
"""simple docstring"""






import unittest

import numpy as np

from transformers import (
    GPTSanJapaneseConfig,
    GPTSanJapaneseForConditionalGeneration,
    GPTSanJapaneseModel,
    GPTSanJapaneseTokenizer,
    is_torch_available,
)
from transformers.generation import GenerationConfig
from transformers.testing_utils import require_torch, slow, tooslow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin

class 				__a    :



							'''simple docstring'''







							def __init__(    self     ,						_lowerCamelCase     ,						_lowerCamelCase=36_000     ,						_lowerCamelCase=13     ,						_lowerCamelCase=7     ,						_lowerCamelCase=True     ,						_lowerCamelCase=32     ,						_lowerCamelCase=42     ,						_lowerCamelCase=5     ,						_lowerCamelCase=2     ,						_lowerCamelCase=4     ,						_lowerCamelCase=2     ,						_lowerCamelCase=32     ,						_lowerCamelCase=80     ,						_lowerCamelCase=33     ,						_lowerCamelCase=0.0     ,						_lowerCamelCase=1e-6     ,						_lowerCamelCase=100     ,						_lowerCamelCase=0.0     ,						) ->       int:




										'''simple docstring'''
										__lowercase						      =					vocab_size
										__lowercase						      =					parent
										__lowercase						      =					batch_size
										__lowercase						      =					num_contexts
										# For common tests
										__lowercase						      =					self.num_contexts
										__lowercase						      =					is_training
										__lowercase						      =					hidden_size
										__lowercase						      =					num_ext_layers
										__lowercase						      =					ext_size
										__lowercase						      =					num_hidden_layers
										__lowercase						      =					num_attention_heads
										__lowercase						      =					num_experts
										__lowercase						      =					d_ff
										__lowercase						      =					d_ext
										__lowercase						      =					d_spout
										__lowercase						      =					dropout_rate
										__lowercase						      =					layer_norm_epsilon
										__lowercase						      =					expert_capacity
										__lowercase						      =					router_jitter_noise
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[Any]:




										'''simple docstring'''
										return GPTSanJapaneseConfig.from_pretrained("Tanrei/GPTSAN-japanese"					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Dict:




										'''simple docstring'''
										__lowercase						      =					ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size					)

										__lowercase						      =					self.get_config()

										return (config, input_ids)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Tuple:




										'''simple docstring'''
										__lowercase						      =					ids_tensor([self.batch_size, self.seq_length]     ,						self.vocab_size					)

										__lowercase						      =					self.get_config()

										return (config, {"input_ids": input_ids})
							def      SCREAMING_SNAKE_CASE	(    self					) ->       str:




										'''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      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						) ->       int:




										'''simple docstring'''
										__lowercase						      =					GPTSanJapaneseForConditionalGeneration(config=_lowerCamelCase					)
										model.to(_lowerCamelCase					)
										model.eval()
										__lowercase						      =					model(
										    input_ids=_lowerCamelCase     ,						)
										self.parent.assertIsNotNone(_lowerCamelCase					)





@require_torch
class 				__a    (   __a				,      __a				,      unittest.TestCase					):



							'''simple docstring'''







							_lowerCamelCase				: str					      =      (GPTSanJapaneseModel,) if is_torch_available() else ()
							_lowerCamelCase				: int					      =      (
							    {
							        """conversational""": GPTSanJapaneseForConditionalGeneration,
							        """feature-extraction""": GPTSanJapaneseForConditionalGeneration,
							        """summarization""": GPTSanJapaneseForConditionalGeneration,
							        """text2text-generation""": GPTSanJapaneseForConditionalGeneration,
							        """translation""": GPTSanJapaneseForConditionalGeneration,
							    }
							    if is_torch_available()
							    else {}
							)
							_lowerCamelCase				: List[Any]					      =      False
							_lowerCamelCase				: Any					      =      False
							_lowerCamelCase				: str					      =      False
							_lowerCamelCase				: List[str]					      =      False
							_lowerCamelCase				: str					      =      False
							_lowerCamelCase				: Any					      =      False
							_lowerCamelCase				: Union[str, Any]					      =      False
							_lowerCamelCase				: Dict					      =      False
							# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
							_lowerCamelCase				: List[str]					      =      [0.8, 0.9]
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase					) ->       Optional[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      SCREAMING_SNAKE_CASE	(    self					) ->       List[str]:




										'''simple docstring'''
										__lowercase						      =					GPTSanJapaneseTester(self					)
										__lowercase						      =					ConfigTester(self     ,						config_class=_lowerCamelCase     ,						d_model=37					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       str:




										'''simple docstring'''
										GPTSanJapaneseConfig()
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Dict:




										'''simple docstring'''
										__lowercase						      =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCamelCase					)
							@unittest.skip(
							    reason="skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`"					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[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      SCREAMING_SNAKE_CASE	(    self					) ->       Union[str, Any]:




										'''simple docstring'''
										pass





@require_torch
class 				__a    (   __a				,      __a				,      unittest.TestCase					):



							'''simple docstring'''







