



'''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  lowerCamelCase_   :
  """simple docstring"""




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




  def 						_lowercase					(			self				:     List[str]       ) -> Any:
    return GPTSanJapaneseConfig.from_pretrained('Tanrei/GPTSAN-japanese'       )




  def 						_lowercase					(			self				:     Tuple       ) -> List[str]:
    __lowerCamelCase	:	Optional[Any]   	=			ids_tensor([self.batch_size, self.seq_length]      ,     self.vocab_size       )

    __lowerCamelCase	:	Optional[int]   	=			self.get_config()

    return (config, input_ids)




  def 						_lowercase					(			self				:     List[str]       ) -> List[Any]:
    __lowerCamelCase	:	int   	=			ids_tensor([self.batch_size, self.seq_length]      ,     self.vocab_size       )

    __lowerCamelCase	:	int   	=			self.get_config()

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




  def 						_lowercase					(			self				:     List[Any]       ) -> List[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				:     Optional[int]      ,     _a				:     Tuple      ,     _a				:     Optional[int]      ,     ) -> str:
    __lowerCamelCase	:	Optional[Any]   	=			GPTSanJapaneseForConditionalGeneration(config=_a       )
    model.to(_a       )
    model.eval()
    __lowerCamelCase	:	Union[str, Any]   	=			model(
        input_ids=_a      ,     )
    self.parent.assertIsNotNone(_a       )






@require_torch
class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       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				:     List[Any]      ,     _a				:     List[str]      ,     _a				:     Any      ,     _a				:     Optional[int]      ,     _a				:     List[Any]      ,     _a				:     int       ) -> Tuple:
    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				:     Any       ) -> Any:
    __lowerCamelCase	:	Dict   	=			GPTSanJapaneseTester(self       )
    __lowerCamelCase	:	int   	=			ConfigTester(self      ,     config_class=_a      ,     d_model=37       )




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




  def 						_lowercase					(			self				:     Optional[int]       ) -> Union[str, Any]:
    __lowerCamelCase	:	Tuple   	=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_a       )




  @unittest.skip(
      reason='skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`'       )
  def 						_lowercase					(			self				:     Union[str, Any]       ) -> Optional[int]:
    super().test_model_parallelism()




  @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'       )
  def 						_lowercase					(			self				:     Any       ) -> List[Any]:
    pass






@require_torch
class  lowerCamelCase_   (    SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       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				:     Optional[int]       ) -> Optional[Any]:
    __lowerCamelCase	:	Optional[int]   	=			GPTSanJapaneseTester(self       )
    __lowerCamelCase	:	List[str]   	=			ConfigTester(self      ,     config_class=_a      ,     d_model=37       )




  def 						_lowercase					(			self				:     Optional[Any]       ) -> List[str]:
    GPTSanJapaneseConfig()




  def 						_lowercase					(			self				:     Optional[Any]       ) -> List[str]:
    __lowerCamelCase	:	str   	=			self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_a       )




  @unittest.skip(
      reason='skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`'       )
  def 						_lowercase					(			self				:     Optional[Any]       ) -> 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				:     Any       ) -> int:
    pass




  @slow
  def 						_lowercase					(			self				:     Dict       ) -> Dict:
    __lowerCamelCase	:	Dict   	=			GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'       )
    __lowerCamelCase	:	Any   	=			GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'       )
    __lowerCamelCase	:	str   	=			tokenizer.encode('武田信玄は'      ,     return_tensors='pt'       )
    __lowerCamelCase	:	Optional[int]   	=			model(_a       )
    __lowerCamelCase	:	Optional[Any]   	=			outputs.logits.detach().cpu().numpy()
    # Output of original model created with mesh-tensoflow
    __lowerCamelCase	:	List[str]   	=			[
        # 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
    ]
    __lowerCamelCase	:	Tuple   	=			np.array(_a       ).flatten()
    __lowerCamelCase	:	Tuple   	=			output_logits[0, :, :20].flatten()

    def check(_a				:     Optional[Any]      ,     _a				:     str      ,     _a				:     int=5e-4       ):
      return abs(a - b       ) < epsilon * max(abs(_a       )      ,     abs(_a       )       )

