
'''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_60_00       ,							__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=1_00       ,							__SCREAMING_SNAKE_CASE=0.0       ,							):


											"""simple docstring"""





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






					def 			_snake_case					(     self	):


											"""simple docstring"""





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






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  str									= ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size	)

											lowercase_			:  Union[str, Any]									= self.get_config()

											return (config, input_ids)






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  int									= ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size	)

											lowercase_			:  Optional[Any]									= self.get_config()

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






					def 			_snake_case					(     self	):


											"""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 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							):


											"""simple docstring"""





											lowercase_			:  List[str]									= GPTSanJapaneseForConditionalGeneration(config=__SCREAMING_SNAKE_CASE	)
											model.to(__SCREAMING_SNAKE_CASE	)
											model.eval()
											lowercase_			:  Union[str, Any]									= model(
											    input_ids=__SCREAMING_SNAKE_CASE       ,							)
											self.parent.assertIsNotNone(__SCREAMING_SNAKE_CASE	)







@require_torch
class 				lowerCAmelCase__     (						lowerCamelCase_					,		lowerCamelCase_					,		unittest.TestCase  ):
					lowerCAmelCase_       =  (GPTSanJapaneseModel,) if is_torch_available() else ()
					lowerCAmelCase_       =  (
					    {
					        '''conversational''': GPTSanJapaneseForConditionalGeneration,
					        '''feature-extraction''': GPTSanJapaneseForConditionalGeneration,
					        '''summarization''': GPTSanJapaneseForConditionalGeneration,
					        '''text2text-generation''': GPTSanJapaneseForConditionalGeneration,
					        '''translation''': GPTSanJapaneseForConditionalGeneration,
					    }
					    if is_torch_available()
					    else {}
					)
					lowerCAmelCase_       =  False
					lowerCAmelCase_       =  False
					lowerCAmelCase_       =  False
					lowerCAmelCase_       =  False
					lowerCAmelCase_       =  False
					lowerCAmelCase_       =  False
					lowerCAmelCase_       =  False
					lowerCAmelCase_       =  False
					# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
					lowerCAmelCase_       =  [0.8, 0.9]






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):


											"""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 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  List[str]									= GPTSanJapaneseTester(self	)
											lowercase_			:  Tuple									= ConfigTester(self       ,							config_class=__SCREAMING_SNAKE_CASE       ,							d_model=37	)






					def 			_snake_case					(     self	):


											"""simple docstring"""





											GPTSanJapaneseConfig()






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  str									= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE	)






					@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 			_snake_case					(     self	):


											"""simple docstring"""





											super().test_model_parallelism()






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


											"""simple docstring"""





											pass







@require_torch
class 				lowerCAmelCase__     (						lowerCamelCase_					,		lowerCamelCase_					,		unittest.TestCase  ):
					lowerCAmelCase_       =  (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
					lowerCAmelCase_       =  False
					lowerCAmelCase_       =  False
					lowerCAmelCase_       =  False
					lowerCAmelCase_       =  False
					lowerCAmelCase_       =  False
					lowerCAmelCase_       =  False
					# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
					lowerCAmelCase_       =  [0.8, 0.9]






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  Optional[int]									= GPTSanJapaneseTester(self	)
											lowercase_			:  int									= ConfigTester(self       ,							config_class=__SCREAMING_SNAKE_CASE       ,							d_model=37	)






					def 			_snake_case					(     self	):


											"""simple docstring"""





											GPTSanJapaneseConfig()






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  str									= self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE	)






					@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 			_snake_case					(     self	):


											"""simple docstring"""





											super().test_model_parallelism()






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


											"""simple docstring"""





											pass






					@slow
					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  Union[str, Any]									= GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
											lowercase_			:  Union[str, Any]									= GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
											lowercase_			:  Union[str, Any]									= tokenizer.encode('''武田信玄は'''       ,							return_tensors='''pt'''	)
											lowercase_			:  List[str]									= model(__SCREAMING_SNAKE_CASE	)
											lowercase_			:  Any									= outputs.logits.detach().cpu().numpy()
											# Output of original model created with mesh-tensoflow
											lowercase_			:  str									= [
											    # 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
											]
											lowercase_			:  int									= np.array(__SCREAMING_SNAKE_CASE	).flatten()
											lowercase_			:  Dict									= 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(__SCREAMING_SNAKE_CASE	)       ,							abs(__SCREAMING_SNAKE_CASE	)	)

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






					@slow
					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  int									= GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
											lowercase_			:  List[Any]									= GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
											model.to(__SCREAMING_SNAKE_CASE	)

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

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

											lowercase_			:  Union[str, Any]									= '''left'''
											lowercase_			:  Optional[int]									= tokenizer(__SCREAMING_SNAKE_CASE       ,							return_tensors='''pt'''       ,							padding=__SCREAMING_SNAKE_CASE	)
											lowercase_			:  Optional[int]									= inputs['''input_ids'''].to(__SCREAMING_SNAKE_CASE	)

