





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 							_UpperCamelCase    :

       def __init__(						self:       str    ,      _SCREAMING_SNAKE_CASE:       Tuple    ,      _SCREAMING_SNAKE_CASE:       Dict=36000    ,      _SCREAMING_SNAKE_CASE:       List[Any]=13    ,      _SCREAMING_SNAKE_CASE:       List[str]=7    ,      _SCREAMING_SNAKE_CASE:       str=True    ,      _SCREAMING_SNAKE_CASE:       int=32    ,      _SCREAMING_SNAKE_CASE:       Optional[int]=42    ,      _SCREAMING_SNAKE_CASE:       List[str]=5    ,      _SCREAMING_SNAKE_CASE:       Dict=2    ,      _SCREAMING_SNAKE_CASE:       Dict=4    ,      _SCREAMING_SNAKE_CASE:       Dict=2    ,      _SCREAMING_SNAKE_CASE:       str=32    ,      _SCREAMING_SNAKE_CASE:       List[Any]=80    ,      _SCREAMING_SNAKE_CASE:       Optional[Any]=33    ,      _SCREAMING_SNAKE_CASE:       Optional[Any]=0.0    ,      _SCREAMING_SNAKE_CASE:       str=1e-6    ,      _SCREAMING_SNAKE_CASE:       Union[str, Any]=100    ,      _SCREAMING_SNAKE_CASE:       Any=0.0    ,      )				->       Dict:




              """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 lowercase       (						self:       List[Any]						)				->       int:




              """simple docstring"""


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

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




              """simple docstring"""


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

              UpperCamelCase_      				=     self.get_config()

              return (config, input_ids)

       def lowercase       (						self:       Dict						)				->       Tuple:




              """simple docstring"""


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

              UpperCamelCase_      				=     self.get_config()

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

       def lowercase       (						self:       Tuple						)				->       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 lowercase       (						self:       List[str]    ,      _SCREAMING_SNAKE_CASE:       List[str]    ,      _SCREAMING_SNAKE_CASE:       List[Any]    ,      )				->       Any:




              """simple docstring"""


              UpperCamelCase_      				=     GPTSanJapaneseForConditionalGeneration(config=_SCREAMING_SNAKE_CASE						)
              model.to(_SCREAMING_SNAKE_CASE						)
              model.eval()
              UpperCamelCase_      				=     model(
                  input_ids=_SCREAMING_SNAKE_CASE    ,      )
              self.parent.assertIsNotNone(_SCREAMING_SNAKE_CASE						)



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

       def lowercase       (						self:       Union[str, Any]    ,      _SCREAMING_SNAKE_CASE:       Dict    ,      _SCREAMING_SNAKE_CASE:       Any    ,      _SCREAMING_SNAKE_CASE:       Tuple    ,      _SCREAMING_SNAKE_CASE:       int    ,      _SCREAMING_SNAKE_CASE:       Union[str, Any]						)				->       Optional[int]:




              """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 lowercase       (						self:       List[str]						)				->       Dict:




              """simple docstring"""


              UpperCamelCase_      				=     GPTSanJapaneseTester(self						)
              UpperCamelCase_      				=     ConfigTester(self    ,      config_class=_SCREAMING_SNAKE_CASE    ,      d_model=37						)

       def lowercase       (						self:       List[Any]						)				->       str:




              """simple docstring"""


              GPTSanJapaneseConfig()

       def lowercase       (						self:       List[Any]						)				->       Optional[int]:




              """simple docstring"""


              UpperCamelCase_      				=     self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE						)

       @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]						)				->       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 lowercase       (						self:       List[Any]						)				->       int:




