
"""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 SCREAMING_SNAKE_CASE       :






	'''simple docstring'''







	def __init__(  self :Dict       ,    __magic_name__ :List[str]       ,    __magic_name__ :Tuple=36000       ,    __magic_name__ :Any=13       ,    __magic_name__ :Dict=7       ,    __magic_name__ :List[Any]=True       ,    __magic_name__ :Optional[int]=32       ,    __magic_name__ :Any=42       ,    __magic_name__ :Union[str, Any]=5       ,    __magic_name__ :Any=2       ,    __magic_name__ :Union[str, Any]=4       ,    __magic_name__ :Dict=2       ,    __magic_name__ :Tuple=32       ,    __magic_name__ :Any=80       ,    __magic_name__ :Tuple=33       ,    __magic_name__ :str=0.0       ,    __magic_name__ :List[Any]=1e-6       ,    __magic_name__ :List[Any]=100       ,    __magic_name__ :List[str]=0.0       ,    )			->   int:

			'''simple docstring'''






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


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

			'''simple docstring'''






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


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

			'''simple docstring'''






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

			a__   		=     self.get_config()

			return (config, input_ids)


	def    _UpperCamelCase							(  self :int			)			->   Optional[int]:

			'''simple docstring'''






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

			a__   		=     self.get_config()

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


	def    _UpperCamelCase							(  self :Any			)			->   List[Any]:

			'''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 :Optional[int]       ,    __magic_name__ :Union[str, Any]       ,    __magic_name__ :Optional[int]       ,    )			->   int:

			'''simple docstring'''






			a__   		=     GPTSanJapaneseForConditionalGeneration(config=__magic_name__			)
			model.to(__magic_name__			)
			model.eval()
			a__   		=     model(
			    input_ids=__magic_name__       ,    )
			self.parent.assertIsNotNone(__magic_name__			)

@require_torch
class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					,   lowerCAmelCase					,   unittest.TestCase					):






	'''simple docstring'''







	snake_case__							:		Optional[int]         =		(GPTSanJapaneseModel,) if is_torch_available() else ()
	snake_case__							:		Dict         =		(
	    {
	        'conversational': GPTSanJapaneseForConditionalGeneration,
	        'feature-extraction': GPTSanJapaneseForConditionalGeneration,
	        'summarization': GPTSanJapaneseForConditionalGeneration,
	        'text2text-generation': GPTSanJapaneseForConditionalGeneration,
	        'translation': GPTSanJapaneseForConditionalGeneration,
	    }
	    if is_torch_available()
	    else {}
	)
	snake_case__							:		int         =		False
	snake_case__							:		Tuple         =		False
	snake_case__							:		Any         =		False
	snake_case__							:		Optional[Any]         =		False
	snake_case__							:		Union[str, Any]         =		False
	snake_case__							:		Any         =		False
	snake_case__							:		List[Any]         =		False
	snake_case__							:		str         =		False
	# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
	snake_case__							:		List[Any]         =		[0.8, 0.9]


	def    _UpperCamelCase							(  self :Optional[Any]       ,    __magic_name__ :Tuple       ,    __magic_name__ :List[Any]       ,    __magic_name__ :Optional[int]       ,    __magic_name__ :str       ,    __magic_name__ :List[str]			)			->   Optional[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    _UpperCamelCase							(  self :int			)			->   str:

			'''simple docstring'''






			a__   		=     GPTSanJapaneseTester(self			)
			a__   		=     ConfigTester(self       ,    config_class=__magic_name__       ,    d_model=37			)


	def    _UpperCamelCase							(  self :List[Any]			)			->   Optional[int]:

			'''simple docstring'''






			GPTSanJapaneseConfig()


	def    _UpperCamelCase							(  self :Optional[Any]			)			->   Dict:

			'''simple docstring'''






			a__   		=     self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*__magic_name__			)


	@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]			)			->   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 :int			)			->   int:

			'''simple docstring'''






			pass

@require_torch
class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					,   lowerCAmelCase					,   unittest.TestCase					):






	'''simple docstring'''







	snake_case__							:		Dict         =		(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
	snake_case__							:		Optional[int]         =		False
	snake_case__							:		List[str]         =		False
	snake_case__							:		Any         =		False
	snake_case__							:		List[str]         =		False
	snake_case__							:		Tuple         =		False
	snake_case__							:		List[Any]         =		False
	# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
	snake_case__							:		List[Any]         =		[0.8, 0.9]


	def    _UpperCamelCase							(  self :int			)			->   Dict:

			'''simple docstring'''






			a__   		=     GPTSanJapaneseTester(self			)
			a__   		=     ConfigTester(self       ,    config_class=__magic_name__       ,    d_model=37			)


	def    _UpperCamelCase							(  self :int			)			->   Optional[int]:

			'''simple docstring'''






			GPTSanJapaneseConfig()


	def    _UpperCamelCase							(  self :Union[str, Any]			)			->   Optional[int]:

			'''simple docstring'''






			a__   		=     self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*__magic_name__			)


	@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 :Any			)			->   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    _UpperCamelCase							(  self :Optional[Any]			)			->   Dict:

			'''simple docstring'''






			pass


	@slow
	def    _UpperCamelCase							(  self :Dict			)			->   str:

			'''simple docstring'''






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

			def check(__magic_name__ :Optional[int]       ,    __magic_name__ :int       ,    __magic_name__ :Tuple=5e-4			):
					return abs(a - b			) < epsilon * max(abs(__magic_name__			)       ,    abs(__magic_name__			)			)

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


	@slow
	def    _UpperCamelCase							(  self :Any			)			->   Optional[int]:

			'''simple docstring'''






			a__   		=     GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''			)
			a__   		=     GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''			)
			model.to(__magic_name__			)

			# set deterministically
			a__   		=     GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''			)
			a__   		=     1

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

			a__   		=     '''left'''
			a__   		=     tokenizer(__magic_name__       ,    return_tensors='''pt'''       ,    padding=__magic_name__			)
			a__   		=     inputs['''input_ids'''].to(__magic_name__			)

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

			a__   		=     model.generate(
			    input_ids=__magic_name__       ,    attention_mask=inputs['''attention_mask'''].to(__magic_name__			)       ,    max_new_tokens=3       ,    generation_config=__magic_name__       ,    )

			a__   		=     tokenizer(sentences[0]       ,    return_tensors='''pt'''			).input_ids.to(__magic_name__			)
			a__   		=     model.generate(
			    input_ids=__magic_name__       ,    max_new_tokens=3       ,    generation_config=__magic_name__			)

			a__   		=     tokenizer(sentences[1]       ,    return_tensors='''pt'''			).input_ids.to(__magic_name__			)
			a__   		=     model.generate(input_ids=__magic_name__       ,    max_new_tokens=3       ,    generation_config=__magic_name__			)

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

			a__   		=     tokenizer.batch_decode(__magic_name__       ,    skip_special_tokens=__magic_name__			)
			a__   		=     tokenizer.decode(output_non_padded[0]       ,    skip_special_tokens=__magic_name__			)
			a__   		=     tokenizer.decode(output_padded[0]       ,    skip_special_tokens=__magic_name__			)

			a__   		=     [
			    '''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(__magic_name__       ,    __magic_name__			)
			self.assertListEqual(__magic_name__       ,    [non_padded_sentence, padded_sentence]			)


	@tooslow
	def    _UpperCamelCase							(  self :Any			)			->   Optional[Any]:

			'''simple docstring'''






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


	@slow
	def    _UpperCamelCase							(  self :str			)			->   List[str]:

			'''simple docstring'''






			a__   		=     GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''			)
			a__   		=     GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''			)
			model.to(__magic_name__			)

			# set deterministically
			a__   		=     GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''			)
			a__   		=     1

			a__   		=     '''武田信玄は、'''
			a__   		=     tokenizer(__magic_name__       ,    return_tensors='''pt'''			).input_ids.to(__magic_name__			)
			a__   		=     tokenizer([input_text, input_text]       ,    return_tensors='''pt'''			).input_ids.to(__magic_name__			)

			# spout from uniform and one-hot
			a__   		=     [
			    # 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
			]

			a__   		=     model.generate(
			    input_ids=__magic_name__       ,    spout=spouts[0]       ,    max_new_tokens=20       ,    generation_config=__magic_name__       ,    )