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




  @slow
  def 						_lowercase					(			self				:     Optional[Any]       ) -> List[Any]:
    __lowerCamelCase	:	List[str]   	=			GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'       )
    __lowerCamelCase	:	List[str]   	=			GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'       )
    model.to(_a       )

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

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

    __lowerCamelCase	:	Optional[Any]   	=			'left'
    __lowerCamelCase	:	str   	=			tokenizer(_a      ,     return_tensors='pt'      ,     padding=_a       )
    __lowerCamelCase	:	Dict   	=			inputs['input_ids'].to(_a       )

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

    __lowerCamelCase	:	str   	=			model.generate(
        input_ids=_a      ,     attention_mask=inputs['attention_mask'].to(_a       )      ,     max_new_tokens=3      ,     generation_config=_a      ,     )

    __lowerCamelCase	:	Optional[int]   	=			tokenizer(sentences[0]      ,     return_tensors='pt'       ).input_ids.to(_a       )
    __lowerCamelCase	:	str   	=			model.generate(
        input_ids=_a      ,     max_new_tokens=3      ,     generation_config=_a       )

    __lowerCamelCase	:	str   	=			tokenizer(sentences[1]      ,     return_tensors='pt'       ).input_ids.to(_a       )
    __lowerCamelCase	:	Tuple   	=			model.generate(input_ids=_a      ,     max_new_tokens=3      ,     generation_config=_a       )

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

    __lowerCamelCase	:	Union[str, Any]   	=			tokenizer.batch_decode(_a      ,     skip_special_tokens=_a       )
    __lowerCamelCase	:	int   	=			tokenizer.decode(output_non_padded[0]      ,     skip_special_tokens=_a       )
    __lowerCamelCase	:	int   	=			tokenizer.decode(output_padded[0]      ,     skip_special_tokens=_a       )

    __lowerCamelCase	:	str   	=			[
        '甲斐なら武田と言うほど甲斐の武田',
        '織田信長は、このような',
    ]
    self.assertListEqual(_a      ,     _a       )
    self.assertListEqual(_a      ,     [non_padded_sentence, padded_sentence]       )




  @tooslow
  def 						_lowercase					(			self				:     int       ) -> Tuple:
    __lowerCamelCase	:	Any   	=			GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'       )
    __lowerCamelCase	:	Optional[int]   	=			GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'       )
    # Output of original model created with mesh-tensoflow
    __lowerCamelCase	:	Union[str, Any]   	=			[
        ('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	:	Dict   	=			tokenizer.encode(_a      ,     return_tensors='pt'       )
      __lowerCamelCase	:	Dict   	=			model(_a       )
      __lowerCamelCase	:	Optional[int]   	=			outputs.logits.detach().cpu().numpy()[0]
      __lowerCamelCase	:	List[str]   	=			np.argmax(output_logits[-1]       )
      self.assertEqual(_a      ,     _a       )




  @slow
  def 						_lowercase					(			self				:     Optional[int]       ) -> List[str]:
    __lowerCamelCase	:	Union[str, Any]   	=			GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'       )
    __lowerCamelCase	:	Optional[int]   	=			GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'       )
    model.to(_a       )

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

    __lowerCamelCase	:	Optional[int]   	=			'武田信玄は、'
    __lowerCamelCase	:	Optional[int]   	=			tokenizer(_a      ,     return_tensors='pt'       ).input_ids.to(_a       )
    __lowerCamelCase	:	Dict   	=			tokenizer([input_text, input_text]      ,     return_tensors='pt'       ).input_ids.to(_a       )

    # spout from uniform and one-hot
    __lowerCamelCase	:	Optional[Any]   	=			[
        # 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
    ]

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

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

    __lowerCamelCase	:	List[Any]   	=			model.generate(
        input_ids=_a      ,     spout=_a      ,     max_new_tokens=20      ,     generation_config=_a      ,     )

    __lowerCamelCase	:	List[str]   	=			tokenizer.decode(outputa[0]       )
    __lowerCamelCase	:	Union[str, Any]   	=			tokenizer.decode(outputa[0]       )
    __lowerCamelCase	:	List[Any]   	=			tokenizer.batch_decode(_a       )

