




"""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  SCREAMING_SNAKE_CASE__   :






						"""simple docstring"""




						def __init__(							self			:     Union[str, Any]  ,  lowerCAmelCase			:     int  ,  lowerCAmelCase			:     int=3_6000  ,  lowerCAmelCase			:     List[str]=13  ,  lowerCAmelCase			:     Tuple=7  ,  lowerCAmelCase			:     Dict=True  ,  lowerCAmelCase			:     int=32  ,  lowerCAmelCase			:     List[str]=42  ,  lowerCAmelCase			:     List[str]=5  ,  lowerCAmelCase			:     Optional[int]=2  ,  lowerCAmelCase			:     Tuple=4  ,  lowerCAmelCase			:     Any=2  ,  lowerCAmelCase			:     List[str]=32  ,  lowerCAmelCase			:     List[Any]=80  ,  lowerCAmelCase			:     List[str]=33  ,  lowerCAmelCase			:     Optional[int]=0.0  ,  lowerCAmelCase			:     Any=1e-6  ,  lowerCAmelCase			:     Optional[int]=100  ,  lowerCAmelCase			:     Optional[Any]=0.0  ,  ):
										lowerCAmelCase		=			vocab_size
										lowerCAmelCase		=			parent
										lowerCAmelCase		=			batch_size
										lowerCAmelCase		=			num_contexts
										# For common tests
										lowerCAmelCase		=			self.num_contexts
										lowerCAmelCase		=			is_training
										lowerCAmelCase		=			hidden_size
										lowerCAmelCase		=			num_ext_layers
										lowerCAmelCase		=			ext_size
										lowerCAmelCase		=			num_hidden_layers
										lowerCAmelCase		=			num_attention_heads
										lowerCAmelCase		=			num_experts
										lowerCAmelCase		=			d_ff
										lowerCAmelCase		=			d_ext
										lowerCAmelCase		=			d_spout
										lowerCAmelCase		=			dropout_rate
										lowerCAmelCase		=			layer_norm_epsilon
										lowerCAmelCase		=			expert_capacity
										lowerCAmelCase		=			router_jitter_noise


						def 					__lowercase	(							self			:     List[Any]   ):
										return GPTSanJapaneseConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""   )


						def 					__lowercase	(							self			:     Tuple   ):
										lowerCAmelCase		=			ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size   )

										lowerCAmelCase		=			self.get_config()

										return (config, input_ids)


						def 					__lowercase	(							self			:     str   ):
										lowerCAmelCase		=			ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size   )

										lowerCAmelCase		=			self.get_config()

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


						def 					__lowercase	(							self			:     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 					__lowercase	(							self			:     List[Any]  ,  lowerCAmelCase			:     Optional[Any]  ,  lowerCAmelCase			:     Dict  ,  ):
										lowerCAmelCase		=			GPTSanJapaneseForConditionalGeneration(config=__lowerCamelCase   )
										model.to(__lowerCamelCase   )
										model.eval()
										lowerCAmelCase		=			model(
										    input_ids=__lowerCamelCase  ,  )
										self.parent.assertIsNotNone(__lowerCamelCase   )






@require_torch
class  SCREAMING_SNAKE_CASE__   (   UpperCamelCase__  ,			UpperCamelCase__  ,			unittest.TestCase   ):






						"""simple docstring"""




						_a        =  (GPTSanJapaneseModel,) if is_torch_available() else ()
						_a        =  (
						    {
						        'conversational': GPTSanJapaneseForConditionalGeneration,
						        'feature-extraction': GPTSanJapaneseForConditionalGeneration,
						        'summarization': GPTSanJapaneseForConditionalGeneration,
						        'text2text-generation': GPTSanJapaneseForConditionalGeneration,
						        'translation': GPTSanJapaneseForConditionalGeneration,
						    }
						    if is_torch_available()
						    else {}
						)
						_a        =  False
						_a        =  False
						_a        =  False
						_a        =  False
						_a        =  False
						_a        =  False
						_a        =  False
						_a        =  False
						# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
						_a        =  [0.8, 0.9]


						def 					__lowercase	(							self			:     int  ,  lowerCAmelCase			:     Dict  ,  lowerCAmelCase			:     Dict  ,  lowerCAmelCase			:     List[Any]  ,  lowerCAmelCase			:     List[str]  ,  lowerCAmelCase			:     str   ):


										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 					__lowercase	(							self			:     Optional[int]   ):
										lowerCAmelCase		=			GPTSanJapaneseTester(self   )
										lowerCAmelCase		=			ConfigTester(self  ,  config_class=__lowerCamelCase  ,  d_model=37   )


						def 					__lowercase	(							self			:     Optional[int]   ):
										GPTSanJapaneseConfig()


						def 					__lowercase	(							self			:     List[str]   ):
										lowerCAmelCase		=			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 					__lowercase	(							self			:     List[Any]   ):
										super().test_model_parallelism()