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

											lowercase_			:  Dict									= model.generate(
											    input_ids=__SCREAMING_SNAKE_CASE       ,							attention_mask=inputs['''attention_mask'''].to(__SCREAMING_SNAKE_CASE	)       ,							max_new_tokens=3       ,							generation_config=__SCREAMING_SNAKE_CASE       ,							)

											lowercase_			:  List[str]									= tokenizer(sentences[0]       ,							return_tensors='''pt'''	).input_ids.to(__SCREAMING_SNAKE_CASE	)
											lowercase_			:  Tuple									= model.generate(
											    input_ids=__SCREAMING_SNAKE_CASE       ,							max_new_tokens=3       ,							generation_config=__SCREAMING_SNAKE_CASE	)

											lowercase_			:  Any									= tokenizer(sentences[1]       ,							return_tensors='''pt'''	).input_ids.to(__SCREAMING_SNAKE_CASE	)
											lowercase_			:  List[Any]									= model.generate(input_ids=__SCREAMING_SNAKE_CASE       ,							max_new_tokens=3       ,							generation_config=__SCREAMING_SNAKE_CASE	)

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

											lowercase_			:  Tuple									= tokenizer.batch_decode(__SCREAMING_SNAKE_CASE       ,							skip_special_tokens=__SCREAMING_SNAKE_CASE	)
											lowercase_			:  List[str]									= tokenizer.decode(output_non_padded[0]       ,							skip_special_tokens=__SCREAMING_SNAKE_CASE	)
											lowercase_			:  List[Any]									= tokenizer.decode(output_padded[0]       ,							skip_special_tokens=__SCREAMING_SNAKE_CASE	)

											lowercase_			:  List[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(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	)
											self.assertListEqual(__SCREAMING_SNAKE_CASE       ,							[non_padded_sentence, padded_sentence]	)






					@tooslow
					def 			_snake_case					(     self	):


											"""simple docstring"""





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






					@slow
					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  int									= GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
											lowercase_			:  Any									= GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
											model.to(__SCREAMING_SNAKE_CASE	)

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

											lowercase_			:  Any									= '''武田信玄は、'''
											lowercase_			:  List[str]									= tokenizer(__SCREAMING_SNAKE_CASE       ,							return_tensors='''pt'''	).input_ids.to(__SCREAMING_SNAKE_CASE	)
											lowercase_			:  Union[str, Any]									= tokenizer([input_text, input_text]       ,							return_tensors='''pt'''	).input_ids.to(__SCREAMING_SNAKE_CASE	)

											# spout from uniform and one-hot
											lowercase_			:  Any									= [
											    # 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
											]

											lowercase_			:  List[Any]									= model.generate(
											    input_ids=__SCREAMING_SNAKE_CASE       ,							spout=spouts[0]       ,							max_new_tokens=20       ,							generation_config=__SCREAMING_SNAKE_CASE       ,							)

											lowercase_			:  Union[str, Any]									= model.generate(
											    input_ids=__SCREAMING_SNAKE_CASE       ,							spout=spouts[1]       ,							max_new_tokens=20       ,							generation_config=__SCREAMING_SNAKE_CASE       ,							)

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

											lowercase_			:  Optional[int]									= tokenizer.decode(outputa[0]	)
											lowercase_			:  Tuple									= tokenizer.decode(outputa[0]	)
											lowercase_			:  Dict									= tokenizer.batch_decode(__SCREAMING_SNAKE_CASE	)

											lowercase_			:  Union[str, Any]									= [
											    '''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(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	)
											self.assertListEqual(__SCREAMING_SNAKE_CASE       ,							[outa_sentence, outa_sentence]	)






					@slow
					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  Union[str, Any]									= GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
											lowercase_			:  Union[str, Any]									= GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
											model.to(__SCREAMING_SNAKE_CASE	)

											# set deterministically
											lowercase_			:  Tuple									= GenerationConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''	)
											lowercase_			:  Dict									= 1

											lowercase_			:  Optional[int]									= '''武田信玄'''
											lowercase_			:  int									= '''織田信長'''
											lowercase_			:  Dict									= '''は、'''
											lowercase_			:  List[str]									= '''が、'''
											lowercase_			:  str									= tokenizer(__SCREAMING_SNAKE_CASE       ,							prefix_text=__SCREAMING_SNAKE_CASE       ,							return_tensors='''pt'''	)
											lowercase_			:  Any									= tokenizer(__SCREAMING_SNAKE_CASE       ,							prefix_text=__SCREAMING_SNAKE_CASE       ,							return_tensors='''pt'''	)
											lowercase_			:  Tuple									= tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]       ,							return_tensors='''pt'''	)

											lowercase_			:  Union[str, Any]									= model.generate(
											    input_ids=input_tok_a.input_ids.to(__SCREAMING_SNAKE_CASE	)       ,							token_type_ids=input_tok_a.token_type_ids.to(__SCREAMING_SNAKE_CASE	)       ,							max_new_tokens=20       ,							generation_config=__SCREAMING_SNAKE_CASE       ,							)

											lowercase_			:  str									= model.generate(
											    input_ids=input_tok_a.input_ids.to(__SCREAMING_SNAKE_CASE	)       ,							token_type_ids=input_tok_a.token_type_ids.to(__SCREAMING_SNAKE_CASE	)       ,							max_new_tokens=20       ,							generation_config=__SCREAMING_SNAKE_CASE       ,							)

