

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					:







	def __init__( self:			Any	,			__UpperCamelCase:			str	,			__UpperCamelCase:			Optional[int]=3_60_00	,			__UpperCamelCase:			Tuple=13	,			__UpperCamelCase:			str=7	,			__UpperCamelCase:			Union[str, Any]=True	,			__UpperCamelCase:			Dict=32	,			__UpperCamelCase:			List[str]=42	,			__UpperCamelCase:			Optional[int]=5	,			__UpperCamelCase:			List[str]=2	,			__UpperCamelCase:			int=4	,			__UpperCamelCase:			Dict=2	,			__UpperCamelCase:			Tuple=32	,			__UpperCamelCase:			List[str]=80	,			__UpperCamelCase:			int=33	,			__UpperCamelCase:			List[Any]=0.0	,			__UpperCamelCase:			Optional[int]=1E-6	,			__UpperCamelCase:			int=1_00	,			__UpperCamelCase:			List[str]=0.0	,			):

						'''simple docstring'''





						__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 						_SCREAMING_SNAKE_CASE				( self:			List[Any]   ):

						'''simple docstring'''





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







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]   ):

						'''simple docstring'''





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

						__magic_name__				=     self.get_config()

						return (config, input_ids)







	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						__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 						_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:			Any	,			__UpperCamelCase:			Dict	,			__UpperCamelCase:			Tuple	,			):

						'''simple docstring'''





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




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







	def 						_SCREAMING_SNAKE_CASE				( self:			str	,			__UpperCamelCase:			int	,			__UpperCamelCase:			Dict	,			__UpperCamelCase:			Optional[Any]	,			__UpperCamelCase:			Dict	,			__UpperCamelCase:			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:			Union[str, Any]   ):

						'''simple docstring'''





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







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]   ):

						'''simple docstring'''





						GPTSanJapaneseConfig()







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]   ):

						'''simple docstring'''





						__magic_name__				=     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCamelCase   )







	@unittest.skip(
	    reason='skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`'   )
	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''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:			Any   ):

						'''simple docstring'''





						pass




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







	def 						_SCREAMING_SNAKE_CASE				( self:			Any   ):

						'''simple docstring'''





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







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						GPTSanJapaneseConfig()







	def 						_SCREAMING_SNAKE_CASE				( self:			List[str]   ):

						'''simple docstring'''





						__magic_name__				=     self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__UpperCamelCase   )







	@unittest.skip(
	    reason='skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`'   )
	def 						_SCREAMING_SNAKE_CASE				( self:			List[str]   ):

						'''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:			int   ):

						'''simple docstring'''





						pass







	@slow
	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						__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(__UpperCamelCase   )
						__magic_name__				=     outputs.logits.detach().cpu().numpy()
						# Output of original model created with mesh-tensoflow
						__magic_name__				=     [
						    # 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
						]
						__magic_name__				=     np.array(__UpperCamelCase   ).flatten()
						__magic_name__				=     output_logits[0, :, :20].flatten()

						def check(__UpperCamelCase:			str	,			__UpperCamelCase:			Any	,			__UpperCamelCase:			Any=5E-4   ):
											return abs(a - b   ) < epsilon * max(abs(__UpperCamelCase   )	,			abs(__UpperCamelCase   )   )

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







	@slow
	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





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

						# 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(__UpperCamelCase	,			return_tensors='pt'	,			padding=__UpperCamelCase   )
						__magic_name__				=     inputs['input_ids'].to(__UpperCamelCase   )

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

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

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

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

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

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

						__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(__UpperCamelCase	,			__UpperCamelCase   )
						self.assertListEqual(__UpperCamelCase	,			[non_padded_sentence, padded_sentence]   )







	@tooslow
	def 						_SCREAMING_SNAKE_CASE				( self:			Any   ):

						'''simple docstring'''





						__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(__UpperCamelCase	,			return_tensors='pt'   )
											__magic_name__				=     model(__UpperCamelCase   )
											__magic_name__				=     outputs.logits.detach().cpu().numpy()[0]
											__magic_name__				=     np.argmax(output_logits[-1]   )
											self.assertEqual(__UpperCamelCase	,			__UpperCamelCase   )







	@slow
	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





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

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

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

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

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

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

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

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

						__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(__UpperCamelCase	,			__UpperCamelCase   )
						self.assertListEqual(__UpperCamelCase	,			[outa_sentence, outa_sentence]   )







	@slow
	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]   ):

						'''simple docstring'''





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

						# 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(__UpperCamelCase	,			prefix_text=__UpperCamelCase	,			return_tensors='pt'   )
						__magic_name__				=     tokenizer(__UpperCamelCase	,			prefix_text=__UpperCamelCase	,			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(__UpperCamelCase   )	,			token_type_ids=input_tok_a.token_type_ids.to(__UpperCamelCase   )	,			max_new_tokens=20	,			generation_config=__UpperCamelCase	,			)

						__magic_name__				=     model.generate(
						    input_ids=input_tok_a.input_ids.to(__UpperCamelCase   )	,			token_type_ids=input_tok_a.token_type_ids.to(__UpperCamelCase   )	,			max_new_tokens=20	,			generation_config=__UpperCamelCase	,			)

						__magic_name__				=     model.generate(
						    input_ids=input_tok_a.input_ids.to(__UpperCamelCase   )	,			token_type_ids=input_tok_a.token_type_ids.to(__UpperCamelCase   )	,			attention_mask=input_tok_a.attention_mask.to(__UpperCamelCase   )	,			max_new_tokens=20	,			generation_config=__UpperCamelCase	,			)

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

						__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(__UpperCamelCase	,			__UpperCamelCase   )
						self.assertListEqual(__UpperCamelCase	,			[outa_sentence, outa_sentence]   )







from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class 					__UpperCamelCase					:







	def __init__( self:			Optional[Any]	,			__UpperCamelCase:			Any   ):

						'''simple docstring'''





						__magic_name__				=     data
						__magic_name__				=     None







	def __str__( self:			Optional[int]   ):

						'''simple docstring'''





						return F'{self.data}'




class 					__UpperCamelCase					:







	def __init__( self:			List[Any]   ):

						'''simple docstring'''





						__magic_name__				=     None
						__magic_name__				=     None







	def __iter__( self:			str   ):

						'''simple docstring'''





						__magic_name__				=     self.front
						while node:
											yield node.data
											__magic_name__				=     node.next







	def __len__( self:			Any   ):

						'''simple docstring'''





						return len(tuple(iter(self   )   )   )







	def __str__( self:			List[str]   ):

						'''simple docstring'''





						return " <- ".join(str(__UpperCamelCase   ) for item in self   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Any   ):

						'''simple docstring'''





						return len(self   ) == 0







	def 						_SCREAMING_SNAKE_CASE				( self:			Any	,			__UpperCamelCase:			Any   ):

						'''simple docstring'''





						__magic_name__				=     Node(__UpperCamelCase   )
						if self.is_empty():
											__magic_name__				=     __magic_name__				=     node
						else:
											assert isinstance(self.rear	,			__UpperCamelCase   )
											__magic_name__				=     node
											__magic_name__				=     node







	def 						_SCREAMING_SNAKE_CASE				( self:			Any   ):

						'''simple docstring'''





						if self.is_empty():
											raise IndexError('dequeue from empty queue'   )
						assert isinstance(self.front	,			__UpperCamelCase   )
						__magic_name__				=     self.front
						__magic_name__				=     self.front.next
						if self.front is None:
											__magic_name__				=     None
						return node.data







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						__magic_name__				=     __magic_name__				=     None


if __name__ == "__main__":
						from doctest import testmod

						testmod()




