





import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_multi_gpu,
    require_single_gpu,
    test_sync,
)
from accelerate.utils import patch_environment



class  SCREAMING_SNAKE_CASE_  ( unittest.TestCase			):


  """simple docstring"""





  def 			__A      (  self						)		->   Any:





       '''simple docstring'''







       __magic_name__				=       inspect.getfile(accelerate.test_utils						)
       __magic_name__				=       os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ['''scripts''', '''test_sync.py''']						)





  @require_cpu
  def 			__A      (  self						)		->   List[str]:





       '''simple docstring'''







       debug_launcher(test_sync.main	,  num_processes=1						)





  @require_cpu
  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       debug_launcher(test_sync.main						)





  @require_single_gpu
  def 			__A      (  self						)		->   Optional[Any]:





       '''simple docstring'''







       test_sync.main()







  @require_multi_gpu
  def 			__A      (  self						)		->   int:





       '''simple docstring'''







       print(F'Found {torch.cuda.device_count()} devices.'						)
       __magic_name__				=       ['''torchrun''', F'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
       with patch_environment(omp_num_threads=1						):
            execute_subprocess_async(A	,  env=os.environ.copy()						)






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	,  A	,  A=3_60_00	,  A=13	,  A=7	,  A=True	,  A=32	,  A=42	,  A=5	,  A=2	,  A=4	,  A=2	,  A=32	,  A=80	,  A=33	,  A=0.0	,  A=1E-6	,  A=1_00	,  A=0.0	,  )		->   int:





       '''simple docstring'''







       __magic_name__				=       vocab_size
       __magic_name__				=       parent
       __magic_name__				=       batch_size
       __magic_name__				=       num_contexts
       # For common tests
       __magic_name__				=       self.num_contexts
       __magic_name__				=       is_training
       __magic_name__				=       hidden_size
       __magic_name__				=       num_ext_layers
       __magic_name__				=       ext_size
       __magic_name__				=       num_hidden_layers
       __magic_name__				=       num_attention_heads
       __magic_name__				=       num_experts
       __magic_name__				=       d_ff
       __magic_name__				=       d_ext
       __magic_name__				=       d_spout
       __magic_name__				=       dropout_rate
       __magic_name__				=       layer_norm_epsilon
       __magic_name__				=       expert_capacity
       __magic_name__				=       router_jitter_noise





  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







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





  def 			__A      (  self						)		->   List[str]:





       '''simple docstring'''







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

       __magic_name__				=       self.get_config()

       return (config, input_ids)





  def 			__A      (  self						)		->   List[str]:





       '''simple docstring'''







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

       __magic_name__				=       self.get_config()

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





  def 			__A      (  self						)		->   List[str]:





       '''simple docstring'''







       return GPTSanJapaneseConfig(
           vocab_size=self.vocab_size	,  num_contexts=self.seq_length	,  d_model=self.hidden_size	,  d_ff=self.d_ff	,  d_ext=self.d_ext	,  d_spout=self.d_spout	,  num_switch_layers=self.num_hidden_layers - self.num_ext_layers	,  num_ext_layers=self.num_ext_layers	,  num_heads=self.num_attention_heads	,  num_experts=self.num_experts	,  expert_capacity=self.expert_capacity	,  dropout_rate=self.dropout_rate	,  layer_norm_epsilon=self.layer_norm_epsilon	,  router_jitter_noise=self.router_jitter_noise	,  )







  def 			__A      (  self	,  A	,  A	,  )		->   List[str]:





       '''simple docstring'''







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



@require_torch
class  SCREAMING_SNAKE_CASE_  ( 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 			__A      (  self	,  A	,  A	,  A	,  A	,  A						)		->   Dict:





       '''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 			__A      (  self						)		->   Tuple:





       '''simple docstring'''







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





  def 			__A      (  self						)		->   Union[str, Any]:





       '''simple docstring'''







