




'''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       lowerCAmelCase				:


       def __init__(				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE=3_6000      ,					__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      ,					) ->					Optional[Any]:



            '''simple docstring'''




            __snake_case         =					vocab_size
            __snake_case         =					parent
            __snake_case         =					batch_size
            __snake_case         =					num_contexts
            # For common tests
            __snake_case         =					self.num_contexts
            __snake_case         =					is_training
            __snake_case         =					hidden_size
            __snake_case         =					num_ext_layers
            __snake_case         =					ext_size
            __snake_case         =					num_hidden_layers
            __snake_case         =					num_attention_heads
            __snake_case         =					num_experts
            __snake_case         =					d_ff
            __snake_case         =					d_ext
            __snake_case         =					d_spout
            __snake_case         =					dropout_rate
            __snake_case         =					layer_norm_epsilon
            __snake_case         =					expert_capacity
            __snake_case         =					router_jitter_noise


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



            '''simple docstring'''




            return GPTSanJapaneseConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''					)


       def  lowerCAmelCase (				self					) ->					Union[str, Any]:



            '''simple docstring'''




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

            __snake_case         =					self.get_config()

            return (config, input_ids)


       def  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




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

            __snake_case         =					self.get_config()

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


       def  lowerCAmelCase (				self					) ->					Optional[Any]:



            '''simple docstring'''




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




       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					) ->					Dict:



            '''simple docstring'''




            __snake_case         =					GPTSanJapaneseForConditionalGeneration(config=__UpperCamelCase					)
            model.to(__UpperCamelCase					)
            model.eval()
            __snake_case         =					model(
                input_ids=__UpperCamelCase      ,					)
            self.parent.assertIsNotNone(__UpperCamelCase					)





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


       def  lowerCAmelCase (				self      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					) ->					Any:



            '''simple docstring'''




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

            return False


       def  lowerCAmelCase (				self					) ->					Optional[Any]:



            '''simple docstring'''




            __snake_case         =					GPTSanJapaneseTester(self					)
            __snake_case         =					ConfigTester(self      ,					config_class=__UpperCamelCase      ,					d_model=37					)


       def  lowerCAmelCase (				self					) ->					List[str]:



            '''simple docstring'''




            GPTSanJapaneseConfig()


       def  lowerCAmelCase (				self					) ->					Union[str, Any]:



            '''simple docstring'''




            __snake_case         =					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  lowerCAmelCase (				self					) ->					Optional[int]:



            '''simple docstring'''




            super().test_model_parallelism()




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



            '''simple docstring'''




            pass





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


       def  lowerCAmelCase (				self					) ->					Union[str, Any]:



            '''simple docstring'''




            __snake_case         =					GPTSanJapaneseTester(self					)
            __snake_case         =					ConfigTester(self      ,					config_class=__UpperCamelCase      ,					d_model=37					)


       def  lowerCAmelCase (				self					) ->					Optional[int]:



            '''simple docstring'''




            GPTSanJapaneseConfig()


       def  lowerCAmelCase (				self					) ->					Optional[int]:



            '''simple docstring'''




            __snake_case         =					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  lowerCAmelCase (				self					) ->					Optional[Any]:



            '''simple docstring'''




            super().test_model_parallelism()


       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''					)
       def  lowerCAmelCase (				self					) ->					List[Any]:



            '''simple docstring'''




            pass


       @slow
       def  lowerCAmelCase (				self					) ->					Optional[Any]:



            '''simple docstring'''




