





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							:



       def __init__(     self	:   List[Any]					,			UpperCAmelCase	:   int					,			UpperCAmelCase	:   str=3_60_00					,			UpperCAmelCase	:   List[str]=13					,			UpperCAmelCase	:   Optional[Any]=7					,			UpperCAmelCase	:   List[str]=True					,			UpperCAmelCase	:   int=32					,			UpperCAmelCase	:   Dict=42					,			UpperCAmelCase	:   Optional[Any]=5					,			UpperCAmelCase	:   Any=2					,			UpperCAmelCase	:   int=4					,			UpperCAmelCase	:   List[str]=2					,			UpperCAmelCase	:   Tuple=32					,			UpperCAmelCase	:   List[str]=80					,			UpperCAmelCase	:   Dict=33					,			UpperCAmelCase	:   Optional[int]=0.0					,			UpperCAmelCase	:   List[Any]=1e-6					,			UpperCAmelCase	:   Dict=1_00					,			UpperCAmelCase	:   List[Any]=0.0					,			):
           lowerCAmelCase_					:	Tuple						=   vocab_size
           lowerCAmelCase_					:	str						=   parent
           lowerCAmelCase_					:	List[Any]						=   batch_size
           lowerCAmelCase_					:	str						=   num_contexts
           # For common tests
           lowerCAmelCase_					:	Dict						=   self.num_contexts
           lowerCAmelCase_					:	Tuple						=   is_training
           lowerCAmelCase_					:	Any						=   hidden_size
           lowerCAmelCase_					:	Optional[int]						=   num_ext_layers
           lowerCAmelCase_					:	int						=   ext_size
           lowerCAmelCase_					:	Tuple						=   num_hidden_layers
           lowerCAmelCase_					:	Optional[Any]						=   num_attention_heads
           lowerCAmelCase_					:	Optional[int]						=   num_experts
           lowerCAmelCase_					:	Dict						=   d_ff
           lowerCAmelCase_					:	int						=   d_ext
           lowerCAmelCase_					:	Optional[int]						=   d_spout
           lowerCAmelCase_					:	Optional[int]						=   dropout_rate
           lowerCAmelCase_					:	Any						=   layer_norm_epsilon
           lowerCAmelCase_					:	str						=   expert_capacity
           lowerCAmelCase_					:	Dict						=   router_jitter_noise



       def  A							(     self	:   List[Any]			):
           return GPTSanJapaneseConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""			)



       def  A							(     self	:   Union[str, Any]			):
           lowerCAmelCase_					:	Tuple						=   ids_tensor([self.batch_size, self.seq_length]					,			self.vocab_size			)

           lowerCAmelCase_					:	List[str]						=   self.get_config()

           return (config, input_ids)



       def  A							(     self	:   List[Any]			):
           lowerCAmelCase_					:	Dict						=   ids_tensor([self.batch_size, self.seq_length]					,			self.vocab_size			)

           lowerCAmelCase_					:	Dict						=   self.get_config()

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



       def  A							(     self	:   Optional[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  A							(     self	:   List[str]					,			UpperCAmelCase	:   str					,			UpperCAmelCase	:   int					,			):
           lowerCAmelCase_					:	str						=   GPTSanJapaneseForConditionalGeneration(config=UpperCAmelCase			)
           model.to(UpperCAmelCase			)
           model.eval()
           lowerCAmelCase_					:	Optional[Any]						=   model(
               input_ids=UpperCAmelCase					,			)
           self.parent.assertIsNotNone(UpperCAmelCase			)






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



       def  A							(     self	:   Union[str, Any]					,			UpperCAmelCase	:   Optional[Any]					,			UpperCAmelCase	:   Dict					,			UpperCAmelCase	:   List[str]					,			UpperCAmelCase	:   List[Any]					,			UpperCAmelCase	:   Optional[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  A							(     self	:   str			):
           lowerCAmelCase_					:	Any						=   GPTSanJapaneseTester(self			)
           lowerCAmelCase_					:	Optional[int]						=   ConfigTester(self					,			config_class=UpperCAmelCase					,			d_model=37			)



       def  A							(     self	:   Tuple			):
           GPTSanJapaneseConfig()



       def  A							(     self	:   List[Any]			):
           lowerCAmelCase_					:	Tuple						=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase			)



