


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   :
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=36000    ,					_SCREAMING_SNAKE_CASE=13    ,					_SCREAMING_SNAKE_CASE=7    ,					_SCREAMING_SNAKE_CASE=True    ,					_SCREAMING_SNAKE_CASE=32    ,					_SCREAMING_SNAKE_CASE=42    ,					_SCREAMING_SNAKE_CASE=5    ,					_SCREAMING_SNAKE_CASE=2    ,					_SCREAMING_SNAKE_CASE=4    ,					_SCREAMING_SNAKE_CASE=2    ,					_SCREAMING_SNAKE_CASE=32    ,					_SCREAMING_SNAKE_CASE=80    ,					_SCREAMING_SNAKE_CASE=33    ,					_SCREAMING_SNAKE_CASE=0.0    ,					_SCREAMING_SNAKE_CASE=1e-6    ,					_SCREAMING_SNAKE_CASE=100    ,					_SCREAMING_SNAKE_CASE=0.0    ,					)				-> Any:




									"""simple docstring"""





									_UpperCAmelCase					=    vocab_size
									_UpperCAmelCase					=    parent
									_UpperCAmelCase					=    batch_size
									_UpperCAmelCase					=    num_contexts
									# For common tests
									_UpperCAmelCase					=    self.num_contexts
									_UpperCAmelCase					=    is_training
									_UpperCAmelCase					=    hidden_size
									_UpperCAmelCase					=    num_ext_layers
									_UpperCAmelCase					=    ext_size
									_UpperCAmelCase					=    num_hidden_layers
									_UpperCAmelCase					=    num_attention_heads
									_UpperCAmelCase					=    num_experts
									_UpperCAmelCase					=    d_ff
									_UpperCAmelCase					=    d_ext
									_UpperCAmelCase					=    d_spout
									_UpperCAmelCase					=    dropout_rate
									_UpperCAmelCase					=    layer_norm_epsilon
									_UpperCAmelCase					=    expert_capacity
									_UpperCAmelCase					=    router_jitter_noise
		def 			UpperCAmelCase__       (    self		)				-> List[str]:




									"""simple docstring"""





									return GPTSanJapaneseConfig.from_pretrained('Tanrei/GPTSAN-japanese'		)
		def 			UpperCAmelCase__       (    self		)				-> Dict:




									"""simple docstring"""





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

									_UpperCAmelCase					=    self.get_config()

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




									"""simple docstring"""





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

									_UpperCAmelCase					=    self.get_config()

									return (config, {"input_ids": input_ids})
		def 			UpperCAmelCase__       (    self		)				-> Optional[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 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE    ,					)				-> str:




									"""simple docstring"""





									_UpperCAmelCase					=    GPTSanJapaneseForConditionalGeneration(config=lowerCamelCase__		)
									model.to(lowerCamelCase__		)
									model.eval()
									_UpperCAmelCase					=    model(
									    input_ids=lowerCamelCase__    ,					)
									self.parent.assertIsNotNone(lowerCamelCase__		)



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




									"""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 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									_UpperCAmelCase					=    GPTSanJapaneseTester(self		)
									_UpperCAmelCase					=    ConfigTester(self    ,					config_class=lowerCamelCase__    ,					d_model=37		)
		def 			UpperCAmelCase__       (    self		)				-> Dict:




									"""simple docstring"""





									GPTSanJapaneseConfig()
		def 			UpperCAmelCase__       (    self		)				-> List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=    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 			UpperCAmelCase__       (    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 			UpperCAmelCase__       (    self		)				-> List[str]:




									"""simple docstring"""





									pass



@require_torch
class 	__a   (       _a					,   _a					,   unittest.TestCase    ):
		_a			:						Any    			=			(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
		_a			:						int    			=			False
		_a			:						Optional[int]    			=			False
		_a			:						Union[str, Any]    			=			False
		_a			:						str    			=			False
		_a			:						Tuple    			=			False
		_a			:						List[Any]    			=			False
		# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
		_a			:						Union[str, Any]    			=			[0.8, 0.9]
		def 			UpperCAmelCase__       (    self		)				-> Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=    GPTSanJapaneseTester(self		)
									_UpperCAmelCase					=    ConfigTester(self    ,					config_class=lowerCamelCase__    ,					d_model=37		)
		def 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									GPTSanJapaneseConfig()
		def 			UpperCAmelCase__       (    self		)				-> Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=    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 			UpperCAmelCase__       (    self		)				-> 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 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





