
"""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 	UpperCamelCase      :

				"""simple docstring"""



				def __init__(							self						,      lowercase__						,      lowercase__=36000						,      lowercase__=13						,      lowercase__=7						,      lowercase__=True						,      lowercase__=32						,      lowercase__=42						,      lowercase__=5						,      lowercase__=2						,      lowercase__=4						,      lowercase__=2						,      lowercase__=32						,      lowercase__=80						,      lowercase__=33						,      lowercase__=0.0						,      lowercase__=1E-6						,      lowercase__=100						,      lowercase__=0.0						,      )       ->       Optional[Any]:
									"""simple docstring"""







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


				def 				A   (							self     )       ->       str:
									"""simple docstring"""







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


				def 				A   (							self     )       ->       Union[str, Any]:
									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE           =      self.get_config()

									return (config, input_ids)


				def 				A   (							self     )       ->       Dict:
									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE           =      self.get_config()

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


				def 				A   (							self     )       ->       Dict:
									"""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 				A   (							self						,      lowercase__						,      lowercase__						,      )       ->       Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      GPTSanJapaneseForConditionalGeneration(config=lowercase__     )
									model.to(lowercase__     )
									model.eval()
									SCREAMING_SNAKE_CASE           =      model(
									    input_ids=lowercase__						,      )
									self.parent.assertIsNotNone(lowercase__     )






@require_torch
class 	UpperCamelCase      (			__magic_name__    , __magic_name__    , unittest.TestCase ):

				"""simple docstring"""



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


				def 				A   (							self						,      lowercase__						,      lowercase__						,      lowercase__						,      lowercase__						,      lowercase__     )       ->       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 				A   (							self     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      GPTSanJapaneseTester(self     )
									SCREAMING_SNAKE_CASE           =      ConfigTester(self						,      config_class=lowercase__						,      d_model=37     )


				def 				A   (							self     )       ->       Dict:
									"""simple docstring"""







									GPTSanJapaneseConfig()


				def 				A   (							self     )       ->       Optional[Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowercase__     )


				@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 				A   (							self     )       ->       List[str]:
									"""simple docstring"""







									super().test_model_parallelism()




				@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'     )
				def 				A   (							self     )       ->       int:
									"""simple docstring"""







									pass






@require_torch
class 	UpperCamelCase      (			__magic_name__    , __magic_name__    , unittest.TestCase ):

				"""simple docstring"""



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


				def 				A   (							self     )       ->       Dict:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      GPTSanJapaneseTester(self     )
									SCREAMING_SNAKE_CASE           =      ConfigTester(self						,      config_class=lowercase__						,      d_model=37     )


				def 				A   (							self     )       ->       Optional[Any]:
									"""simple docstring"""







									GPTSanJapaneseConfig()


				def 				A   (							self     )       ->       Union[str, Any]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowercase__     )


				@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 				A   (							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 				A   (							self     )       ->       int:
									"""simple docstring"""







									pass


				@slow
				def 				A   (							self     )       ->       Dict:
									"""simple docstring"""







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

									def check(lowercase__						,      lowercase__						,      lowercase__=5E-4     ):
														return abs(a - b     ) < epsilon * max(abs(lowercase__     )						,      abs(lowercase__     )     )

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


				@slow
				def 				A   (							self     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'     )
									SCREAMING_SNAKE_CASE           =      GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'     )
									model.to(lowercase__     )

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

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

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

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

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

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

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

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

									SCREAMING_SNAKE_CASE           =      tokenizer.batch_decode(lowercase__						,      skip_special_tokens=lowercase__     )
									SCREAMING_SNAKE_CASE           =      tokenizer.decode(output_non_padded[0]						,      skip_special_tokens=lowercase__     )
									SCREAMING_SNAKE_CASE           =      tokenizer.decode(output_padded[0]						,      skip_special_tokens=lowercase__     )

									SCREAMING_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(lowercase__						,      lowercase__     )
									self.assertListEqual(lowercase__						,      [non_padded_sentence, padded_sentence]     )


				@tooslow
				def 				A   (							self     )       ->       Any:
									"""simple docstring"""







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


				@slow
				def 				A   (							self     )       ->       Optional[int]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'     )
									SCREAMING_SNAKE_CASE           =      GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'     )
									model.to(lowercase__     )

