ,				snake_case__ ,				shots=1_000						)

			return job.result().get_counts(snake_case__						)


if __name__ == "__main__":
				print(f'Counts for classical state send: {superdense_coding(1,1)}')



