

'''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     lowercase_		:

  '''simple docstring'''





  def __init__(       self				,	a_				,	a_=3_6_0_0_0				,	a_=1_3				,	a_=7				,	a_=True				,	a_=3_2				,	a_=4_2				,	a_=5				,	a_=2				,	a_=4				,	a_=2				,	a_=3_2				,	a_=8_0				,	a_=3_3				,	a_=0.0				,	a_=1E-6				,	a_=1_0_0				,	a_=0.0				,	)							->			str:


    """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   snake_case_     (       self       )							->			Union[str, Any]:


    """simple docstring"""
    return GPTSanJapaneseConfig.from_pretrained('Tanrei/GPTSAN-japanese'       )
  def   snake_case_     (       self       )							->			Tuple:


    """simple docstring"""
    UpperCAmelCase  							=		ids_tensor([self.batch_size, self.seq_length]				,	self.vocab_size       )

    UpperCAmelCase  							=		self.get_config()

    return (config, input_ids)
  def   snake_case_     (       self       )							->			Optional[int]:


    """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   snake_case_     (       self       )							->			List[Any]:


    """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_				,	)							->			Any:


    """simple docstring"""
    UpperCAmelCase  							=		GPTSanJapaneseForConditionalGeneration(config=lowercase__       )
    model.to(lowercase__       )
    model.eval()
    UpperCAmelCase  							=		model(
        input_ids=lowercase__				,	)
    self.parent.assertIsNotNone(lowercase__       )




@require_torch
class     lowercase_		(						_UpperCAmelCase	,    _UpperCAmelCase	,    unittest.TestCase	):

  '''simple docstring'''





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


    """simple docstring"""
    UpperCAmelCase  							=		GPTSanJapaneseTester(self       )
    UpperCAmelCase  							=		ConfigTester(self				,	config_class=lowercase__				,	d_model=3_7       )
  def   snake_case_     (       self       )							->			Tuple:


    """simple docstring"""
    GPTSanJapaneseConfig()
  def   snake_case_     (       self       )							->			List[str]:


    """simple docstring"""
    UpperCAmelCase  							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowercase__       )
  @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       )							->			Union[str, 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       )							->			Optional[int]:


    """simple docstring"""
    pass




@require_torch
class     lowercase_		(						_UpperCAmelCase	,    _UpperCAmelCase	,    unittest.TestCase	):

  '''simple docstring'''





  __lowerCAmelCase   :	Tuple      					=				(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
  __lowerCAmelCase   :	str      					=				False
  __lowerCAmelCase   :	Dict      					=				False
  __lowerCAmelCase   :	List[str]      					=				False
  __lowerCAmelCase   :	Optional[Any]      					=				False
  __lowerCAmelCase   :	Tuple      					=				False
  __lowerCAmelCase   :	int      					=				False
  # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
  __lowerCAmelCase   :	List[Any]      					=				[0.8, 0.9]
  def   snake_case_     (       self       )							->			Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase  							=		GPTSanJapaneseTester(self       )
    UpperCAmelCase  							=		ConfigTester(self				,	config_class=lowercase__				,	d_model=3_7       )
  def   snake_case_     (       self       )							->			Tuple:


    """simple docstring"""
    GPTSanJapaneseConfig()
  def   snake_case_     (       self       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowercase__       )
  @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       )							->			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   snake_case_     (       self       )							->			Tuple:


    """simple docstring"""
    pass
  @slow
  def   snake_case_     (       self       )							->			Dict:


    """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(lowercase__       )
    UpperCAmelCase  							=		outputs.logits.detach().cpu().numpy()
    # Output of original model created with mesh-tensoflow
    UpperCAmelCase  							=		[
        # 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
    ]
    UpperCAmelCase  							=		np.array(lowercase__       ).flatten()
    UpperCAmelCase  							=		output_logits[0, :, :2_0].flatten()

    def check(a_				,	a_				,	a_=5E-4       ):
      return abs(a - b       ) < epsilon * max(abs(lowercase__       )				,	abs(lowercase__       )       )

    self.assertTrue(np.all([check(target[i]				,	predict[i]       ) for i in range(len(lowercase__       )       )]       )       )
  @slow
  def   snake_case_     (       self       )							->			List[Any]:


    """simple docstring"""
    UpperCAmelCase  							=		GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'       )
    UpperCAmelCase  							=		GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'       )
    model.to(lowercase__       )

    # 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(lowercase__				,	return_tensors='pt'				,	padding=lowercase__       )
    UpperCAmelCase  							=		inputs["input_ids"].to(lowercase__       )

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

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

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

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

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

    UpperCAmelCase  							=		tokenizer.batch_decode(lowercase__				,	skip_special_tokens=lowercase__       )
    UpperCAmelCase  							=		tokenizer.decode(output_non_padded[0]				,	skip_special_tokens=lowercase__       )
    UpperCAmelCase  							=		tokenizer.decode(output_padded[0]				,	skip_special_tokens=lowercase__       )