       GPTSanJapaneseConfig()





  def 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







       __magic_name__				=       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 			__A      (  self						)		->   List[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 			__A      (  self						)		->   Optional[int]:





       '''simple docstring'''







       pass



@require_torch
class  SCREAMING_SNAKE_CASE_  ( 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 			__A      (  self						)		->   Tuple:





       '''simple docstring'''







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





  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       GPTSanJapaneseConfig()





  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       __magic_name__				=       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 			__A      (  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 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       pass





  @slow
  def 			__A      (  self						)		->   Dict:





       '''simple docstring'''







       __magic_name__				=       GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
       __magic_name__				=       GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
       __magic_name__				=       tokenizer.encode('''武田信玄は'''	,  return_tensors='''pt'''						)
       __magic_name__				=       model(A						)
       __magic_name__				=       outputs.logits.detach().cpu().numpy()
       # Output of original model created with mesh-tensoflow
       __magic_name__				=       [
           # fmt: off
           [-12.0_37_83_98_89_52_63_67, -12.4_33_06_15_99_73_14_45, -14.3_33_84_03_70_17_82_23, -12.4_50_34_59_93_04_19_92, -11.1_66_13_76_95_31_25,
           -11.9_30_13_76_34_27_73_44, -10.6_59_74_04_47_99_80_47, -12.9_09_57_45_08_66_69_92, -13.2_41_04_30_90_82_03_12, -13.3_98_57_95_97_47_31_45,
           -11.1_07_52_48_71_82_61_72, -12.3_68_59_41_69_61_67, -22.97_94_31_15_23_43_75, -10.4_81_06_76_57_47_07_03, -12.4_84_03_07_23_57_17_77,
           -12.8_07_36_06_49_10_88_87, -14.7_69_70_00_50_35_40_04, -12.2_33_57_96_35_62_01_17, -13.4_28_14_54_08_63_03_71, -22.6_24_17_79_32_73_92_58],
           [-7.5_11_14_98_83_27_02_64, -8.2_81_85_17_68_49_36_52, -7.9_43_12_71_55_30_39_55, -7.55_02_13_33_69_44_58, -6.49_86_99_66_50_69_58,
           -7.5_86_79_62_83_72_19_24, -6.9_78_08_59_94_72_04_59, -7.8_39_14_51_83_56_32_32, -8.21_96_41_68_54_85_84, -8.6_95_09_12_47_55_85_94,
           -6.7_06_91_06_10_19_89_75, -6.65_85_79_82_63_54_98_05, -19.5_65_69_86_23_65_72_27, -5.3_53_84_27_35_29_05_27, -8.3_50_68_60_73_30_32_23,
           -8.0_39_38_86_56_61_62_11, -10.8_56_56_92_90_16_11_33, -7.75_15_44_47_55_55_42, -8.8_19_02_21_78_64_99_02, -19.51_53_27_45_36_13_28],
           [-9.73_06_62_34_58_86_23, -10.2_23_92_27_29_49_21_88, -9.9_32_98_14_91_08_88_67, -11.8_57_83_67_23_32_76_37, -7.6_62_62_62_66_47_94_92,
           -11.13_52_91_09_95_48_34, -7.7_65_09_76_18_10_30_27, -11.4_72_92_32_78_80_85_94, -9.5_43_14_99_48_12_01_17, -11.9_05_63_39_26_39_16_02,
           -9.3_66_16_42_07_45_84_96, -11.5_73_42_81_53_99_17, -23.6_99_00_32_19_60_44_92, -9.4_29_59_02_25_21_97_27, -10.42_83_92_41_02_78_32,
           -10.5_85_24_03_64_07_47_07, -10.94_77_19_57_39_74_61, -11.0_95_41_60_69_03_07_62, -10.3_90_24_06_69_25_04_88, -23.7_69_37_29_40_06_34_77],
           [-9.7_28_26_57_62_32_91_02, -9.8_59_71_26_00_70_80_08, -10.09_72_92_90_00_85_45, -9.6_78_52_21_09_98_53_52, -6.8_79_51_99_39_42_26_07,
           -9.68_48_75_48_82_81_25, -4.28_03_42_57_88_87_93_95, -10.0_18_91_42_22_71_72_85, -9.3_08_44_59_30_48_09_57, -10.63_39_45_46_50_87_89,
           -8.0_83_64_67_74_29_19_92, -9.06_30_14_98_41_30_86, -21.9_04_26_63_57_42_18_75, -8.90_16_08_46_71_02_05, -8.8_41_87_60_29_96_82_62,
           -11.8_56_71_99_70_70_31_25, -12.0_79_39_81_55_21_24_02, -11.2_33_75_32_04_34_57_03, -10.1_77_33_86_00_15_86_91, -21.87_25_66_22_31_44_53],
           [-9.6_69_76_45_18_73_77_93, -9.6_14_19_86_84_69_23_83, -9.8_14_51_03_45_45_89_84, -9.9_96_50_19_22_60_74_22, -11.3_75_69_04_60_20_50_78,
           -10.1_13_40_52_27_66_11_33, -10.5_46_86_73_70_60_54_69, -10.04_36_90_68_14_57_52, -10.9_07_80_92_57_50_73_24, -10.5_04_21_61_94_15_28_32,
           -11.1_29_19_90_28_01_51_37, -10.1_51_12_40_00_54_93_16, -21.96_58_67_99_62_15_82, -9.0_86_34_94_87_30_46_88, -11.7_30_33_90_50_29_29_69,
           -10.4_60_66_76_10_16_84_57, -10.2_98_04_99_26_75_78_12, -10.7_84_14_82_16_24_75_59, -10.8_40_69_34_73_81_59_18, -22.03_15_28_47_29_00_39],
           # fmt: on
       ]
       __magic_name__				=       np.array(A						).flatten()
       __magic_name__				=       output_logits[0, :, :20].flatten()

