


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       :					Any     ,						SCREAMING_SNAKE_CASE_       :					str     ,						SCREAMING_SNAKE_CASE_       :					Optional[int]=36000     ,						SCREAMING_SNAKE_CASE_       :					Tuple=13     ,						SCREAMING_SNAKE_CASE_       :					str=7     ,						SCREAMING_SNAKE_CASE_       :					Union[str, Any]=True     ,						SCREAMING_SNAKE_CASE_       :					Dict=32     ,						SCREAMING_SNAKE_CASE_       :					List[str]=42     ,						SCREAMING_SNAKE_CASE_       :					Optional[int]=5     ,						SCREAMING_SNAKE_CASE_       :					List[str]=2     ,						SCREAMING_SNAKE_CASE_       :					int=4     ,						SCREAMING_SNAKE_CASE_       :					Dict=2     ,						SCREAMING_SNAKE_CASE_       :					Tuple=32     ,						SCREAMING_SNAKE_CASE_       :					List[str]=80     ,						SCREAMING_SNAKE_CASE_       :					int=33     ,						SCREAMING_SNAKE_CASE_       :					List[Any]=0.0     ,						SCREAMING_SNAKE_CASE_       :					Optional[int]=1E-6     ,						SCREAMING_SNAKE_CASE_       :					int=100     ,						SCREAMING_SNAKE_CASE_       :					List[str]=0.0     ,						):
        __lowerCamelCase:					List[str]	 =						vocab_size
        __lowerCamelCase:					Optional[int]	 =						parent
        __lowerCamelCase:					str	 =						batch_size
        __lowerCamelCase:					List[str]	 =						num_contexts
        # For common tests
        __lowerCamelCase:					List[str]	 =						self.num_contexts
        __lowerCamelCase:					Union[str, Any]	 =						is_training
        __lowerCamelCase:					Optional[Any]	 =						hidden_size
        __lowerCamelCase:					List[str]	 =						num_ext_layers
        __lowerCamelCase:					int	 =						ext_size
        __lowerCamelCase:					int	 =						num_hidden_layers
        __lowerCamelCase:					Tuple	 =						num_attention_heads
        __lowerCamelCase:					Any	 =						num_experts
        __lowerCamelCase:					List[str]	 =						d_ff
        __lowerCamelCase:					Optional[Any]	 =						d_ext
        __lowerCamelCase:					List[Any]	 =						d_spout
        __lowerCamelCase:					Optional[int]	 =						dropout_rate
        __lowerCamelCase:					int	 =						layer_norm_epsilon
        __lowerCamelCase:					str	 =						expert_capacity
        __lowerCamelCase:					Union[str, Any]	 =						router_jitter_noise





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





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        __lowerCamelCase:					Tuple	 =						ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size				)

        __lowerCamelCase:					Dict	 =						self.get_config()

        return (config, input_ids)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					str				):
        __lowerCamelCase:					List[Any]	 =						ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size				)

        __lowerCamelCase:					Dict	 =						self.get_config()

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





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







     def 			SCREAMING_SNAKE_CASE__							(     self       :					Any     ,						SCREAMING_SNAKE_CASE_       :					Dict     ,						SCREAMING_SNAKE_CASE_       :					Tuple     ,						):
        __lowerCamelCase:					Union[str, Any]	 =						GPTSanJapaneseForConditionalGeneration(config=__UpperCamelCase				)
        model.to(__UpperCamelCase				)
        model.eval()
        __lowerCamelCase:					List[Any]	 =						model(
            input_ids=__UpperCamelCase     ,						)
        self.parent.assertIsNotNone(__UpperCamelCase				)



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





     def 			SCREAMING_SNAKE_CASE__							(     self       :					str     ,						SCREAMING_SNAKE_CASE_       :					int     ,						SCREAMING_SNAKE_CASE_       :					Dict     ,						SCREAMING_SNAKE_CASE_       :					Optional[Any]     ,						SCREAMING_SNAKE_CASE_       :					Dict     ,						SCREAMING_SNAKE_CASE_       :					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 			SCREAMING_SNAKE_CASE__							(     self       :					Union[str, Any]				):
        __lowerCamelCase:					Any	 =						GPTSanJapaneseTester(self				)
        __lowerCamelCase:					Tuple	 =						ConfigTester(self     ,						config_class=__UpperCamelCase     ,						d_model=37				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        GPTSanJapaneseConfig()





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        __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 			SCREAMING_SNAKE_CASE__							(     self       :					Dict				):
        super().test_model_parallelism()







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



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





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Any				):
        __lowerCamelCase:					Optional[Any]	 =						GPTSanJapaneseTester(self				)
        __lowerCamelCase:					str	 =						ConfigTester(self     ,						config_class=__UpperCamelCase     ,						d_model=37				)





     def 			SCREAMING_SNAKE_CASE__							(     self       :					Dict				):
        GPTSanJapaneseConfig()





     def 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]				):
        __lowerCamelCase:					Union[str, Any]	 =						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 			SCREAMING_SNAKE_CASE__							(     self       :					List[str]				):
        super().test_model_parallelism()





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





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

        def check(SCREAMING_SNAKE_CASE_       :					str     ,						SCREAMING_SNAKE_CASE_       :					Any     ,						SCREAMING_SNAKE_CASE_       :					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 			SCREAMING_SNAKE_CASE__							(     self       :					Tuple				):
        __lowerCamelCase:					Tuple	 =						GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""				)
        __lowerCamelCase:					List[str]	 =						GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""				)
        model.to(__UpperCamelCase				)

        # set deterministically
        __lowerCamelCase:					Optional[Any]	 =						GenerationConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""				)
        __lowerCamelCase:					int	 =						1

