






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





								'''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        SCREAMING_SNAKE_CASE_       (  self						:    Tuple    ):





								'''simple docstring'''



								return GPTSanJapaneseConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
							def        SCREAMING_SNAKE_CASE_       (  self						:    Union[str, Any]    ):





								'''simple docstring'''



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

								a				=	self.get_config()

								return (config, input_ids)
							def        SCREAMING_SNAKE_CASE_       (  self						:    Dict    ):





								'''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        SCREAMING_SNAKE_CASE_       (  self						:    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        SCREAMING_SNAKE_CASE_       (  self						:    Tuple							,__lowerCamelCase						:    Tuple							,__lowerCamelCase						:    str							,):





								'''simple docstring'''



								a				=	GPTSanJapaneseForConditionalGeneration(config=__lowerCamelCase    )
								model.to(__lowerCamelCase    )
								model.eval()
								a				=	model(
								    input_ids=__lowerCamelCase							,)
								self.parent.assertIsNotNone(__lowerCamelCase    )







@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							,__lowerCamelCase						:    Union[str, Any]							,__lowerCamelCase						:    List[str]							,__lowerCamelCase						:    Optional[Any]							,__lowerCamelCase						:    Tuple							,__lowerCamelCase						:    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        SCREAMING_SNAKE_CASE_       (  self						:    List[Any]    ):





								'''simple docstring'''



								a				=	GPTSanJapaneseTester(self    )
								a				=	ConfigTester(self							,config_class=__lowerCamelCase							,d_model=37    )
							def        SCREAMING_SNAKE_CASE_       (  self						:    str    ):





								'''simple docstring'''



								GPTSanJapaneseConfig()
							def        SCREAMING_SNAKE_CASE_       (  self						:    Any    ):





								'''simple docstring'''



								a				=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__lowerCamelCase    )
							@unittest.skip(
							    reason='''skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`'''    )
							def        SCREAMING_SNAKE_CASE_       (  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        SCREAMING_SNAKE_CASE_       (  self						:    str    ):





								'''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						:    Tuple    ):





								'''simple docstring'''



								a				=	GPTSanJapaneseTester(self    )
								a				=	ConfigTester(self							,config_class=__lowerCamelCase							,d_model=37    )
							def        SCREAMING_SNAKE_CASE_       (  self						:    Tuple    ):





								'''simple docstring'''



								GPTSanJapaneseConfig()
							def        SCREAMING_SNAKE_CASE_       (  self						:    List[Any]    ):





								'''simple docstring'''



								a				=	self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__lowerCamelCase    )
							@unittest.skip(
							    reason='''skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`'''    )
							def        SCREAMING_SNAKE_CASE_       (  self						:    Tuple    ):





								'''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						:    str    ):





								'''simple docstring'''



								pass
							@slow
							def        SCREAMING_SNAKE_CASE_       (  self						:    int    ):