       def check(A	,  A	,  A=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 			__A      (  self						)		->   List[Any]:





       '''simple docstring'''







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

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

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

       __magic_name__				=       '''left'''
       __magic_name__				=       tokenizer(A	,  return_tensors='''pt'''	,  padding=A						)
       __magic_name__				=       inputs['''input_ids'''].to(A						)

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

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

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

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

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

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

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





  @tooslow
  def 			__A      (  self						)		->   int:





       '''simple docstring'''







       __magic_name__				=       GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
       __magic_name__				=       GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
       # Output of original model created with mesh-tensoflow
       __magic_name__				=       [
           ('''武田信玄は''', 3_56_75),
           ('''武田信玄は、''', 45),
           ('''武田信玄は、この''', 29),
           ('''武田信玄は、このよう''', 3_06_42),
           ('''武田信玄は、このような''', 3_56_80),
           ('''武田信玄は、このような「''', 86_40),
           ('''武田信玄は、このような「武田''', 3_16_17),
           ('''武田信玄は、このような「武田家''', 3_06_46),
           ('''武田信玄は、このような「武田家の''', 3_16_17),
           ('''武田信玄は、このような「武田家の家''', 3_13_81),
       ]
       for input, output in target:
            __magic_name__				=       tokenizer.encode(A	,  return_tensors='''pt'''						)
            __magic_name__				=       model(A						)
            __magic_name__				=       outputs.logits.detach().cpu().numpy()[0]
            __magic_name__				=       np.argmax(output_logits[-1]						)
            self.assertEqual(A	,  A						)





  @slow
  def 			__A      (  self						)		->   str:





       '''simple docstring'''