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

        __lowerCamelCase:					List[str]	 =						"""left"""
        __lowerCamelCase:					str	 =						tokenizer(__UpperCamelCase     ,						return_tensors="""pt"""     ,						padding=__UpperCamelCase				)
        __lowerCamelCase:					List[Any]	 =						inputs["""input_ids"""].to(__UpperCamelCase				)

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

        __lowerCamelCase:					Dict	 =						model.generate(
            input_ids=__UpperCamelCase     ,						attention_mask=inputs["""attention_mask"""].to(__UpperCamelCase				)     ,						max_new_tokens=3     ,						generation_config=__UpperCamelCase     ,						)

        __lowerCamelCase:					Dict	 =						tokenizer(sentences[0]     ,						return_tensors="""pt"""				).input_ids.to(__UpperCamelCase				)
        __lowerCamelCase:					Optional[Any]	 =						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:					str	 =						tokenizer.batch_decode(__UpperCamelCase     ,						skip_special_tokens=__UpperCamelCase				)
        __lowerCamelCase:					int	 =						tokenizer.decode(output_non_padded[0]     ,						skip_special_tokens=__UpperCamelCase				)
        __lowerCamelCase:					Any	 =						tokenizer.decode(output_padded[0]     ,						skip_special_tokens=__UpperCamelCase				)

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





     @slow
     def 			SCREAMING_SNAKE_CASE__							(     self       :					str				):
        __lowerCamelCase:					int	 =						GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""				)
        __lowerCamelCase:					List[str]	 =						GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""				)
        model.to(__UpperCamelCase				)

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

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

        # spout from uniform and one-hot
        __lowerCamelCase:					Union[str, Any]	 =						[
            # 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:					Optional[Any]	 =						model.generate(
            input_ids=__UpperCamelCase     ,						spout=spouts[1]     ,						max_new_tokens=20     ,						generation_config=__UpperCamelCase     ,						)

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

        __lowerCamelCase:					List[str]	 =						tokenizer.decode(outputa[0]				)
        __lowerCamelCase:					int	 =						tokenizer.decode(outputa[0]				)
        __lowerCamelCase:					Optional[Any]	 =						tokenizer.batch_decode(__UpperCamelCase				)

        __lowerCamelCase:					Tuple	 =						[
            """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 			SCREAMING_SNAKE_CASE__							(     self       :					Optional[int]				):
        __lowerCamelCase:					Optional[int]	 =						GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""				)
        __lowerCamelCase:					Tuple	 =						GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""				)
        model.to(__UpperCamelCase				)

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

        __lowerCamelCase:					str	 =						"""武田信玄"""
        __lowerCamelCase:					List[str]	 =						"""織田信長"""
        __lowerCamelCase:					str	 =						"""は、"""
        __lowerCamelCase:					Tuple	 =						"""が、"""
        __lowerCamelCase:					List[Any]	 =						tokenizer(__UpperCamelCase     ,						prefix_text=__UpperCamelCase     ,						return_tensors="""pt"""				)
        __lowerCamelCase:					List[str]	 =						tokenizer(__UpperCamelCase     ,						prefix_text=__UpperCamelCase     ,						return_tensors="""pt"""				)
        __lowerCamelCase:					Union[str, 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:					str	 =						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:					Tuple	 =						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:					Dict	 =						tokenizer.decode(outputa[0]				)
        __lowerCamelCase:					List[str]	 =						tokenizer.decode(outputa[0]				)
        __lowerCamelCase:					Union[str, Any]	 =						tokenizer.batch_decode(__UpperCamelCase				)

        __lowerCamelCase:					Union[str, Any]	 =						[
            """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]				)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__							=				logging.get_logger(__name__)

A__							=				{
    "tau/splinter-base": "https://huggingface.co/tau/splinter-base/resolve/main/config.json",
    "tau/splinter-base-qass": "https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json",
    "tau/splinter-large": "https://huggingface.co/tau/splinter-large/resolve/main/config.json",
    "tau/splinter-large-qass": "https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json",
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}

class 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE      ):
	_lowercase	: int												=	"splinter"







	def __init__( self:			str	,			__UpperCamelCase:			Any=3_05_22	,			__UpperCamelCase:			List[Any]=7_68	,			__UpperCamelCase:			str=12	,			__UpperCamelCase:			Optional[Any]=12	,			__UpperCamelCase:			Optional[int]=30_72	,			__UpperCamelCase:			List[str]="gelu"	,			__UpperCamelCase:			Union[str, Any]=0.1	,			__UpperCamelCase:			Dict=0.1	,			__UpperCamelCase:			str=5_12	,			__UpperCamelCase:			Dict=2	,			__UpperCamelCase:			str=0.02	,			__UpperCamelCase:			Tuple=1E-12	,			__UpperCamelCase:			Optional[int]=True	,			__UpperCamelCase:			Union[str, Any]=0	,			__UpperCamelCase:			Optional[int]=1_04	,			**__UpperCamelCase:			Tuple	,			):

						'''simple docstring'''





						super().__init__(pad_token_id=__UpperCamelCase	,			**__UpperCamelCase   )

						__magic_name__				=     vocab_size
						__magic_name__				=     max_position_embeddings
						__magic_name__				=     hidden_size
						__magic_name__				=     num_hidden_layers
						__magic_name__				=     num_attention_heads
						__magic_name__				=     intermediate_size
						__magic_name__				=     hidden_act
						__magic_name__				=     hidden_dropout_prob
						__magic_name__				=     attention_probs_dropout_prob
						__magic_name__				=     initializer_range
						__magic_name__				=     type_vocab_size
						__magic_name__				=     layer_norm_eps
						__magic_name__				=     use_cache
						__magic_name__				=     question_token_id




