code: 
  
  
  
  
  
  '''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				,	A				,	A=3_6000				,	A=13				,	A=7				,	A=True				,	A=32				,	A=42				,	A=5				,	A=2				,	A=4				,	A=2				,	A=32				,	A=80				,	A=33				,	A=0.0				,	A=1e-6				,	A=100				,	A=0.0				,	)	->     Optional[Any]:
  								_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     snake_case_(	self )	->     List[str]:
  								return GPTSanJapaneseConfig.from_pretrained("""Tanrei/GPTSAN-japanese""" )
  
  
  				def     snake_case_(	self )	->     Any:
  								_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     snake_case_(	self )	->     List[Any]:
  								_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     snake_case_(	self )	->     str:
  								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     snake_case_(	self				,	A				,	A				,	)	->     Any:
  								_SCREAMING_SNAKE_CASE             =    GPTSanJapaneseForConditionalGeneration(config=a_ )
  								model.to(a_ )
  								model.eval()
  								_SCREAMING_SNAKE_CASE             =    model(
  								    input_ids=a_				,	)
  								self.parent.assertIsNotNone(a_ )
  
  
  
  
  @require_torch
  class       a_  (       snake_case_   ,					snake_case_   ,					unittest.TestCase ):
  
  				'''simple docstring'''
  				UpperCamelCase									=				(GPTSanJapaneseModel,) if is_torch_available() else ()
  				UpperCamelCase									=				(
  				    {
  				        '''conversational''': GPTSanJapaneseForConditionalGeneration,
  				        '''feature-extraction''': GPTSanJapaneseForConditionalGeneration,
  				        '''summarization''': GPTSanJapaneseForConditionalGeneration,
  				        '''text2text-generation''': GPTSanJapaneseForConditionalGeneration,
  				        '''translation''': GPTSanJapaneseForConditionalGeneration,
  				    }
  				    if is_torch_available()
  				    else {}
  				)
  				UpperCamelCase									=				False
  				UpperCamelCase									=				False
  				UpperCamelCase									=				False
  				UpperCamelCase									=				False
  				UpperCamelCase									=				False
  				UpperCamelCase									=				False
  				UpperCamelCase									=				False
  				UpperCamelCase									=				False
  				# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
  				UpperCamelCase									=				[0.8, 0.9]
  
  
  				def     snake_case_(	self				,	A				,	A				,	A				,	A				,	A )	->     int:
  								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     snake_case_(	self )	->     Union[str, Any]:
  								_SCREAMING_SNAKE_CASE             =    GPTSanJapaneseTester(self )
  								_SCREAMING_SNAKE_CASE             =    ConfigTester(self				,	config_class=a_				,	d_model=37 )
  
  
  				def     snake_case_(	self )	->     Optional[Any]:
  								GPTSanJapaneseConfig()
  
  
  				def     snake_case_(	self )	->     Dict:
  								_SCREAMING_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     snake_case_(	self )	->     Any:
  								super().test_model_parallelism()
  
  
  
  
  
  				@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
  				def     snake_case_(	self )	->     int:
  								pass
  
  
  
  
  @require_torch
  class       a_  (       snake_case_   ,					snake_case_   ,					unittest.TestCase ):
  
  				'''simple docstring'''
  				UpperCamelCase									=				(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
  				UpperCamelCase									=				False
  				UpperCamelCase									=				False
  				UpperCamelCase									=				False
  				UpperCamelCase									=				False
  				UpperCamelCase									=				False
  				UpperCamelCase									=				False
  				# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
  				UpperCamelCase									=				[0.8, 0.9]
  
  
  				def     snake_case_(	self )	->     Dict:
  								_SCREAMING_SNAKE_CASE             =    GPTSanJapaneseTester(self )
  								_SCREAMING_SNAKE_CASE             =    ConfigTester(self				,	config_class=a_				,	d_model=37 )
  
  
  				def     snake_case_(	self )	->     Tuple:
  								GPTSanJapaneseConfig()
  
  
  				def     snake_case_(	self )	->     str:
  								_SCREAMING_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     snake_case_(	self )	->     Any:
  								super().test_model_parallelism()
  
