






"""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							,					_A							,					_A=36000							,					_A=13							,					_A=7							,					_A=True							,					_A=32							,					_A=42							,					_A=5							,					_A=2							,					_A=4							,					_A=2							,					_A=32							,					_A=80							,					_A=33							,					_A=0.0							,					_A=1e-6							,					_A=100							,					_A=0.0							,					)	->						Any:

     """simple docstring"""



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


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

     """simple docstring"""



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


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

     """simple docstring"""



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

     _UpperCAmelCase			: List[str]     					=			self.get_config()

     return (config, input_ids)


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

     """simple docstring"""



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

     _UpperCAmelCase			: Any     					=			self.get_config()

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


 def 	snake_case__			(		self)	->						int:

     """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							,					_A							,					_A							,					)	->						Optional[int]:

     """simple docstring"""



     _UpperCAmelCase			: str     					=			GPTSanJapaneseForConditionalGeneration(config=_A)
     model.to(_A)
     model.eval()
     _UpperCAmelCase			: Dict     					=			model(
         input_ids=_A							,					)
     self.parent.assertIsNotNone(_A)



@require_torch
class 							A_      (  __lowercase	,				__lowercase	,				unittest.TestCase     ):






 '''simple docstring'''




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


 def 	snake_case__			(		self							,					_A							,					_A							,					_A							,					_A							,					_A)	->						int:

     """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)	->						Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase			: List[Any]     					=			GPTSanJapaneseTester(self)
     _UpperCAmelCase			: str     					=			ConfigTester(self							,					config_class=_A							,					d_model=37)


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

     """simple docstring"""



     GPTSanJapaneseConfig()


 def 	snake_case__			(		self)	->						str:

     """simple docstring"""



     _UpperCAmelCase			: Optional[int]     					=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_A)


 @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)	->						Tuple:

     """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)	->						Union[str, Any]:

     """simple docstring"""



     pass



@require_torch
class 							A_      (  __lowercase	,				__lowercase	,				unittest.TestCase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Optional[int]	=   (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
 _SCREAMING_SNAKE_CASE							:     Tuple	=   False
 _SCREAMING_SNAKE_CASE							:     Optional[int]	=   False
 _SCREAMING_SNAKE_CASE							:     Optional[Any]	=   False
 _SCREAMING_SNAKE_CASE							:     Optional[int]	=   False
 _SCREAMING_SNAKE_CASE							:     Union[str, Any]	=   False
 _SCREAMING_SNAKE_CASE							:     Optional[Any]	=   False
 # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
 _SCREAMING_SNAKE_CASE							:     int	=   [0.8, 0.9]


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

     """simple docstring"""



     _UpperCAmelCase			: Optional[int]     					=			GPTSanJapaneseTester(self)
     _UpperCAmelCase			: Union[str, Any]     					=			ConfigTester(self							,					config_class=_A							,					d_model=37)


 def 	snake_case__			(		self)	->						int:

     """simple docstring"""



     GPTSanJapaneseConfig()


 def 	snake_case__			(		self)	->						int:

     """simple docstring"""



     _UpperCAmelCase			: Optional[Any]     					=			self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_A)


 @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)	->						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 	snake_case__			(		self)	->						Dict:

     """simple docstring"""



     pass


 @slow
 def 	snake_case__			(		self)	->						Any:

     """simple docstring"""