    __lowerCamelCase	:	Any   	=			[
        '武田信玄は、武田氏の滅亡後、武田氏の居城であった甲斐武田氏の居城である',
        '武田信玄は、武田家の滅亡を防ぐため、武田家の家臣である武田信虎を討',
    ]
    self.assertListEqual(_a      ,     _a       )
    self.assertListEqual(_a      ,     [outa_sentence, outa_sentence]       )




  @slow
  def 						_lowercase					(			self				:     List[Any]       ) -> Union[str, Any]:
    __lowerCamelCase	:	int   	=			GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'       )
    __lowerCamelCase	:	int   	=			GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'       )
    model.to(_a       )

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

    __lowerCamelCase	:	str   	=			'武田信玄'
    __lowerCamelCase	:	int   	=			'織田信長'
    __lowerCamelCase	:	Optional[Any]   	=			'は、'
    __lowerCamelCase	:	Union[str, Any]   	=			'が、'
    __lowerCamelCase	:	Tuple   	=			tokenizer(_a      ,     prefix_text=_a      ,     return_tensors='pt'       )
    __lowerCamelCase	:	Tuple   	=			tokenizer(_a      ,     prefix_text=_a      ,     return_tensors='pt'       )
    __lowerCamelCase	:	int   	=			tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]      ,     return_tensors='pt'       )

    __lowerCamelCase	:	Tuple   	=			model.generate(
        input_ids=input_tok_a.input_ids.to(_a       )      ,     token_type_ids=input_tok_a.token_type_ids.to(_a       )      ,     max_new_tokens=20      ,     generation_config=_a      ,     )

    __lowerCamelCase	:	Tuple   	=			model.generate(
        input_ids=input_tok_a.input_ids.to(_a       )      ,     token_type_ids=input_tok_a.token_type_ids.to(_a       )      ,     max_new_tokens=20      ,     generation_config=_a      ,     )

    __lowerCamelCase	:	Tuple   	=			model.generate(
        input_ids=input_tok_a.input_ids.to(_a       )      ,     token_type_ids=input_tok_a.token_type_ids.to(_a       )      ,     attention_mask=input_tok_a.attention_mask.to(_a       )      ,     max_new_tokens=20      ,     generation_config=_a      ,     )

    __lowerCamelCase	:	Tuple   	=			tokenizer.decode(outputa[0]       )
    __lowerCamelCase	:	Dict   	=			tokenizer.decode(outputa[0]       )
    __lowerCamelCase	:	Optional[int]   	=			tokenizer.batch_decode(_a       )

    __lowerCamelCase	:	str   	=			[
        '武田信玄は、武田氏の祖である武田信虎を、その子・武田信友を擁して',
        '織田信長が、織田信長の妻・お市の方を妻として迎えたという逸話が残',
    ]
    self.assertListEqual(_a      ,     _a       )
    self.assertListEqual(_a      ,     [outa_sentence, outa_sentence]       )










'''simple docstring'''





import math
from timeit import timeit

def 	a_					(							_lowerCAmelCase	)				->				int:
  __lowerCamelCase	:	int   	=			0
  __lowerCamelCase	:	Optional[int]   	=			abs(_lowerCAmelCase	)
  while True:
    __lowerCamelCase	:	List[str]   	=			n // 10
    digits += 1
    if n == 0:
      break
  return digits

def 	a_					(							_lowerCAmelCase	)				->				int:
  return 1 if n == 0 else math.floor(math.log(abs(_lowerCAmelCase	)	,10	) + 1	)

def 	a_					(							_lowerCAmelCase	)				->				int:
  return len(str(abs(_lowerCAmelCase	)	)	)

def 	a_					(							)				->				None:
  from collections.abc import Callable

  def benchmark_a_function(_lowerCAmelCase	,_lowerCAmelCase	) -> None:
    __lowerCamelCase	:	Any   	=			F'{func.__name__}({value})'
    __lowerCamelCase	:	int   	=			timeit(F'__main__.{call}'	,setup='import __main__'	)
    print(F'{call}: {func(_lowerCAmelCase	)} -- {timing} seconds'	)

  for value in (262144, 1125899906842624, 1267650600228229401496703205376):
    for func in (num_digits, num_digits_fast, num_digits_faster):
      benchmark_a_function(_lowerCAmelCase	,_lowerCAmelCase	)
    print()


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   benchmark()






