




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	,				lowerCamelCase	,				lowerCamelCase=36000	,				lowerCamelCase=13	,				lowerCamelCase=7	,				lowerCamelCase=True	,				lowerCamelCase=32	,				lowerCamelCase=42	,				lowerCamelCase=5	,				lowerCamelCase=2	,				lowerCamelCase=4	,				lowerCamelCase=2	,				lowerCamelCase=32	,				lowerCamelCase=80	,				lowerCamelCase=33	,				lowerCamelCase=0.0	,				lowerCamelCase=1E-6	,				lowerCamelCase=100	,				lowerCamelCase=0.0	,				) ->      Optional[int]:





								"""simple docstring"""


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

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





								"""simple docstring"""


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

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





								"""simple docstring"""


								lowercase__  :	Tuple        =							ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size    )

								lowercase__  :	str        =							self.get_config()

								return (config, input_ids)

	def    __a     (					self    ) ->      int:





								"""simple docstring"""


								lowercase__  :	int        =							ids_tensor([self.batch_size, self.seq_length]	,				self.vocab_size    )

								lowercase__  :	int        =							self.get_config()

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

	def    __a     (					self    ) ->      Dict:





								"""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	,				lowerCamelCase	,				lowerCamelCase	,				) ->      str:





								"""simple docstring"""


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







@require_torch
class  UpperCAmelCase(					UpperCAmelCase__   ,     UpperCAmelCase__   ,     unittest.TestCase					):



	"""simple docstring"""






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

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





								"""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    ) ->      Dict:





								"""simple docstring"""


								lowercase__  :	Union[str, Any]        =							GPTSanJapaneseTester(self    )
								lowercase__  :	int        =							ConfigTester(self	,				config_class=snake_case_	,				d_model=37    )

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





								"""simple docstring"""


								GPTSanJapaneseConfig()

	def    __a     (					self    ) ->      str:





								"""simple docstring"""


								lowercase__  :	List[Any]        =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*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    __a     (					self    ) ->      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    ) ->      Any:





								"""simple docstring"""


								pass







@require_torch
class  UpperCAmelCase(					UpperCAmelCase__   ,     UpperCAmelCase__   ,     unittest.TestCase					):



	"""simple docstring"""






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

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





								"""simple docstring"""


								lowercase__  :	Optional[int]        =							GPTSanJapaneseTester(self    )
								lowercase__  :	List[str]        =							ConfigTester(self	,				config_class=snake_case_	,				d_model=37    )

	def    __a     (					self    ) ->      Any:





								"""simple docstring"""


								GPTSanJapaneseConfig()

	def    __a     (					self    ) ->      int:





								"""simple docstring"""


								lowercase__  :	str        =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*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    __a     (					self    ) ->      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    ) ->      Union[str, Any]:





								"""simple docstring"""


								pass

	@slow
	def    __a     (					self    ) ->      Dict:





								"""simple docstring"""


								lowercase__  :	int        =							GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"    )
								lowercase__  :	Any        =							GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"    )
								lowercase__  :	Any        =							tokenizer.encode("武田信玄は"	,				return_tensors="pt"    )
								lowercase__  :	Tuple        =							model(snake_case_    )
								lowercase__  :	Any        =							outputs.logits.detach().cpu().numpy()
								# Output of original model created with mesh-tensoflow
								lowercase__  :	Union[str, Any]        =							[
								    # 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
								]
								lowercase__  :	str        =							np.array(snake_case_    ).flatten()
								lowercase__  :	Optional[int]        =							output_logits[0, :, :20].flatten()

								def check(lowerCamelCase	,				lowerCamelCase	,				lowerCamelCase=5E-4    ):
															return abs(a - b    ) < epsilon * max(abs(snake_case_    )	,				abs(snake_case_    )    )

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

	@slow
	def    __a     (					self    ) ->      Dict:





								"""simple docstring"""


								lowercase__  :	Dict        =							GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"    )
								lowercase__  :	List[str]        =							GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"    )
								model.to(snake_case_    )

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

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

								lowercase__  :	Optional[Any]        =							'left'
								lowercase__  :	Tuple        =							tokenizer(snake_case_	,				return_tensors="pt"	,				padding=snake_case_    )
								lowercase__  :	Optional[int]        =							inputs['input_ids'].to(snake_case_    )