     _UpperCAmelCase			: Any     					=			GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese''')
     _UpperCAmelCase			: int     					=			GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese''')
     _UpperCAmelCase			: Union[str, Any]     					=			tokenizer.encode('''武田信玄は'''							,					return_tensors='''pt''')
     _UpperCAmelCase			: Union[str, Any]     					=			model(_A)
     _UpperCAmelCase			: Dict     					=			outputs.logits.detach().cpu().numpy()
     # Output of original model created with mesh-tensoflow
     _UpperCAmelCase			: Union[str, Any]     					=			[
         # fmt: off
         [-12.037839889526367, -12.433061599731445, -14.333840370178223, -12.450345993041992, -11.1661376953125,
         -11.930137634277344, -10.659740447998047, -12.909574508666992, -13.241043090820312, -13.398579597473145,
         -11.107524871826172, -12.3685941696167, -22.97943115234375, -10.481067657470703, -12.484030723571777,
         -12.807360649108887, -14.769700050354004, -12.233579635620117, -13.428145408630371, -22.624177932739258],
         [-7.511149883270264, -8.281851768493652, -7.943127155303955, -7.55021333694458, -6.49869966506958,
         -7.586796283721924, -6.978085994720459, -7.839145183563232, -8.21964168548584, -8.695091247558594,
         -6.706910610198975, -6.6585798263549805, -19.565698623657227, -5.353842735290527, -8.350686073303223,
         -8.039388656616211, -10.856569290161133, -7.75154447555542, -8.819022178649902, -19.51532745361328],
         [-9.73066234588623, -10.223922729492188, -9.932981491088867, -11.857836723327637, -7.662626266479492,
         -11.13529109954834, -7.765097618103027, -11.472923278808594, -9.543149948120117, -11.905633926391602,
         -9.366164207458496, -11.5734281539917, -23.699003219604492, -9.429590225219727, -10.42839241027832,
         -10.585240364074707, -10.94771957397461, -11.095416069030762, -10.390240669250488, -23.769372940063477],
         [-9.728265762329102, -9.859712600708008, -10.09729290008545, -9.678522109985352, -6.879519939422607,
         -9.68487548828125, -4.2803425788879395, -10.018914222717285, -9.308445930480957, -10.63394546508789,
         -8.083646774291992, -9.06301498413086, -21.904266357421875, -8.90160846710205, -8.841876029968262,
         -11.856719970703125, -12.079398155212402, -11.233753204345703, -10.177338600158691, -21.87256622314453],
         [-9.669764518737793, -9.614198684692383, -9.814510345458984, -9.996501922607422, -11.375690460205078,
         -10.113405227661133, -10.546867370605469, -10.04369068145752, -10.907809257507324, -10.504216194152832,
         -11.129199028015137, -10.151124000549316, -21.96586799621582, -9.086349487304688, -11.730339050292969,
         -10.460667610168457, -10.298049926757812, -10.784148216247559, -10.840693473815918, -22.03152847290039],
         # fmt: on
     ]
     _UpperCAmelCase			: List[str]     					=			np.array(_A).flatten()
     _UpperCAmelCase			: Tuple     					=			output_logits[0, :, :20].flatten()

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

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


 @slow
 def 	snake_case__			(		self)	->						Optional[int]:

     """simple docstring"""



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

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

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

     _UpperCAmelCase			: Tuple     					=			'''left'''
     _UpperCAmelCase			: Optional[int]     					=			tokenizer(_A							,					return_tensors='''pt'''							,					padding=_A)
     _UpperCAmelCase			: Optional[Any]     					=			inputs['''input_ids'''].to(_A)

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

     _UpperCAmelCase			: Any     					=			model.generate(
         input_ids=_A							,					attention_mask=inputs['''attention_mask'''].to(_A)							,					max_new_tokens=3							,					generation_config=_A							,					)

     _UpperCAmelCase			: str     					=			tokenizer(sentences[0]							,					return_tensors='''pt''').input_ids.to(_A)
     _UpperCAmelCase			: str     					=			model.generate(
         input_ids=_A							,					max_new_tokens=3							,					generation_config=_A)

     _UpperCAmelCase			: int     					=			tokenizer(sentences[1]							,					return_tensors='''pt''').input_ids.to(_A)
     _UpperCAmelCase			: Dict     					=			model.generate(input_ids=_A							,					max_new_tokens=3							,					generation_config=_A)

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

     _UpperCAmelCase			: List[Any]     					=			tokenizer.batch_decode(_A							,					skip_special_tokens=_A)
     _UpperCAmelCase			: Dict     					=			tokenizer.decode(output_non_padded[0]							,					skip_special_tokens=_A)
     _UpperCAmelCase			: int     					=			tokenizer.decode(output_padded[0]							,					skip_special_tokens=_A)