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

			a__   		=     model.generate(
			    input_ids=__magic_name__       ,    spout=__magic_name__       ,    max_new_tokens=20       ,    generation_config=__magic_name__       ,    )

			a__   		=     tokenizer.decode(outputa[0]			)
			a__   		=     tokenizer.decode(outputa[0]			)
			a__   		=     tokenizer.batch_decode(__magic_name__			)

			a__   		=     [
			    '''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(__magic_name__       ,    __magic_name__			)
			self.assertListEqual(__magic_name__       ,    [outa_sentence, outa_sentence]			)






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

			'''simple docstring'''






			a__   		=     GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''			)
			a__   		=     GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''			)
			model.to(__magic_name__			)

			# set deterministically
			a__   		=     GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''			)
			a__   		=     1

			a__   		=     '''武田信玄'''
			a__   		=     '''織田信長'''
			a__   		=     '''は、'''
			a__   		=     '''が、'''
			a__   		=     tokenizer(__magic_name__       ,    prefix_text=__magic_name__       ,    return_tensors='''pt'''			)
			a__   		=     tokenizer(__magic_name__       ,    prefix_text=__magic_name__       ,    return_tensors='''pt'''			)
			a__   		=     tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]       ,    return_tensors='''pt'''			)

			a__   		=     model.generate(
			    input_ids=input_tok_a.input_ids.to(__magic_name__			)       ,    token_type_ids=input_tok_a.token_type_ids.to(__magic_name__			)       ,    max_new_tokens=20       ,    generation_config=__magic_name__       ,    )

			a__   		=     model.generate(
			    input_ids=input_tok_a.input_ids.to(__magic_name__			)       ,    token_type_ids=input_tok_a.token_type_ids.to(__magic_name__			)       ,    max_new_tokens=20       ,    generation_config=__magic_name__       ,    )

			a__   		=     model.generate(
			    input_ids=input_tok_a.input_ids.to(__magic_name__			)       ,    token_type_ids=input_tok_a.token_type_ids.to(__magic_name__			)       ,    attention_mask=input_tok_a.attention_mask.to(__magic_name__			)       ,    max_new_tokens=20       ,    generation_config=__magic_name__       ,    )

			a__   		=     tokenizer.decode(outputa[0]			)
			a__   		=     tokenizer.decode(outputa[0]			)
			a__   		=     tokenizer.batch_decode(__magic_name__			)

			a__   		=     [
			    '''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(__magic_name__       ,    __magic_name__			)
			self.assertListEqual(__magic_name__       ,    [outa_sentence, outa_sentence]			)






"""simple docstring"""




__lowerCAmelCase							:  Any 				=		{
    '''0''': 1,
    '''1''': 1,
    '''2''': 2,
    '''3''': 6,
    '''4''': 24,
    '''5''': 120,
    '''6''': 720,
    '''7''': 5040,
    '''8''': 40320,
    '''9''': 362880,
}

__lowerCAmelCase							:  List[Any] 				=		{145: 145}

__lowerCAmelCase							:  Any 				=		{
    145: 0,
    169: 3,
    36301: 3,
    1454: 3,
    871: 2,
    45361: 2,
    872: 2,
}

def __snake_case (      UpperCamelCase    )       ->    int:


		"""simple docstring"""





		if n in CACHE_SUM_DIGIT_FACTORIALS:
				return CACHE_SUM_DIGIT_FACTORIALS[n]
		a__   		=     sum(DIGIT_FACTORIALS[let] for let in str(UpperCamelCase    )    )
		a__   		=     ret
		return ret

def __snake_case (      UpperCamelCase						,					UpperCamelCase = None    )       ->    int:


		"""simple docstring"""





		a__   		=     previous or set()
		if n in CHAIN_LENGTH_CACHE:
				return CHAIN_LENGTH_CACHE[n]
		a__   		=     sum_digit_factorials(UpperCamelCase    )
		if next_number in previous:
				a__   		=     0
				return 0
		else:
				previous.add(UpperCamelCase    )
				a__   		=     1 + chain_length(UpperCamelCase						,					UpperCamelCase    )
				a__   		=     ret
				return ret

def __snake_case (      UpperCamelCase = 60						,					UpperCamelCase = 1_000_000    )       ->    int:


		"""simple docstring"""





		return sum(1 for i in range(1						,					UpperCamelCase    ) if chain_length(UpperCamelCase    ) == num_terms    )


if __name__ == "__main__":
					print(f"{solution() = }")




