





'''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       lowerCamelCase       :


					def __init__(       self						:	Optional[int]							,  __snake_case						:	Optional[Any]							,  __snake_case						:	Tuple=3_60_00							,  __snake_case						:	str=13							,  __snake_case						:	List[Any]=7							,  __snake_case						:	Tuple=True							,  __snake_case						:	List[Any]=32							,  __snake_case						:	int=42							,  __snake_case						:	List[str]=5							,  __snake_case						:	Tuple=2							,  __snake_case						:	Dict=4							,  __snake_case						:	int=2							,  __snake_case						:	Union[str, Any]=32							,  __snake_case						:	Tuple=80							,  __snake_case						:	str=33							,  __snake_case						:	Dict=0.0							,  __snake_case						:	Optional[Any]=1e-6							,  __snake_case						:	str=1_00							,  __snake_case						:	Optional[int]=0.0							,  ):





											'''simple docstring'''





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


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





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


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							):





											'''simple docstring'''





											_snake_case: int	       =		ids_tensor([self.batch_size, self.seq_length]							,  self.vocab_size							)

											_snake_case: Optional[int]	       =		self.get_config()

											return (config, input_ids)


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[Any]							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		ids_tensor([self.batch_size, self.seq_length]							,  self.vocab_size							)

											_snake_case: Dict	       =		self.get_config()

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


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[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  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							,  __snake_case						:	str							,  __snake_case						:	List[Any]							,  ):





											'''simple docstring'''





											_snake_case: str	       =		GPTSanJapaneseForConditionalGeneration(config=__snake_case							)
											model.to(__snake_case							)
											model.eval()
											_snake_case: List[Any]	       =		model(
											    input_ids=__snake_case							,  )
											self.parent.assertIsNotNone(__snake_case							)







@require_torch
class       lowerCamelCase       (    __UpperCAmelCase  ,				__UpperCAmelCase  ,				unittest.TestCase	):
					_SCREAMING_SNAKE_CASE				 =     (GPTSanJapaneseModel,) if is_torch_available() else ()
					_SCREAMING_SNAKE_CASE				 =     (
					    {
					        "conversational": GPTSanJapaneseForConditionalGeneration,
					        "feature-extraction": GPTSanJapaneseForConditionalGeneration,
					        "summarization": GPTSanJapaneseForConditionalGeneration,
					        "text2text-generation": GPTSanJapaneseForConditionalGeneration,
					        "translation": GPTSanJapaneseForConditionalGeneration,
					    }
					    if is_torch_available()
					    else {}
					)
					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     False
					# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
					_SCREAMING_SNAKE_CASE				 =     [0.8, 0.9]


					def  SCREAMING_SNAKE_CASE_		(       self						:	int							,  __snake_case						:	Dict							,  __snake_case						:	Tuple							,  __snake_case						:	List[Any]							,  __snake_case						:	Any							,  __snake_case						:	List[Any]							):





											'''simple docstring'''





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

											return False


					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							):





											'''simple docstring'''





											_snake_case: int	       =		GPTSanJapaneseTester(self							)
											_snake_case: Optional[int]	       =		ConfigTester(self							,  config_class=__snake_case							,  d_model=37							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											GPTSanJapaneseConfig()


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: List[str]	       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case							)


					@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  SCREAMING_SNAKE_CASE_		(       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  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											pass







@require_torch
class       lowerCamelCase       (    __UpperCAmelCase  ,				__UpperCAmelCase  ,				unittest.TestCase	):
					_SCREAMING_SNAKE_CASE				 =     (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     False
					_SCREAMING_SNAKE_CASE				 =     False
					# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
					_SCREAMING_SNAKE_CASE				 =     [0.8, 0.9]


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





											_snake_case: str	       =		GPTSanJapaneseTester(self							)
											_snake_case: Dict	       =		ConfigTester(self							,  config_class=__snake_case							,  d_model=37							)


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											GPTSanJapaneseConfig()


					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: Optional[Any]	       =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__snake_case							)


					@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  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





											super().test_model_parallelism()


