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



   """simple docstring"""





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



   """simple docstring"""





   return GPTSanJapaneseConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





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

   __A		:  List[Any]       				=		self.get_config()

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



   """simple docstring"""





   __A		:  Dict       				=		ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size							)

   __A		:  List[str]       				=		self.get_config()

   return (config, {"input_ids": input_ids})
 def 		lowercase_( self      :     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 		lowercase_( self      :     Optional[Any]					, lowerCamelCase      :     List[Any]					, lowerCamelCase      :     int					, ):



   """simple docstring"""





   __A		:  Union[str, Any]       				=		GPTSanJapaneseForConditionalGeneration(config=lowerCamelCase							)
   model.to(lowerCamelCase							)
   model.eval()
   __A		:  int       				=		model(
       input_ids=lowerCamelCase					, )
   self.parent.assertIsNotNone(lowerCamelCase							)







@require_torch
class 			__A					(			_SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    unittest.TestCase      ):
 lowerCamelCase												=(GPTSanJapaneseModel,) if is_torch_available() else ()
 lowerCamelCase												=(
     {
         '''conversational''': GPTSanJapaneseForConditionalGeneration,
         '''feature-extraction''': GPTSanJapaneseForConditionalGeneration,
         '''summarization''': GPTSanJapaneseForConditionalGeneration,
         '''text2text-generation''': GPTSanJapaneseForConditionalGeneration,
         '''translation''': GPTSanJapaneseForConditionalGeneration,
     }
     if is_torch_available()
     else {}
 )
 lowerCamelCase												=False
 lowerCamelCase												=False
 lowerCamelCase												=False
 lowerCamelCase												=False
 lowerCamelCase												=False
 lowerCamelCase												=False
 lowerCamelCase												=False
 lowerCamelCase												=False
 # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
 lowerCamelCase												=[0.8, 0.9]
 def 		lowercase_( self      :     str					, lowerCamelCase      :     Dict					, lowerCamelCase      :     List[str]					, lowerCamelCase      :     str					, lowerCamelCase      :     Union[str, Any]					, lowerCamelCase      :     Union[str, 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 		lowercase_( self      :     Optional[Any]							):



   """simple docstring"""





   __A		:  int       				=		GPTSanJapaneseTester(self							)
   __A		:  Optional[Any]       				=		ConfigTester(self					, config_class=lowerCamelCase					, d_model=37							)
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   GPTSanJapaneseConfig()
 def 		lowercase_( self      :     Union[str, Any]							):



   """simple docstring"""





   __A		:  Dict       				=		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 		lowercase_( self      :     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 		lowercase_( self      :     Optional[Any]							):



   """simple docstring"""





   pass







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



   """simple docstring"""





   __A		:  List[Any]       				=		GPTSanJapaneseTester(self							)
   __A		:  str       				=		ConfigTester(self					, config_class=lowerCamelCase					, d_model=37							)
 def 		lowercase_( self      :     Any							):



   """simple docstring"""





   GPTSanJapaneseConfig()
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





   __A		:  Tuple       				=		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 		lowercase_( self      :     int							):



   """simple docstring"""





   super().test_model_parallelism()
 @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""							)
 def 		lowercase_( self      :     Optional[Any]							):



   """simple docstring"""





   pass
 @slow
 def 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





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

   def check(lowerCamelCase      :     Any					, lowerCamelCase      :     Optional[int]					, lowerCamelCase      :     List[Any]=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 		lowercase_( self      :     List[Any]							):



   """simple docstring"""





   __A		:  List[Any]       				=		GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
   __A		:  int       				=		GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
   model.to(lowerCamelCase							)

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

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

   __A		:  List[str]       				=		"""left"""
   __A		:  Any       				=		tokenizer(lowerCamelCase					, return_tensors="""pt"""					, padding=lowerCamelCase							)
   __A		:  List[str]       				=		inputs["""input_ids"""].to(lowerCamelCase							)

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

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

   __A		:  str       				=		tokenizer(sentences[0]					, return_tensors="""pt"""							).input_ids.to(lowerCamelCase							)
   __A		:  str       				=		model.generate(
       input_ids=lowerCamelCase					, max_new_tokens=3					, generation_config=lowerCamelCase							)

   __A		:  str       				=		tokenizer(sentences[1]					, return_tensors="""pt"""							).input_ids.to(lowerCamelCase							)
   __A		:  int       				=		model.generate(input_ids=lowerCamelCase					, max_new_tokens=3					, generation_config=lowerCamelCase							)

