


'''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 		UpperCamelCase_		:





						"""simple docstring"""




						def __init__( self				:					Union[str, Any]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Tuple=3_60_00	,				_lowerCamelCase				:					str=13	,				_lowerCamelCase				:					Optional[Any]=7	,				_lowerCamelCase				:					List[str]=True	,				_lowerCamelCase				:					Tuple=32	,				_lowerCamelCase				:					Any=42	,				_lowerCamelCase				:					Union[str, Any]=5	,				_lowerCamelCase				:					int=2	,				_lowerCamelCase				:					Dict=4	,				_lowerCamelCase				:					Any=2	,				_lowerCamelCase				:					str=32	,				_lowerCamelCase				:					Union[str, Any]=80	,				_lowerCamelCase				:					Union[str, Any]=33	,				_lowerCamelCase				:					List[Any]=0.0	,				_lowerCamelCase				:					List[Any]=1e-6	,				_lowerCamelCase				:					List[str]=1_00	,				_lowerCamelCase				:					str=0.0	,				)							->					Optional[int]:
										__magic_name__	  = vocab_size
										__magic_name__	  = parent
										__magic_name__	  = batch_size
										__magic_name__	  = num_contexts
										# For common tests
										__magic_name__	  = self.num_contexts
										__magic_name__	  = is_training
										__magic_name__	  = hidden_size
										__magic_name__	  = num_ext_layers
										__magic_name__	  = ext_size
										__magic_name__	  = num_hidden_layers
										__magic_name__	  = num_attention_heads
										__magic_name__	  = num_experts
										__magic_name__	  = d_ff
										__magic_name__	  = d_ext
										__magic_name__	  = d_spout
										__magic_name__	  = dropout_rate
										__magic_name__	  = layer_norm_epsilon
										__magic_name__	  = expert_capacity
										__magic_name__	  = router_jitter_noise
						def 					__A					( self				:					Dict  )							->					List[Any]:
										return GPTSanJapaneseConfig.from_pretrained("Tanrei/GPTSAN-japanese"  )
						def 					__A					( self				:					Union[str, Any]  )							->					int:
										__magic_name__	  = ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size  )

										__magic_name__	  = self.get_config()

										return (config, input_ids)
						def 					__A					( self				:					Tuple  )							->					Dict:
										__magic_name__	  = ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size  )

										__magic_name__	  = self.get_config()

										return (config, {"input_ids": input_ids})
						def 					__A					( self				:					List[Any]  )							->					List[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 					__A					( self				:					int	,				_lowerCamelCase				:					Any	,				_lowerCamelCase				:					Optional[Any]	,				)							->					Tuple:
										__magic_name__	  = GPTSanJapaneseForConditionalGeneration(config=_lowerCamelCase  )
										model.to(_lowerCamelCase  )
										model.eval()
										__magic_name__	  = model(
										    input_ids=_lowerCamelCase	,				)
										self.parent.assertIsNotNone(_lowerCamelCase  )






@require_torch
class 		UpperCamelCase_		(					A   ,		A   ,		unittest.TestCase   ):





						"""simple docstring"""




