





'''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							:
		def __init__(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=3_60_00,__SCREAMING_SNAKE_CASE=13,__SCREAMING_SNAKE_CASE=7,__SCREAMING_SNAKE_CASE=True,__SCREAMING_SNAKE_CASE=32,__SCREAMING_SNAKE_CASE=42,__SCREAMING_SNAKE_CASE=5,__SCREAMING_SNAKE_CASE=2,__SCREAMING_SNAKE_CASE=4,__SCREAMING_SNAKE_CASE=2,__SCREAMING_SNAKE_CASE=32,__SCREAMING_SNAKE_CASE=80,__SCREAMING_SNAKE_CASE=33,__SCREAMING_SNAKE_CASE=0.0,__SCREAMING_SNAKE_CASE=1e-6,__SCREAMING_SNAKE_CASE=1_00,__SCREAMING_SNAKE_CASE=0.0,):



					'''simple docstring'''

					__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			):



					'''simple docstring'''

					return GPTSanJapaneseConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""			)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

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

					__lowerCAmelCase											=				self.get_config()

					return (config, input_ids)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__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			):



					'''simple docstring'''

					return GPTSanJapaneseConfig(
					    vocab_size=self.vocab_size,num_contexts=self.seq_length,d_model=self.hidden_size,d_ff=self.d_ff,d_ext=self.d_ext,d_spout=self.d_spout,num_switch_layers=self.num_hidden_layers - self.num_ext_layers,num_ext_layers=self.num_ext_layers,num_heads=self.num_attention_heads,num_experts=self.num_experts,expert_capacity=self.expert_capacity,dropout_rate=self.dropout_rate,layer_norm_epsilon=self.layer_norm_epsilon,router_jitter_noise=self.router_jitter_noise,)







		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,):



					'''simple docstring'''

					__lowerCAmelCase											=				GPTSanJapaneseForConditionalGeneration(config=__SCREAMING_SNAKE_CASE			)
					model.to(__SCREAMING_SNAKE_CASE			)
					model.eval()
					__lowerCAmelCase											=				model(
					    input_ids=__SCREAMING_SNAKE_CASE,)
					self.parent.assertIsNotNone(__SCREAMING_SNAKE_CASE			)





@require_torch
class     _UpperCAmelCase							(						lowerCAmelCase_	,					lowerCAmelCase_	,					unittest.TestCase   ):
		a   :   str     =(GPTSanJapaneseModel,) if is_torch_available() else ()
		a   :   Tuple     =(
		    {
		        """conversational""": GPTSanJapaneseForConditionalGeneration,
		        """feature-extraction""": GPTSanJapaneseForConditionalGeneration,
		        """summarization""": GPTSanJapaneseForConditionalGeneration,
		        """text2text-generation""": GPTSanJapaneseForConditionalGeneration,
		        """translation""": GPTSanJapaneseForConditionalGeneration,
		    }
		    if is_torch_available()
		    else {}
		)
		a   :   Tuple     =False
		a   :   int     =False
		a   :   str     =False
		a   :   Optional[int]     =False
		a   :   Any     =False
		a   :   Dict     =False
		a   :   Optional[int]     =False
		a   :   Dict     =False
		# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
		a   :   Optional[Any]     =[0.8, 0.9]
		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					if pipeline_test_casse_name == "SummarizationPipelineTests":
								# TODO: fix `_reorder_cache` is not implemented for this model
								return True
					elif pipeline_test_casse_name == "Text2TextGenerationPipelineTests":
								# TODO: check this.
								return True

					return False
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				GPTSanJapaneseTester(self			)
					__lowerCAmelCase											=				ConfigTester(self,config_class=__SCREAMING_SNAKE_CASE,d_model=37			)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					GPTSanJapaneseConfig()
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE			)
		@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			):



					'''simple docstring'''

					super().test_model_parallelism()