								'''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(__lowerCamelCase    )
								a				=	outputs.logits.detach().cpu().numpy()
								# Output of original model created with mesh-tensoflow
								a				=	[
								    # fmt: off
								    [-12.0_37_83_98_89_52_63_67, -12.4_33_06_15_99_73_14_45, -14.3_33_84_03_70_17_82_23, -12.4_50_34_59_93_04_19_92, -11.1_66_13_76_95_31_25,
								    -11.9_30_13_76_34_27_73_44, -10.6_59_74_04_47_99_80_47, -12.9_09_57_45_08_66_69_92, -13.2_41_04_30_90_82_03_12, -13.3_98_57_95_97_47_31_45,
								    -11.1_07_52_48_71_82_61_72, -12.3_68_59_41_69_61_67, -22.97_94_31_15_23_43_75, -10.4_81_06_76_57_47_07_03, -12.4_84_03_07_23_57_17_77,
								    -12.8_07_36_06_49_10_88_87, -14.7_69_70_00_50_35_40_04, -12.2_33_57_96_35_62_01_17, -13.4_28_14_54_08_63_03_71, -22.6_24_17_79_32_73_92_58],
								    [-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, -19.5_65_69_86_23_65_72_27, -5.353_842_735_290_527, -8.350_686_073_303_223,
								    -8.039_388_656_616_211, -10.8_56_56_92_90_16_11_33, -7.75_154_447_555_542, -8.819_022_178_649_902, -19.51_53_27_45_36_13_28],
								    [-9.73_066_234_588_623, -10.2_23_92_27_29_49_21_88, -9.932_981_491_088_867, -11.8_57_83_67_23_32_76_37, -7.662_626_266_479_492,
								    -11.13_52_91_09_95_48_34, -7.765_097_618_103_027, -11.4_72_92_32_78_80_85_94, -9.543_149_948_120_117, -11.9_05_63_39_26_39_16_02,
								    -9.366_164_207_458_496, -11.5_73_42_81_53_99_17, -23.6_99_00_32_19_60_44_92, -9.429_590_225_219_727, -10.42_83_92_41_02_78_32,
								    -10.5_85_24_03_64_07_47_07, -10.94_77_19_57_39_74_61, -11.0_95_41_60_69_03_07_62, -10.3_90_24_06_69_25_04_88, -23.7_69_37_29_40_06_34_77],
								    [-9.728_265_762_329_102, -9.859_712_600_708_008, -10.09_72_92_90_00_85_45, -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, -10.0_18_91_42_22_71_72_85, -9.308_445_930_480_957, -10.63_39_45_46_50_87_89,
								    -8.083_646_774_291_992, -9.06_301_498_413_086, -21.9_04_26_63_57_42_18_75, -8.90_160_846_710_205, -8.841_876_029_968_262,
								    -11.8_56_71_99_70_70_31_25, -12.0_79_39_81_55_21_24_02, -11.2_33_75_32_04_34_57_03, -10.1_77_33_86_00_15_86_91, -21.87_25_66_22_31_44_53],
								    [-9.669_764_518_737_793, -9.614_198_684_692_383, -9.814_510_345_458_984, -9.996_501_922_607_422, -11.3_75_69_04_60_20_50_78,
								    -10.1_13_40_52_27_66_11_33, -10.5_46_86_73_70_60_54_69, -10.04_36_90_68_14_57_52, -10.9_07_80_92_57_50_73_24, -10.5_04_21_61_94_15_28_32,
								    -11.1_29_19_90_28_01_51_37, -10.1_51_12_40_00_54_93_16, -21.96_58_67_99_62_15_82, -9.086_349_487_304_688, -11.7_30_33_90_50_29_29_69,
								    -10.4_60_66_76_10_16_84_57, -10.2_98_04_99_26_75_78_12, -10.7_84_14_82_16_24_75_59, -10.8_40_69_34_73_81_59_18, -22.03_15_28_47_29_00_39],
								    # fmt: on
								]
								a				=	np.array(__lowerCamelCase    ).flatten()
								a				=	output_logits[0, :, :20].flatten()

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

								self.assertTrue(np.all([check(target[i]							,predict[i]    ) for i in range(len(__lowerCamelCase    )    )]    )    )
							@slow
							def        SCREAMING_SNAKE_CASE_       (  self						:    Optional[Any]    ):





								'''simple docstring'''



								a				=	GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
								a				=	GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
								model.to(__lowerCamelCase    )

								# 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(__lowerCamelCase							,return_tensors='''pt'''							,padding=__lowerCamelCase    )
								a				=	inputs['''input_ids'''].to(__lowerCamelCase    )

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

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

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

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

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

								a				=	tokenizer.batch_decode(__lowerCamelCase							,skip_special_tokens=__lowerCamelCase    )
								a				=	tokenizer.decode(output_non_padded[0]							,skip_special_tokens=__lowerCamelCase    )
								a				=	tokenizer.decode(output_padded[0]							,skip_special_tokens=__lowerCamelCase    )

								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(__lowerCamelCase							,__lowerCamelCase    )
								self.assertListEqual(__lowerCamelCase							,[non_padded_sentence, padded_sentence]    )
							@tooslow
							def        SCREAMING_SNAKE_CASE_       (  self						:    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''', 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:
									a				=	tokenizer.encode(__lowerCamelCase							,return_tensors='''pt'''    )
									a				=	model(__lowerCamelCase    )
									a				=	outputs.logits.detach().cpu().numpy()[0]
									a				=	np.argmax(output_logits[-1]    )
									self.assertEqual(__lowerCamelCase							,__lowerCamelCase    )
							@slow
							def        SCREAMING_SNAKE_CASE_       (  self						:    Any    ):





