'''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   UpperCamelCase					:







    """simple docstring"""





    def __init__(					self     :						Any							,							UpperCAmelCase_     :						Dict							,							UpperCAmelCase_     :						Optional[int]=3_6_0_0_0							,							UpperCAmelCase_     :						Dict=1_3							,							UpperCAmelCase_     :						Optional[Any]=7							,							UpperCAmelCase_     :						str=True							,							UpperCAmelCase_     :						int=3_2							,							UpperCAmelCase_     :						List[str]=4_2							,							UpperCAmelCase_     :						Dict=5							,							UpperCAmelCase_     :						int=2							,							UpperCAmelCase_     :						Tuple=4							,							UpperCAmelCase_     :						List[str]=2							,							UpperCAmelCase_     :						Tuple=3_2							,							UpperCAmelCase_     :						Any=8_0							,							UpperCAmelCase_     :						Optional[int]=3_3							,							UpperCAmelCase_     :						Optional[Any]=0.0							,							UpperCAmelCase_     :						Optional[int]=1e-6							,							UpperCAmelCase_     :						List[Any]=1_0_0							,							UpperCAmelCase_     :						List[Any]=0.0							,							):


         """simple docstring"""







         a					:							List[str]							= vocab_size
         a					:							List[str]							= parent
         a					:							Optional[Any]							= batch_size
         a					:							List[str]							= num_contexts
         # For common tests
         a					:							Union[str, Any]							= self.num_contexts
         a					:							Dict							= is_training
         a					:							List[Any]							= hidden_size
         a					:							str							= num_ext_layers
         a					:							Optional[Any]							= ext_size
         a					:							Optional[Any]							= num_hidden_layers
         a					:							Any							= num_attention_heads
         a					:							List[str]							= num_experts
         a					:							List[Any]							= d_ff
         a					:							Dict							= d_ext
         a					:							Optional[int]							= d_spout
         a					:							Any							= dropout_rate
         a					:							Tuple							= layer_norm_epsilon
         a					:							List[str]							= expert_capacity
         a					:							Union[str, Any]							= router_jitter_noise





    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[int]):


         """simple docstring"""







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





    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple):


         """simple docstring"""







         a					:							List[str]							= ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size)

         a					:							Any							= self.get_config()

         return (config, input_ids)





    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple):


         """simple docstring"""







         a					:							List[str]							= ids_tensor([self.batch_size, self.seq_length]							,							self.vocab_size)

         a					:							Optional[Any]							= self.get_config()

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





    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[int]):


         """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    SCREAMING_SNAKE_CASE_     (					self     :						Tuple							,							UpperCAmelCase_     :						Tuple							,							UpperCAmelCase_     :						Optional[int]							,							):


         """simple docstring"""







         a					:							Union[str, Any]							= GPTSanJapaneseForConditionalGeneration(config=lowerCamelCase_)
         model.to(lowerCamelCase_)
         model.eval()
         a					:							Optional[Any]							= model(
             input_ids=lowerCamelCase_							,							)
         self.parent.assertIsNotNone(lowerCamelCase_)






@require_torch
class   UpperCamelCase					(       lowerCamelCase_    ,     lowerCamelCase_    ,     unittest.TestCase       ):







    """simple docstring"""





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





    def    SCREAMING_SNAKE_CASE_     (					self     :						Any							,							UpperCAmelCase_     :						Optional[int]							,							UpperCAmelCase_     :						Optional[Any]							,							UpperCAmelCase_     :						Optional[int]							,							UpperCAmelCase_     :						List[Any]							,							UpperCAmelCase_     :						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    SCREAMING_SNAKE_CASE_     (					self     :						Optional[int]):


         """simple docstring"""







         a					:							Union[str, Any]							= GPTSanJapaneseTester(self)
         a					:							List[Any]							= ConfigTester(self							,							config_class=lowerCamelCase_							,							d_model=3_7)





    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[int]):


         """simple docstring"""







         GPTSanJapaneseConfig()





    def    SCREAMING_SNAKE_CASE_     (					self     :						Any):