  
  				@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
  				def     snake_case_(	self )	->     Tuple:
  								pass
  
  
  				@slow
  				def     snake_case_(	self )	->     Dict:
  								_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(a_ )
  								_SCREAMING_SNAKE_CASE             =    outputs.logits.detach().cpu().numpy()
  								# Output of original model created with mesh-tensoflow
  								_SCREAMING_SNAKE_CASE             =    [
  								    # 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
  								]
  								_SCREAMING_SNAKE_CASE             =    np.array(a_ ).flatten()
  								_SCREAMING_SNAKE_CASE             =    output_logits[0, :, :20].flatten()
  
  								def check(A				,	A				,	A=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     snake_case_(	self )	->     Optional[Any]:
  								_SCREAMING_SNAKE_CASE             =    GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese""" )
  								_SCREAMING_SNAKE_CASE             =    GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese""" )
  								model.to(a_ )
  
  								# 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(a_				,	return_tensors="""pt"""				,	padding=a_ )
  								_SCREAMING_SNAKE_CASE             =    inputs["""input_ids"""].to(a_ )
  
  								self.assertNotEqual(inputs["""attention_mask"""][0].numpy().tolist()				,	inputs["""attention_mask"""][1].numpy().tolist() )
  
  								_SCREAMING_SNAKE_CASE             =    model.generate(
  								    input_ids=a_				,	attention_mask=inputs["""attention_mask"""].to(a_ )				,	max_new_tokens=3				,	generation_config=a_				,	)
  
  								_SCREAMING_SNAKE_CASE             =    tokenizer(sentences[0]				,	return_tensors="""pt""" ).input_ids.to(a_ )
  								_SCREAMING_SNAKE_CASE             =    model.generate(
  								    input_ids=a_				,	max_new_tokens=3				,	generation_config=a_ )
  
  								_SCREAMING_SNAKE_CASE             =    tokenizer(sentences[1]				,	return_tensors="""pt""" ).input_ids.to(a_ )
  								_SCREAMING_SNAKE_CASE             =    model.generate(input_ids=a_				,	max_new_tokens=3				,	generation_config=a_ )
  
  								self.assertNotEqual(inputs_non_padded.shape				,	inputs_padded.shape )
  
  								_SCREAMING_SNAKE_CASE             =    tokenizer.batch_decode(a_				,	skip_special_tokens=a_ )
  								_SCREAMING_SNAKE_CASE             =    tokenizer.decode(output_non_padded[0]				,	skip_special_tokens=a_ )
  								_SCREAMING_SNAKE_CASE             =    tokenizer.decode(output_padded[0]				,	skip_special_tokens=a_ )
  
  								_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(a_				,	a_ )
  								self.assertListEqual(a_				,	[non_padded_sentence, padded_sentence] )
  
  
  				@tooslow
  				def     snake_case_(	self )	->     List[Any]:
  								_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""", 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:
  												_SCREAMING_SNAKE_CASE             =    tokenizer.encode(a_				,	return_tensors="""pt""" )
  												_SCREAMING_SNAKE_CASE             =    model(a_ )
  												_SCREAMING_SNAKE_CASE             =    outputs.logits.detach().cpu().numpy()[0]
  												_SCREAMING_SNAKE_CASE             =    np.argmax(output_logits[-1] )
  												self.assertEqual(a_				,	a_ )
  
  
  				@slow
  				def     snake_case_(	self )	->     Dict:
  								_SCREAMING_SNAKE_CASE             =    GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese""" )
  								_SCREAMING_SNAKE_CASE             =    GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese""" )
  								model.to(a_ )
  