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

   __A		:  List[Any]       				=		tokenizer.batch_decode(lowerCamelCase					, skip_special_tokens=lowerCamelCase							)
   __A		:  Union[str, Any]       				=		tokenizer.decode(output_non_padded[0]					, skip_special_tokens=lowerCamelCase							)
   __A		:  Optional[int]       				=		tokenizer.decode(output_padded[0]					, skip_special_tokens=lowerCamelCase							)

   __A		:  int       				=		[
       """甲斐なら武田と言うほど甲斐の武田""",
       """織田信長は、このような""",
   ]
   self.assertListEqual(lowerCamelCase					, lowerCamelCase							)
   self.assertListEqual(lowerCamelCase					, [non_padded_sentence, padded_sentence]							)
 @tooslow
 def 		lowercase_( self      :     Optional[Any]							):



   """simple docstring"""





   __A		:  Any       				=		GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
   __A		:  str       				=		GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
   # Output of original model created with mesh-tensoflow
   __A		:  Union[str, Any]       				=		[
       ("""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		:  int       				=		tokenizer.encode(lowerCamelCase					, return_tensors="""pt"""							)
     __A		:  Any       				=		model(lowerCamelCase							)
     __A		:  List[Any]       				=		outputs.logits.detach().cpu().numpy()[0]
     __A		:  Dict       				=		np.argmax(output_logits[-1]							)
     self.assertEqual(lowerCamelCase					, lowerCamelCase							)
 @slow
 def 		lowercase_( self      :     str							):



   """simple docstring"""





   __A		:  List[str]       				=		GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
   __A		:  str       				=		GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
   model.to(lowerCamelCase							)

   # set deterministically
   __A		:  Dict       				=		GenerationConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
   __A		:  List[str]       				=		1

   __A		:  Optional[Any]       				=		"""武田信玄は、"""
   __A		:  Optional[Any]       				=		tokenizer(lowerCamelCase					, return_tensors="""pt"""							).input_ids.to(lowerCamelCase							)
   __A		:  Any       				=		tokenizer([input_text, input_text]					, return_tensors="""pt"""							).input_ids.to(lowerCamelCase							)

   # spout from uniform and one-hot
   __A		:  List[Any]       				=		[
       # fmt: off
       [0.87_882_208, 0.38_426_396, 0.33_220_248, 0.43_890_406, 0.16_562_252,
       0.04_803_985, 0.211_572  , 0.23_188_473, 0.37_153_068, 0.7_836_377 ,
       0.02_160_172, 0.38_761_719, 0.75_290_772, 0.90_198_857, 0.34_365_777,
       0.64_168_169, 0.44_318_471, 0.14_575_746, 0.92_562_881, 0.40_812_148,
       0.29_019_122, 0.88_861_599, 0.65_524_846, 0.43_563_456, 0.38_177_187,
       0.70_832_965, 0.81_527_892, 0.68_832_812, 0.38_833_192, 0.4_561_522 ,
       0.14_828_817, 0.47_248_213, 0.54_357_335, 0.82_009_566, 0.1_338_884 ,
       0.02_755_417, 0.19_764_677, 0.2_422_084 , 0.04_757_674, 0.65_409_606,
       0.0_824_589 , 0.03_304_383, 0.94_387_689, 0.98_764_509, 0.82_433_901,
       0.27_646_741, 0.64_907_493, 0.76_009_406, 0.30_087_915, 0.17_904_689,
       0.41_601_714, 0.67_046_398, 0.10_422_822, 0.08_447_374, 0.07_354_344,
       0.61_423_565, 0.70_284_866, 0.7_532_333 , 0.1_972_038 , 0.29_575_659,
       0.90_583_886, 0.29_265_307, 0.50_000_175, 0.70_407_655, 0.889_363  ,
       0.81_904_418, 0.66_829_128, 0.64_468_815, 0.56_563_723, 0.85_601_875,
       0.94_924_672, 0.00_166_762, 0.25_220_643, 0.74_540_219, 0.67_993_247,
       0.1_549_675 , 0.39_385_352, 0.92_153_607, 0.63_745_931, 0.27_759_043,
       0.84_702_295, 0.65_904_271, 0.58_676_614, 0.8_666_936 , 0.39_607_438,
       0.79_954_983, 0.42_220_697, 0.39_650_381, 0.7_849_864 , 0.56_150_201,
       0.15_678_925, 0.14_746_032, 0.34_542_114, 0.47_026_783, 0.11_956_489,
       0.25_421_435, 0.33_788_901, 0.68_934_842, 0.36_424_685, 0.71_737_898,
       0.38_983_449, 0.94_393_779, 0.39_575_588, 0.36_616_553, 0.87_104_665,
       0.64_630_203, 0.22_516_905, 0.88_270_804, 0.15_031_338, 0.75_144_345,
       0.46_459_025, 0.85_396_454, 0.86_355_643, 0.65_139_851, 0.70_266_061,
       0.30_241_389, 0.81_056_497, 0.88_865_969, 0.38_773_807, 0.70_635_849,
       0.90_718_459, 0.43_245_789, 0.28_000_654, 0.45_935_562, 0.08_773_519,
       0.9_552_151 , 0.93_901_511, 0.22_489_288], # uniform
       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
        0., 0., 0., 0., 0., 0., 0., 0.],
       # fmt: on
   ]