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





											'''simple docstring'''





											pass


					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	int							):





											'''simple docstring'''





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

											def check(__snake_case						:	Optional[int]							,  __snake_case						:	str							,  __snake_case						:	Union[str, Any]=5e-4							):
																	return abs(a - b							) < epsilon * max(abs(__snake_case							)							,  abs(__snake_case							)							)

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


					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[Any]							):





											'''simple docstring'''





											_snake_case: Dict	       =		GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'							)
											_snake_case: Dict	       =		GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'							)
											model.to(__snake_case							)

											# set deterministically
											_snake_case: Optional[Any]	       =		GenerationConfig.from_pretrained('Tanrei/GPTSAN-japanese'							)
											_snake_case: Tuple	       =		1

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

											_snake_case: Union[str, Any]	       =		'left'
											_snake_case: str	       =		tokenizer(__snake_case							,  return_tensors='pt'							,  padding=__snake_case							)
											_snake_case: str	       =		inputs['input_ids'].to(__snake_case							)

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

											_snake_case: Optional[int]	       =		model.generate(
											    input_ids=__snake_case							,  attention_mask=inputs['attention_mask'].to(__snake_case							)							,  max_new_tokens=3							,  generation_config=__snake_case							,  )

											_snake_case: List[Any]	       =		tokenizer(sentences[0]							,  return_tensors='pt'							).input_ids.to(__snake_case							)
											_snake_case: Optional[Any]	       =		model.generate(
											    input_ids=__snake_case							,  max_new_tokens=3							,  generation_config=__snake_case							)

											_snake_case: int	       =		tokenizer(sentences[1]							,  return_tensors='pt'							).input_ids.to(__snake_case							)
											_snake_case: List[str]	       =		model.generate(input_ids=__snake_case							,  max_new_tokens=3							,  generation_config=__snake_case							)

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

											_snake_case: List[Any]	       =		tokenizer.batch_decode(__snake_case							,  skip_special_tokens=__snake_case							)
											_snake_case: List[str]	       =		tokenizer.decode(output_non_padded[0]							,  skip_special_tokens=__snake_case							)
											_snake_case: Optional[Any]	       =		tokenizer.decode(output_padded[0]							,  skip_special_tokens=__snake_case							)

											_snake_case: 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(__snake_case							,  __snake_case							)
											self.assertListEqual(__snake_case							,  [non_padded_sentence, padded_sentence]							)


					@tooslow
					def  SCREAMING_SNAKE_CASE_		(       self						:	str							):





											'''simple docstring'''





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


					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							):





											'''simple docstring'''





											_snake_case: List[Any]	       =		GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'							)
											_snake_case: Union[str, Any]	       =		GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'							)
											model.to(__snake_case							)

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

											_snake_case: Optional[Any]	       =		'武田信玄は、'
											_snake_case: str	       =		tokenizer(__snake_case							,  return_tensors='pt'							).input_ids.to(__snake_case							)
											_snake_case: Any	       =		tokenizer([input_text, input_text]							,  return_tensors='pt'							).input_ids.to(__snake_case							)

											# spout from uniform and one-hot
											_snake_case: Dict	       =		[
											    # 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
											]

											_snake_case: Dict	       =		model.generate(
											    input_ids=__snake_case							,  spout=spouts[0]							,  max_new_tokens=20							,  generation_config=__snake_case							,  )

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

											_snake_case: int	       =		model.generate(
											    input_ids=__snake_case							,  spout=__snake_case							,  max_new_tokens=20							,  generation_config=__snake_case							,  )

											_snake_case: int	       =		tokenizer.decode(outputa[0]							)
											_snake_case: Optional[int]	       =		tokenizer.decode(outputa[0]							)
											_snake_case: Tuple	       =		tokenizer.batch_decode(__snake_case							)

											_snake_case: 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(__snake_case							,  __snake_case							)
											self.assertListEqual(__snake_case							,  [outa_sentence, outa_sentence]							)




					@slow
					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'							)
											_snake_case: str	       =		GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'							)
											model.to(__snake_case							)