     _UpperCAmelCase			: 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(_A							,					_A)
     self.assertListEqual(_A							,					[non_padded_sentence, padded_sentence])


 @tooslow
 def 	snake_case__			(		self)	->						Any:

     """simple docstring"""



     _UpperCAmelCase			: Optional[Any]     					=			GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese''')
     _UpperCAmelCase			: Optional[Any]     					=			GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese''')
     # Output of original model created with mesh-tensoflow
     _UpperCAmelCase			: Optional[Any]     					=			[
         ('''武田信玄は''', 35675),
         ('''武田信玄は、''', 45),
         ('''武田信玄は、この''', 29),
         ('''武田信玄は、このよう''', 30642),
         ('''武田信玄は、このような''', 35680),
         ('''武田信玄は、このような「''', 8640),
         ('''武田信玄は、このような「武田''', 31617),
         ('''武田信玄は、このような「武田家''', 30646),
         ('''武田信玄は、このような「武田家の''', 31617),
         ('''武田信玄は、このような「武田家の家''', 31381),
     ]
     for input, output in target:
         _UpperCAmelCase			: Tuple     					=			tokenizer.encode(_A							,					return_tensors='''pt''')
         _UpperCAmelCase			: Dict     					=			model(_A)
         _UpperCAmelCase			: Union[str, Any]     					=			outputs.logits.detach().cpu().numpy()[0]
         _UpperCAmelCase			: str     					=			np.argmax(output_logits[-1])
         self.assertEqual(_A							,					_A)


 @slow
 def 	snake_case__			(		self)	->						Optional[int]:

     """simple docstring"""



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

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

     _UpperCAmelCase			: int     					=			'''武田信玄は、'''
     _UpperCAmelCase			: str     					=			tokenizer(_A							,					return_tensors='''pt''').input_ids.to(_A)
     _UpperCAmelCase			: Optional[int]     					=			tokenizer([input_text, input_text]							,					return_tensors='''pt''').input_ids.to(_A)

     # spout from uniform and one-hot
     _UpperCAmelCase			: int     					=			[
         # fmt: off
         [0.87882208, 0.38426396, 0.33220248, 0.43890406, 0.16562252,
         0.04803985, 0.211572  , 0.23188473, 0.37153068, 0.7836377 ,
         0.02160172, 0.38761719, 0.75290772, 0.90198857, 0.34365777,
         0.64168169, 0.44318471, 0.14575746, 0.92562881, 0.40812148,
         0.29019122, 0.88861599, 0.65524846, 0.43563456, 0.38177187,
         0.70832965, 0.81527892, 0.68832812, 0.38833192, 0.4561522 ,
         0.14828817, 0.47248213, 0.54357335, 0.82009566, 0.1338884 ,
         0.02755417, 0.19764677, 0.2422084 , 0.04757674, 0.65409606,
         0.0824589 , 0.03304383, 0.94387689, 0.98764509, 0.82433901,
         0.27646741, 0.64907493, 0.76009406, 0.30087915, 0.17904689,
         0.41601714, 0.67046398, 0.10422822, 0.08447374, 0.07354344,
         0.61423565, 0.70284866, 0.7532333 , 0.1972038 , 0.29575659,
         0.90583886, 0.29265307, 0.50000175, 0.70407655, 0.889363  ,
         0.81904418, 0.66829128, 0.64468815, 0.56563723, 0.85601875,
         0.94924672, 0.00166762, 0.25220643, 0.74540219, 0.67993247,
         0.1549675 , 0.39385352, 0.92153607, 0.63745931, 0.27759043,
         0.84702295, 0.65904271, 0.58676614, 0.8666936 , 0.39607438,
         0.79954983, 0.42220697, 0.39650381, 0.7849864 , 0.56150201,
         0.15678925, 0.14746032, 0.34542114, 0.47026783, 0.11956489,
         0.25421435, 0.33788901, 0.68934842, 0.36424685, 0.71737898,
         0.38983449, 0.94393779, 0.39575588, 0.36616553, 0.87104665,
         0.64630203, 0.22516905, 0.88270804, 0.15031338, 0.75144345,
         0.46459025, 0.85396454, 0.86355643, 0.65139851, 0.70266061,
         0.30241389, 0.81056497, 0.88865969, 0.38773807, 0.70635849,
         0.90718459, 0.43245789, 0.28000654, 0.45935562, 0.08773519,
         0.9552151 , 0.93901511, 0.22489288], # 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
     ]

