


"""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				:   List[Any]		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   Union[str, Any]=36_000		,							UpperCAmelCase_				:   Any=13		,							UpperCAmelCase_				:   Tuple=7		,							UpperCAmelCase_				:   Union[str, Any]=True		,							UpperCAmelCase_				:   List[Any]=32		,							UpperCAmelCase_				:   Any=42		,							UpperCAmelCase_				:   int=5		,							UpperCAmelCase_				:   Optional[int]=2		,							UpperCAmelCase_				:   Optional[int]=4		,							UpperCAmelCase_				:   Dict=2		,							UpperCAmelCase_				:   str=32		,							UpperCAmelCase_				:   Dict=80		,							UpperCAmelCase_				:   Tuple=33		,							UpperCAmelCase_				:   Optional[int]=0.0		,							UpperCAmelCase_				:   List[str]=1E-6		,							UpperCAmelCase_				:   Optional[Any]=100		,							UpperCAmelCase_				:   str=0.0		,							)       ->List[str]:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   List[str]							)       ->Dict:



														"""simple docstring"""



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



							def 		lowerCAmelCase							(					self				:   List[str]							)       ->Any:



														"""simple docstring"""



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

														snake_case_     							=		self.get_config()

														return (config, input_ids)



							def 		lowerCAmelCase							(					self				:   Optional[Any]							)       ->str:



														"""simple docstring"""



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

														snake_case_     							=		self.get_config()

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



							def 		lowerCAmelCase							(					self				:   Optional[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 		lowerCAmelCase							(					self				:   str		,							UpperCAmelCase_				:   Union[str, Any]		,							UpperCAmelCase_				:   Union[str, Any]		,							)       ->Dict:



														"""simple docstring"""



														snake_case_     							=		GPTSanJapaneseForConditionalGeneration(config=a_							)
														model.to(a_							)
														model.eval()
														snake_case_     							=		model(
														    input_ids=a_		,							)
														self.parent.assertIsNotNone(a_							)





@require_torch
class   __A  (__snake_case    ,		__snake_case    ,		unittest.TestCase):





							'''simple docstring'''

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



							def 		lowerCAmelCase							(					self				:   str		,							UpperCAmelCase_				:   Dict		,							UpperCAmelCase_				:   Tuple		,							UpperCAmelCase_				:   Tuple		,							UpperCAmelCase_				:   Any		,							UpperCAmelCase_				:   Dict							)       ->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 		lowerCAmelCase							(					self				:   List[Any]							)       ->Optional[int]:



														"""simple docstring"""



														snake_case_     							=		GPTSanJapaneseTester(self							)
														snake_case_     							=		ConfigTester(self		,							config_class=a_		,							d_model=37							)



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



														"""simple docstring"""



														GPTSanJapaneseConfig()



							def 		lowerCAmelCase							(					self				:   Optional[Any]							)       ->Tuple:



														"""simple docstring"""



														snake_case_     							=		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*a_							)



							@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 		lowerCAmelCase							(					self				:   Dict							)       ->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 		lowerCAmelCase							(					self				:   List[Any]							)       ->List[str]:



														"""simple docstring"""



														pass





@require_torch
class   __A  (__snake_case    ,		__snake_case    ,		unittest.TestCase):





							'''simple docstring'''

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



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



														"""simple docstring"""



														snake_case_     							=		GPTSanJapaneseTester(self							)
														snake_case_     							=		ConfigTester(self		,							config_class=a_		,							d_model=37							)



							def 		lowerCAmelCase							(					self				:   Tuple							)       ->List[Any]:



														"""simple docstring"""



														GPTSanJapaneseConfig()



							def 		lowerCAmelCase							(					self				:   Any							)       ->int:



														"""simple docstring"""



														snake_case_     							=		self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*a_							)



							@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 		lowerCAmelCase							(					self				:   str							)       ->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 		lowerCAmelCase							(					self				:   int							)       ->Union[str, Any]:



														"""simple docstring"""



														pass



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



														"""simple docstring"""



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

														def check(UpperCAmelCase_				:   List[Any]		,							UpperCAmelCase_				:   str		,							UpperCAmelCase_				:   Any=5E-4							):
																					return abs(a - b							) < epsilon * max(abs(a_							)		,							abs(a_							)							)

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



							@slow
							def 		lowerCAmelCase							(					self				:   Dict							)       ->Dict:



														"""simple docstring"""



														snake_case_     							=		GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
														snake_case_     							=		GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
														model.to(a_							)

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

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

														snake_case_     							=		'''left'''
														snake_case_     							=		tokenizer(a_		,							return_tensors="""pt"""		,							padding=a_							)
														snake_case_     							=		inputs['''input_ids'''].to(a_							)