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



					'''simple docstring'''

					pass





@require_torch
class     _UpperCAmelCase							(						lowerCAmelCase_	,					lowerCAmelCase_	,					unittest.TestCase   ):
		a   :   Dict     =(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
		a   :   List[str]     =False
		a   :   Tuple     =False
		a   :   Optional[int]     =False
		a   :   Union[str, Any]     =False
		a   :   List[Any]     =False
		a   :   str     =False
		# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
		a   :   Optional[Any]     =[0.8, 0.9]
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				GPTSanJapaneseTester(self			)
					__lowerCAmelCase											=				ConfigTester(self,config_class=__SCREAMING_SNAKE_CASE,d_model=37			)
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					GPTSanJapaneseConfig()
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE			)
		@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			):



					'''simple docstring'''

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



					'''simple docstring'''

					pass
		@slow
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__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(__SCREAMING_SNAKE_CASE			)
					__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(__SCREAMING_SNAKE_CASE			).flatten()
					__lowerCAmelCase											=				output_logits[0, :, :20].flatten()

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

					self.assertTrue(np.all([check(target[i],predict[i]			) for i in range(len(__SCREAMING_SNAKE_CASE			)			)]			)			)
		@slow
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""			)
					__lowerCAmelCase											=				GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""			)
					model.to(__SCREAMING_SNAKE_CASE			)

					# 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(__SCREAMING_SNAKE_CASE,return_tensors="""pt""",padding=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				inputs["""input_ids"""].to(__SCREAMING_SNAKE_CASE			)

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

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

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

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

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

					__lowerCAmelCase											=				tokenizer.batch_decode(__SCREAMING_SNAKE_CASE,skip_special_tokens=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				tokenizer.decode(output_non_padded[0],skip_special_tokens=__SCREAMING_SNAKE_CASE			)
					__lowerCAmelCase											=				tokenizer.decode(output_padded[0],skip_special_tokens=__SCREAMING_SNAKE_CASE			)

					__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(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			)
					self.assertListEqual(__SCREAMING_SNAKE_CASE,[non_padded_sentence, padded_sentence]			)
		@tooslow
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__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_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:
								__lowerCAmelCase											=				tokenizer.encode(__SCREAMING_SNAKE_CASE,return_tensors="""pt"""			)
								__lowerCAmelCase											=				model(__SCREAMING_SNAKE_CASE			)
								__lowerCAmelCase											=				outputs.logits.detach().cpu().numpy()[0]
								__lowerCAmelCase											=				np.argmax(output_logits[-1]			)
								self.assertEqual(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			)
		@slow
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""			)
					__lowerCAmelCase											=				GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""			)
					model.to(__SCREAMING_SNAKE_CASE			)

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

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

					# 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=__SCREAMING_SNAKE_CASE,spout=spouts[0],max_new_tokens=20,generation_config=__SCREAMING_SNAKE_CASE,)

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

					__lowerCAmelCase											=				model.generate(
					    input_ids=__SCREAMING_SNAKE_CASE,spout=__SCREAMING_SNAKE_CASE,max_new_tokens=20,generation_config=__SCREAMING_SNAKE_CASE,)

					__lowerCAmelCase											=				tokenizer.decode(outputa[0]			)
					__lowerCAmelCase											=				tokenizer.decode(outputa[0]			)
					__lowerCAmelCase											=				tokenizer.batch_decode(__SCREAMING_SNAKE_CASE			)

					__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(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			)
					self.assertListEqual(__SCREAMING_SNAKE_CASE,[outa_sentence, outa_sentence]			)







		@slow
		def   lowerCamelCase__			(  self			):



					'''simple docstring'''

					__lowerCAmelCase											=				GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""			)
					__lowerCAmelCase											=				GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""			)
					model.to(__SCREAMING_SNAKE_CASE			)

					# 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(__SCREAMING_SNAKE_CASE,prefix_text=__SCREAMING_SNAKE_CASE,return_tensors="""pt"""			)
					__lowerCAmelCase											=				tokenizer(__SCREAMING_SNAKE_CASE,prefix_text=__SCREAMING_SNAKE_CASE,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(__SCREAMING_SNAKE_CASE			),token_type_ids=input_tok_a.token_type_ids.to(__SCREAMING_SNAKE_CASE			),max_new_tokens=20,generation_config=__SCREAMING_SNAKE_CASE,)