              """simple docstring"""


              pass



@require_torch
class 							_UpperCamelCase    (		lowerCAmelCase_     ,  lowerCAmelCase_     ,  unittest.TestCase   ):
       _UpperCamelCase :    List[Any]   			=      (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
       _UpperCamelCase :    Optional[int]   			=      False
       _UpperCamelCase :    str   			=      False
       _UpperCamelCase :    List[Any]   			=      False
       _UpperCamelCase :    List[str]   			=      False
       _UpperCamelCase :    Any   			=      False
       _UpperCamelCase :    Union[str, Any]   			=      False
       # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
       _UpperCamelCase :    List[str]   			=      [0.8, 0.9]

       def lowercase       (						self:       Tuple						)				->       List[Any]:




              """simple docstring"""


              UpperCamelCase_      				=     GPTSanJapaneseTester(self						)
              UpperCamelCase_      				=     ConfigTester(self    ,      config_class=_SCREAMING_SNAKE_CASE    ,      d_model=37						)

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




              """simple docstring"""


              GPTSanJapaneseConfig()

       def lowercase       (						self:       List[str]						)				->       Any:




              """simple docstring"""


              UpperCamelCase_      				=     self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE						)

       @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]						)				->       Optional[int]:




              """simple docstring"""


              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[Any]						)				->       List[Any]:




              """simple docstring"""


              pass

       @slow
       def lowercase       (						self:       Union[str, Any]						)				->       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(_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     outputs.logits.detach().cpu().numpy()
              # Output of original model created with mesh-tensoflow
              UpperCamelCase_      				=     [
                  # 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
              ]
              UpperCamelCase_      				=     np.array(_SCREAMING_SNAKE_CASE						).flatten()
              UpperCamelCase_      				=     output_logits[0, :, :20].flatten()

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

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

       @slow
       def lowercase       (						self:       Tuple						)				->       Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_      				=     GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"						)
              UpperCamelCase_      				=     GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"						)
              model.to(_SCREAMING_SNAKE_CASE						)

              # 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(_SCREAMING_SNAKE_CASE    ,      return_tensors="pt"    ,      padding=_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     inputs["input_ids"].to(_SCREAMING_SNAKE_CASE						)

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

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

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

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

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

              UpperCamelCase_      				=     tokenizer.batch_decode(_SCREAMING_SNAKE_CASE    ,      skip_special_tokens=_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     tokenizer.decode(output_non_padded[0]    ,      skip_special_tokens=_SCREAMING_SNAKE_CASE						)
              UpperCamelCase_      				=     tokenizer.decode(output_padded[0]    ,      skip_special_tokens=_SCREAMING_SNAKE_CASE						)

              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(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)
              self.assertListEqual(_SCREAMING_SNAKE_CASE    ,      [non_padded_sentence, padded_sentence]						)

       @tooslow
       def lowercase       (						self:       Any						)				->       str:




              """simple docstring"""


              UpperCamelCase_      				=     GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"						)
              UpperCamelCase_      				=     GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"						)
              # Output of original model created with mesh-tensoflow
              UpperCamelCase_      				=     [
                  ("武田信玄は", 35675),
                  ("武田信玄は、", 45),
                  ("武田信玄は、この", 29),
                  ("武田信玄は、このよう", 30642),
                  ("武田信玄は、このような", 35680),
                  ("武田信玄は、このような「", 8640),
                  ("武田信玄は、このような「武田", 31617),
                  ("武田信玄は、このような「武田家", 30646),
                  ("武田信玄は、このような「武田家の", 31617),
                  ("武田信玄は、このような「武田家の家", 31381),
              ]
              for input, output in target:
                     UpperCamelCase_      				=     tokenizer.encode(_SCREAMING_SNAKE_CASE    ,      return_tensors="pt"						)
                     UpperCamelCase_      				=     model(_SCREAMING_SNAKE_CASE						)
                     UpperCamelCase_      				=     outputs.logits.detach().cpu().numpy()[0]
                     UpperCamelCase_      				=     np.argmax(output_logits[-1]						)
                     self.assertEqual(_SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE						)

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




              """simple docstring"""


              UpperCamelCase_      				=     GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"						)
              UpperCamelCase_      				=     GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"						)
              model.to(_SCREAMING_SNAKE_CASE						)