       @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	:   Optional[Any]			):
           super().test_model_parallelism()



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






@require_torch
class 		__a							(	__UpperCamelCase			,__UpperCamelCase			,unittest.TestCase      ):
       __snake_case						:   List[Any]      =		(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
       __snake_case						:   Any      =		False
       __snake_case						:   List[str]      =		False
       __snake_case						:   Any      =		False
       __snake_case						:   List[str]      =		False
       __snake_case						:   Optional[int]      =		False
       __snake_case						:   Tuple      =		False
       # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
       __snake_case						:   Any      =		[0.8, 0.9]



       def  A							(     self	:   str			):
           lowerCAmelCase_					:	Dict						=   GPTSanJapaneseTester(self			)
           lowerCAmelCase_					:	List[str]						=   ConfigTester(self					,			config_class=UpperCAmelCase					,			d_model=37			)



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



       def  A							(     self	:   Dict			):
           lowerCAmelCase_					:	int						=   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase			)



       @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	:   Dict			):
           super().test_model_parallelism()



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



       @slow
       def  A							(     self	:   Dict			):
           lowerCAmelCase_					:	List[Any]						=   GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""			)
           lowerCAmelCase_					:	str						=   GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""			)
           lowerCAmelCase_					:	Dict						=   tokenizer.encode("""武田信玄は"""					,			return_tensors="""pt"""			)
           lowerCAmelCase_					:	Tuple						=   model(UpperCAmelCase			)
           lowerCAmelCase_					:	Any						=   outputs.logits.detach().cpu().numpy()
           # Output of original model created with mesh-tensoflow
           lowerCAmelCase_					:	Union[str, Any]						=   [
               # 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_					:	Optional[Any]						=   np.array(UpperCAmelCase			).flatten()
           lowerCAmelCase_					:	Optional[Any]						=   output_logits[0, :, :20].flatten()

           def check(UpperCAmelCase	:   Tuple					,			UpperCAmelCase	:   Tuple					,			UpperCAmelCase	:   Optional[Any]=5e-4			):
               return abs(a - b			) < epsilon * max(abs(UpperCAmelCase			)					,			abs(UpperCAmelCase			)			)

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



       @slow
       def  A							(     self	:   Optional[int]			):
           lowerCAmelCase_					:	Optional[Any]						=   GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""			)
           lowerCAmelCase_					:	int						=   GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""			)
           model.to(UpperCAmelCase			)

           # set deterministically
           lowerCAmelCase_					:	str						=   GenerationConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""			)
           lowerCAmelCase_					:	List[str]						=   1

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

           lowerCAmelCase_					:	str						=   """left"""
           lowerCAmelCase_					:	Tuple						=   tokenizer(UpperCAmelCase					,			return_tensors="""pt"""					,			padding=UpperCAmelCase			)
           lowerCAmelCase_					:	Optional[int]						=   inputs["""input_ids"""].to(UpperCAmelCase			)

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

           lowerCAmelCase_					:	str						=   model.generate(
               input_ids=UpperCAmelCase					,			attention_mask=inputs["""attention_mask"""].to(UpperCAmelCase			)					,			max_new_tokens=3					,			generation_config=UpperCAmelCase					,			)

           lowerCAmelCase_					:	List[Any]						=   tokenizer(sentences[0]					,			return_tensors="""pt"""			).input_ids.to(UpperCAmelCase			)
           lowerCAmelCase_					:	Optional[int]						=   model.generate(
               input_ids=UpperCAmelCase					,			max_new_tokens=3					,			generation_config=UpperCAmelCase			)

           lowerCAmelCase_					:	List[str]						=   tokenizer(sentences[1]					,			return_tensors="""pt"""			).input_ids.to(UpperCAmelCase			)
           lowerCAmelCase_					:	Dict						=   model.generate(input_ids=UpperCAmelCase					,			max_new_tokens=3					,			generation_config=UpperCAmelCase			)