     _UpperCAmelCase			: Tuple     					=			model.generate(
         input_ids=_A							,					spout=spouts[0]							,					max_new_tokens=20							,					generation_config=_A							,					)

     _UpperCAmelCase			: int     					=			model.generate(
         input_ids=_A							,					spout=spouts[1]							,					max_new_tokens=20							,					generation_config=_A							,					)

     _UpperCAmelCase			: Optional[int]     					=			model.generate(
         input_ids=_A							,					spout=_A							,					max_new_tokens=20							,					generation_config=_A							,					)

     _UpperCAmelCase			: int     					=			tokenizer.decode(outputa[0])
     _UpperCAmelCase			: Any     					=			tokenizer.decode(outputa[0])
     _UpperCAmelCase			: Optional[Any]     					=			tokenizer.batch_decode(_A)

     _UpperCAmelCase			: List[str]     					=			[
         '''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(_A							,					_A)
     self.assertListEqual(_A							,					[outa_sentence, outa_sentence])


 @slow
 def 	snake_case__			(		self)	->						List[str]:

     """simple docstring"""



     _UpperCAmelCase			: Any     					=			GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese''')
     _UpperCAmelCase			: Optional[int]     					=			GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese''')
     model.to(_A)

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

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

     _UpperCAmelCase			: Optional[Any]     					=			model.generate(
         input_ids=input_tok_a.input_ids.to(_A)							,					token_type_ids=input_tok_a.token_type_ids.to(_A)							,					max_new_tokens=20							,					generation_config=_A							,					)

     _UpperCAmelCase			: Dict     					=			model.generate(
         input_ids=input_tok_a.input_ids.to(_A)							,					token_type_ids=input_tok_a.token_type_ids.to(_A)							,					max_new_tokens=20							,					generation_config=_A							,					)

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

     _UpperCAmelCase			: List[Any]     					=			tokenizer.decode(outputa[0])
     _UpperCAmelCase			: List[str]     					=			tokenizer.decode(outputa[0])
     _UpperCAmelCase			: Dict     					=			tokenizer.batch_decode(_A)

     _UpperCAmelCase			: 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(_A							,					_A)
     self.assertListEqual(_A							,					[outa_sentence, outa_sentence])












def        _lowerCamelCase		(  __A      :							list	) ->   list:
    _UpperCAmelCase			: Dict     					=			1.3
    _UpperCAmelCase			: Any     					=			len(__A	)
    _UpperCAmelCase			: List[Any]     					=			False

    while not completed:
        # Update the gap value for a next comb
        _UpperCAmelCase			: Optional[Any]     					=			int(gap / shrink_factor	)
        if gap <= 1:
            _UpperCAmelCase			: Any     					=			True

        _UpperCAmelCase			: Optional[Any]     					=			0
        while index + gap < len(__A	):
            if data[index] > data[index + gap]:
                # Swap values
                _UpperCAmelCase					,	_UpperCAmelCase			: Union[str, Any]     					=			data[index + gap], data[index]
                _UpperCAmelCase			: Any     					=			False
            index += 1

    return data


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  SCREAMING_SNAKE_CASE								=  input('Enter numbers separated by a comma:\n').strip()
  SCREAMING_SNAKE_CASE								=  [int(item) for item in user_input.split(',')]
  print(comb_sort(unsorted))




