





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 					snake_case					:




 """simple docstring"""



 def __init__(	self	:							List[Any]							,     __A	:							List[Any]							,     __A	:							Dict=3_6_0_0_0							,     __A	:							Any=1_3							,     __A	:							Dict=7							,     __A	:							Optional[int]=True							,     __A	:							Dict=3_2							,     __A	:							List[Any]=4_2							,     __A	:							str=5							,     __A	:							str=2							,     __A	:							List[Any]=4							,     __A	:							List[str]=2							,     __A	:							List[str]=3_2							,     __A	:							Optional[int]=8_0							,     __A	:							Dict=3_3							,     __A	:							str=0.0							,     __A	:							Dict=1e-6							,     __A	:							str=1_0_0							,     __A	:							List[str]=0.0							,     ):
        __UpperCamelCase	=  vocab_size
        __UpperCamelCase	=  parent
        __UpperCamelCase	=  batch_size
        __UpperCamelCase	=  num_contexts
        # For common tests
        __UpperCamelCase	=  self.num_contexts
        __UpperCamelCase	=  is_training
        __UpperCamelCase	=  hidden_size
        __UpperCamelCase	=  num_ext_layers
        __UpperCamelCase	=  ext_size
        __UpperCamelCase	=  num_hidden_layers
        __UpperCamelCase	=  num_attention_heads
        __UpperCamelCase	=  num_experts
        __UpperCamelCase	=  d_ff
        __UpperCamelCase	=  d_ext
        __UpperCamelCase	=  d_spout
        __UpperCamelCase	=  dropout_rate
        __UpperCamelCase	=  layer_norm_epsilon
        __UpperCamelCase	=  expert_capacity
        __UpperCamelCase	=  router_jitter_noise






 def 	_lowerCamelCase (	self	:							Optional[Any]     ):
        return GPTSanJapaneseConfig.from_pretrained('Tanrei/GPTSAN-japanese'     )






 def 	_lowerCamelCase (	self	:							Optional[int]     ):
        __UpperCamelCase	=  ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size     )

        __UpperCamelCase	=  self.get_config()

        return (config, input_ids)






 def 	_lowerCamelCase (	self	:							List[str]     ):
        __UpperCamelCase	=  ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size     )

        __UpperCamelCase	=  self.get_config()

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






 def 	_lowerCamelCase (	self	:							Any     ):
        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	:							List[Any]							,     __A	:							Any							,     __A	:							str							,     ):
        __UpperCamelCase	=  GPTSanJapaneseForConditionalGeneration(config=__A     )
        model.to(__A     )
        model.eval()
        __UpperCamelCase	=  model(
            input_ids=__A							,     )
        self.parent.assertIsNotNone(__A     )






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




 """simple docstring"""



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






 def 	_lowerCamelCase (	self	:							str							,     __A	:							List[str]							,     __A	:							Any							,     __A	:							int							,     __A	:							str							,     __A	:							Union[str, Any]     ):







        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	:							List[Any]     ):
        __UpperCamelCase	=  GPTSanJapaneseTester(self     )
        __UpperCamelCase	=  ConfigTester(self							,     config_class=__A							,     d_model=3_7     )






 def 	_lowerCamelCase (	self	:							List[Any]     ):
        GPTSanJapaneseConfig()






 def 	_lowerCamelCase (	self	:							str     ):
        __UpperCamelCase	=  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 	_lowerCamelCase (	self	:							int     ):
        super().test_model_parallelism()






 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'     )
 def 	_lowerCamelCase (	self	:							List[str]     ):
        pass






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




 """simple docstring"""



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






 def 	_lowerCamelCase (	self	:							str     ):
        __UpperCamelCase	=  GPTSanJapaneseTester(self     )
        __UpperCamelCase	=  ConfigTester(self							,     config_class=__A							,     d_model=3_7     )






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






 def 	_lowerCamelCase (	self	:							Dict     ):
        __UpperCamelCase	=  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 	_lowerCamelCase (	self	:							Dict     ):
        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






 @slow
 def 	_lowerCamelCase (	self	:							List[Any]     ):
        __UpperCamelCase	=  GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'     )
        __UpperCamelCase	=  GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'     )
        __UpperCamelCase	=  tokenizer.encode('武田信玄は'							,     return_tensors='pt'     )
        __UpperCamelCase	=  model(__A     )
        __UpperCamelCase	=  outputs.logits.detach().cpu().numpy()
        # Output of original model created with mesh-tensoflow
        __UpperCamelCase	=  [
            # 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
        ]
        __UpperCamelCase	=  np.array(__A     ).flatten()
        __UpperCamelCase	=  output_logits[0, :, :2_0].flatten()

        def check(__A	:							List[Any]							,     __A	:							Tuple							,     __A	:							str=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 	_lowerCamelCase (	self	:							Union[str, Any]     ):
        __UpperCamelCase	=  GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'     )
        __UpperCamelCase	=  GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'     )
        model.to(__A     )

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

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

        __UpperCamelCase	=  "left"
        __UpperCamelCase	=  tokenizer(__A							,     return_tensors='pt'							,     padding=__A     )
        __UpperCamelCase	=  inputs["input_ids"].to(__A     )