         """simple docstring"""







         a					:							Dict							= 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    SCREAMING_SNAKE_CASE_     (					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    SCREAMING_SNAKE_CASE_     (					self     :						Dict):


         """simple docstring"""







         pass






@require_torch
class   UpperCamelCase					(       lowerCamelCase_    ,     lowerCamelCase_    ,     unittest.TestCase       ):







    """simple docstring"""





    A     :    List[Any]				=			(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
    A     :    Optional[Any]				=			False
    A     :    int				=			False
    A     :    Optional[Any]				=			False
    A     :    List[str]				=			False
    A     :    Dict				=			False
    A     :    str				=			False
    # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
    A     :    str				=			[0.8, 0.9]





    def    SCREAMING_SNAKE_CASE_     (					self     :						Any):


         """simple docstring"""







         a					:							List[Any]							= GPTSanJapaneseTester(self)
         a					:							int							= ConfigTester(self							,							config_class=lowerCamelCase_							,							d_model=3_7)





    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple):


         """simple docstring"""







         GPTSanJapaneseConfig()





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[str]):


         """simple docstring"""







         a					:							Union[str, Any]							= 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    SCREAMING_SNAKE_CASE_     (					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    SCREAMING_SNAKE_CASE_     (					self     :						Optional[Any]):


         """simple docstring"""







         pass





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


         """simple docstring"""







         a					:							str							= GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese')
         a					:							List[Any]							= GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese')
         a					:							Optional[Any]							= tokenizer.encode('武田信玄は'							,							return_tensors='pt')
         a					:							List[str]							= model(lowerCamelCase_)
         a					:							List[Any]							= outputs.logits.detach().cpu().numpy()
         # Output of original model created with mesh-tensoflow
         a					:							Tuple							= [
             # 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
         ]
         a					:							Union[str, Any]							= np.array(lowerCamelCase_).flatten()
         a					:							List[Any]							= output_logits[0, :, :2_0].flatten()

         def check(UpperCAmelCase_     :						Optional[int]							,							UpperCAmelCase_     :						Union[str, Any]							,							UpperCAmelCase_     :						Union[str, Any]=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    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]):


         """simple docstring"""







         a					:							Dict							= GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese')
         a					:							List[str]							= GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese')
         model.to(lowerCamelCase_)

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

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

         a					:							int							= 'left'
         a					:							Optional[int]							= tokenizer(lowerCamelCase_							,							return_tensors='pt'							,							padding=lowerCamelCase_)
         a					:							Optional[Any]							= inputs['input_ids'].to(lowerCamelCase_)

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

         a					:							Optional[Any]							= model.generate(
             input_ids=lowerCamelCase_							,							attention_mask=inputs['attention_mask'].to(lowerCamelCase_)							,							max_new_tokens=3							,							generation_config=lowerCamelCase_							,							)

         a					:							Union[str, Any]							= tokenizer(sentences[0]							,							return_tensors='pt').input_ids.to(lowerCamelCase_)
         a					:							Any							= model.generate(
             input_ids=lowerCamelCase_							,							max_new_tokens=3							,							generation_config=lowerCamelCase_)

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

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

         a					:							Union[str, Any]							= tokenizer.batch_decode(lowerCamelCase_							,							skip_special_tokens=lowerCamelCase_)
         a					:							str							= tokenizer.decode(output_non_padded[0]							,							skip_special_tokens=lowerCamelCase_)
         a					:							Any							= tokenizer.decode(output_padded[0]							,							skip_special_tokens=lowerCamelCase_)

         a					:							Any							= [
             '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    SCREAMING_SNAKE_CASE_     (					self     :						Optional[int]):