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

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

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

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

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

														snake_case_     							=		tokenizer.batch_decode(a_		,							skip_special_tokens=a_							)
														snake_case_     							=		tokenizer.decode(output_non_padded[0]		,							skip_special_tokens=a_							)
														snake_case_     							=		tokenizer.decode(output_padded[0]		,							skip_special_tokens=a_							)

														snake_case_     							=		[
														    '''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(a_		,							a_							)
														self.assertListEqual(a_		,							[non_padded_sentence, padded_sentence]							)



							@tooslow
							def 		lowerCAmelCase							(					self				:   List[Any]							)       ->str:



														"""simple docstring"""



														snake_case_     							=		GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
														snake_case_     							=		GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
														# Output of original model created with mesh-tensoflow
														snake_case_     							=		[
														    ('''武田信玄は''', 35_675),
														    ('''武田信玄は、''', 45),
														    ('''武田信玄は、この''', 29),
														    ('''武田信玄は、このよう''', 30_642),
														    ('''武田信玄は、このような''', 35_680),
														    ('''武田信玄は、このような「''', 8_640),
														    ('''武田信玄は、このような「武田''', 31_617),
														    ('''武田信玄は、このような「武田家''', 30_646),
														    ('''武田信玄は、このような「武田家の''', 31_617),
														    ('''武田信玄は、このような「武田家の家''', 31_381),
														]
														for input, output in target:
																					snake_case_     							=		tokenizer.encode(a_		,							return_tensors="""pt"""							)
																					snake_case_     							=		model(a_							)
																					snake_case_     							=		outputs.logits.detach().cpu().numpy()[0]
																					snake_case_     							=		np.argmax(output_logits[-1]							)
																					self.assertEqual(a_		,							a_							)



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



														"""simple docstring"""



														snake_case_     							=		GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
														snake_case_     							=		GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
														model.to(a_							)

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

														snake_case_     							=		'''武田信玄は、'''
														snake_case_     							=		tokenizer(a_		,							return_tensors="""pt"""							).input_ids.to(a_							)
														snake_case_     							=		tokenizer([input_text, input_text]		,							return_tensors="""pt"""							).input_ids.to(a_							)

														# spout from uniform and one-hot
														snake_case_     							=		[
														    # 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_     							=		model.generate(
														    input_ids=a_		,							spout=spouts[0]		,							max_new_tokens=20		,							generation_config=a_		,							)

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

														snake_case_     							=		model.generate(
														    input_ids=a_		,							spout=a_		,							max_new_tokens=20		,							generation_config=a_		,							)

														snake_case_     							=		tokenizer.decode(outputa[0]							)
														snake_case_     							=		tokenizer.decode(outputa[0]							)
														snake_case_     							=		tokenizer.batch_decode(a_							)

														snake_case_     							=		[
														    '''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(a_		,							a_							)
														self.assertListEqual(a_		,							[outa_sentence, outa_sentence]							)



							@slow
							def 		lowerCAmelCase							(					self				:   Any							)       ->Dict:



														"""simple docstring"""



														snake_case_     							=		GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
														snake_case_     							=		GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
														model.to(a_							)

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

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

														snake_case_     							=		model.generate(
														    input_ids=input_tok_a.input_ids.to(a_							)		,							token_type_ids=input_tok_a.token_type_ids.to(a_							)		,							max_new_tokens=20		,							generation_config=a_		,							)

														snake_case_     							=		model.generate(
														    input_ids=input_tok_a.input_ids.to(a_							)		,							token_type_ids=input_tok_a.token_type_ids.to(a_							)		,							max_new_tokens=20		,							generation_config=a_		,							)

														snake_case_     							=		model.generate(
														    input_ids=input_tok_a.input_ids.to(a_							)		,							token_type_ids=input_tok_a.token_type_ids.to(a_							)		,							attention_mask=input_tok_a.attention_mask.to(a_							)		,							max_new_tokens=20		,							generation_config=a_		,							)

														snake_case_     							=		tokenizer.decode(outputa[0]							)
														snake_case_     							=		tokenizer.decode(outputa[0]							)
														snake_case_     							=		tokenizer.batch_decode(a_							)

														snake_case_     							=		[
														    '''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(a_		,							a_							)
														self.assertListEqual(a_		,							[outa_sentence, outa_sentence]							)







"""simple docstring"""
import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput



@dataclass
class      _UpperCAmelCase					(				__snake_case							):