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






@require_torch
class  SCREAMING_SNAKE_CASE__   (   UpperCamelCase__  ,			UpperCamelCase__  ,			unittest.TestCase   ):






						"""simple docstring"""




						_a        =  (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
						_a        =  False
						_a        =  False
						_a        =  False
						_a        =  False
						_a        =  False
						_a        =  False
						# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
						_a        =  [0.8, 0.9]


						def 					__lowercase	(							self			:     Any   ):
										lowerCAmelCase		=			GPTSanJapaneseTester(self   )
										lowerCAmelCase		=			ConfigTester(self  ,  config_class=__lowerCamelCase  ,  d_model=37   )


						def 					__lowercase	(							self			:     Any   ):
										GPTSanJapaneseConfig()


						def 					__lowercase	(							self			:     Tuple   ):
										lowerCAmelCase		=			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 					__lowercase	(							self			:     Optional[Any]   ):
										super().test_model_parallelism()


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


						@slow
						def 					__lowercase	(							self			:     Optional[int]   ):
										lowerCAmelCase		=			GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""   )
										lowerCAmelCase		=			GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""   )
										lowerCAmelCase		=			tokenizer.encode("""武田信玄は"""  ,  return_tensors="""pt"""   )
										lowerCAmelCase		=			model(__lowerCamelCase   )
										lowerCAmelCase		=			outputs.logits.detach().cpu().numpy()
										# Output of original model created with mesh-tensoflow
										lowerCAmelCase		=			[
										    # fmt: off
										    [-12.037_8398_8952_6367, -12.433_0615_9973_1445, -14.333_8403_7017_8223, -12.450_3459_9304_1992, -11.1_6613_7695_3125,
										    -11.930_1376_3427_7344, -10.659_7404_4799_8047, -12.909_5745_0866_6992, -13.241_0430_9082_0312, -13.398_5795_9747_3145,
										    -11.107_5248_7182_6172, -12.3_6859_4169_6167, -22.97_9431_1523_4375, -10.481_0676_5747_0703, -12.484_0307_2357_1777,
										    -12.807_3606_4910_8887, -14.769_7000_5035_4004, -12.233_5796_3562_0117, -13.428_1454_0863_0371, -22.624_1779_3273_9258],
										    [-7.511_1498_8327_0264, -8.281_8517_6849_3652, -7.943_1271_5530_3955, -7.55_0213_3369_4458, -6.49_8699_6650_6958,
										    -7.586_7962_8372_1924, -6.978_0859_9472_0459, -7.839_1451_8356_3232, -8.21_9641_6854_8584, -8.695_0912_4755_8594,
										    -6.706_9106_1019_8975, -6.6585_7982_6354_9805, -19.565_6986_2365_7227, -5.353_8427_3529_0527, -8.350_6860_7330_3223,
										    -8.039_3886_5661_6211, -10.856_5692_9016_1133, -7.75_1544_4755_5542, -8.819_0221_7864_9902, -19.51_5327_4536_1328],
										    [-9.73_0662_3458_8623, -10.223_9227_2949_2188, -9.932_9814_9108_8867, -11.857_8367_2332_7637, -7.662_6262_6647_9492,
										    -11.13_5291_0995_4834, -7.765_0976_1810_3027, -11.472_9232_7880_8594, -9.543_1499_4812_0117, -11.905_6339_2639_1602,
										    -9.366_1642_0745_8496, -11.5_7342_8153_9917, -23.699_0032_1960_4492, -9.429_5902_2521_9727, -10.42_8392_4102_7832,
										    -10.585_2403_6407_4707, -10.94_7719_5739_7461, -11.095_4160_6903_0762, -10.390_2406_6925_0488, -23.769_3729_4006_3477],
										    [-9.728_2657_6232_9102, -9.859_7126_0070_8008, -10.09_7292_9000_8545, -9.678_5221_0998_5352, -6.879_5199_3942_2607,
										    -9.68_4875_4882_8125, -4.2803_4257_8887_9395, -10.018_9142_2271_7285, -9.308_4459_3048_0957, -10.63_3945_4650_8789,
										    -8.083_6467_7429_1992, -9.06_3014_9841_3086, -21.904_2663_5742_1875, -8.90_1608_4671_0205, -8.841_8760_2996_8262,
										    -11.856_7199_7070_3125, -12.079_3981_5521_2402, -11.233_7532_0434_5703, -10.177_3386_0015_8691, -21.87_2566_2231_4453],
										    [-9.669_7645_1873_7793, -9.614_1986_8469_2383, -9.814_5103_4545_8984, -9.996_5019_2260_7422, -11.375_6904_6020_5078,
										    -10.113_4052_2766_1133, -10.546_8673_7060_5469, -10.04_3690_6814_5752, -10.907_8092_5750_7324, -10.504_2161_9415_2832,
										    -11.129_1990_2801_5137, -10.151_1240_0054_9316, -21.96_5867_9962_1582, -9.086_3494_8730_4688, -11.730_3390_5029_2969,
										    -10.460_6676_1016_8457, -10.298_0499_2675_7812, -10.784_1482_1624_7559, -10.840_6934_7381_5918, -22.03_1528_4729_0039],
										    # fmt: on
										]
										lowerCAmelCase		=			np.array(__lowerCamelCase   ).flatten()
										lowerCAmelCase		=			output_logits[0, :, :20].flatten()