         """simple docstring"""







         a					:							Optional[Any]							= GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese')
         a					:							Tuple							= GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese')
         # Output of original model created with mesh-tensoflow
         a					:							Tuple							= [
             ('武田信玄は', 3_5_6_7_5),
             ('武田信玄は、', 4_5),
             ('武田信玄は、この', 2_9),
             ('武田信玄は、このよう', 3_0_6_4_2),
             ('武田信玄は、このような', 3_5_6_8_0),
             ('武田信玄は、このような「', 8_6_4_0),
             ('武田信玄は、このような「武田', 3_1_6_1_7),
             ('武田信玄は、このような「武田家', 3_0_6_4_6),
             ('武田信玄は、このような「武田家の', 3_1_6_1_7),
             ('武田信玄は、このような「武田家の家', 3_1_3_8_1),
         ]
         for input, output in target:
              a					:							Any							= tokenizer.encode(lowerCamelCase_							,							return_tensors='pt')
              a					:							Optional[int]							= model(lowerCamelCase_)
              a					:							Union[str, Any]							= outputs.logits.detach().cpu().numpy()[0]
              a					:							List[str]							= np.argmax(output_logits[-1])
              self.assertEqual(lowerCamelCase_							,							lowerCamelCase_)





    @slow
    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple):


         """simple docstring"""







         a					:							int							= GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese')
         a					:							List[str]							= GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese')
         model.to(lowerCamelCase_)

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

         a					:							List[str]							= '武田信玄は、'
         a					:							Optional[int]							= tokenizer(lowerCamelCase_							,							return_tensors='pt').input_ids.to(lowerCamelCase_)
         a					:							Optional[Any]							= tokenizer([input_text, input_text]							,							return_tensors='pt').input_ids.to(lowerCamelCase_)

         # spout from uniform and one-hot
         a					:							Optional[int]							= [
             # 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
         ]

         a					:							str							= model.generate(
             input_ids=lowerCamelCase_							,							spout=spouts[0]							,							max_new_tokens=2_0							,							generation_config=lowerCamelCase_							,							)

         a					:							Dict							= model.generate(
             input_ids=lowerCamelCase_							,							spout=spouts[1]							,							max_new_tokens=2_0							,							generation_config=lowerCamelCase_							,							)

         a					:							Tuple							= model.generate(
             input_ids=lowerCamelCase_							,							spout=lowerCamelCase_							,							max_new_tokens=2_0							,							generation_config=lowerCamelCase_							,							)

         a					:							List[Any]							= tokenizer.decode(outputa[0])
         a					:							int							= tokenizer.decode(outputa[0])
         a					:							str							= tokenizer.batch_decode(lowerCamelCase_)

         a					:							Optional[int]							= [
             '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    SCREAMING_SNAKE_CASE_     (					self     :						Optional[Any]):


         """simple docstring"""







         a					:							Dict							= GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese')
         a					:							Dict							= GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese')
         model.to(lowerCamelCase_)

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

         a					:							Any							= '武田信玄'
         a					:							Any							= '織田信長'
         a					:							Dict							= 'は、'
         a					:							Dict							= 'が、'
         a					:							Any							= tokenizer(lowerCamelCase_							,							prefix_text=lowerCamelCase_							,							return_tensors='pt')
         a					:							Tuple							= tokenizer(lowerCamelCase_							,							prefix_text=lowerCamelCase_							,							return_tensors='pt')
         a					:							Optional[Any]							= tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]							,							return_tensors='pt')

         a					:							Optional[int]							= 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=2_0							,							generation_config=lowerCamelCase_							,							)

         a					:							Any							= 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=2_0							,							generation_config=lowerCamelCase_							,							)

         a					:							str							= 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=2_0							,							generation_config=lowerCamelCase_							,							)

         a					:							Optional[Any]							= tokenizer.decode(outputa[0])
         a					:							str							= tokenizer.decode(outputa[0])
         a					:							Optional[Any]							= tokenizer.batch_decode(lowerCamelCase_)

         a					:							int							= [
             '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

__SCREAMING_SNAKE_CASE	:							Union[str, Any]           =  [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]


class        __lowerCamelCase      :


				"""simple docstring"""







				def __init__(				self     :    Dict	,							lowerCamelCase_     :    int					):
								_lowerCAmelCase  						=-1
								_lowerCAmelCase  						=-1
								_lowerCAmelCase  						=[-1] * length
								_lowerCAmelCase  						=None
								_lowerCAmelCase  						=None






				def __repr__(				self     :    Tuple					):
								return F"Node(min_value={self.minn} max_value={self.maxx})"

def 		snake_case_   (				lowercase__     :    list[int]     ):






