
'''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    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE=36000    ,__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=100    ,__SCREAMING_SNAKE_CASE=0.0    ,):
    SCREAMING_SNAKE_CASE  :  str								=		vocab_size
    SCREAMING_SNAKE_CASE  :  List[str]								=		parent
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		batch_size
    SCREAMING_SNAKE_CASE  :  str								=		num_contexts
    # For common tests
    SCREAMING_SNAKE_CASE  :  List[str]								=		self.num_contexts
    SCREAMING_SNAKE_CASE  :  List[str]								=		is_training
    SCREAMING_SNAKE_CASE  :  Tuple								=		hidden_size
    SCREAMING_SNAKE_CASE  :  int								=		num_ext_layers
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		ext_size
    SCREAMING_SNAKE_CASE  :  Tuple								=		num_hidden_layers
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		num_attention_heads
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		num_experts
    SCREAMING_SNAKE_CASE  :  List[str]								=		d_ff
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		d_ext
    SCREAMING_SNAKE_CASE  :  Tuple								=		d_spout
    SCREAMING_SNAKE_CASE  :  Dict								=		dropout_rate
    SCREAMING_SNAKE_CASE  :  str								=		layer_norm_epsilon
    SCREAMING_SNAKE_CASE  :  str								=		expert_capacity
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		router_jitter_noise






   def 					__a			(       self      ):
    return GPTSanJapaneseConfig.from_pretrained('Tanrei/GPTSAN-japanese'      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		ids_tensor([self.batch_size, self.seq_length]    ,self.vocab_size      )

    SCREAMING_SNAKE_CASE  :  Dict								=		self.get_config()

    return (config, input_ids)






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  List[Any]								=		ids_tensor([self.batch_size, self.seq_length]    ,self.vocab_size      )

    SCREAMING_SNAKE_CASE  :  List[Any]								=		self.get_config()

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






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






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		GPTSanJapaneseForConditionalGeneration(config=lowerCamelCase__      )
    model.to(lowerCamelCase__      )
    model.eval()
    SCREAMING_SNAKE_CASE  :  List[str]								=		model(
        input_ids=lowerCamelCase__    ,)
    self.parent.assertIsNotNone(lowerCamelCase__      )



@require_torch
class 		_a     (							__lowerCAmelCase			,    __lowerCAmelCase			,    unittest.TestCase  ):







   """simple docstring"""




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






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):






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

    return False






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		GPTSanJapaneseTester(self      )
    SCREAMING_SNAKE_CASE  :  Dict								=		ConfigTester(self    ,config_class=lowerCamelCase__    ,d_model=37      )






   def 					__a			(       self      ):
    GPTSanJapaneseConfig()






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Any								=		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 					__a			(       self      ):
    super().test_model_parallelism()






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



@require_torch
class 		_a     (							__lowerCAmelCase			,    __lowerCAmelCase			,    unittest.TestCase  ):







   """simple docstring"""




   A		= (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
   A		= False
   A		= False
   A		= False
   A		= False
   A		= False
   A		= False
   # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
   A		= [0.8, 0.9]






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Any								=		GPTSanJapaneseTester(self      )
    SCREAMING_SNAKE_CASE  :  Tuple								=		ConfigTester(self    ,config_class=lowerCamelCase__    ,d_model=37      )






   def 					__a			(       self      ):
    GPTSanJapaneseConfig()






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		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 					__a			(       self      ):
    super().test_model_parallelism()






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






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		tokenizer.encode('武田信玄は'    ,return_tensors='pt'      )
    SCREAMING_SNAKE_CASE  :  Tuple								=		model(lowerCamelCase__      )
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		outputs.logits.detach().cpu().numpy()
    # Output of original model created with mesh-tensoflow
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		[
        # 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  :  Dict								=		np.array(lowerCamelCase__      ).flatten()
    SCREAMING_SNAKE_CASE  :  Any								=		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(lowerCamelCase__      )    ,abs(lowerCamelCase__      )      )

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






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Any								=		GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'      )
    model.to(lowerCamelCase__      )

    # set deterministically
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		GenerationConfig.from_pretrained('Tanrei/GPTSAN-japanese'      )
    SCREAMING_SNAKE_CASE  :  Dict								=		1

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

    SCREAMING_SNAKE_CASE  :  Tuple								=		'''left'''
    SCREAMING_SNAKE_CASE  :  str								=		tokenizer(lowerCamelCase__    ,return_tensors='pt'    ,padding=lowerCamelCase__      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		inputs['''input_ids'''].to(lowerCamelCase__      )

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

    SCREAMING_SNAKE_CASE  :  List[str]								=		model.generate(
        input_ids=lowerCamelCase__    ,attention_mask=inputs['attention_mask'].to(lowerCamelCase__      )    ,max_new_tokens=3    ,generation_config=lowerCamelCase__    ,)

    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		tokenizer(sentences[0]    ,return_tensors='pt'      ).input_ids.to(lowerCamelCase__      )
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		model.generate(
        input_ids=lowerCamelCase__    ,max_new_tokens=3    ,generation_config=lowerCamelCase__      )

    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		tokenizer(sentences[1]    ,return_tensors='pt'      ).input_ids.to(lowerCamelCase__      )
    SCREAMING_SNAKE_CASE  :  int								=		model.generate(input_ids=lowerCamelCase__    ,max_new_tokens=3    ,generation_config=lowerCamelCase__      )