										def check(lowerCAmelCase			:     Optional[int]  ,  lowerCAmelCase			:     Optional[int]  ,  lowerCAmelCase			:     Union[str, Any]=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 					__lowercase	(							self			:     List[str]   ):
										lowerCAmelCase		=			GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""   )
										lowerCAmelCase		=			GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""   )
										model.to(__lowerCamelCase   )

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

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

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

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

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

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

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

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

										lowerCAmelCase		=			tokenizer.batch_decode(__lowerCamelCase  ,  skip_special_tokens=__lowerCamelCase   )
										lowerCAmelCase		=			tokenizer.decode(output_non_padded[0]  ,  skip_special_tokens=__lowerCamelCase   )
										lowerCAmelCase		=			tokenizer.decode(output_padded[0]  ,  skip_special_tokens=__lowerCamelCase   )

										lowerCAmelCase		=			[
										    "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 					__lowercase	(							self			:     Optional[int]   ):
										lowerCAmelCase		=			GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""   )
										lowerCAmelCase		=			GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""   )
										# Output of original model created with mesh-tensoflow
										lowerCAmelCase		=			[
										    ("武田信玄は", 3_5675),
										    ("武田信玄は、", 45),
										    ("武田信玄は、この", 29),
										    ("武田信玄は、このよう", 3_0642),
										    ("武田信玄は、このような", 3_5680),
										    ("武田信玄は、このような「", 8640),
										    ("武田信玄は、このような「武田", 3_1617),
										    ("武田信玄は、このような「武田家", 3_0646),
										    ("武田信玄は、このような「武田家の", 3_1617),
										    ("武田信玄は、このような「武田家の家", 3_1381),
										]
										for input, output in target:
														lowerCAmelCase		=			tokenizer.encode(__lowerCamelCase  ,  return_tensors="""pt"""   )
														lowerCAmelCase		=			model(__lowerCamelCase   )
														lowerCAmelCase		=			outputs.logits.detach().cpu().numpy()[0]
														lowerCAmelCase		=			np.argmax(output_logits[-1]   )
														self.assertEqual(__lowerCamelCase  ,  __lowerCamelCase   )


						@slow
						def 					__lowercase	(							self			:     Tuple   ):
										lowerCAmelCase		=			GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""   )
										lowerCAmelCase		=			GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""   )
										model.to(__lowerCamelCase   )