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




									"""simple docstring"""





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

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

									self.assertTrue(np.all([check(target[i]    ,					predict[i]		) for i in range(len(lowerCamelCase__		)		)]		)		)
		@slow
		def 			UpperCAmelCase__       (    self		)				-> int:




									"""simple docstring"""





									_UpperCAmelCase					=    GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'		)
									_UpperCAmelCase					=    GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'		)
									model.to(lowerCamelCase__		)

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

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

									_UpperCAmelCase					=    "left"
									_UpperCAmelCase					=    tokenizer(lowerCamelCase__    ,					return_tensors='pt'    ,					padding=lowerCamelCase__		)
									_UpperCAmelCase					=    inputs["input_ids"].to(lowerCamelCase__		)

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

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

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

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

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

									_UpperCAmelCase					=    tokenizer.batch_decode(lowerCamelCase__    ,					skip_special_tokens=lowerCamelCase__		)
									_UpperCAmelCase					=    tokenizer.decode(output_non_padded[0]    ,					skip_special_tokens=lowerCamelCase__		)
									_UpperCAmelCase					=    tokenizer.decode(output_padded[0]    ,					skip_special_tokens=lowerCamelCase__		)

									_UpperCAmelCase					=    [
									    "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 			UpperCAmelCase__       (    self		)				-> Any:




									"""simple docstring"""





									_UpperCAmelCase					=    GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'		)
									_UpperCAmelCase					=    GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'		)
									# Output of original model created with mesh-tensoflow
									_UpperCAmelCase					=    [
									    ("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					=    tokenizer.encode(lowerCamelCase__    ,					return_tensors='pt'		)
																_UpperCAmelCase					=    model(lowerCamelCase__		)
																_UpperCAmelCase					=    outputs.logits.detach().cpu().numpy()[0]
																_UpperCAmelCase					=    np.argmax(output_logits[-1]		)
																self.assertEqual(lowerCamelCase__    ,					lowerCamelCase__		)
		@slow
		def 			UpperCAmelCase__       (    self		)				-> Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'		)
									_UpperCAmelCase					=    GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'		)
									model.to(lowerCamelCase__		)

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

									_UpperCAmelCase					=    "武田信玄は、"
									_UpperCAmelCase					=    tokenizer(lowerCamelCase__    ,					return_tensors='pt'		).input_ids.to(lowerCamelCase__		)
									_UpperCAmelCase					=    tokenizer([input_text, input_text]    ,					return_tensors='pt'		).input_ids.to(lowerCamelCase__		)

									# spout from uniform and one-hot
									_UpperCAmelCase					=    [
									    # 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					=    model.generate(
									    input_ids=lowerCamelCase__    ,					spout=spouts[0]    ,					max_new_tokens=20    ,					generation_config=lowerCamelCase__    ,					)

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

									_UpperCAmelCase					=    model.generate(
									    input_ids=lowerCamelCase__    ,					spout=lowerCamelCase__    ,					max_new_tokens=20    ,					generation_config=lowerCamelCase__    ,					)

									_UpperCAmelCase					=    tokenizer.decode(outputa[0]		)
									_UpperCAmelCase					=    tokenizer.decode(outputa[0]		)
									_UpperCAmelCase					=    tokenizer.batch_decode(lowerCamelCase__		)

									_UpperCAmelCase					=    [
									    "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 			UpperCAmelCase__       (    self		)				-> Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=    GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'		)
									_UpperCAmelCase					=    GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'		)
									model.to(lowerCamelCase__		)

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

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

									_UpperCAmelCase					=    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__    ,					)

									_UpperCAmelCase					=    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__    ,					)

									_UpperCAmelCase					=    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__    ,					)

									_UpperCAmelCase					=    tokenizer.decode(outputa[0]		)
									_UpperCAmelCase					=    tokenizer.decode(outputa[0]		)
									_UpperCAmelCase					=    tokenizer.batch_decode(lowerCamelCase__		)

									_UpperCAmelCase					=    [
									    "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]		)




from __future__ import annotations

def  lowerCAmelCase__				(    a__:	list[int]    ,						a__:	int )						->	list[int]:


							'''simple docstring'''





							_UpperCAmelCase					=    {}
							for index, val in enumerate(a__ ):
														_UpperCAmelCase					=    target - val
														if compl in chk_map:
																					return [chk_map[compl], index]
														_UpperCAmelCase					=    index
							return []


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(f'''{two_sum([2, 7, 1_1, 1_5], 9) = }''')