						UpperCAmelCase__	:       List[Any]         =     (GPTSanJapaneseModel,) if is_torch_available() else ()
						UpperCAmelCase__	:       Optional[Any]         =     (
						    {
						        '''conversational''': GPTSanJapaneseForConditionalGeneration,
						        '''feature-extraction''': GPTSanJapaneseForConditionalGeneration,
						        '''summarization''': GPTSanJapaneseForConditionalGeneration,
						        '''text2text-generation''': GPTSanJapaneseForConditionalGeneration,
						        '''translation''': GPTSanJapaneseForConditionalGeneration,
						    }
						    if is_torch_available()
						    else {}
						)
						UpperCAmelCase__	:       List[Any]         =     False
						UpperCAmelCase__	:       int         =     False
						UpperCAmelCase__	:       Tuple         =     False
						UpperCAmelCase__	:       int         =     False
						UpperCAmelCase__	:       str         =     False
						UpperCAmelCase__	:       Tuple         =     False
						UpperCAmelCase__	:       str         =     False
						UpperCAmelCase__	:       str         =     False
						# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
						UpperCAmelCase__	:       str         =     [0.8, 0.9]
						def 					__A					( self				:					List[Any]	,				_lowerCamelCase				:					Optional[int]	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					str	,				_lowerCamelCase				:					Dict	,				_lowerCamelCase				:					Optional[Any]  )							->					List[Any]:
										if pipeline_test_casse_name == "SummarizationPipelineTests":
														# TODO: fix `_reorder_cache` is not implemented for this model
														return True
										elif pipeline_test_casse_name == "Text2TextGenerationPipelineTests":
														# TODO: check this.
														return True

										return False
						def 					__A					( self				:					Any  )							->					Optional[Any]:
										__magic_name__	  = GPTSanJapaneseTester(self  )
										__magic_name__	  = ConfigTester(self	,				config_class=_lowerCamelCase	,				d_model=37  )
						def 					__A					( self				:					Union[str, Any]  )							->					str:
										GPTSanJapaneseConfig()
						def 					__A					( self				:					int  )							->					Optional[Any]:
										__magic_name__	  = 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 					__A					( self				:					Tuple  )							->					Union[str, Any]:
										super().test_model_parallelism()

						@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."  )
						def 					__A					( self				:					List[str]  )							->					Optional[int]:
										pass






@require_torch
class 		UpperCamelCase_		(					A   ,		A   ,		unittest.TestCase   ):





						"""simple docstring"""




						UpperCAmelCase__	:       Tuple         =     (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
						UpperCAmelCase__	:       Optional[int]         =     False
						UpperCAmelCase__	:       Tuple         =     False
						UpperCAmelCase__	:       Optional[Any]         =     False
						UpperCAmelCase__	:       Optional[int]         =     False
						UpperCAmelCase__	:       Optional[Any]         =     False
						UpperCAmelCase__	:       Optional[int]         =     False
						# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
						UpperCAmelCase__	:       List[str]         =     [0.8, 0.9]
						def 					__A					( self				:					List[Any]  )							->					List[str]:
										__magic_name__	  = GPTSanJapaneseTester(self  )
										__magic_name__	  = ConfigTester(self	,				config_class=_lowerCamelCase	,				d_model=37  )
						def 					__A					( self				:					Optional[int]  )							->					Any:
										GPTSanJapaneseConfig()
						def 					__A					( self				:					Any  )							->					List[str]:
										__magic_name__	  = 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 					__A					( self				:					str  )							->					List[Any]:
										super().test_model_parallelism()
						@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."  )
						def 					__A					( self				:					List[str]  )							->					Any:
										pass
						@slow
						def 					__A					( self				:					Optional[int]  )							->					List[str]:
										__magic_name__	  = GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"  )
										__magic_name__	  = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"  )
										__magic_name__	  = tokenizer.encode("武田信玄は"	,				return_tensors="pt"  )
										__magic_name__	  = model(_lowerCamelCase  )
										__magic_name__	  = outputs.logits.detach().cpu().numpy()
										# Output of original model created with mesh-tensoflow
										__magic_name__	  = [
										    # 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
										]
										__magic_name__	  = np.array(_lowerCamelCase  ).flatten()
										__magic_name__	  = output_logits[0, :, :20].flatten()

										def check(_lowerCamelCase				:					Any	,				_lowerCamelCase				:					Optional[Any]	,				_lowerCamelCase				:					Optional[int]=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 					__A					( self				:					int  )							->					Dict:
										__magic_name__	  = GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"  )
										__magic_name__	  = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"  )
										model.to(_lowerCamelCase  )

