


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      :


				"""simple docstring"""







				def __init__(				self     :    Any	,							lowerCamelCase_     :    Dict	,							lowerCamelCase_     :    Optional[int]=3_6000	,							lowerCamelCase_     :    Dict=13	,							lowerCamelCase_     :    Optional[Any]=7	,							lowerCamelCase_     :    str=True	,							lowerCamelCase_     :    int=32	,							lowerCamelCase_     :    List[str]=42	,							lowerCamelCase_     :    Dict=5	,							lowerCamelCase_     :    int=2	,							lowerCamelCase_     :    Tuple=4	,							lowerCamelCase_     :    List[str]=2	,							lowerCamelCase_     :    Tuple=32	,							lowerCamelCase_     :    Any=80	,							lowerCamelCase_     :    Optional[int]=33	,							lowerCamelCase_     :    Optional[Any]=0.0	,							lowerCamelCase_     :    Optional[int]=1e-6	,							lowerCamelCase_     :    List[Any]=100	,							lowerCamelCase_     :    List[Any]=0.0	,							):
								_lowerCAmelCase  						=vocab_size
								_lowerCAmelCase  						=parent
								_lowerCAmelCase  						=batch_size
								_lowerCAmelCase  						=num_contexts
								# For common tests
								_lowerCAmelCase  						=self.num_contexts
								_lowerCAmelCase  						=is_training
								_lowerCAmelCase  						=hidden_size
								_lowerCAmelCase  						=num_ext_layers
								_lowerCAmelCase  						=ext_size
								_lowerCAmelCase  						=num_hidden_layers
								_lowerCAmelCase  						=num_attention_heads
								_lowerCAmelCase  						=num_experts
								_lowerCAmelCase  						=d_ff
								_lowerCAmelCase  						=d_ext
								_lowerCAmelCase  						=d_spout
								_lowerCAmelCase  						=dropout_rate
								_lowerCAmelCase  						=layer_norm_epsilon
								_lowerCAmelCase  						=expert_capacity
								_lowerCAmelCase  						=router_jitter_noise






				def        lowerCAmelCase__			(				self     :    Optional[int]					):
								return GPTSanJapaneseConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""					)






				def        lowerCAmelCase__			(				self     :    Tuple					):
								_lowerCAmelCase  						=ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size					)

								_lowerCAmelCase  						=self.get_config()

								return (config, input_ids)






				def        lowerCAmelCase__			(				self     :    Tuple					):
								_lowerCAmelCase  						=ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size					)

								_lowerCAmelCase  						=self.get_config()

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






				def        lowerCAmelCase__			(				self     :    Optional[int]					):
								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     :    Tuple	,							lowerCamelCase_     :    Tuple	,							lowerCamelCase_     :    Optional[int]	,							):
								_lowerCAmelCase  						=GPTSanJapaneseForConditionalGeneration(config=lowerCamelCase_					)
								model.to(lowerCamelCase_					)
								model.eval()
								_lowerCAmelCase  						=model(
								    input_ids=lowerCamelCase_	,							)
								self.parent.assertIsNotNone(lowerCamelCase_					)


@require_torch
class        __lowerCamelCase      (				lowerCamelCase_				,		lowerCamelCase_				,		unittest.TestCase ):


				"""simple docstring"""







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






				def        lowerCAmelCase__			(				self     :    Any	,							lowerCamelCase_     :    Optional[int]	,							lowerCamelCase_     :    Optional[Any]	,							lowerCamelCase_     :    Optional[int]	,							lowerCamelCase_     :    List[Any]	,							lowerCamelCase_     :    Dict					):
								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     :    Optional[int]					):
								_lowerCAmelCase  						=GPTSanJapaneseTester(self					)
								_lowerCAmelCase  						=ConfigTester(self	,							config_class=lowerCamelCase_	,							d_model=37					)






				def        lowerCAmelCase__			(				self     :    Optional[int]					):
								GPTSanJapaneseConfig()






				def        lowerCAmelCase__			(				self     :    Any					):
								_lowerCAmelCase  						=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        lowerCAmelCase__			(				self     :    List[str]					):
								super().test_model_parallelism()






				@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""					)
				def        lowerCAmelCase__			(				self     :    Dict					):
								pass


@require_torch
class        __lowerCamelCase      (				lowerCamelCase_				,		lowerCamelCase_				,		unittest.TestCase ):