		'''simple docstring'''

		lowerCamelCase__           =42
		lowerCamelCase__           =None
		lowerCamelCase__           =None



class      _UpperCAmelCase					(				unittest.TestCase							):






		'''simple docstring'''

		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							ModelOutputTest(a=30   )
			self.assertEqual(x.a							,			30   )
			self.assertIsNone(x.b   )
			self.assertIsNone(x.c   )
			with self.assertRaises(a_   ):
				__snake_case      :		str         =							x.d



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							ModelOutputTest(a=30							,			b=10   )
			self.assertEqual(x[0]							,			30   )
			self.assertEqual(x[1]							,			10   )
			self.assertEqual(x[:2]							,			(30, 10)   )
			self.assertEqual(x[:]							,			(30, 10)   )

			__snake_case      :		List[Any]         =							ModelOutputTest(a=30							,			c=10   )
			self.assertEqual(x[0]							,			30   )
			self.assertEqual(x[1]							,			10   )
			self.assertEqual(x[:2]							,			(30, 10)   )
			self.assertEqual(x[:]							,			(30, 10)   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Optional[int]         =							ModelOutputTest(a=30							,			b=10   )
			self.assertEqual(x['''a''']							,			30   )
			self.assertEqual(x['''b''']							,			10   )
			with self.assertRaises(a_   ):
				__snake_case      :		Optional[int]         =							x['''c''']

			__snake_case      :		Any         =							ModelOutputTest(a=30							,			c=10   )
			self.assertEqual(x['''a''']							,			30   )
			self.assertEqual(x['''c''']							,			10   )
			with self.assertRaises(a_   ):
				__snake_case      :		Optional[int]         =							x['''b''']



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Optional[int]         =							ModelOutputTest(a=30   )
			self.assertEqual(list(x.keys()   )							,			['''a''']   )
			self.assertEqual(list(x.values()   )							,			[30]   )
			self.assertEqual(list(x.items()   )							,			[('''a''', 30)]   )
			self.assertEqual(list(a_   )							,			['''a''']   )

			__snake_case      :		Optional[int]         =							ModelOutputTest(a=30							,			b=10   )
			self.assertEqual(list(x.keys()   )							,			['''a''', '''b''']   )
			self.assertEqual(list(x.values()   )							,			[30, 10]   )
			self.assertEqual(list(x.items()   )							,			[('''a''', 30), ('''b''', 10)]   )
			self.assertEqual(list(a_   )							,			['''a''', '''b''']   )

			__snake_case      :		Optional[Any]         =							ModelOutputTest(a=30							,			c=10   )
			self.assertEqual(list(x.keys()   )							,			['''a''', '''c''']   )
			self.assertEqual(list(x.values()   )							,			[30, 10]   )
			self.assertEqual(list(x.items()   )							,			[('''a''', 30), ('''c''', 10)]   )
			self.assertEqual(list(a_   )							,			['''a''', '''c''']   )

			with self.assertRaises(a_   ):
				__snake_case      :		Optional[int]         =							x.update({'''d''': 20}   )
			with self.assertRaises(a_   ):
				del x["a"]
			with self.assertRaises(a_   ):
				__snake_case      :		Union[str, Any]         =							x.pop('''a'''   )
			with self.assertRaises(a_   ):
				__snake_case      :		Optional[Any]         =							x.setdefault('''d'''							,			32   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		List[str]         =							ModelOutputTest(a=30   )
			__snake_case      :		Tuple         =							10
			self.assertEqual(x.a							,			10   )
			self.assertEqual(x['''a''']							,			10   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		List[str]         =							ModelOutputTest(a=30   )
			__snake_case      :		Any         =							10
			self.assertEqual(x.a							,			10   )
			self.assertEqual(x['''a''']							,			10   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		int         =							ModelOutputTest({'''a''': 30, '''b''': 10}   )
			self.assertEqual(list(x.keys()   )							,			['''a''', '''b''']   )
			self.assertEqual(x.a							,			30   )
			self.assertEqual(x.b							,			10   )



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Any         =							ModelOutputTest([('''a''', 30), ('''b''', 10)]   )
			self.assertEqual(list(x.keys()   )							,			['''a''', '''b''']   )
			self.assertEqual(x.a							,			30   )
			self.assertEqual(x.b							,			10   )

			with self.assertRaises(a_   ):
				__snake_case      :		List[Any]         =							ModelOutputTest([('''a''', 30), (10, 10)]   )

			__snake_case      :		Union[str, Any]         =							ModelOutputTest(a=(30, 30)   )
			self.assertEqual(list(x.keys()   )							,			['''a''']   )
			self.assertEqual(x.a							,			(30, 30)   )