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

								lowercase__  :	int        =							model.generate(
								    input_ids=snake_case_	,				attention_mask=inputs["attention_mask"].to(snake_case_    )	,				max_new_tokens=3	,				generation_config=snake_case_	,				)

								lowercase__  :	Any        =							tokenizer(sentences[0]	,				return_tensors="pt"    ).input_ids.to(snake_case_    )
								lowercase__  :	Optional[Any]        =							model.generate(
								    input_ids=snake_case_	,				max_new_tokens=3	,				generation_config=snake_case_    )

								lowercase__  :	List[Any]        =							tokenizer(sentences[1]	,				return_tensors="pt"    ).input_ids.to(snake_case_    )
								lowercase__  :	Tuple        =							model.generate(input_ids=snake_case_	,				max_new_tokens=3	,				generation_config=snake_case_    )

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

								lowercase__  :	Optional[int]        =							tokenizer.batch_decode(snake_case_	,				skip_special_tokens=snake_case_    )
								lowercase__  :	Tuple        =							tokenizer.decode(output_non_padded[0]	,				skip_special_tokens=snake_case_    )
								lowercase__  :	Dict        =							tokenizer.decode(output_padded[0]	,				skip_special_tokens=snake_case_    )

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

	@tooslow
	def    __a     (					self    ) ->      Any:





								"""simple docstring"""


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

	@slow
	def    __a     (					self    ) ->      int:





								"""simple docstring"""


								lowercase__  :	str        =							GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"    )
								lowercase__  :	List[Any]        =							GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"    )
								model.to(snake_case_    )

								# set deterministically
								lowercase__  :	Tuple        =							GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"    )
								lowercase__  :	Union[str, Any]        =							1

								lowercase__  :	List[str]        =							'武田信玄は、'
								lowercase__  :	Union[str, Any]        =							tokenizer(snake_case_	,				return_tensors="pt"    ).input_ids.to(snake_case_    )
								lowercase__  :	List[Any]        =							tokenizer([input_text, input_text]	,				return_tensors="pt"    ).input_ids.to(snake_case_    )

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

								lowercase__  :	Optional[Any]        =							model.generate(
								    input_ids=snake_case_	,				spout=spouts[0]	,				max_new_tokens=20	,				generation_config=snake_case_	,				)

								lowercase__  :	Tuple        =							model.generate(
								    input_ids=snake_case_	,				spout=spouts[1]	,				max_new_tokens=20	,				generation_config=snake_case_	,				)

								lowercase__  :	str        =							model.generate(
								    input_ids=snake_case_	,				spout=snake_case_	,				max_new_tokens=20	,				generation_config=snake_case_	,				)

								lowercase__  :	Optional[Any]        =							tokenizer.decode(outputa[0]    )
								lowercase__  :	Tuple        =							tokenizer.decode(outputa[0]    )
								lowercase__  :	Any        =							tokenizer.batch_decode(snake_case_    )

								lowercase__  :	Tuple        =							[
								    '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(snake_case_	,				snake_case_    )
								self.assertListEqual(snake_case_	,				[outa_sentence, outa_sentence]    )







	@slow
	def    __a     (					self    ) ->      Tuple:





								"""simple docstring"""


								lowercase__  :	Tuple        =							GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"    )
								lowercase__  :	List[str]        =							GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"    )
								model.to(snake_case_    )

								# set deterministically
								lowercase__  :	Tuple        =							GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"    )
								lowercase__  :	List[str]        =							1

								lowercase__  :	str        =							'武田信玄'
								lowercase__  :	List[Any]        =							'織田信長'
								lowercase__  :	Tuple        =							'は、'
								lowercase__  :	Optional[Any]        =							'が、'
								lowercase__  :	int        =							tokenizer(snake_case_	,				prefix_text=snake_case_	,				return_tensors="pt"    )
								lowercase__  :	Any        =							tokenizer(snake_case_	,				prefix_text=snake_case_	,				return_tensors="pt"    )
								lowercase__  :	Optional[Any]        =							tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]	,				return_tensors="pt"    )

								lowercase__  :	Optional[int]        =							model.generate(
								    input_ids=input_tok_a.input_ids.to(snake_case_    )	,				token_type_ids=input_tok_a.token_type_ids.to(snake_case_    )	,				max_new_tokens=20	,				generation_config=snake_case_	,				)

								lowercase__  :	List[Any]        =							model.generate(
								    input_ids=input_tok_a.input_ids.to(snake_case_    )	,				token_type_ids=input_tok_a.token_type_ids.to(snake_case_    )	,				max_new_tokens=20	,				generation_config=snake_case_	,				)