				"""simple docstring"""







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






				def        lowerCAmelCase__			(				self     :    Any					):
								_lowerCAmelCase  						=GPTSanJapaneseTester(self					)
								_lowerCAmelCase  						=ConfigTester(self	,							config_class=lowerCamelCase_	,							d_model=37					)






				def        lowerCAmelCase__			(				self     :    Tuple					):
								GPTSanJapaneseConfig()






				def        lowerCAmelCase__			(				self     :    List[str]					):
								_lowerCAmelCase  						=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        lowerCAmelCase__			(				self     :    List[Any]					):
								super().test_model_parallelism()






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






				@slow
				def        lowerCAmelCase__			(				self     :    Optional[int]					):
								_lowerCAmelCase  						=GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""					)
								_lowerCAmelCase  						=GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""					)
								_lowerCAmelCase  						=tokenizer.encode("""武田信玄は"""	,							return_tensors="""pt"""					)
								_lowerCAmelCase  						=model(lowerCamelCase_					)
								_lowerCAmelCase  						=outputs.logits.detach().cpu().numpy()
								# Output of original model created with mesh-tensoflow
								_lowerCAmelCase  						=[
								    # fmt: off
								    [-12.037_8398_8952_6367, -12.433_0615_9973_1445, -14.333_8403_7017_8223, -12.450_3459_9304_1992, -11.1_6613_7695_3125,
								    -11.930_1376_3427_7344, -10.659_7404_4799_8047, -12.909_5745_0866_6992, -13.241_0430_9082_0312, -13.398_5795_9747_3145,
								    -11.107_5248_7182_6172, -12.3_6859_4169_6167, -22.97_9431_1523_4375, -10.481_0676_5747_0703, -12.484_0307_2357_1777,
								    -12.807_3606_4910_8887, -14.769_7000_5035_4004, -12.233_5796_3562_0117, -13.428_1454_0863_0371, -22.624_1779_3273_9258],
								    [-7.511_1498_8327_0264, -8.281_8517_6849_3652, -7.943_1271_5530_3955, -7.55_0213_3369_4458, -6.49_8699_6650_6958,
								    -7.586_7962_8372_1924, -6.978_0859_9472_0459, -7.839_1451_8356_3232, -8.21_9641_6854_8584, -8.695_0912_4755_8594,
								    -6.706_9106_1019_8975, -6.6585_7982_6354_9805, -19.565_6986_2365_7227, -5.353_8427_3529_0527, -8.350_6860_7330_3223,
								    -8.039_3886_5661_6211, -10.856_5692_9016_1133, -7.75_1544_4755_5542, -8.819_0221_7864_9902, -19.51_5327_4536_1328],
								    [-9.73_0662_3458_8623, -10.223_9227_2949_2188, -9.932_9814_9108_8867, -11.857_8367_2332_7637, -7.662_6262_6647_9492,
								    -11.13_5291_0995_4834, -7.765_0976_1810_3027, -11.472_9232_7880_8594, -9.543_1499_4812_0117, -11.905_6339_2639_1602,
								    -9.366_1642_0745_8496, -11.5_7342_8153_9917, -23.699_0032_1960_4492, -9.429_5902_2521_9727, -10.42_8392_4102_7832,
								    -10.585_2403_6407_4707, -10.94_7719_5739_7461, -11.095_4160_6903_0762, -10.390_2406_6925_0488, -23.769_3729_4006_3477],
								    [-9.728_2657_6232_9102, -9.859_7126_0070_8008, -10.09_7292_9000_8545, -9.678_5221_0998_5352, -6.879_5199_3942_2607,
								    -9.68_4875_4882_8125, -4.2803_4257_8887_9395, -10.018_9142_2271_7285, -9.308_4459_3048_0957, -10.63_3945_4650_8789,
								    -8.083_6467_7429_1992, -9.06_3014_9841_3086, -21.904_2663_5742_1875, -8.90_1608_4671_0205, -8.841_8760_2996_8262,
								    -11.856_7199_7070_3125, -12.079_3981_5521_2402, -11.233_7532_0434_5703, -10.177_3386_0015_8691, -21.87_2566_2231_4453],
								    [-9.669_7645_1873_7793, -9.614_1986_8469_2383, -9.814_5103_4545_8984, -9.996_5019_2260_7422, -11.375_6904_6020_5078,
								    -10.113_4052_2766_1133, -10.546_8673_7060_5469, -10.04_3690_6814_5752, -10.907_8092_5750_7324, -10.504_2161_9415_2832,
								    -11.129_1990_2801_5137, -10.151_1240_0054_9316, -21.96_5867_9962_1582, -9.086_3494_8730_4688, -11.730_3390_5029_2969,
								    -10.460_6676_1016_8457, -10.298_0499_2675_7812, -10.784_1482_1624_7559, -10.840_6934_7381_5918, -22.03_1528_4729_0039],
								    # fmt: on
								]
								_lowerCAmelCase  						=np.array(lowerCamelCase_					).flatten()
								_lowerCAmelCase  						=output_logits[0, :, :20].flatten()