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

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

									# spout from uniform and one-hot
									SCREAMING_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
									]

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

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

									SCREAMING_SNAKE_CASE           =      model.generate(
									    input_ids=lowercase__						,      spout=lowercase__						,      max_new_tokens=20						,      generation_config=lowercase__						,      )

									SCREAMING_SNAKE_CASE           =      tokenizer.decode(outputa[0]     )
									SCREAMING_SNAKE_CASE           =      tokenizer.decode(outputa[0]     )
									SCREAMING_SNAKE_CASE           =      tokenizer.batch_decode(lowercase__     )

									SCREAMING_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(lowercase__						,      lowercase__     )
									self.assertListEqual(lowercase__						,      [outa_sentence, outa_sentence]     )




				@slow
				def 				A   (							self     )       ->       str:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'     )
									SCREAMING_SNAKE_CASE           =      GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'     )
									model.to(lowercase__     )

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

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

									SCREAMING_SNAKE_CASE           =      model.generate(
									    input_ids=input_tok_a.input_ids.to(lowercase__     )						,      token_type_ids=input_tok_a.token_type_ids.to(lowercase__     )						,      max_new_tokens=20						,      generation_config=lowercase__						,      )

									SCREAMING_SNAKE_CASE           =      model.generate(
									    input_ids=input_tok_a.input_ids.to(lowercase__     )						,      token_type_ids=input_tok_a.token_type_ids.to(lowercase__     )						,      max_new_tokens=20						,      generation_config=lowercase__						,      )

									SCREAMING_SNAKE_CASE           =      model.generate(
									    input_ids=input_tok_a.input_ids.to(lowercase__     )						,      token_type_ids=input_tok_a.token_type_ids.to(lowercase__     )						,      attention_mask=input_tok_a.attention_mask.to(lowercase__     )						,      max_new_tokens=20						,      generation_config=lowercase__						,      )

									SCREAMING_SNAKE_CASE           =      tokenizer.decode(outputa[0]     )
									SCREAMING_SNAKE_CASE           =      tokenizer.decode(outputa[0]     )
									SCREAMING_SNAKE_CASE           =      tokenizer.batch_decode(lowercase__     )

									SCREAMING_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(lowercase__						,      lowercase__     )
									self.assertListEqual(lowercase__						,      [outa_sentence, outa_sentence]     )


"""simple docstring"""




from __future__ import annotations

from math import pi, pow

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					if side_length < 0:
										raise ValueError('vol_cube() only accepts non-negative values'     )
					return pow(SCREAMING_SNAKE_CASE_,	3     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if height < 0 or radius < 0:
										raise ValueError('vol_spherical_cap() only accepts non-negative values'     )
					# Volume is 1/3 pi * height squared * (3 * radius - height)
					return 1 / 3 * pi * pow(SCREAMING_SNAKE_CASE_,	2     ) * (3 * radius - height)

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if radius_a < 0 or radius_a < 0 or centers_distance < 0:
										raise ValueError('vol_spheres_intersect() only accepts non-negative values'     )
					if centers_distance == 0:
										return vol_sphere(min(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )     )

					SCREAMING_SNAKE_CASE           =      (
					    (radius_a - radius_a + centers_distance)
					    * (radius_a + radius_a - centers_distance)
					    / (2 * centers_distance)
					)
					SCREAMING_SNAKE_CASE           =      (
					    (radius_a - radius_a + centers_distance)
					    * (radius_a + radius_a - centers_distance)
					    / (2 * centers_distance)
					)

					return vol_spherical_cap(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ) + vol_spherical_cap(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):

					if radius_a <= 0 or radius_a <= 0 or centers_distance < 0:
										raise ValueError(
										    'vol_spheres_union() only accepts non-negative values, non-zero radius'     )

					if centers_distance == 0:
										return vol_sphere(max(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )     )

					return (
					    vol_sphere(SCREAMING_SNAKE_CASE_     )
					    + vol_sphere(SCREAMING_SNAKE_CASE_     )
					    - vol_spheres_intersect(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )
					)

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if width < 0 or height < 0 or length < 0:
										raise ValueError('vol_cuboid() only accepts non-negative values'     )
					return float(width * height * length     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if height < 0 or area_of_base < 0:
										raise ValueError('vol_cone() only accepts non-negative values'     )
					return area_of_base * height / 3.0