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

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

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

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

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

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

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

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

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

										__magic_name__	  = [
										    "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 					__A					( self				:					int  )							->					List[Any]:
										__magic_name__	  = GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"  )
										__magic_name__	  = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"  )
										# Output of original model created with mesh-tensoflow
										__magic_name__	  = [
										    ("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:
														__magic_name__	  = tokenizer.encode(_lowerCamelCase	,				return_tensors="pt"  )
														__magic_name__	  = model(_lowerCamelCase  )
														__magic_name__	  = outputs.logits.detach().cpu().numpy()[0]
														__magic_name__	  = np.argmax(output_logits[-1]  )
														self.assertEqual(_lowerCamelCase	,				_lowerCamelCase  )
						@slow
						def 					__A					( self				:					Any  )							->					List[str]:
										__magic_name__	  = GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"  )
										__magic_name__	  = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"  )
										model.to(_lowerCamelCase  )

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

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

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

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

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

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

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

										__magic_name__	  = [
										    "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 					__A					( self				:					Any  )							->					int:
										__magic_name__	  = GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"  )
										__magic_name__	  = GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"  )
										model.to(_lowerCamelCase  )

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

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

										__magic_name__	  = 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	,				)

										__magic_name__	  = 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	,				)

										__magic_name__	  = 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	,				)

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

										__magic_name__	  = [
										    "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'''
import random
import sys

__magic_name__   :   Optional[Any]  							='ABCDEFGHIJKLMNOPQRSTUVWXYZ'




def      __snake_case       (       ):
				'''simple docstring'''




				__magic_name__	  = input("Enter message: "						)
				__magic_name__	  = "LFWOAYUISVKMNXPBDCRJTQEGHZ"
				__magic_name__	  = input("Encrypt/Decrypt [e/d]: "						)

				check_valid_key(lowerCamelCase_						)

				if resp.lower().startswith("e"						):
								__magic_name__	  = "encrypt"
								__magic_name__	  = encrypt_message(lowerCamelCase_	,  lowerCamelCase_						)
				elif resp.lower().startswith("d"						):
								__magic_name__	  = "decrypt"
								__magic_name__	  = decrypt_message(lowerCamelCase_	,  lowerCamelCase_						)

				print(F'\n{mode.title()}ion: \n{translated}'						)




def      __snake_case       (       lowerCamelCase_ :				str						):
				'''simple docstring'''




				__magic_name__	  = list(lowerCamelCase_						)
				__magic_name__	  = list(lowerCamelCase_						)
				key_list.sort()
				letters_list.sort()

				if key_list != letters_list:
								sys.exit("Error in the key or symbol set."						)




def      __snake_case       (       lowerCamelCase_ :				str	,  lowerCamelCase_ :				str						):
				'''simple docstring'''




				return translate_message(lowerCamelCase_	,  lowerCamelCase_	,  "encrypt"						)




def      __snake_case       (       lowerCamelCase_ :				str	,  lowerCamelCase_ :				str						):
				'''simple docstring'''




				return translate_message(lowerCamelCase_	,  lowerCamelCase_	,  "decrypt"						)




def      __snake_case       (       lowerCamelCase_ :				str	,  lowerCamelCase_ :				str	,  lowerCamelCase_ :				str						):
				'''simple docstring'''




				__magic_name__	  = ""
				__magic_name__	  = LETTERS
				__magic_name__	  = key

				if mode == "decrypt":
								__magic_name__  ,      __magic_name__	  = chars_b, chars_a

				for symbol in message:
								if symbol.upper() in chars_a:
												__magic_name__	  = chars_a.find(symbol.upper()						)
												if symbol.isupper():
																translated += chars_b[sym_index].upper()
												else:
																translated += chars_b[sym_index].lower()
								else:
												translated += symbol

				return translated




def      __snake_case       (       ):
				'''simple docstring'''




				__magic_name__	  = list(lowerCamelCase_						)
				random.shuffle(lowerCamelCase_						)
				return "".join(lowerCamelCase_						)


if __name__ == "__main__":
						main()