            __snake_case         =					GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
            __snake_case         =					GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
            __snake_case         =					tokenizer.encode('''武田信玄は'''      ,					return_tensors='''pt'''					)
            __snake_case         =					model(__UpperCamelCase					)
            __snake_case         =					outputs.logits.detach().cpu().numpy()
            # Output of original model created with mesh-tensoflow
            __snake_case         =					[
                # fmt: off
                [-12.037_839_889_526_367, -12.433_061_599_731_445, -14.333_840_370_178_223, -12.450_345_993_041_992, -11.1_661_376_953_125,
                -11.930_137_634_277_344, -10.659_740_447_998_047, -12.909_574_508_666_992, -13.241_043_090_820_312, -13.398_579_597_473_145,
                -11.107_524_871_826_172, -12.3_685_941_696_167, -22.97_943_115_234_375, -10.481_067_657_470_703, -12.484_030_723_571_777,
                -12.807_360_649_108_887, -14.769_700_050_354_004, -12.233_579_635_620_117, -13.428_145_408_630_371, -22.624_177_932_739_258],
                [-7.511_149_883_270_264, -8.281_851_768_493_652, -7.943_127_155_303_955, -7.55_021_333_694_458, -6.49_869_966_506_958,
                -7.586_796_283_721_924, -6.978_085_994_720_459, -7.839_145_183_563_232, -8.21_964_168_548_584, -8.695_091_247_558_594,
                -6.706_910_610_198_975, -6.6_585_798_263_549_805, -19.565_698_623_657_227, -5.353_842_735_290_527, -8.350_686_073_303_223,
                -8.039_388_656_616_211, -10.856_569_290_161_133, -7.75_154_447_555_542, -8.819_022_178_649_902, -19.51_532_745_361_328],
                [-9.73_066_234_588_623, -10.223_922_729_492_188, -9.932_981_491_088_867, -11.857_836_723_327_637, -7.662_626_266_479_492,
                -11.13_529_109_954_834, -7.765_097_618_103_027, -11.472_923_278_808_594, -9.543_149_948_120_117, -11.905_633_926_391_602,
                -9.366_164_207_458_496, -11.5_734_281_539_917, -23.699_003_219_604_492, -9.429_590_225_219_727, -10.42_839_241_027_832,
                -10.585_240_364_074_707, -10.94_771_957_397_461, -11.095_416_069_030_762, -10.390_240_669_250_488, -23.769_372_940_063_477],
                [-9.728_265_762_329_102, -9.859_712_600_708_008, -10.09_729_290_008_545, -9.678_522_109_985_352, -6.879_519_939_422_607,
                -9.68_487_548_828_125, -4.2_803_425_788_879_395, -10.018_914_222_717_285, -9.308_445_930_480_957, -10.63_394_546_508_789,
                -8.083_646_774_291_992, -9.06_301_498_413_086, -21.904_266_357_421_875, -8.90_160_846_710_205, -8.841_876_029_968_262,
                -11.856_719_970_703_125, -12.079_398_155_212_402, -11.233_753_204_345_703, -10.177_338_600_158_691, -21.87_256_622_314_453],
                [-9.669_764_518_737_793, -9.614_198_684_692_383, -9.814_510_345_458_984, -9.996_501_922_607_422, -11.375_690_460_205_078,
                -10.113_405_227_661_133, -10.546_867_370_605_469, -10.04_369_068_145_752, -10.907_809_257_507_324, -10.504_216_194_152_832,
                -11.129_199_028_015_137, -10.151_124_000_549_316, -21.96_586_799_621_582, -9.086_349_487_304_688, -11.730_339_050_292_969,
                -10.460_667_610_168_457, -10.298_049_926_757_812, -10.784_148_216_247_559, -10.840_693_473_815_918, -22.03_152_847_290_039],
                # fmt: on
            ]
            __snake_case         =					np.array(__UpperCamelCase					).flatten()
            __snake_case         =					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(__UpperCamelCase					)      ,					abs(__UpperCamelCase					)					)

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


       @slow
       def  lowerCAmelCase (				self					) ->					List[Any]:



            '''simple docstring'''




            __snake_case         =					GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
            __snake_case         =					GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
            model.to(__UpperCamelCase					)

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

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

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

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

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

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

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

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

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

            __snake_case         =					[
                '''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  lowerCAmelCase (				self					) ->					Dict:



            '''simple docstring'''