								lowercase__  :	Optional[Any]        =							model.generate(
								    input_ids=input_tok_a.input_ids.to(snake_case_    )	,				token_type_ids=input_tok_a.token_type_ids.to(snake_case_    )	,				attention_mask=input_tok_a.attention_mask.to(snake_case_    )	,				max_new_tokens=20	,				generation_config=snake_case_	,				)

								lowercase__  :	str        =							tokenizer.decode(outputa[0]    )
								lowercase__  :	List[str]        =							tokenizer.decode(outputa[0]    )
								lowercase__  :	str        =							tokenizer.batch_decode(snake_case_    )

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





import hashlib
import importlib.util
import json
import os
import pathlib
from types import ModuleType

import pytest
import requests

__a  :		str		     =   pathlib.Path.cwd().joinpath('''project_euler''')
__a  :		Tuple		     =   pathlib.Path.cwd().joinpath(
    '''scripts''', '''project_euler_answers.json'''
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
						__a  :		dict[str, str]		     =   json.load(file_handle)
def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			ModuleType:
							lowercase__  :	List[str]        =							importlib.util.spec_from_file_location(file_path.name    ,str(SCREAMING_SNAKE_CASE_    )    )
							lowercase__  :	int        =							importlib.util.module_from_spec(SCREAMING_SNAKE_CASE_    )  # type: ignore
							spec.loader.exec_module(SCREAMING_SNAKE_CASE_    )  # type: ignore
							return module
def  snake_case_					(		)      ->			list[pathlib.Path]:
							lowercase__  :	List[str]        =							[]
							for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
														if problem_dir_path.is_file() or problem_dir_path.name.startswith("_"    ):
																					continue
														for file_path in problem_dir_path.iterdir():
																					if file_path.suffix != ".py" or file_path.name.startswith(("_", "test")    ):
																												continue
																					solution_file_paths.append(SCREAMING_SNAKE_CASE_    )
							return solution_file_paths
def  snake_case_					(		)      ->			str:
							with open(os.environ["GITHUB_EVENT_PATH"]    ) as file:
														lowercase__  :	str        =							json.load(SCREAMING_SNAKE_CASE_    )
							return event["pull_request"]["url"] + "/files"
def  snake_case_					(		)      ->			list[pathlib.Path]:
							lowercase__  :	Optional[Any]        =							[]
							lowercase__  :	Dict        =							{
							    "Accept": "application/vnd.github.v3+json",
							    "Authorization": "token " + os.environ["GITHUB_TOKEN"],
							}
							lowercase__  :	Union[str, Any]        =							requests.get(get_files_url()    ,headers=SCREAMING_SNAKE_CASE_    ).json()
							for file in files:
														lowercase__  :	Optional[Any]        =							pathlib.Path.cwd().joinpath(file["filename"]    )
														if (
														    filepath.suffix != ".py"
														    or filepath.name.startswith(("_", "test")    )
														    or not filepath.name.startswith("sol"    )
														):
																					continue
														solution_file_paths.append(SCREAMING_SNAKE_CASE_    )
							return solution_file_paths
def  snake_case_					(		)      ->			list[pathlib.Path]:
							if os.environ.get("CI"    ) and os.environ.get("GITHUB_EVENT_NAME"    ) == "pull_request":
														# Return only if there are any, otherwise default to all solutions
														if filepaths := added_solution_file_path():
																					return filepaths
							return all_solution_file_paths()




@pytest.mark.parametrize(
    "solution_path"    ,collect_solution_file_paths()    ,ids=lambda SCREAMING_SNAKE_CASE_    : F"""{path.parent.name}/{path.name}"""    ,)
def  snake_case_					(		SCREAMING_SNAKE_CASE_    )      ->			None:
							lowercase__  :	str        =							solution_path.parent.name[8:].zfill(3    )
							lowercase__  :	str        =							PROBLEM_ANSWERS[problem_number]
							lowercase__  :	Optional[Any]        =							convert_path_to_module(SCREAMING_SNAKE_CASE_    )
							lowercase__  :	str        =							str(solution_module.solution()    )  # type: ignore
							lowercase__  :	List[str]        =							hashlib.shaaaa(answer.encode()    ).hexdigest()
							assert (
							    answer == expected
							), F"""Expected solution to {problem_number} to have hash {expected}, got {answer}"""