								def check(lowerCamelCase_     :    Optional[int]	,							lowerCamelCase_     :    Union[str, Any]	,							lowerCamelCase_     :    Union[str, 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        lowerCAmelCase__			(				self     :    List[Any]					):
								_lowerCAmelCase  						=GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""					)
								_lowerCAmelCase  						=GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""					)
								model.to(lowerCamelCase_					)

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

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

								_lowerCAmelCase  						="""left"""
								_lowerCAmelCase  						=tokenizer(lowerCamelCase_	,							return_tensors="""pt"""	,							padding=lowerCamelCase_					)
								_lowerCAmelCase  						=inputs["""input_ids"""].to(lowerCamelCase_					)

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

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

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

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

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

								_lowerCAmelCase  						=tokenizer.batch_decode(lowerCamelCase_	,							skip_special_tokens=lowerCamelCase_					)
								_lowerCAmelCase  						=tokenizer.decode(output_non_padded[0]	,							skip_special_tokens=lowerCamelCase_					)
								_lowerCAmelCase  						=tokenizer.decode(output_padded[0]	,							skip_special_tokens=lowerCamelCase_					)

								_lowerCAmelCase  						=[
								    """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        lowerCAmelCase__			(				self     :    Optional[int]					):
								_lowerCAmelCase  						=GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""					)
								_lowerCAmelCase  						=GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""					)
								# Output of original model created with mesh-tensoflow
								_lowerCAmelCase  						=[
								    ("""武田信玄は""", 3_5675),
								    ("""武田信玄は、""", 45),
								    ("""武田信玄は、この""", 29),
								    ("""武田信玄は、このよう""", 3_0642),
								    ("""武田信玄は、このような""", 3_5680),
								    ("""武田信玄は、このような「""", 8640),
								    ("""武田信玄は、このような「武田""", 3_1617),
								    ("""武田信玄は、このような「武田家""", 3_0646),
								    ("""武田信玄は、このような「武田家の""", 3_1617),
								    ("""武田信玄は、このような「武田家の家""", 3_1381),
								]
								for input, output in target:
												_lowerCAmelCase  						=tokenizer.encode(lowerCamelCase_	,							return_tensors="""pt"""					)
												_lowerCAmelCase  						=model(lowerCamelCase_					)
												_lowerCAmelCase  						=outputs.logits.detach().cpu().numpy()[0]
												_lowerCAmelCase  						=np.argmax(output_logits[-1]					)
												self.assertEqual(lowerCamelCase_	,							lowerCamelCase_					)






				@slow
				def        lowerCAmelCase__			(				self     :    Tuple					):
								_lowerCAmelCase  						=GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""					)
								_lowerCAmelCase  						=GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""					)
								model.to(lowerCamelCase_					)

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

								_lowerCAmelCase  						="""武田信玄は、"""
								_lowerCAmelCase  						=tokenizer(lowerCamelCase_	,							return_tensors="""pt"""					).input_ids.to(lowerCamelCase_					)
								_lowerCAmelCase  						=tokenizer([input_text, input_text]	,							return_tensors="""pt"""					).input_ids.to(lowerCamelCase_					)