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

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

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

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

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

              UpperCamelCase_      				=     model.generate(
                  input_ids=_SCREAMING_SNAKE_CASE    ,      spout=_SCREAMING_SNAKE_CASE    ,      max_new_tokens=20    ,      generation_config=_SCREAMING_SNAKE_CASE    ,      )

              UpperCamelCase_      				=     tokenizer.decode(outputa[0]						)
              UpperCamelCase_      				=     tokenizer.decode(outputa[0]						)
              UpperCamelCase_      				=     tokenizer.batch_decode(_SCREAMING_SNAKE_CASE						)

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



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




              """simple docstring"""


              UpperCamelCase_      				=     GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"						)
              UpperCamelCase_      				=     GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"						)
              model.to(_SCREAMING_SNAKE_CASE						)

              # 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(_SCREAMING_SNAKE_CASE    ,      prefix_text=_SCREAMING_SNAKE_CASE    ,      return_tensors="pt"						)
              UpperCamelCase_      				=     tokenizer(_SCREAMING_SNAKE_CASE    ,      prefix_text=_SCREAMING_SNAKE_CASE    ,      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(_SCREAMING_SNAKE_CASE						)    ,      token_type_ids=input_tok_a.token_type_ids.to(_SCREAMING_SNAKE_CASE						)    ,      max_new_tokens=20    ,      generation_config=_SCREAMING_SNAKE_CASE    ,      )

              UpperCamelCase_      				=     model.generate(
                  input_ids=input_tok_a.input_ids.to(_SCREAMING_SNAKE_CASE						)    ,      token_type_ids=input_tok_a.token_type_ids.to(_SCREAMING_SNAKE_CASE						)    ,      max_new_tokens=20    ,      generation_config=_SCREAMING_SNAKE_CASE    ,      )

              UpperCamelCase_      				=     model.generate(
                  input_ids=input_tok_a.input_ids.to(_SCREAMING_SNAKE_CASE						)    ,      token_type_ids=input_tok_a.token_type_ids.to(_SCREAMING_SNAKE_CASE						)    ,      attention_mask=input_tok_a.attention_mask.to(_SCREAMING_SNAKE_CASE						)    ,      max_new_tokens=20    ,      generation_config=_SCREAMING_SNAKE_CASE    ,      )

              UpperCamelCase_      				=     tokenizer.decode(outputa[0]						)
              UpperCamelCase_      				=     tokenizer.decode(outputa[0]						)
              UpperCamelCase_      				=     tokenizer.batch_decode(_SCREAMING_SNAKE_CASE						)

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








import math




def        lowerCAmelCase_       (							UpperCamelCase_     )   ->						bool:

       if 1 < number < 4:
              # 2 and 3 are primes
              return True
       elif number < 2 or number % 2 == 0 or number % 3 == 0:
              # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
              return False

       # All primes number are in format of 6k +/- 1
       for i in range(5    ,						int(math.sqrt(UpperCamelCase_     ) + 1     )    ,						6     ):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True




def        lowerCAmelCase_       (							UpperCamelCase_ = 600851475143     )   ->						int:

       try:
              UpperCamelCase_      				=     int(UpperCamelCase_     )
       except (TypeError, ValueError):
              raise TypeError("Parameter n must be int or castable to int."     )
       if n <= 0:
              raise ValueError("Parameter n must be greater than or equal to one."     )
       UpperCamelCase_      				=     0
       if is_prime(UpperCamelCase_     ):
              return n
       while n % 2 == 0:
              n //= 2
       if is_prime(UpperCamelCase_     ):
              return n
       for i in range(3    ,						int(math.sqrt(UpperCamelCase_     )     ) + 1    ,						2     ):
              if n % i == 0:
                     if is_prime(n // i     ):
                            UpperCamelCase_      				=     n // i
                            break
                     elif is_prime(UpperCamelCase_     ):
                            UpperCamelCase_      				=     i
       return max_number


if __name__ == "__main__":
     print(f'''{solution() = }''')