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

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

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

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

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

        __UpperCamelCase	=  tokenizer.batch_decode(__A							,     skip_special_tokens=__A     )
        __UpperCamelCase	=  tokenizer.decode(output_non_padded[0]							,     skip_special_tokens=__A     )
        __UpperCamelCase	=  tokenizer.decode(output_padded[0]							,     skip_special_tokens=__A     )

        __UpperCamelCase	=  [
            "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 	_lowerCamelCase (	self	:							Optional[Any]     ):
        __UpperCamelCase	=  GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'     )
        __UpperCamelCase	=  GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'     )
        # Output of original model created with mesh-tensoflow
        __UpperCamelCase	=  [
            ("武田信玄は", 3_5_6_7_5),
            ("武田信玄は、", 4_5),
            ("武田信玄は、この", 2_9),
            ("武田信玄は、このよう", 3_0_6_4_2),
            ("武田信玄は、このような", 3_5_6_8_0),
            ("武田信玄は、このような「", 8_6_4_0),
            ("武田信玄は、このような「武田", 3_1_6_1_7),
            ("武田信玄は、このような「武田家", 3_0_6_4_6),
            ("武田信玄は、このような「武田家の", 3_1_6_1_7),
            ("武田信玄は、このような「武田家の家", 3_1_3_8_1),
        ]
        for input, output in target:
               __UpperCamelCase	=  tokenizer.encode(__A							,     return_tensors='pt'     )
               __UpperCamelCase	=  model(__A     )
               __UpperCamelCase	=  outputs.logits.detach().cpu().numpy()[0]
               __UpperCamelCase	=  np.argmax(output_logits[-1]     )
               self.assertEqual(__A							,     __A     )






 @slow
 def 	_lowerCamelCase (	self	:							Optional[Any]     ):
        __UpperCamelCase	=  GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'     )
        __UpperCamelCase	=  GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'     )
        model.to(__A     )

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

        __UpperCamelCase	=  "武田信玄は、"
        __UpperCamelCase	=  tokenizer(__A							,     return_tensors='pt'     ).input_ids.to(__A     )
        __UpperCamelCase	=  tokenizer([input_text, input_text]							,     return_tensors='pt'     ).input_ids.to(__A     )

        # spout from uniform and one-hot
        __UpperCamelCase	=  [
            # 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
        ]

        __UpperCamelCase	=  model.generate(
            input_ids=__A							,     spout=spouts[0]							,     max_new_tokens=2_0							,     generation_config=__A							,     )

        __UpperCamelCase	=  model.generate(
            input_ids=__A							,     spout=spouts[1]							,     max_new_tokens=2_0							,     generation_config=__A							,     )

        __UpperCamelCase	=  model.generate(
            input_ids=__A							,     spout=__A							,     max_new_tokens=2_0							,     generation_config=__A							,     )

        __UpperCamelCase	=  tokenizer.decode(outputa[0]     )
        __UpperCamelCase	=  tokenizer.decode(outputa[0]     )
        __UpperCamelCase	=  tokenizer.batch_decode(__A     )

        __UpperCamelCase	=  [
            "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 	_lowerCamelCase (	self	:							Any     ):
        __UpperCamelCase	=  GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'     )
        __UpperCamelCase	=  GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'     )
        model.to(__A     )

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

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

        __UpperCamelCase	=  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=2_0							,     generation_config=__A							,     )

        __UpperCamelCase	=  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=2_0							,     generation_config=__A							,     )

        __UpperCamelCase	=  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=2_0							,     generation_config=__A							,     )

        __UpperCamelCase	=  tokenizer.decode(outputa[0]     )
        __UpperCamelCase	=  tokenizer.decode(outputa[0]     )
        __UpperCamelCase	=  tokenizer.batch_decode(__A     )

        __UpperCamelCase	=  [
            "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]     )







'''simple docstring'''
def     lowercase__       (       __lowercase						:  list    ,  __lowercase						:  int    ,  __lowercase						:  int    ,  __lowercase						:  int					)			->				int:



       """simple docstring"""






       __UpperCamelCase	=  int((lower_bound + upper_bound) // 2					)
       if array[r] == value:
              return r
       if lower_bound >= upper_bound:
              return -1
       if array[r] < value:
              return binary_search(__lowercase    ,  r + 1    ,  __lowercase    ,  __lowercase					)
       else:
              return binary_search(__lowercase    ,  __lowercase    ,  r - 1    ,  __lowercase					)
def     lowercase__       (       __lowercase						:  int    ,  __lowercase						:  list					)			->				list:



       """simple docstring"""






       __UpperCamelCase	=  0
       if matrix[index][0] == value:
              return [index, 0]
       while index < len(__lowercase					) and matrix[index][0] < value:
              __UpperCamelCase	=  binary_search(matrix[index]    ,  0    ,  len(matrix[index]					) - 1    ,  __lowercase					)
              if r != -1:
                     return [index, r]
              index += 1
       return [-1, -1]


if __name__ == "__main__":
    import doctest

    doctest.testmod()