											# set deterministically
											_snake_case: List[str]	       =		GenerationConfig.from_pretrained('Tanrei/GPTSAN-japanese'							)
											_snake_case: List[Any]	       =		1

											_snake_case: Optional[int]	       =		'武田信玄'
											_snake_case: List[Any]	       =		'織田信長'
											_snake_case: Optional[int]	       =		'は、'
											_snake_case: str	       =		'が、'
											_snake_case: Dict	       =		tokenizer(__snake_case							,  prefix_text=__snake_case							,  return_tensors='pt'							)
											_snake_case: str	       =		tokenizer(__snake_case							,  prefix_text=__snake_case							,  return_tensors='pt'							)
											_snake_case: str	       =		tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]							,  return_tensors='pt'							)

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

											_snake_case: Dict	       =		model.generate(
											    input_ids=input_tok_a.input_ids.to(__snake_case							)							,  token_type_ids=input_tok_a.token_type_ids.to(__snake_case							)							,  max_new_tokens=20							,  generation_config=__snake_case							,  )

											_snake_case: str	       =		model.generate(
											    input_ids=input_tok_a.input_ids.to(__snake_case							)							,  token_type_ids=input_tok_a.token_type_ids.to(__snake_case							)							,  attention_mask=input_tok_a.attention_mask.to(__snake_case							)							,  max_new_tokens=20							,  generation_config=__snake_case							,  )

											_snake_case: List[Any]	       =		tokenizer.decode(outputa[0]							)
											_snake_case: List[str]	       =		tokenizer.decode(outputa[0]							)
											_snake_case: str	       =		tokenizer.batch_decode(__snake_case							)

											_snake_case: Dict	       =		[
											    '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(__snake_case							,  __snake_case							)
											self.assertListEqual(__snake_case							,  [outa_sentence, outa_sentence]							)












'''simple docstring'''






import math
from collections.abc import Generator





def    lowercase_   (		lowercase__    ) ->Generator[int, None, None]:
						_snake_case: Generator	       =		(i for i in range(1					,	(max_n + 1)    ))
						for i in (n for n in numbers if n > 1):
												for j in range(2					,	lowercase__    ):
																		if (i % j) == 0:
																								break
												else:
																		yield i





def    lowercase_   (		lowercase__    ) ->Generator[int, None, None]:
						_snake_case: Generator	       =		(i for i in range(1					,	(max_n + 1)    ))
						for i in (n for n in numbers if n > 1):
												# only need to check for factors up to sqrt(i)
												_snake_case: int	       =		int(math.sqrt(lowercase__    )    ) + 1
												for j in range(2					,	lowercase__    ):
																		if (i % j) == 0:
																								break
												else:
																		yield i





def    lowercase_   (		lowercase__    ) ->Generator[int, None, None]:
						_snake_case: Generator	       =		(i for i in range(1					,	(max_n + 1)					,	2    ))
						# It's useless to test even numbers as they will not be prime
						if max_n > 2:
												yield 2  # Because 2 will not be tested, it's necessary to yield it now
						for i in (n for n in numbers if n > 1):
												_snake_case: List[str]	       =		int(math.sqrt(lowercase__    )    ) + 1
												for j in range(3					,	lowercase__					,	2    ):
																		# As we removed the even numbers, we don't need them now
																		if (i % j) == 0:
																								break
												else:
																		yield i





def    lowercase_   (		) ->Optional[int]:
						from timeit import timeit

						_snake_case: Dict	       =		'from __main__ import slow_primes, primes, fast_primes'
						print(timeit('slow_primes(1_000_000_000_000)'					,	setup=lowercase__					,	number=100_0000    )    )
						print(timeit('primes(1_000_000_000_000)'					,	setup=lowercase__					,	number=100_0000    )    )
						print(timeit('fast_primes(1_000_000_000_000)'					,	setup=lowercase__					,	number=100_0000    )    )


if __name__ == "__main__":
		A :			List[str]							   =      int(input('Calculate primes up to:\n>> ').strip())
		for ret in primes(number):
				print(ret)
		benchmark()