								# spout from uniform and one-hot
								_lowerCAmelCase  						=[
								    # fmt: off
								    [0.8788_2208, 0.3842_6396, 0.3322_0248, 0.4389_0406, 0.1656_2252,
								    0.0480_3985, 0.21_1572  , 0.2318_8473, 0.3715_3068, 0.783_6377 ,
								    0.0216_0172, 0.3876_1719, 0.7529_0772, 0.9019_8857, 0.3436_5777,
								    0.6416_8169, 0.4431_8471, 0.1457_5746, 0.9256_2881, 0.4081_2148,
								    0.2901_9122, 0.8886_1599, 0.6552_4846, 0.4356_3456, 0.3817_7187,
								    0.7083_2965, 0.8152_7892, 0.6883_2812, 0.3883_3192, 0.456_1522 ,
								    0.1482_8817, 0.4724_8213, 0.5435_7335, 0.8200_9566, 0.133_8884 ,
								    0.0275_5417, 0.1976_4677, 0.242_2084 , 0.0475_7674, 0.6540_9606,
								    0.082_4589 , 0.0330_4383, 0.9438_7689, 0.9876_4509, 0.8243_3901,
								    0.2764_6741, 0.6490_7493, 0.7600_9406, 0.3008_7915, 0.1790_4689,
								    0.4160_1714, 0.6704_6398, 0.1042_2822, 0.0844_7374, 0.0735_4344,
								    0.6142_3565, 0.7028_4866, 0.753_2333 , 0.197_2038 , 0.2957_5659,
								    0.9058_3886, 0.2926_5307, 0.5000_0175, 0.7040_7655, 0.88_9363  ,
								    0.8190_4418, 0.6682_9128, 0.6446_8815, 0.5656_3723, 0.8560_1875,
								    0.9492_4672, 0.0016_6762, 0.2522_0643, 0.7454_0219, 0.6799_3247,
								    0.154_9675 , 0.3938_5352, 0.9215_3607, 0.6374_5931, 0.2775_9043,
								    0.8470_2295, 0.6590_4271, 0.5867_6614, 0.866_6936 , 0.3960_7438,
								    0.7995_4983, 0.4222_0697, 0.3965_0381, 0.784_9864 , 0.5615_0201,
								    0.1567_8925, 0.1474_6032, 0.3454_2114, 0.4702_6783, 0.1195_6489,
								    0.2542_1435, 0.3378_8901, 0.6893_4842, 0.3642_4685, 0.7173_7898,
								    0.3898_3449, 0.9439_3779, 0.3957_5588, 0.3661_6553, 0.8710_4665,
								    0.6463_0203, 0.2251_6905, 0.8827_0804, 0.1503_1338, 0.7514_4345,
								    0.4645_9025, 0.8539_6454, 0.8635_5643, 0.6513_9851, 0.7026_6061,
								    0.3024_1389, 0.8105_6497, 0.8886_5969, 0.3877_3807, 0.7063_5849,
								    0.9071_8459, 0.4324_5789, 0.2800_0654, 0.4593_5562, 0.0877_3519,
								    0.955_2151 , 0.9390_1511, 0.2248_9288], # 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
								]

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

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

								_lowerCAmelCase  						=model.generate(
								    input_ids=lowerCamelCase_	,							spout=lowerCamelCase_	,							max_new_tokens=20	,							generation_config=lowerCamelCase_	,							)

								_lowerCAmelCase  						=tokenizer.decode(outputa[0]					)
								_lowerCAmelCase  						=tokenizer.decode(outputa[0]					)
								_lowerCAmelCase  						=tokenizer.batch_decode(lowerCamelCase_					)

								_lowerCAmelCase  						=[
								    """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        lowerCAmelCase__			(				self     :    Optional[Any]					):
								_lowerCAmelCase  						=GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""					)
								_lowerCAmelCase  						=GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""					)
								model.to(lowerCamelCase_					)

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

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

								_lowerCAmelCase  						=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_	,							)

								_lowerCAmelCase  						=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_	,							)

								_lowerCAmelCase  						=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_	,							)

								_lowerCAmelCase  						=tokenizer.decode(outputa[0]					)
								_lowerCAmelCase  						=tokenizer.decode(outputa[0]					)
								_lowerCAmelCase  						=tokenizer.batch_decode(lowerCamelCase_					)

								_lowerCAmelCase  						=[
								    """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]					)





def 		snake_case_   (				lowercase__     :    str     ):






				'''simple docstring'''






				_lowerCAmelCase  						=53_81
				for x in s:
								_lowerCAmelCase  						=((hash_value << 5) + hash_value) + ord(lowercase__     )
				return hash_value & 0Xffff_ffff