  								# 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(a_				,	return_tensors="""pt""" ).input_ids.to(a_ )
  								_SCREAMING_SNAKE_CASE             =    tokenizer([input_text, input_text]				,	return_tensors="""pt""" ).input_ids.to(a_ )
  
  								# spout from uniform and one-hot
  								_SCREAMING_SNAKE_CASE             =    [
  								    # 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
  								]
  
  								_SCREAMING_SNAKE_CASE             =    model.generate(
  								    input_ids=a_				,	spout=spouts[0]				,	max_new_tokens=20				,	generation_config=a_				,	)
  
  								_SCREAMING_SNAKE_CASE             =    model.generate(
  								    input_ids=a_				,	spout=spouts[1]				,	max_new_tokens=20				,	generation_config=a_				,	)
  
  								_SCREAMING_SNAKE_CASE             =    model.generate(
  								    input_ids=a_				,	spout=a_				,	max_new_tokens=20				,	generation_config=a_				,	)
  
  								_SCREAMING_SNAKE_CASE             =    tokenizer.decode(outputa[0] )
  								_SCREAMING_SNAKE_CASE             =    tokenizer.decode(outputa[0] )
  								_SCREAMING_SNAKE_CASE             =    tokenizer.batch_decode(a_ )
  
  								_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(a_				,	a_ )
  								self.assertListEqual(a_				,	[outa_sentence, outa_sentence] )
  
  
  
  
  
  				@slow
  				def     snake_case_(	self )	->     int:
  								_SCREAMING_SNAKE_CASE             =    GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese""" )
  								_SCREAMING_SNAKE_CASE             =    GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese""" )
  								model.to(a_ )
  
  								# 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(a_				,	prefix_text=a_				,	return_tensors="""pt""" )
  								_SCREAMING_SNAKE_CASE             =    tokenizer(a_				,	prefix_text=a_				,	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(a_ )				,	token_type_ids=input_tok_a.token_type_ids.to(a_ )				,	max_new_tokens=20				,	generation_config=a_				,	)
  
  								_SCREAMING_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_				,	)
  
  								_SCREAMING_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_				,	)
  
  								_SCREAMING_SNAKE_CASE             =    tokenizer.decode(outputa[0] )
  								_SCREAMING_SNAKE_CASE             =    tokenizer.decode(outputa[0] )
  								_SCREAMING_SNAKE_CASE             =    tokenizer.batch_decode(a_ )
  
  								_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(a_				,	a_ )
  								self.assertListEqual(a_				,	[outa_sentence, outa_sentence] )
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  def lowerCamelCase		(  __lowerCamelCase				:					str					)		->str:
      _SCREAMING_SNAKE_CASE             =    word.upper()
      _SCREAMING_SNAKE_CASE             =    0
      _SCREAMING_SNAKE_CASE             =    {}
      _SCREAMING_SNAKE_CASE             =    []
  
      for letter in word:
          if letter not in letter_nums:
              _SCREAMING_SNAKE_CASE             =    str(__lowerCamelCase					)
              next_num += 1
          word_pattern.append(letter_nums[letter]					)
      return ".".join(__lowerCamelCase					)
  
  
  if __name__ == "__main__":
        import pprint
        import time
  
        lowercase_			       =				time.time()
        with open("""dictionary.txt""") as in_file:
              lowercase_			       =				in_file.read().splitlines()
  
        lowercase_			       =				{}
        for word in word_list:
              lowercase_			       =				get_word_pattern(word)
              if pattern in all_patterns:
                    all_patterns[pattern].append(word)
              else:
                    lowercase_			       =				[word]
  
        with open("""word_patterns.txt""", """w""") as out_file:
              out_file.write(pprint.pformat(all_patterns))
  
        lowercase_			       =				round(time.time() - start_time, 2)
        print(f"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
        # Done!  9,581 word patterns found in 0.58 seconds.
  
  
  
  