    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(lowercase__				,	lowercase__       )
    self.assertListEqual(lowercase__				,	[non_padded_sentence, padded_sentence]       )
  @tooslow
  def   snake_case_     (       self       )							->			Union[str, 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", 3_5_6_7_5),
        ("武田信玄は、", 4_5),
        ("武田信玄は、この", 2_9),
        ("武田信玄は、このよう", 3_0_6_4_2),
        ("武田信玄は、このような", 3_5_6_8_0),
        ("武田信玄は、このような「", 8_6_4_0),
        ("武田信玄は、このような「武田", 3_1_6_1_7),
        ("武田信玄は、このような「武田家", 3_0_6_4_6),
        ("武田信玄は、このような「武田家の", 3_1_6_1_7),
        ("武田信玄は、このような「武田家の家", 3_1_3_8_1),
    ]
    for input, output in target:
      UpperCAmelCase  							=		tokenizer.encode(lowercase__				,	return_tensors='pt'       )
      UpperCAmelCase  							=		model(lowercase__       )
      UpperCAmelCase  							=		outputs.logits.detach().cpu().numpy()[0]
      UpperCAmelCase  							=		np.argmax(output_logits[-1]       )
      self.assertEqual(lowercase__				,	lowercase__       )
  @slow
  def   snake_case_     (       self       )							->			Optional[int]:


    """simple docstring"""
    UpperCAmelCase  							=		GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'       )
    UpperCAmelCase  							=		GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'       )
    model.to(lowercase__       )

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

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

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

    UpperCAmelCase  							=		model.generate(
        input_ids=lowercase__				,	spout=spouts[0]				,	max_new_tokens=2_0				,	generation_config=lowercase__				,	)

    UpperCAmelCase  							=		model.generate(
        input_ids=lowercase__				,	spout=spouts[1]				,	max_new_tokens=2_0				,	generation_config=lowercase__				,	)

    UpperCAmelCase  							=		model.generate(
        input_ids=lowercase__				,	spout=lowercase__				,	max_new_tokens=2_0				,	generation_config=lowercase__				,	)

    UpperCAmelCase  							=		tokenizer.decode(outputa[0]       )
    UpperCAmelCase  							=		tokenizer.decode(outputa[0]       )
    UpperCAmelCase  							=		tokenizer.batch_decode(lowercase__       )

    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(lowercase__				,	lowercase__       )
    self.assertListEqual(lowercase__				,	[outa_sentence, outa_sentence]       )





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


    """simple docstring"""
    UpperCAmelCase  							=		GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'       )
    UpperCAmelCase  							=		GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'       )
    model.to(lowercase__       )

    # 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(lowercase__				,	prefix_text=lowercase__				,	return_tensors='pt'       )
    UpperCAmelCase  							=		tokenizer(lowercase__				,	prefix_text=lowercase__				,	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(lowercase__       )				,	token_type_ids=input_tok_a.token_type_ids.to(lowercase__       )				,	max_new_tokens=2_0				,	generation_config=lowercase__				,	)

    UpperCAmelCase  							=		model.generate(
        input_ids=input_tok_a.input_ids.to(lowercase__       )				,	token_type_ids=input_tok_a.token_type_ids.to(lowercase__       )				,	max_new_tokens=2_0				,	generation_config=lowercase__				,	)

    UpperCAmelCase  							=		model.generate(
        input_ids=input_tok_a.input_ids.to(lowercase__       )				,	token_type_ids=input_tok_a.token_type_ids.to(lowercase__       )				,	attention_mask=input_tok_a.attention_mask.to(lowercase__       )				,	max_new_tokens=2_0				,	generation_config=lowercase__				,	)

    UpperCAmelCase  							=		tokenizer.decode(outputa[0]       )
    UpperCAmelCase  							=		tokenizer.decode(outputa[0]       )
    UpperCAmelCase  							=		tokenizer.batch_decode(lowercase__       )

    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(lowercase__				,	lowercase__       )
    self.assertListEqual(lowercase__				,	[outa_sentence, outa_sentence]       )





'''simple docstring'''




from typing import TypeVar

from .arrow_dataset import Dataset, _split_by_node_map_style_dataset
from .iterable_dataset import IterableDataset, _split_by_node_iterable_dataset


_a			:	Optional[int]								=  TypeVar('DatasetType', Dataset, IterableDataset)


def  lowerCamelCase__						(  SCREAMING_SNAKE_CASE :				DatasetType    ,				SCREAMING_SNAKE_CASE :				int    ,				SCREAMING_SNAKE_CASE :				int  ):
  if isinstance(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE  ):
    return _split_by_node_map_style_dataset(SCREAMING_SNAKE_CASE    ,				rank=SCREAMING_SNAKE_CASE    ,				world_size=SCREAMING_SNAKE_CASE  )
  else:
    return _split_by_node_iterable_dataset(SCREAMING_SNAKE_CASE    ,				rank=SCREAMING_SNAKE_CASE    ,				world_size=SCREAMING_SNAKE_CASE  )