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

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

										# spout from uniform and one-hot
										lowerCAmelCase		=			[
										    # fmt: off
										    [0.8788_2208, 0.3842_6396, 0.3322_0248, 0.4389_0406, 0.1656_2252,
										    0.0480_3985, 0.21_1572  , 0.2318_8473, 0.3715_3068, 0.783_6377 ,
										    0.0216_0172, 0.3876_1719, 0.7529_0772, 0.9019_8857, 0.3436_5777,
										    0.6416_8169, 0.4431_8471, 0.1457_5746, 0.9256_2881, 0.4081_2148,
										    0.2901_9122, 0.8886_1599, 0.6552_4846, 0.4356_3456, 0.3817_7187,
										    0.7083_2965, 0.8152_7892, 0.6883_2812, 0.3883_3192, 0.456_1522 ,
										    0.1482_8817, 0.4724_8213, 0.5435_7335, 0.8200_9566, 0.133_8884 ,
										    0.0275_5417, 0.1976_4677, 0.242_2084 , 0.0475_7674, 0.6540_9606,
										    0.082_4589 , 0.0330_4383, 0.9438_7689, 0.9876_4509, 0.8243_3901,
										    0.2764_6741, 0.6490_7493, 0.7600_9406, 0.3008_7915, 0.1790_4689,
										    0.4160_1714, 0.6704_6398, 0.1042_2822, 0.0844_7374, 0.0735_4344,
										    0.6142_3565, 0.7028_4866, 0.753_2333 , 0.197_2038 , 0.2957_5659,
										    0.9058_3886, 0.2926_5307, 0.5000_0175, 0.7040_7655, 0.88_9363  ,
										    0.8190_4418, 0.6682_9128, 0.6446_8815, 0.5656_3723, 0.8560_1875,
										    0.9492_4672, 0.0016_6762, 0.2522_0643, 0.7454_0219, 0.6799_3247,
										    0.154_9675 , 0.3938_5352, 0.9215_3607, 0.6374_5931, 0.2775_9043,
										    0.8470_2295, 0.6590_4271, 0.5867_6614, 0.866_6936 , 0.3960_7438,
										    0.7995_4983, 0.4222_0697, 0.3965_0381, 0.784_9864 , 0.5615_0201,
										    0.1567_8925, 0.1474_6032, 0.3454_2114, 0.4702_6783, 0.1195_6489,
										    0.2542_1435, 0.3378_8901, 0.6893_4842, 0.3642_4685, 0.7173_7898,
										    0.3898_3449, 0.9439_3779, 0.3957_5588, 0.3661_6553, 0.8710_4665,
										    0.6463_0203, 0.2251_6905, 0.8827_0804, 0.1503_1338, 0.7514_4345,
										    0.4645_9025, 0.8539_6454, 0.8635_5643, 0.6513_9851, 0.7026_6061,
										    0.3024_1389, 0.8105_6497, 0.8886_5969, 0.3877_3807, 0.7063_5849,
										    0.9071_8459, 0.4324_5789, 0.2800_0654, 0.4593_5562, 0.0877_3519,
										    0.955_2151 , 0.9390_1511, 0.2248_9288], # uniform
										    [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
										     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
										     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
										     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
										     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
										     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
										     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
										     0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
										     0., 0., 0., 0., 0., 0., 0., 0.],
										    # fmt: on
										]

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

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

										lowerCAmelCase		=			model.generate(
										    input_ids=__lowerCamelCase  ,  spout=__lowerCamelCase  ,  max_new_tokens=20  ,  generation_config=__lowerCamelCase  ,  )

										lowerCAmelCase		=			tokenizer.decode(outputa[0]   )
										lowerCAmelCase		=			tokenizer.decode(outputa[0]   )
										lowerCAmelCase		=			tokenizer.batch_decode(__lowerCamelCase   )

										lowerCAmelCase		=			[
										    "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 					__lowercase	(							self			:     Dict   ):
										lowerCAmelCase		=			GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""   )
										lowerCAmelCase		=			GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""   )
										model.to(__lowerCamelCase   )

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

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

										lowerCAmelCase		=			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  ,  )

										lowerCAmelCase		=			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  ,  )

										lowerCAmelCase		=			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  ,  )

										lowerCAmelCase		=			tokenizer.decode(outputa[0]   )
										lowerCAmelCase		=			tokenizer.decode(outputa[0]   )
										lowerCAmelCase		=			tokenizer.batch_decode(__lowerCamelCase   )

										lowerCAmelCase		=			[
										    "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"""







def 							lowercase   (snake_case__				:	list	)       ->					bool:


				'''simple docstring'''






				if not isinstance(snake_case__ ,    snake_case__	):
								raise ValueError("""Input series is not valid, valid series - [1, 2/3, 2]"""	)
				if len(snake_case__	) == 0:
								raise ValueError("""Input list must be a non empty list"""	)
				if len(snake_case__	) == 1 and series[0] != 0:
								return True
				lowerCAmelCase		=			[]
				lowerCAmelCase		=			len(snake_case__	)
				for i in range(0 ,    snake_case__	):
								if series[i] == 0:
												raise ValueError("""Input series cannot have 0 as an element"""	)
								rec_series.append(1 / series[i]	)
				lowerCAmelCase		=			rec_series[1] - rec_series[0]
				for index in range(2 ,    snake_case__	):
								if rec_series[index] - rec_series[index - 1] != common_diff:
												return False
				return True





def 							lowercase   (snake_case__				:	list	)       ->					float:


				'''simple docstring'''






				if not isinstance(snake_case__ ,    snake_case__	):
								raise ValueError("""Input series is not valid, valid series - [2, 4, 6]"""	)
				if len(snake_case__	) == 0:
								raise ValueError("""Input list must be a non empty list"""	)
				lowerCAmelCase		=			0
				for val in series:
								answer += 1 / val
				return len(snake_case__	) / answer


if __name__ == "__main__":
		import doctest

		doctest.testmod()