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

    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		tokenizer.batch_decode(lowerCamelCase__    ,skip_special_tokens=lowerCamelCase__      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		tokenizer.decode(output_non_padded[0]    ,skip_special_tokens=lowerCamelCase__      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		tokenizer.decode(output_padded[0]    ,skip_special_tokens=lowerCamelCase__      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		[
        '''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 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'      )
    SCREAMING_SNAKE_CASE  :  str								=		GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'      )
    # Output of original model created with mesh-tensoflow
    SCREAMING_SNAKE_CASE  :  str								=		[
        ('''武田信玄は''', 35675),
        ('''武田信玄は、''', 45),
        ('''武田信玄は、この''', 29),
        ('''武田信玄は、このよう''', 30642),
        ('''武田信玄は、このような''', 35680),
        ('''武田信玄は、このような「''', 8640),
        ('''武田信玄は、このような「武田''', 31617),
        ('''武田信玄は、このような「武田家''', 30646),
        ('''武田信玄は、このような「武田家の''', 31617),
        ('''武田信玄は、このような「武田家の家''', 31381),
    ]
    for input, output in target:
     SCREAMING_SNAKE_CASE  :  Any								=		tokenizer.encode(lowerCamelCase__    ,return_tensors='pt'      )
     SCREAMING_SNAKE_CASE  :  Any								=		model(lowerCamelCase__      )
     SCREAMING_SNAKE_CASE  :  Any								=		outputs.logits.detach().cpu().numpy()[0]
     SCREAMING_SNAKE_CASE  :  Optional[int]								=		np.argmax(output_logits[-1]      )
     self.assertEqual(lowerCamelCase__    ,lowerCamelCase__      )






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'      )
    SCREAMING_SNAKE_CASE  :  int								=		GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'      )
    model.to(lowerCamelCase__      )

    # set deterministically
    SCREAMING_SNAKE_CASE  :  List[Any]								=		GenerationConfig.from_pretrained('Tanrei/GPTSAN-japanese'      )
    SCREAMING_SNAKE_CASE  :  Dict								=		1

    SCREAMING_SNAKE_CASE  :  Any								=		'''武田信玄は、'''
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		tokenizer(lowerCamelCase__    ,return_tensors='pt'      ).input_ids.to(lowerCamelCase__      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		tokenizer([input_text, input_text]    ,return_tensors='pt'      ).input_ids.to(lowerCamelCase__      )

    # spout from uniform and one-hot
    SCREAMING_SNAKE_CASE  :  int								=		[
        # 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  :  List[str]								=		model.generate(
        input_ids=lowerCamelCase__    ,spout=spouts[0]    ,max_new_tokens=20    ,generation_config=lowerCamelCase__    ,)

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		model.generate(
        input_ids=lowerCamelCase__    ,spout=spouts[1]    ,max_new_tokens=20    ,generation_config=lowerCamelCase__    ,)

    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		model.generate(
        input_ids=lowerCamelCase__    ,spout=lowerCamelCase__    ,max_new_tokens=20    ,generation_config=lowerCamelCase__    ,)

    SCREAMING_SNAKE_CASE  :  Dict								=		tokenizer.decode(outputa[0]      )
    SCREAMING_SNAKE_CASE  :  int								=		tokenizer.decode(outputa[0]      )
    SCREAMING_SNAKE_CASE  :  Tuple								=		tokenizer.batch_decode(lowerCamelCase__      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		[
        '''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 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Any								=		GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'      )
    SCREAMING_SNAKE_CASE  :  Any								=		GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'      )
    model.to(lowerCamelCase__      )

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

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		'''武田信玄'''
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		'''織田信長'''
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		'''は、'''
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		'''が、'''
    SCREAMING_SNAKE_CASE  :  Any								=		tokenizer(lowerCamelCase__    ,prefix_text=lowerCamelCase__    ,return_tensors='pt'      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		tokenizer(lowerCamelCase__    ,prefix_text=lowerCamelCase__    ,return_tensors='pt'      )
    SCREAMING_SNAKE_CASE  :  List[Any]								=		tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]    ,return_tensors='pt'      )

    SCREAMING_SNAKE_CASE  :  Tuple								=		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__    ,)

    SCREAMING_SNAKE_CASE  :  List[Any]								=		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__    ,)

    SCREAMING_SNAKE_CASE  :  Dict								=		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__    ,)

    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		tokenizer.decode(outputa[0]      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		tokenizer.decode(outputa[0]      )
    SCREAMING_SNAKE_CASE  :  Any								=		tokenizer.batch_decode(lowerCamelCase__      )

    SCREAMING_SNAKE_CASE  :  int								=		[
        '''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]      )






import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir

@dataclass
class    __magic_name__						:
     A:		str



class    __magic_name__						(  unittest.TestCase):


     def   UpperCAmelCase__						(   self				:  Any      )	->   List[Any]:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[Any] 							= get_tests_dir('''fixtures/test_sentencepiece.model'''      )
      UpperCamelCase__       :     Any 							= get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model'''      )

      UpperCamelCase__       :     List[str] 							= FakeOriginalTokenizer(vocab_file=lowerCamelCase__      )

      with warnings.catch_warnings(record=lowerCamelCase__      ) as w:
       UpperCamelCase__       :     Optional[int] 							= SpmConverter(lowerCamelCase__      )
      self.assertEqual(len(lowerCamelCase__      )				,						0      )

      UpperCamelCase__       :     Tuple 							= FakeOriginalTokenizer(vocab_file=lowerCamelCase__      )

      with warnings.catch_warnings(record=lowerCamelCase__      ) as w:
       UpperCamelCase__       :     str 							= SpmConverter(lowerCamelCase__      )
      self.assertEqual(len(lowerCamelCase__      )				,						1      )

      self.assertIn(
          '''The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'''
          ''' which is not implemented in the fast tokenizers.'''				,						str(w[0].message      )				,						)