							_lowerCamelCase				: Dict					      =      (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
							_lowerCamelCase				: List[Any]					      =      False
							_lowerCamelCase				: int					      =      False
							_lowerCamelCase				: Dict					      =      False
							_lowerCamelCase				: str					      =      False
							_lowerCamelCase				: Any					      =      False
							_lowerCamelCase				: Union[str, Any]					      =      False
							# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
							_lowerCamelCase				: Dict					      =      [0.8, 0.9]
							def      SCREAMING_SNAKE_CASE	(    self					) ->       str:




										'''simple docstring'''
										__lowercase						      =					GPTSanJapaneseTester(self					)
										__lowercase						      =					ConfigTester(self     ,						config_class=_lowerCamelCase     ,						d_model=37					)
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[str]:




										'''simple docstring'''
										GPTSanJapaneseConfig()
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Optional[int]:




										'''simple docstring'''
										__lowercase						      =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*_lowerCamelCase					)
							@unittest.skip(
							    reason="skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`"					)
							def      SCREAMING_SNAKE_CASE	(    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      SCREAMING_SNAKE_CASE	(    self					) ->       Union[str, Any]:




										'''simple docstring'''
										pass
							@slow
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[Any]:




										'''simple docstring'''
										__lowercase						      =					GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"					)
										__lowercase						      =					GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"					)
										__lowercase						      =					tokenizer.encode("武田信玄は"     ,						return_tensors="pt"					)
										__lowercase						      =					model(_lowerCamelCase					)
										__lowercase						      =					outputs.logits.detach().cpu().numpy()
										# Output of original model created with mesh-tensoflow
										__lowercase						      =					[
										    # 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
										]
										__lowercase						      =					np.array(_lowerCamelCase					).flatten()
										__lowercase						      =					output_logits[0, :, :20].flatten()

										def check(_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase=5e-4					):
													return abs(a - b					) < epsilon * max(abs(_lowerCamelCase					)     ,						abs(_lowerCamelCase					)					)

										self.assertTrue(np.all([check(target[i]     ,						predict[i]					) for i in range(len(_lowerCamelCase					)					)]					)					)
							@slow
							def      SCREAMING_SNAKE_CASE	(    self					) ->       List[str]:




										'''simple docstring'''
										__lowercase						      =					GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"					)
										__lowercase						      =					GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"					)
										model.to(_lowerCamelCase					)

										# set deterministically
										__lowercase						      =					GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"					)
										__lowercase						      =					1

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

										__lowercase						      =					"left"
										__lowercase						      =					tokenizer(_lowerCamelCase     ,						return_tensors="pt"     ,						padding=_lowerCamelCase					)
										__lowercase						      =					inputs["input_ids"].to(_lowerCamelCase					)

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

										__lowercase						      =					model.generate(
										    input_ids=_lowerCamelCase     ,						attention_mask=inputs["attention_mask"].to(_lowerCamelCase					)     ,						max_new_tokens=3     ,						generation_config=_lowerCamelCase     ,						)

										__lowercase						      =					tokenizer(sentences[0]     ,						return_tensors="pt"					).input_ids.to(_lowerCamelCase					)
										__lowercase						      =					model.generate(
										    input_ids=_lowerCamelCase     ,						max_new_tokens=3     ,						generation_config=_lowerCamelCase					)

										__lowercase						      =					tokenizer(sentences[1]     ,						return_tensors="pt"					).input_ids.to(_lowerCamelCase					)
										__lowercase						      =					model.generate(input_ids=_lowerCamelCase     ,						max_new_tokens=3     ,						generation_config=_lowerCamelCase					)

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

										__lowercase						      =					tokenizer.batch_decode(_lowerCamelCase     ,						skip_special_tokens=_lowerCamelCase					)
										__lowercase						      =					tokenizer.decode(output_non_padded[0]     ,						skip_special_tokens=_lowerCamelCase					)
										__lowercase						      =					tokenizer.decode(output_padded[0]     ,						skip_special_tokens=_lowerCamelCase					)

										__lowercase						      =					[
										    "甲斐なら武田と言うほど甲斐の武田",
										    "織田信長は、このような",
										]
										self.assertListEqual(_lowerCamelCase     ,						_lowerCamelCase					)
										self.assertListEqual(_lowerCamelCase     ,						[non_padded_sentence, padded_sentence]					)
							@tooslow
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Tuple:




										'''simple docstring'''
										__lowercase						      =					GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"					)
										__lowercase						      =					GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"					)
										# Output of original model created with mesh-tensoflow
										__lowercase						      =					[
										    ("武田信玄は", 35_675),
										    ("武田信玄は、", 45),
										    ("武田信玄は、この", 29),
										    ("武田信玄は、このよう", 30_642),
										    ("武田信玄は、このような", 35_680),
										    ("武田信玄は、このような「", 8_640),
										    ("武田信玄は、このような「武田", 31_617),
										    ("武田信玄は、このような「武田家", 30_646),
										    ("武田信玄は、このような「武田家の", 31_617),
										    ("武田信玄は、このような「武田家の家", 31_381),
										]
										for input, output in target:
													__lowercase						      =					tokenizer.encode(_lowerCamelCase     ,						return_tensors="pt"					)
													__lowercase						      =					model(_lowerCamelCase					)
													__lowercase						      =					outputs.logits.detach().cpu().numpy()[0]
													__lowercase						      =					np.argmax(output_logits[-1]					)
													self.assertEqual(_lowerCamelCase     ,						_lowerCamelCase					)
							@slow
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Union[str, Any]:




										'''simple docstring'''
										__lowercase						      =					GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"					)
										__lowercase						      =					GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"					)
										model.to(_lowerCamelCase					)

										# set deterministically
										__lowercase						      =					GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"					)
										__lowercase						      =					1

										__lowercase						      =					"武田信玄は、"
										__lowercase						      =					tokenizer(_lowerCamelCase     ,						return_tensors="pt"					).input_ids.to(_lowerCamelCase					)
										__lowercase						      =					tokenizer([input_text, input_text]     ,						return_tensors="pt"					).input_ids.to(_lowerCamelCase					)

										# spout from uniform and one-hot
										__lowercase						      =					[
										    # 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
										]

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

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

										__lowercase						      =					model.generate(
										    input_ids=_lowerCamelCase     ,						spout=_lowerCamelCase     ,						max_new_tokens=20     ,						generation_config=_lowerCamelCase     ,						)

										__lowercase						      =					tokenizer.decode(outputa[0]					)
										__lowercase						      =					tokenizer.decode(outputa[0]					)
										__lowercase						      =					tokenizer.batch_decode(_lowerCamelCase					)

										__lowercase						      =					[
										    "武田信玄は、武田氏の滅亡後、武田氏の居城であった甲斐武田氏の居城である",
										    "武田信玄は、武田家の滅亡を防ぐため、武田家の家臣である武田信虎を討",
										]
										self.assertListEqual(_lowerCamelCase     ,						_lowerCamelCase					)
										self.assertListEqual(_lowerCamelCase     ,						[outa_sentence, outa_sentence]					)





							@slow
							def      SCREAMING_SNAKE_CASE	(    self					) ->       Tuple:




										'''simple docstring'''
										__lowercase						      =					GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"					)
										__lowercase						      =					GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"					)
										model.to(_lowerCamelCase					)

										# set deterministically
										__lowercase						      =					GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"					)
										__lowercase						      =					1

										__lowercase						      =					"武田信玄"
										__lowercase						      =					"織田信長"
										__lowercase						      =					"は、"
										__lowercase						      =					"が、"
										__lowercase						      =					tokenizer(_lowerCamelCase     ,						prefix_text=_lowerCamelCase     ,						return_tensors="pt"					)
										__lowercase						      =					tokenizer(_lowerCamelCase     ,						prefix_text=_lowerCamelCase     ,						return_tensors="pt"					)
										__lowercase						      =					tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]     ,						return_tensors="pt"					)

										__lowercase						      =					model.generate(
										    input_ids=input_tok_a.input_ids.to(_lowerCamelCase					)     ,						token_type_ids=input_tok_a.token_type_ids.to(_lowerCamelCase					)     ,						max_new_tokens=20     ,						generation_config=_lowerCamelCase     ,						)

										__lowercase						      =					model.generate(
										    input_ids=input_tok_a.input_ids.to(_lowerCamelCase					)     ,						token_type_ids=input_tok_a.token_type_ids.to(_lowerCamelCase					)     ,						max_new_tokens=20     ,						generation_config=_lowerCamelCase     ,						)

										__lowercase						      =					model.generate(
										    input_ids=input_tok_a.input_ids.to(_lowerCamelCase					)     ,						token_type_ids=input_tok_a.token_type_ids.to(_lowerCamelCase					)     ,						attention_mask=input_tok_a.attention_mask.to(_lowerCamelCase					)     ,						max_new_tokens=20     ,						generation_config=_lowerCamelCase     ,						)

										__lowercase						      =					tokenizer.decode(outputa[0]					)
										__lowercase						      =					tokenizer.decode(outputa[0]					)
										__lowercase						      =					tokenizer.batch_decode(_lowerCamelCase					)

										__lowercase						      =					[
										    "武田信玄は、武田氏の祖である武田信虎を、その子・武田信友を擁して",
										    "織田信長が、織田信長の妻・お市の方を妻として迎えたという逸話が残",
										]
										self.assertListEqual(_lowerCamelCase     ,						_lowerCamelCase					)
										self.assertListEqual(_lowerCamelCase     ,						[outa_sentence, outa_sentence]					)







"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase							 =							{'''configuration_sew_d''': ['''SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWDConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							 =							[
					    '''SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''SEWDForCTC''',
					    '''SEWDForSequenceClassification''',
					    '''SEWDModel''',
					    '''SEWDPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sew_d import (
										    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SEWDForCTC,
										    SEWDForSequenceClassification,
										    SEWDModel,
										    SEWDPreTrainedModel,
										)


else:
					import sys

					_lowercase							 =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