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if height < 0 or radius < 0:
										raise ValueError('vol_right_circ_cone() only accepts non-negative values'     )
					return pi * pow(SCREAMING_SNAKE_CASE_,	2     ) * height / 3.0

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if height < 0 or area_of_base < 0:
										raise ValueError('vol_prism() only accepts non-negative values'     )
					return float(area_of_base * height     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if height < 0 or area_of_base < 0:
										raise ValueError('vol_pyramid() only accepts non-negative values'     )
					return area_of_base * height / 3.0

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					if radius < 0:
										raise ValueError('vol_sphere() only accepts non-negative values'     )
					# Volume is 4/3 * pi * radius cubed
					return 4 / 3 * pi * pow(SCREAMING_SNAKE_CASE_,	3     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					if radius < 0:
										raise ValueError('vol_hemisphere() only accepts non-negative values'     )
					# Volume is radius cubed * pi * 2/3
					return pow(SCREAMING_SNAKE_CASE_,	3     ) * pi * 2 / 3

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if height < 0 or radius < 0:
										raise ValueError('vol_circular_cylinder() only accepts non-negative values'     )
					# Volume is radius squared * height * pi
					return pow(SCREAMING_SNAKE_CASE_,	2     ) * height * pi

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					# Volume - (outer_radius squared - inner_radius squared) * pi * height
					if inner_radius < 0 or outer_radius < 0 or height < 0:
										raise ValueError(
										    'vol_hollow_circular_cylinder() only accepts non-negative values'     )
					if outer_radius <= inner_radius:
										raise ValueError('outer_radius must be greater than inner_radius'     )
					return pi * (pow(SCREAMING_SNAKE_CASE_,	2     ) - pow(SCREAMING_SNAKE_CASE_,	2     )) * height

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					# Volume is 1/3 * pi * height *
					#           (radius_1 squared + radius_2 squared + radius_1 * radius_2)
					if radius_a < 0 or radius_a < 0 or height < 0:
										raise ValueError('vol_conical_frustum() only accepts non-negative values'     )
					return (
					    1
					    / 3
					    * pi
					    * height
					    * (pow(SCREAMING_SNAKE_CASE_,	2     ) + pow(SCREAMING_SNAKE_CASE_,	2     ) + radius_a * radius_a)
					)

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if torus_radius < 0 or tube_radius < 0:
										raise ValueError('vol_torus() only accepts non-negative values'     )
					return 2 * pow(SCREAMING_SNAKE_CASE_,	2     ) * torus_radius * pow(SCREAMING_SNAKE_CASE_,	2     )

def     UpperCamelCase_    (						):
					print('Volumes:'     )
					print(f'''Cube: {vol_cube(2     ) = }'''     )  # = 8
					print(f'''Cuboid: {vol_cuboid(2,	2,	2     ) = }'''     )  # = 8
					print(f'''Cone: {vol_cone(2,	2     ) = }'''     )  # ~= 1.33
					print(f'''Right Circular Cone: {vol_right_circ_cone(2,	2     ) = }'''     )  # ~= 8.38
					print(f'''Prism: {vol_prism(2,	2     ) = }'''     )  # = 4
					print(f'''Pyramid: {vol_pyramid(2,	2     ) = }'''     )  # ~= 1.33
					print(f'''Sphere: {vol_sphere(2     ) = }'''     )  # ~= 33.5
					print(f'''Hemisphere: {vol_hemisphere(2     ) = }'''     )  # ~= 16.75
					print(f'''Circular Cylinder: {vol_circular_cylinder(2,	2     ) = }'''     )  # ~= 25.1
					print(f'''Torus: {vol_torus(2,	2     ) = }'''     )  # ~= 157.9
					print(f'''Conical Frustum: {vol_conical_frustum(2,	2,	4     ) = }'''     )  # ~= 58.6
					print(f'''Spherical cap: {vol_spherical_cap(1,	2     ) = }'''     )  # ~= 5.24
					print(f'''Spheres intersetion: {vol_spheres_intersect(2,	2,	1     ) = }'''     )  # ~= 21.21
					print(f'''Spheres union: {vol_spheres_union(2,	2,	1     ) = }'''     )  # ~= 45.81
					print(
					    f'''Hollow Circular Cylinder: {vol_hollow_circular_cylinder(1,	2,	3     ) = }'''     )  # ~= 28.3


if __name__ == "__main__":
	main()
