




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_		:



      def __init__( self  :      int				,       snake_case__  :      Optional[int]				,       snake_case__  :      Dict=3_60_00				,       snake_case__  :      Optional[int]=13				,       snake_case__  :      Optional[int]=7				,       snake_case__  :      int=True				,       snake_case__  :      List[str]=32				,       snake_case__  :      Optional[Any]=42				,       snake_case__  :      int=5				,       snake_case__  :      str=2				,       snake_case__  :      Tuple=4				,       snake_case__  :      List[Any]=2				,       snake_case__  :      Tuple=32				,       snake_case__  :      Tuple=80				,       snake_case__  :      str=33				,       snake_case__  :      Dict=0.0				,       snake_case__  :      int=1e-6				,       snake_case__  :      Optional[Any]=1_00				,       snake_case__  :      Dict=0.0				,       ):

        """simple docstring"""

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



      def   __a				( self  :      Optional[Any]     ):

        """simple docstring"""

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



      def   __a				( self  :      Dict     ):

        """simple docstring"""

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

        SCREAMING_SNAKE_CASE_							=       self.get_config()

        return (config, input_ids)



      def   __a				( self  :      List[str]     ):

        """simple docstring"""

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

        SCREAMING_SNAKE_CASE_							=       self.get_config()

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



      def   __a				( self  :      Optional[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   __a				( self  :      List[str]				,       snake_case__  :      Tuple				,       snake_case__  :      int				,       ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       GPTSanJapaneseForConditionalGeneration(config=lowerCamelCase__     )
        model.to(lowerCamelCase__     )
        model.eval()
        SCREAMING_SNAKE_CASE_							=       model(
            input_ids=lowerCamelCase__				,       )
        self.parent.assertIsNotNone(lowerCamelCase__     )



@require_torch
class       lowercase_		(_a       ,  _a       ,  unittest.TestCase   ):
      lowerCAmelCase__ 			=(GPTSanJapaneseModel,) if is_torch_available() else ()
      lowerCAmelCase__ 			=(
          {
              "conversational": GPTSanJapaneseForConditionalGeneration,
              "feature-extraction": GPTSanJapaneseForConditionalGeneration,
              "summarization": GPTSanJapaneseForConditionalGeneration,
              "text2text-generation": GPTSanJapaneseForConditionalGeneration,
              "translation": GPTSanJapaneseForConditionalGeneration,
          }
          if is_torch_available()
          else {}
      )
      lowerCAmelCase__ 			=False
      lowerCAmelCase__ 			=False
      lowerCAmelCase__ 			=False
      lowerCAmelCase__ 			=False
      lowerCAmelCase__ 			=False
      lowerCAmelCase__ 			=False
      lowerCAmelCase__ 			=False
      lowerCAmelCase__ 			=False
      # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
      lowerCAmelCase__ 			=[0.8, 0.9]



      def   __a				( self  :      int				,       snake_case__  :      int				,       snake_case__  :      List[Any]				,       snake_case__  :      Any				,       snake_case__  :      str				,       snake_case__  :      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   __a				( self  :      Tuple     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       GPTSanJapaneseTester(self     )
        SCREAMING_SNAKE_CASE_							=       ConfigTester(self				,       config_class=lowerCamelCase__				,       d_model=37     )



      def   __a				( self  :      Union[str, Any]     ):

        """simple docstring"""

        GPTSanJapaneseConfig()



      def   __a				( self  :      List[Any]     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase__     )



      @unittest.skip(
          reason='skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`'     )
      def   __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  :      int     ):

        """simple docstring"""

        pass



@require_torch
class       lowercase_		(_a       ,  _a       ,  unittest.TestCase   ):
      lowerCAmelCase__ 			=(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
      lowerCAmelCase__ 			=False
      lowerCAmelCase__ 			=False
      lowerCAmelCase__ 			=False
      lowerCAmelCase__ 			=False
      lowerCAmelCase__ 			=False
      lowerCAmelCase__ 			=False
      # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
      lowerCAmelCase__ 			=[0.8, 0.9]



      def   __a				( self  :      str     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       GPTSanJapaneseTester(self     )
        SCREAMING_SNAKE_CASE_							=       ConfigTester(self				,       config_class=lowerCamelCase__				,       d_model=37     )



      def   __a				( self  :      str     ):

        """simple docstring"""