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

											lowercase_			:  Optional[int]									= tokenizer.decode(outputa[0]	)
											lowercase_			:  Any									= tokenizer.decode(outputa[0]	)
											lowercase_			:  Any									= tokenizer.batch_decode(__SCREAMING_SNAKE_CASE	)

											lowercase_			:  Optional[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(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	)
											self.assertListEqual(__SCREAMING_SNAKE_CASE       ,							[outa_sentence, outa_sentence]	)





'''simple docstring'''







from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
							import torch






class 				lowerCAmelCase__     (						lowerCamelCase_  ):
					lowerCAmelCase_       =  ['''image_processor''', '''tokenizer''']
					lowerCAmelCase_       =  '''OneFormerImageProcessor'''
					lowerCAmelCase_       =  ('''CLIPTokenizer''', '''CLIPTokenizerFast''')






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE = 77       ,							__SCREAMING_SNAKE_CASE = 77       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if image_processor is None:
																	raise ValueError('''You need to specify an `image_processor`.'''	)
											if tokenizer is None:
																	raise ValueError('''You need to specify a `tokenizer`.'''	)

											lowercase_			:  Dict									= max_seq_length
											lowercase_			:  Dict									= task_seq_length

											super().__init__(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=77	):


											"""simple docstring"""





											if text_list is None:
																	raise ValueError('''tokens cannot be None.'''	)

											lowercase_			:  str									= self.tokenizer(__SCREAMING_SNAKE_CASE       ,							padding='''max_length'''       ,							max_length=__SCREAMING_SNAKE_CASE       ,							truncation=__SCREAMING_SNAKE_CASE	)

											lowercase_		,				lowercase_			:  List[str]									= tokens['''attention_mask'''], tokens['''input_ids''']

											lowercase_			:  List[str]									= []
											for attn_mask, input_id in zip(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  Optional[Any]									= torch.tensor(__SCREAMING_SNAKE_CASE	) * torch.tensor(__SCREAMING_SNAKE_CASE	)
																	token_inputs.append(token.unsqueeze(0	)	)

											lowercase_			:  str									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)
											return token_inputs






					def __call__(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if task_inputs is None:
																	raise ValueError('''You have to specify the task_input. Found None.'''	)
											elif images is None:
																	raise ValueError('''You have to specify the image. Found None.'''	)

											if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs	):
																	raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''	)

											lowercase_			:  Optional[int]									= self.image_processor(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  List[str]									= [task_inputs]

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) and all(isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) for task_input in task_inputs	):
																	lowercase_			:  List[str]									= []
																	for task in task_inputs:
																							lowercase_			:  Tuple									= F'''the task is {task}'''
																							task_token_inputs.append(__SCREAMING_SNAKE_CASE	)
																	lowercase_			:  Tuple									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.task_seq_length	)
											else:
																	raise TypeError('''Task Inputs should be a string or a list of strings.'''	)

											if hasattr(__SCREAMING_SNAKE_CASE       ,							'''text_inputs'''	):
																	lowercase_			:  Any									= encoded_inputs.text_inputs

																	lowercase_			:  int									= []
																	for texts in texts_list:
																							lowercase_			:  Any									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.max_seq_length	)
																							text_inputs.append(text_input_list.unsqueeze(0	)	)

																	lowercase_			:  Tuple									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)

											return encoded_inputs






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if task_inputs is None:
																	raise ValueError('''You have to specify the task_input. Found None.'''	)
											elif images is None:
																	raise ValueError('''You have to specify the image. Found None.'''	)

											if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs	):
																	raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''	)

											lowercase_			:  Dict									= self.image_processor.encode_inputs(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  List[Any]									= [task_inputs]

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) and all(isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) for task_input in task_inputs	):
																	lowercase_			:  int									= []
																	for task in task_inputs:
																							lowercase_			:  int									= F'''the task is {task}'''
																							task_token_inputs.append(__SCREAMING_SNAKE_CASE	)
																	lowercase_			:  str									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.task_seq_length	)
											else:
																	raise TypeError('''Task Inputs should be a string or a list of strings.'''	)

											if hasattr(__SCREAMING_SNAKE_CASE       ,							'''text_inputs'''	):
																	lowercase_			:  Dict									= encoded_inputs.text_inputs

																	lowercase_			:  str									= []
																	for texts in texts_list:
																							lowercase_			:  List[str]									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.max_seq_length	)
																							text_inputs.append(text_input_list.unsqueeze(0	)	)

																	lowercase_			:  Union[str, Any]									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)

											return encoded_inputs






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_semantic_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_instance_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_panoptic_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)