            __snake_case         =					GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
            __snake_case         =					GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
            # Output of original model created with mesh-tensoflow
            __snake_case         =					[
                ('''武田信玄は''', 3_5675),
                ('''武田信玄は、''', 45),
                ('''武田信玄は、この''', 29),
                ('''武田信玄は、このよう''', 3_0642),
                ('''武田信玄は、このような''', 3_5680),
                ('''武田信玄は、このような「''', 8640),
                ('''武田信玄は、このような「武田''', 3_1617),
                ('''武田信玄は、このような「武田家''', 3_0646),
                ('''武田信玄は、このような「武田家の''', 3_1617),
                ('''武田信玄は、このような「武田家の家''', 3_1381),
            ]
            for input, output in target:
                 __snake_case         =					tokenizer.encode(__UpperCamelCase      ,					return_tensors='''pt'''					)
                 __snake_case         =					model(__UpperCamelCase					)
                 __snake_case         =					outputs.logits.detach().cpu().numpy()[0]
                 __snake_case         =					np.argmax(output_logits[-1]					)
                 self.assertEqual(__UpperCamelCase      ,					__UpperCamelCase					)


       @slow
       def  lowerCAmelCase (				self					) ->					List[Any]:



            '''simple docstring'''




            __snake_case         =					GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
            __snake_case         =					GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
            model.to(__UpperCamelCase					)

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

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

            # spout from uniform and one-hot
            __snake_case         =					[
                # fmt: off
                [0.87_882_208, 0.38_426_396, 0.33_220_248, 0.43_890_406, 0.16_562_252,
                0.04_803_985, 0.211_572  , 0.23_188_473, 0.37_153_068, 0.7_836_377 ,
                0.02_160_172, 0.38_761_719, 0.75_290_772, 0.90_198_857, 0.34_365_777,
                0.64_168_169, 0.44_318_471, 0.14_575_746, 0.92_562_881, 0.40_812_148,
                0.29_019_122, 0.88_861_599, 0.65_524_846, 0.43_563_456, 0.38_177_187,
                0.70_832_965, 0.81_527_892, 0.68_832_812, 0.38_833_192, 0.4_561_522 ,
                0.14_828_817, 0.47_248_213, 0.54_357_335, 0.82_009_566, 0.1_338_884 ,
                0.02_755_417, 0.19_764_677, 0.2_422_084 , 0.04_757_674, 0.65_409_606,
                0.0_824_589 , 0.03_304_383, 0.94_387_689, 0.98_764_509, 0.82_433_901,
                0.27_646_741, 0.64_907_493, 0.76_009_406, 0.30_087_915, 0.17_904_689,
                0.41_601_714, 0.67_046_398, 0.10_422_822, 0.08_447_374, 0.07_354_344,
                0.61_423_565, 0.70_284_866, 0.7_532_333 , 0.1_972_038 , 0.29_575_659,
                0.90_583_886, 0.29_265_307, 0.50_000_175, 0.70_407_655, 0.889_363  ,
                0.81_904_418, 0.66_829_128, 0.64_468_815, 0.56_563_723, 0.85_601_875,
                0.94_924_672, 0.00_166_762, 0.25_220_643, 0.74_540_219, 0.67_993_247,
                0.1_549_675 , 0.39_385_352, 0.92_153_607, 0.63_745_931, 0.27_759_043,
                0.84_702_295, 0.65_904_271, 0.58_676_614, 0.8_666_936 , 0.39_607_438,
                0.79_954_983, 0.42_220_697, 0.39_650_381, 0.7_849_864 , 0.56_150_201,
                0.15_678_925, 0.14_746_032, 0.34_542_114, 0.47_026_783, 0.11_956_489,
                0.25_421_435, 0.33_788_901, 0.68_934_842, 0.36_424_685, 0.71_737_898,
                0.38_983_449, 0.94_393_779, 0.39_575_588, 0.36_616_553, 0.87_104_665,
                0.64_630_203, 0.22_516_905, 0.88_270_804, 0.15_031_338, 0.75_144_345,
                0.46_459_025, 0.85_396_454, 0.86_355_643, 0.65_139_851, 0.70_266_061,
                0.30_241_389, 0.81_056_497, 0.88_865_969, 0.38_773_807, 0.70_635_849,
                0.90_718_459, 0.43_245_789, 0.28_000_654, 0.45_935_562, 0.08_773_519,
                0.9_552_151 , 0.93_901_511, 0.22_489_288], # 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
            ]