        GPTSanJapaneseConfig()



      def   __a				( self  :      str     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCamelCase__     )



      @unittest.skip(
          reason='skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`'     )
      def   __a				( self  :      List[str]     ):

        """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  :      List[str]     ):

        """simple docstring"""

        pass



      @slow
      def   __a				( self  :      Tuple     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'     )
        SCREAMING_SNAKE_CASE_							=       GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'     )
        SCREAMING_SNAKE_CASE_							=       tokenizer.encode('武田信玄は'				,       return_tensors='pt'     )
        SCREAMING_SNAKE_CASE_							=       model(lowerCamelCase__     )
        SCREAMING_SNAKE_CASE_							=       outputs.logits.detach().cpu().numpy()
        # Output of original model created with mesh-tensoflow
        SCREAMING_SNAKE_CASE_							=       [
            # 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
        ]
        SCREAMING_SNAKE_CASE_							=       np.array(lowerCamelCase__     ).flatten()
        SCREAMING_SNAKE_CASE_							=       output_logits[0, :, :20].flatten()

        def check(snake_case__  :      int				,       snake_case__  :      str				,       snake_case__  :      str=5e-4     ):
          return abs(a - b     ) < epsilon * max(abs(lowerCamelCase__     )				,       abs(lowerCamelCase__     )     )

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



      @slow
      def   __a				( self  :      Optional[int]     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'     )
        SCREAMING_SNAKE_CASE_							=       GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'     )
        model.to(lowerCamelCase__     )

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

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

        SCREAMING_SNAKE_CASE_							=       "left"
        SCREAMING_SNAKE_CASE_							=       tokenizer(lowerCamelCase__				,       return_tensors='pt'				,       padding=lowerCamelCase__     )
        SCREAMING_SNAKE_CASE_							=       inputs["input_ids"].to(lowerCamelCase__     )

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

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

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

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

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

        SCREAMING_SNAKE_CASE_							=       tokenizer.batch_decode(lowerCamelCase__				,       skip_special_tokens=lowerCamelCase__     )
        SCREAMING_SNAKE_CASE_							=       tokenizer.decode(output_non_padded[0]				,       skip_special_tokens=lowerCamelCase__     )
        SCREAMING_SNAKE_CASE_							=       tokenizer.decode(output_padded[0]				,       skip_special_tokens=lowerCamelCase__     )

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



      @tooslow
      def   __a				( self  :      Any     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'     )
        SCREAMING_SNAKE_CASE_							=       GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'     )
        # Output of original model created with mesh-tensoflow
        SCREAMING_SNAKE_CASE_							=       [
            ("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:
          SCREAMING_SNAKE_CASE_							=       tokenizer.encode(lowerCamelCase__				,       return_tensors='pt'     )
          SCREAMING_SNAKE_CASE_							=       model(lowerCamelCase__     )
          SCREAMING_SNAKE_CASE_							=       outputs.logits.detach().cpu().numpy()[0]
          SCREAMING_SNAKE_CASE_							=       np.argmax(output_logits[-1]     )
          self.assertEqual(lowerCamelCase__				,       lowerCamelCase__     )



      @slow
      def   __a				( self  :      str     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'     )
        SCREAMING_SNAKE_CASE_							=       GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'     )
        model.to(lowerCamelCase__     )

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

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

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

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

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

        SCREAMING_SNAKE_CASE_							=       model.generate(
            input_ids=lowerCamelCase__				,       spout=lowerCamelCase__				,       max_new_tokens=20				,       generation_config=lowerCamelCase__				,       )

        SCREAMING_SNAKE_CASE_							=       tokenizer.decode(outputa[0]     )
        SCREAMING_SNAKE_CASE_							=       tokenizer.decode(outputa[0]     )
        SCREAMING_SNAKE_CASE_							=       tokenizer.batch_decode(lowerCamelCase__     )

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






      @slow
      def   __a				( self  :      List[Any]     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'     )
        SCREAMING_SNAKE_CASE_							=       GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'     )
        model.to(lowerCamelCase__     )

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

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

        SCREAMING_SNAKE_CASE_							=       model.generate(
            input_ids=input_tok_a.input_ids.to(lowerCamelCase__     )				,       token_type_ids=input_tok_a.token_type_ids.to(lowerCamelCase__     )				,       max_new_tokens=20				,       generation_config=lowerCamelCase__				,       )