								'''simple docstring'''



								a				=	GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
								a				=	GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
								model.to(__lowerCamelCase    )

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

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

								# 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=__lowerCamelCase							,spout=spouts[0]							,max_new_tokens=20							,generation_config=__lowerCamelCase							,)

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

								a				=	model.generate(
								    input_ids=__lowerCamelCase							,spout=__lowerCamelCase							,max_new_tokens=20							,generation_config=__lowerCamelCase							,)

								a				=	tokenizer.decode(outputa[0]    )
								a				=	tokenizer.decode(outputa[0]    )
								a				=	tokenizer.batch_decode(__lowerCamelCase    )

								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(__lowerCamelCase							,__lowerCamelCase    )
								self.assertListEqual(__lowerCamelCase							,[outa_sentence, outa_sentence]    )




							@slow
							def        SCREAMING_SNAKE_CASE_       (  self						:    Any    ):





								'''simple docstring'''



								a				=	GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
								a				=	GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
								model.to(__lowerCamelCase    )

								# 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(__lowerCamelCase							,prefix_text=__lowerCamelCase							,return_tensors='''pt'''    )
								a				=	tokenizer(__lowerCamelCase							,prefix_text=__lowerCamelCase							,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(__lowerCamelCase    )							,token_type_ids=input_tok_a.token_type_ids.to(__lowerCamelCase    )							,max_new_tokens=20							,generation_config=__lowerCamelCase							,)

								a				=	model.generate(
								    input_ids=input_tok_a.input_ids.to(__lowerCamelCase    )							,token_type_ids=input_tok_a.token_type_ids.to(__lowerCamelCase    )							,max_new_tokens=20							,generation_config=__lowerCamelCase							,)

								a				=	model.generate(
								    input_ids=input_tok_a.input_ids.to(__lowerCamelCase    )							,token_type_ids=input_tok_a.token_type_ids.to(__lowerCamelCase    )							,attention_mask=input_tok_a.attention_mask.to(__lowerCamelCase    )							,max_new_tokens=20							,generation_config=__lowerCamelCase							,)

								a				=	tokenizer.decode(outputa[0]    )
								a				=	tokenizer.decode(outputa[0]    )
								a				=	tokenizer.batch_decode(__lowerCamelCase    )

								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(__lowerCamelCase							,__lowerCamelCase    )
								self.assertListEqual(__lowerCamelCase							,[outa_sentence, outa_sentence]    )











from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__      :			Optional[Any]           =					logging.get_logger(__name__)

lowercase__      :			Optional[int]           =					{
    "uw-madison/yoso-4096": "https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}




class 			a__       (					UpperCamelCase__      ):
     a		:			Dict							=	"""yoso"""







     def __init__(	self  ,		A=50265  ,		A=768  ,		A=12  ,		A=12  ,		A=3072  ,		A="gelu"  ,		A=0.1  ,		A=0.1  ,		A=4096  ,		A=1  ,		A=0.0_2  ,		A=1e-12  ,		A="absolute"  ,		A=True  ,		A=9  ,		A=64  ,		A=None  ,		A=True  ,		A=True  ,		A=1  ,		A=0  ,		A=2  ,		**A  ,		)  ->    Dict:



          '''simple docstring'''



          super().__init__(pad_token_id=A  ,		bos_token_id=A  ,		eos_token_id=A  ,		**A )

          a      =      vocab_size
          a      =      max_position_embeddings
          a      =      hidden_size
          a      =      num_hidden_layers
          a      =      num_attention_heads
          a      =      intermediate_size
          a      =      hidden_act
          a      =      hidden_dropout_prob
          a      =      attention_probs_dropout_prob
          a      =      initializer_range
          a      =      type_vocab_size
          a      =      layer_norm_eps
          a      =      position_embedding_type
          a      =      use_expectation
          a      =      hash_code_len
          a      =      num_hash
          a      =      conv_window
          a      =      use_fast_hash
          a      =      lsh_backward