				'''simple docstring'''






				_lowerCAmelCase  						=Node(len(lowercase__     )     )
				_lowerCAmelCase						,							_lowerCAmelCase  						=min(lowercase__     ), max(lowercase__     )
				# Leaf node case where the node contains only one unique value
				if root.minn == root.maxx:
								return root
				_lowerCAmelCase  						=(root.minn + root.maxx) // 2

				_lowerCAmelCase  						=[]
				_lowerCAmelCase  						=[]

				for index, num in enumerate(lowercase__     ):
								if num <= pivot:
												left_arr.append(lowercase__     )
								else:
												right_arr.append(lowercase__     )
								_lowerCAmelCase  						=len(lowercase__     )
				_lowerCAmelCase  						=build_tree(lowercase__     )
				_lowerCAmelCase  						=build_tree(lowercase__     )
				return root

def 		snake_case_   (				lowercase__     :    Node | None       ,     lowercase__     :    int       ,     lowercase__     :    int     ):






				'''simple docstring'''






				if index < 0 or node is None:
								return 0
				# Leaf node cases
				if node.minn == node.maxx:
								return index + 1 if node.minn == num else 0
				_lowerCAmelCase  						=(node.minn + node.maxx) // 2
				if num <= pivot:
								# go the left subtree and map index to the left subtree
								return rank_till_index(node.left       ,     lowercase__       ,     node.map_left[index] - 1     )
				else:
								# go to the right subtree and map index to the right subtree
								return rank_till_index(node.right       ,     lowercase__       ,     index - node.map_left[index]     )

def 		snake_case_   (				lowercase__     :    Node | None       ,     lowercase__     :    int       ,     lowercase__     :    int       ,     lowercase__     :    int     ):






				'''simple docstring'''






				if start > end:
								return 0
				_lowerCAmelCase  						=rank_till_index(lowercase__       ,     lowercase__       ,     lowercase__     )
				_lowerCAmelCase  						=rank_till_index(lowercase__       ,     lowercase__       ,     start - 1     )
				return rank_till_end - rank_before_start

def 		snake_case_   (				lowercase__     :    Node | None       ,     lowercase__     :    int       ,     lowercase__     :    int       ,     lowercase__     :    int     ):






				'''simple docstring'''






				if index > (end - start) or start > end or node is None:
								return -1
				# Leaf node case
				if node.minn == node.maxx:
								return node.minn
				# Number of elements in the left subtree in interval [start, end]
				_lowerCAmelCase  						=node.map_left[end] - (
				    node.map_left[start - 1] if start else 0
				)
				if num_elements_in_left_tree > index:
								return quantile(
								    node.left       ,     lowercase__       ,     (node.map_left[start - 1] if start else 0)       ,     node.map_left[end] - 1       ,     )
				else:
								return quantile(
								    node.right       ,     index - num_elements_in_left_tree       ,     start - (node.map_left[start - 1] if start else 0)       ,     end - node.map_left[end]       ,     )

def 		snake_case_   (				lowercase__     :    Node | None       ,     lowercase__     :    int       ,     lowercase__     :    int       ,     lowercase__     :    int       ,     lowercase__     :    int     ):






				'''simple docstring'''






				if (
				    start > end
				    or node is None
				    or start_num > end_num
				    or node.minn > end_num
				    or node.maxx < start_num
				):
								return 0
				if start_num <= node.minn and node.maxx <= end_num:
								return end - start + 1
				_lowerCAmelCase  						=range_counting(
				    node.left       ,     (node.map_left[start - 1] if start else 0)       ,     node.map_left[end] - 1       ,     lowercase__       ,     lowercase__       ,     )
				_lowerCAmelCase  						=range_counting(
				    node.right       ,     start - (node.map_left[start - 1] if start else 0)       ,     end - node.map_left[end]       ,     lowercase__       ,     lowercase__       ,     )
				return left + right


if __name__ == "__main__":
		import doctest

		doctest.testmod()