					__lowerCAmelCase											=				model.generate(
					    input_ids=input_tok_a.input_ids.to(__SCREAMING_SNAKE_CASE			),token_type_ids=input_tok_a.token_type_ids.to(__SCREAMING_SNAKE_CASE			),max_new_tokens=20,generation_config=__SCREAMING_SNAKE_CASE,)

					__lowerCAmelCase											=				model.generate(
					    input_ids=input_tok_a.input_ids.to(__SCREAMING_SNAKE_CASE			),token_type_ids=input_tok_a.token_type_ids.to(__SCREAMING_SNAKE_CASE			),attention_mask=input_tok_a.attention_mask.to(__SCREAMING_SNAKE_CASE			),max_new_tokens=20,generation_config=__SCREAMING_SNAKE_CASE,)

					__lowerCAmelCase											=				tokenizer.decode(outputa[0]			)
					__lowerCAmelCase											=				tokenizer.decode(outputa[0]			)
					__lowerCAmelCase											=				tokenizer.batch_decode(__SCREAMING_SNAKE_CASE			)

					__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(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE			)
					self.assertListEqual(__SCREAMING_SNAKE_CASE,[outa_sentence, outa_sentence]			)








'''simple docstring'''







# Lint as: python3

import hashlib
from typing import Union





def    _lowerCAmelCase						(						lowercase	)					->					bytes:
			if isinstance(lowercase					,	lowercase	):
						# Data already in bytes, returns as it as
						return hash_data
			elif isinstance(lowercase					,	lowercase	):
						# We keep the data as it as for it ot be later encoded to UTF-8
						# However replace `\\` with `/` for Windows compatibility
						__lowerCAmelCase											=				hash_data.replace("""\\"""					,	"""/"""	)
			elif isinstance(lowercase					,	lowercase	):
						__lowerCAmelCase											=				str(lowercase	)
			else:
						# If data is not of the required type, raise error
						raise InvalidKeyError(lowercase	)

			return hash_data.encode("""utf-8"""	)





class     _UpperCAmelCase							(						lowerCAmelCase_   ):
		def __init__(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				"""\nFAILURE TO GENERATE DATASET: Invalid key type detected"""
					__lowerCAmelCase											=				f'\nFound Key {hash_data} of type {type(__SCREAMING_SNAKE_CASE			)}'
					__lowerCAmelCase											=				"""\nKeys should be either str, int or bytes type"""
					super().__init__(f'{self.prefix}{self.err_msg}{self.suffix}'			)





class     _UpperCAmelCase							(						lowerCAmelCase_   ):
		def __init__(  self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=""			):



					'''simple docstring'''

					__lowerCAmelCase											=				key
					__lowerCAmelCase											=				duplicate_key_indices
					__lowerCAmelCase											=				fix_msg
					__lowerCAmelCase											=				"""Found multiple examples generated with the same key"""
					if len(__SCREAMING_SNAKE_CASE			) <= 20:
								__lowerCAmelCase											=				f'\nThe examples at index {", ".join(__SCREAMING_SNAKE_CASE			)} have the key {key}'
					else:
								__lowerCAmelCase											=				f'\nThe examples at index {", ".join(duplicate_key_indices[:20]			)}... ({len(__SCREAMING_SNAKE_CASE			) - 20} more) have the key {key}'
					__lowerCAmelCase											=				"""\n""" + fix_msg if fix_msg else """"""
					super().__init__(f'{self.prefix}{self.err_msg}{self.suffix}'			)





class     _UpperCAmelCase							:
		def __init__(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				hashlib.mda(_as_bytes(__SCREAMING_SNAKE_CASE			)			)







		def   lowerCamelCase__			(  self,__SCREAMING_SNAKE_CASE			):



					'''simple docstring'''

					__lowerCAmelCase											=				self._split_mda.copy()
					__lowerCAmelCase											=				_as_bytes(__SCREAMING_SNAKE_CASE			)
					mda.update(__SCREAMING_SNAKE_CASE			)
					# Convert to integer with hexadecimal conversion
					return int(mda.hexdigest(),16			)