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

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

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

       # spout from uniform and one-hot
       __magic_name__				=       [
           # fmt: off
           [0.87_88_22_08, 0.38_42_63_96, 0.33_22_02_48, 0.43_89_04_06, 0.16_56_22_52,
           0.04_80_39_85, 0.21_15_72  , 0.23_18_84_73, 0.37_15_30_68, 0.7_83_63_77 ,
           0.02_16_01_72, 0.38_76_17_19, 0.75_29_07_72, 0.90_19_88_57, 0.34_36_57_77,
           0.64_16_81_69, 0.44_31_84_71, 0.14_57_57_46, 0.92_56_28_81, 0.40_81_21_48,
           0.29_01_91_22, 0.88_86_15_99, 0.65_52_48_46, 0.43_56_34_56, 0.38_17_71_87,
           0.70_83_29_65, 0.81_52_78_92, 0.68_83_28_12, 0.38_83_31_92, 0.4_56_15_22 ,
           0.14_82_88_17, 0.47_24_82_13, 0.54_35_73_35, 0.82_00_95_66, 0.1_33_88_84 ,
           0.02_75_54_17, 0.19_76_46_77, 0.2_42_20_84 , 0.04_75_76_74, 0.65_40_96_06,
           0.0_82_45_89 , 0.03_30_43_83, 0.94_38_76_89, 0.98_76_45_09, 0.82_43_39_01,
           0.27_64_67_41, 0.64_90_74_93, 0.76_00_94_06, 0.30_08_79_15, 0.17_90_46_89,
           0.41_60_17_14, 0.67_04_63_98, 0.10_42_28_22, 0.08_44_73_74, 0.07_35_43_44,
           0.61_42_35_65, 0.70_28_48_66, 0.7_53_23_33 , 0.1_97_20_38 , 0.29_57_56_59,
           0.90_58_38_86, 0.29_26_53_07, 0.50_00_01_75, 0.70_40_76_55, 0.88_93_63  ,
           0.81_90_44_18, 0.66_82_91_28, 0.64_46_88_15, 0.56_56_37_23, 0.85_60_18_75,
           0.94_92_46_72, 0.00_16_67_62, 0.25_22_06_43, 0.74_54_02_19, 0.67_99_32_47,
           0.1_54_96_75 , 0.39_38_53_52, 0.92_15_36_07, 0.63_74_59_31, 0.27_75_90_43,
           0.84_70_22_95, 0.65_90_42_71, 0.58_67_66_14, 0.8_66_69_36 , 0.39_60_74_38,
           0.79_95_49_83, 0.42_22_06_97, 0.39_65_03_81, 0.7_84_98_64 , 0.56_15_02_01,
           0.15_67_89_25, 0.14_74_60_32, 0.34_54_21_14, 0.47_02_67_83, 0.11_95_64_89,
           0.25_42_14_35, 0.33_78_89_01, 0.68_93_48_42, 0.36_42_46_85, 0.71_73_78_98,
           0.38_98_34_49, 0.94_39_37_79, 0.39_57_55_88, 0.36_61_65_53, 0.87_10_46_65,
           0.64_63_02_03, 0.22_51_69_05, 0.88_27_08_04, 0.15_03_13_38, 0.75_14_43_45,
           0.46_45_90_25, 0.85_39_64_54, 0.86_35_56_43, 0.65_13_98_51, 0.70_26_60_61,
           0.30_24_13_89, 0.81_05_64_97, 0.88_86_59_69, 0.38_77_38_07, 0.70_63_58_49,
           0.90_71_84_59, 0.43_24_57_89, 0.28_00_06_54, 0.45_93_55_62, 0.08_77_35_19,
           0.9_55_21_51 , 0.93_90_15_11, 0.22_48_92_88], # uniform
           [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
            0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
            0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
            0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
            0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
            0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
            0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
            0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
            0., 0., 0., 0., 0., 0., 0., 0.],
           # fmt: on
       ]

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

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

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

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

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







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





       '''simple docstring'''







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

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

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

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

       __magic_name__				=       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	,  )

       __magic_name__				=       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	,  )

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

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