   __A		:  Optional[Any]       				=		model.generate(
       input_ids=lowerCamelCase					, spout=spouts[0]					, max_new_tokens=20					, generation_config=lowerCamelCase					, )

   __A		:  int       				=		model.generate(
       input_ids=lowerCamelCase					, spout=spouts[1]					, max_new_tokens=20					, generation_config=lowerCamelCase					, )

   __A		:  List[str]       				=		model.generate(
       input_ids=lowerCamelCase					, spout=lowerCamelCase					, max_new_tokens=20					, generation_config=lowerCamelCase					, )

   __A		:  Dict       				=		tokenizer.decode(outputa[0]							)
   __A		:  Dict       				=		tokenizer.decode(outputa[0]							)
   __A		:  int       				=		tokenizer.batch_decode(lowerCamelCase							)

   __A		:  int       				=		[
       """武田信玄は、武田氏の滅亡後、武田氏の居城であった甲斐武田氏の居城である""",
       """武田信玄は、武田家の滅亡を防ぐため、武田家の家臣である武田信虎を討""",
   ]
   self.assertListEqual(lowerCamelCase					, lowerCamelCase							)
   self.assertListEqual(lowerCamelCase					, [outa_sentence, outa_sentence]							)






 @slow
 def 		lowercase_( self      :     str							):



   """simple docstring"""





   __A		:  Any       				=		GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
   __A		:  Union[str, Any]       				=		GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""							)
   model.to(lowerCamelCase							)

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

   __A		:  Dict       				=		"""武田信玄"""
   __A		:  Dict       				=		"""織田信長"""
   __A		:  Union[str, Any]       				=		"""は、"""
   __A		:  Any       				=		"""が、"""
   __A		:  List[str]       				=		tokenizer(lowerCamelCase					, prefix_text=lowerCamelCase					, return_tensors="""pt"""							)
   __A		:  Any       				=		tokenizer(lowerCamelCase					, prefix_text=lowerCamelCase					, return_tensors="""pt"""							)
   __A		:  Union[str, Any]       				=		tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]					, return_tensors="""pt"""							)

   __A		:  Dict       				=		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		:  Any       				=		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		:  List[str]       				=		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		:  Optional[Any]       				=		tokenizer.decode(outputa[0]							)
   __A		:  str       				=		tokenizer.decode(outputa[0]							)
   __A		:  List[Any]       				=		tokenizer.batch_decode(lowerCamelCase							)

   __A		:  Union[str, Any]       				=		[
       """武田信玄は、武田氏の祖である武田信虎を、その子・武田信友を擁して""",
       """織田信長が、織田信長の妻・お市の方を妻として迎えたという逸話が残""",
   ]
   self.assertListEqual(lowerCamelCase					, lowerCamelCase							)
   self.assertListEqual(lowerCamelCase					, [outa_sentence, outa_sentence]							)





'''simple docstring'''
A__ :		dict[str, float]					={
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.0_0_0_9_8_4_2_0_7_3,
    "short-ton": 0.0_0_1_1_0_2_3_1_2_2,
    "pound": 2.2_0_4_6_2_4_4_2_0_2,
    "stone": 0.1_5_7_4_7_3_1_7_2_8,
    "ounce": 3_5.2_7_3_9_9_0_7_2_3,
    "carrat": 5_000,
    "atomic-mass-unit": 6.0_2_2_1_3_6_6_5_2E2_6,
}

A__ :		dict[str, float]					={
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1_0_1_6.0_4_6_0_8,
    "short-ton": 9_0_7.1_8_4,
    "pound": 0.4_5_3_5_9_2,
    "stone": 6.3_5_0_2_9,
    "ounce": 0.0_2_8_3_4_9_5,
    "carrat": 0.0_0_0_2,
    "atomic-mass-unit": 1.6_6_0_5_4_0_1_9_9E-2_7,
}







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	float			)					->							float:





  """simple docstring"""

  if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
    __A		:  Tuple       				=		(
        F"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
        F"Supported values are: {', '.join(__SCREAMING_SNAKE_CASE			)}"
    )
    raise ValueError(__SCREAMING_SNAKE_CASE			)
  return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
     import doctest

     doctest.testmod()