            __snake_case         =					model.generate(
                input_ids=__UpperCamelCase      ,					spout=spouts[0]      ,					max_new_tokens=20      ,					generation_config=__UpperCamelCase      ,					)

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

            __snake_case         =					model.generate(
                input_ids=__UpperCamelCase      ,					spout=__UpperCamelCase      ,					max_new_tokens=20      ,					generation_config=__UpperCamelCase      ,					)

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

            __snake_case         =					[
                '''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  lowerCAmelCase (				self					) ->					List[str]:



            '''simple docstring'''




            __snake_case         =					GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
            __snake_case         =					GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''					)
            model.to(__UpperCamelCase					)

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

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

            __snake_case         =					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      ,					)

            __snake_case         =					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      ,					)

            __snake_case         =					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      ,					)

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

            __snake_case         =					[
                '''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]					)











'''simple docstring'''



import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax





@require_flax
class       lowerCAmelCase				:


       def  lowerCAmelCase (				self					) ->					int:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            __snake_case         =					model.init(inputs_dict['''prng_key''']      ,					inputs_dict['''sample''']					)
            jax.lax.stop_gradient(__SCREAMING_SNAKE_CASE					)

            __snake_case         =					model.apply(__SCREAMING_SNAKE_CASE      ,					inputs_dict['''sample''']					)

            if isinstance(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					):
                 __snake_case         =					output.sample

            self.assertIsNotNone(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					inputs_dict['''sample'''].shape
            self.assertEqual(output.shape      ,					__SCREAMING_SNAKE_CASE      ,					'''Input and output shapes do not match'''					)


       def  lowerCAmelCase (				self					) ->					Tuple:



            '''simple docstring'''




            __snake_case   ,      __snake_case         =					self.prepare_init_args_and_inputs_for_common()

            __snake_case         =					16
            __snake_case         =					(16, 32)

            __snake_case         =					self.model_class(**__SCREAMING_SNAKE_CASE					)
            __snake_case         =					model.init(inputs_dict['''prng_key''']      ,					inputs_dict['''sample''']					)
            jax.lax.stop_gradient(__SCREAMING_SNAKE_CASE					)

            __snake_case         =					model.apply(__SCREAMING_SNAKE_CASE      ,					inputs_dict['''sample''']					)

            if isinstance(__SCREAMING_SNAKE_CASE      ,					__SCREAMING_SNAKE_CASE					):
                 __snake_case         =					output.sample

            self.assertIsNotNone(__SCREAMING_SNAKE_CASE					)
            __snake_case         =					inputs_dict['''sample'''].shape
            self.assertEqual(output.shape      ,					__SCREAMING_SNAKE_CASE      ,					'''Input and output shapes do not match'''					)




       def  lowerCAmelCase (				self					) ->					str:



            '''simple docstring'''




            __snake_case         =					'''kwargs''' in inspect.signature(self.model_class.__init__					).parameters
            __snake_case         =					len(self.model_class._deprecated_kwargs					) > 0

            if has_kwarg_in_model_class and not has_deprecated_kwarg:
                 raise ValueError(
                     F'''{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs'''
                     ''' under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are'''
                     ''' no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ='''
                     ''' [<deprecated_argument>]`'''					)

            if not has_kwarg_in_model_class and has_deprecated_kwarg:
                 raise ValueError(
                     F'''{self.model_class} doesn\'t have `**kwargs` in its __init__ method but has defined deprecated kwargs'''
                     ''' under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to'''
                     F''' {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument'''
                     ''' from `_deprecated_kwargs = [<deprecated_argument>]`'''					)