        SCREAMING_SNAKE_CASE_							=       model.generate(
            input_ids=input_tok_a.input_ids.to(lowerCamelCase__     )				,       token_type_ids=input_tok_a.token_type_ids.to(lowerCamelCase__     )				,       max_new_tokens=20				,       generation_config=lowerCamelCase__				,       )

        SCREAMING_SNAKE_CASE_							=       model.generate(
            input_ids=input_tok_a.input_ids.to(lowerCamelCase__     )				,       token_type_ids=input_tok_a.token_type_ids.to(lowerCamelCase__     )				,       attention_mask=input_tok_a.attention_mask.to(lowerCamelCase__     )				,       max_new_tokens=20				,       generation_config=lowerCamelCase__				,       )

        SCREAMING_SNAKE_CASE_							=       tokenizer.decode(outputa[0]     )
        SCREAMING_SNAKE_CASE_							=       tokenizer.decode(outputa[0]     )
        SCREAMING_SNAKE_CASE_							=       tokenizer.batch_decode(lowerCamelCase__     )

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





from __future__ import annotations


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Union[str, Any]:
		SCREAMING_SNAKE_CASE_							=       0
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   ) - 1
		while left <= right:
				SCREAMING_SNAKE_CASE_							=       (left + right) // 2
				if left == right:
						if sorted_list[middle] < item:
								SCREAMING_SNAKE_CASE_							=       middle + 1
						break
				elif sorted_list[middle] < item:
						SCREAMING_SNAKE_CASE_							=       middle + 1
				else:
						SCREAMING_SNAKE_CASE_							=       middle - 1
		sorted_list.insert(lowerCAmelCase						,	lowerCAmelCase   )
		return sorted_list


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Any:
		SCREAMING_SNAKE_CASE_							=       []
		while left and right:
				if left[0][0] < right[0][0]:
						result.append(left.pop(0   )   )
				else:
						result.append(right.pop(0   )   )
		return result + left + right


def 				_a       (				lowerCAmelCase   )->  List[str]:
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   )
		if length <= 1:
				return list_ad
		SCREAMING_SNAKE_CASE_							=       length // 2
		return merge(sortlist_ad(list_ad[:middle]   )						,	sortlist_ad(list_ad[middle:]   )   )


def 				_a       (				lowerCAmelCase   )->  list[int]:

		if len(lowerCAmelCase   ) <= 1:
				return collection
		SCREAMING_SNAKE_CASE_							=       []
		SCREAMING_SNAKE_CASE_							=       False
		for i in range(0						,	len(lowerCAmelCase   )						,	2   ):
				if i == len(lowerCAmelCase   ) - 1:
						SCREAMING_SNAKE_CASE_							=       True
				else:
						if collection[i] < collection[i + 1]:
								two_paired_list.append([collection[i], collection[i + 1]]   )
						else:
								two_paired_list.append([collection[i + 1], collection[i]]   )
		SCREAMING_SNAKE_CASE_							=       sortlist_ad(lowerCAmelCase   )
		SCREAMING_SNAKE_CASE_							=       [i[0] for i in sorted_list_ad]
		result.append(sorted_list_ad[-1][1]   )
		if has_last_odd_item:
				SCREAMING_SNAKE_CASE_							=       collection[-1]
				SCREAMING_SNAKE_CASE_							=       binary_search_insertion(lowerCAmelCase						,	lowerCAmelCase   )
		SCREAMING_SNAKE_CASE_							=       False
		for i in range(len(lowerCAmelCase   ) - 1   ):
				if result[i] == collection[-1] and has_last_odd_item:
						SCREAMING_SNAKE_CASE_							=       True
				SCREAMING_SNAKE_CASE_							=       sorted_list_ad[i][1]
				# If last_odd_item is inserted before the item's index,
				# you should forward index one more.
				if is_last_odd_item_inserted_before_this_index:
						SCREAMING_SNAKE_CASE_							=       result[: i + 2] + binary_search_insertion(result[i + 2 :]						,	lowerCAmelCase   )
				else:
						SCREAMING_SNAKE_CASE_							=       result[: i + 1] + binary_search_insertion(result[i + 1 :]						,	lowerCAmelCase   )

		return result


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:    List[str]											=   input('''Enter numbers separated by a comma:\n''').strip()
						SCREAMING_SNAKE_CASE:    Any											=   [int(item) for item in user_input.split(''',''')]
						print(merge_insertion_sort(unsorted))