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

           lowerCAmelCase_					:	Tuple						=   tokenizer.batch_decode(UpperCAmelCase					,			skip_special_tokens=UpperCAmelCase			)
           lowerCAmelCase_					:	int						=   tokenizer.decode(output_non_padded[0]					,			skip_special_tokens=UpperCAmelCase			)
           lowerCAmelCase_					:	List[Any]						=   tokenizer.decode(output_padded[0]					,			skip_special_tokens=UpperCAmelCase			)

           lowerCAmelCase_					:	Optional[Any]						=   [
               """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(UpperCAmelCase					,			UpperCAmelCase			)
           self.assertListEqual(UpperCAmelCase					,			[non_padded_sentence, padded_sentence]			)



       @tooslow
       def  A							(     self	:   int			):
           lowerCAmelCase_					:	Dict						=   GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""			)
           lowerCAmelCase_					:	Optional[int]						=   GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""			)
           # Output of original model created with mesh-tensoflow
           lowerCAmelCase_					:	Optional[Any]						=   [
               ("""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_					:	List[str]						=   tokenizer.encode(UpperCAmelCase					,			return_tensors="""pt"""			)
               lowerCAmelCase_					:	str						=   model(UpperCAmelCase			)
               lowerCAmelCase_					:	Tuple						=   outputs.logits.detach().cpu().numpy()[0]
               lowerCAmelCase_					:	List[Any]						=   np.argmax(output_logits[-1]			)
               self.assertEqual(UpperCAmelCase					,			UpperCAmelCase			)



       @slow
       def  A							(     self	:   Union[str, Any]			):
           lowerCAmelCase_					:	List[Any]						=   GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""			)
           lowerCAmelCase_					:	List[str]						=   GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""			)
           model.to(UpperCAmelCase			)

           # set deterministically
           lowerCAmelCase_					:	Tuple						=   GenerationConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""			)
           lowerCAmelCase_					:	str						=   1

           lowerCAmelCase_					:	Dict						=   """武田信玄は、"""
           lowerCAmelCase_					:	str						=   tokenizer(UpperCAmelCase					,			return_tensors="""pt"""			).input_ids.to(UpperCAmelCase			)
           lowerCAmelCase_					:	Any						=   tokenizer([input_text, input_text]					,			return_tensors="""pt"""			).input_ids.to(UpperCAmelCase			)

           # spout from uniform and one-hot
           lowerCAmelCase_					:	Tuple						=   [
               # 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_					:	Dict						=   model.generate(
               input_ids=UpperCAmelCase					,			spout=spouts[0]					,			max_new_tokens=20					,			generation_config=UpperCAmelCase					,			)

           lowerCAmelCase_					:	List[str]						=   model.generate(
               input_ids=UpperCAmelCase					,			spout=spouts[1]					,			max_new_tokens=20					,			generation_config=UpperCAmelCase					,			)

           lowerCAmelCase_					:	int						=   model.generate(
               input_ids=UpperCAmelCase					,			spout=UpperCAmelCase					,			max_new_tokens=20					,			generation_config=UpperCAmelCase					,			)

           lowerCAmelCase_					:	List[str]						=   tokenizer.decode(outputa[0]			)
           lowerCAmelCase_					:	Tuple						=   tokenizer.decode(outputa[0]			)
           lowerCAmelCase_					:	int						=   tokenizer.batch_decode(UpperCAmelCase			)

           lowerCAmelCase_					:	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(UpperCAmelCase					,			UpperCAmelCase			)
           self.assertListEqual(UpperCAmelCase					,			[outa_sentence, outa_sentence]			)



       @slow
       def  A							(     self	:   Tuple			):
           lowerCAmelCase_					:	List[Any]						=   GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""			)
           lowerCAmelCase_					:	List[Any]						=   GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""			)
           model.to(UpperCAmelCase			)

           # set deterministically
           lowerCAmelCase_					:	List[Any]						=   GenerationConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""			)
           lowerCAmelCase_					:	List[str]						=   1

           lowerCAmelCase_					:	List[Any]						=   """武田信玄"""
           lowerCAmelCase_					:	Optional[int]						=   """織田信長"""
           lowerCAmelCase_					:	Optional[Any]						=   """は、"""
           lowerCAmelCase_					:	List[Any]						=   """が、"""
           lowerCAmelCase_					:	int						=   tokenizer(UpperCAmelCase					,			prefix_text=UpperCAmelCase					,			return_tensors="""pt"""			)
           lowerCAmelCase_					:	str						=   tokenizer(UpperCAmelCase					,			prefix_text=UpperCAmelCase					,			return_tensors="""pt"""			)
           lowerCAmelCase_					:	Any						=   tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]					,			return_tensors="""pt"""			)

           lowerCAmelCase_					:	Optional[Any]						=   model.generate(
               input_ids=input_tok_a.input_ids.to(UpperCAmelCase			)					,			token_type_ids=input_tok_a.token_type_ids.to(UpperCAmelCase			)					,			max_new_tokens=20					,			generation_config=UpperCAmelCase					,			)

           lowerCAmelCase_					:	int						=   model.generate(
               input_ids=input_tok_a.input_ids.to(UpperCAmelCase			)					,			token_type_ids=input_tok_a.token_type_ids.to(UpperCAmelCase			)					,			max_new_tokens=20					,			generation_config=UpperCAmelCase					,			)

           lowerCAmelCase_					:	List[str]						=   model.generate(
               input_ids=input_tok_a.input_ids.to(UpperCAmelCase			)					,			token_type_ids=input_tok_a.token_type_ids.to(UpperCAmelCase			)					,			attention_mask=input_tok_a.attention_mask.to(UpperCAmelCase			)					,			max_new_tokens=20					,			generation_config=UpperCAmelCase					,			)

           lowerCAmelCase_					:	int						=   tokenizer.decode(outputa[0]			)
           lowerCAmelCase_					:	Union[str, Any]						=   tokenizer.decode(outputa[0]			)
           lowerCAmelCase_					:	Any						=   tokenizer.batch_decode(UpperCAmelCase			)

           lowerCAmelCase_					:	Dict						=   [
               """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(UpperCAmelCase					,			UpperCAmelCase			)
           self.assertListEqual(UpperCAmelCase					,			[outa_sentence, outa_sentence]			)









import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest

class 		__a							(	__UpperCamelCase      ):
       __snake_case						:   Optional[Any]      =		(VQDiffusionScheduler,)



       def  A							(     self	:   Optional[int]					,			**UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Any						=   {
               """num_vec_classes""": 40_97,
               """num_train_timesteps""": 1_00,
           }

           config.update(**UpperCAmelCase			)
           return config



       def  A							(     self	:   str					,			UpperCAmelCase	:   List[Any]			):
           lowerCAmelCase_					:	Union[str, Any]						=   4
           lowerCAmelCase_					:	str						=   8
           lowerCAmelCase_					:	Optional[Any]						=   8

           lowerCAmelCase_					:	str						=   torch.randint(0					,			UpperCAmelCase					,			(batch_size, height * width)			)

           return sample



       @property
       def  A							(     self	:   Optional[int]			):
           assert False



       def  A							(     self	:   int					,			UpperCAmelCase	:   Dict			):
           def model(UpperCAmelCase	:   Union[str, Any]					,			UpperCAmelCase	:   List[str]					,			*UpperCAmelCase	:   List[Any]			):
               lowerCAmelCase_		,		lowerCAmelCase_					:	Tuple						=   sample.shape
               lowerCAmelCase_					:	int						=   torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)			)
               lowerCAmelCase_					:	str						=   F.log_softmax(logits.double()					,			dim=1			).float()
               return return_value

           return model



       def  A							(     self	:   Union[str, Any]			):
           for timesteps in [2, 5, 1_00, 10_00]:
               self.check_over_configs(num_train_timesteps=UpperCAmelCase			)



       def  A							(     self	:   str			):
           for num_vec_classes in [5, 1_00, 10_00, 40_00]:
               self.check_over_configs(num_vec_classes=UpperCAmelCase			)



       def  A							(     self	:   Optional[Any]			):
           for t in [0, 50, 99]:
               self.check_over_forward(time_step=UpperCAmelCase			)



       def  A							(     self	:   int			):
           pass


