

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  a     :


						def __init__(				self  ,    A_  ,    A_=36000  ,    A_=13  ,    A_=7  ,    A_=True  ,    A_=32  ,    A_=42  ,    A_=5  ,    A_=2  ,    A_=4  ,    A_=2  ,    A_=32  ,    A_=80  ,    A_=33  ,    A_=0.0  ,    A_=1e-6  ,    A_=100  ,    A_=0.0  ,    ):







										'''simple docstring'''


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


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


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


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[Any]       						=			ids_tensor([self.batch_size, self.seq_length]  ,    self.vocab_size		)

										_UpperCAmelCase				:						Union[str, Any]       						=			self.get_config()

										return (config, input_ids)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


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

										_UpperCAmelCase				:						Union[str, Any]       						=			self.get_config()

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


						def 				_UpperCAmelCase (				self		):







										'''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 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						Tuple       						=			GPTSanJapaneseForConditionalGeneration(config=A_		)
										model.to(A_		)
										model.eval()
										_UpperCAmelCase				:						Optional[int]       						=			model(
										    input_ids=A_  ,    )
										self.parent.assertIsNotNone(A_		)







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


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_  ,    A_  ,    A_		):







										'''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 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			GPTSanJapaneseTester(self		)
										_UpperCAmelCase				:						str       						=			ConfigTester(self  ,    config_class=A_  ,    d_model=37		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										GPTSanJapaneseConfig()


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A_		)


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







										'''simple docstring'''


										super().test_model_parallelism()





						@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										pass







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


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			GPTSanJapaneseTester(self		)
										_UpperCAmelCase				:						Dict       						=			ConfigTester(self  ,    config_class=A_  ,    d_model=37		)


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										GPTSanJapaneseConfig()


						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						str       						=			self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A_		)


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







										'''simple docstring'''


										super().test_model_parallelism()


						@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."		)
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										pass


						@slow
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


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

										def check(A_  ,    A_  ,    A_=5e-4		):
														return abs(a - b		) < epsilon * max(abs(A_		)  ,    abs(A_		)		)

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


						@slow
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"		)
										_UpperCAmelCase				:						Dict       						=			GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"		)
										model.to(A_		)

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

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

										_UpperCAmelCase				:						Optional[Any]       						=			"left"
										_UpperCAmelCase				:						str       						=			tokenizer(A_  ,    return_tensors="pt"  ,    padding=A_		)
										_UpperCAmelCase				:						int       						=			inputs["input_ids"].to(A_		)

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

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

										_UpperCAmelCase				:						List[Any]       						=			tokenizer(sentences[0]  ,    return_tensors="pt"		).input_ids.to(A_		)
										_UpperCAmelCase				:						Union[str, Any]       						=			model.generate(
										    input_ids=A_  ,    max_new_tokens=3  ,    generation_config=A_		)

										_UpperCAmelCase				:						int       						=			tokenizer(sentences[1]  ,    return_tensors="pt"		).input_ids.to(A_		)
										_UpperCAmelCase				:						Optional[int]       						=			model.generate(input_ids=A_  ,    max_new_tokens=3  ,    generation_config=A_		)

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

										_UpperCAmelCase				:						Tuple       						=			tokenizer.batch_decode(A_  ,    skip_special_tokens=A_		)
										_UpperCAmelCase				:						Union[str, Any]       						=			tokenizer.decode(output_non_padded[0]  ,    skip_special_tokens=A_		)
										_UpperCAmelCase				:						str       						=			tokenizer.decode(output_padded[0]  ,    skip_special_tokens=A_		)

										_UpperCAmelCase				:						List[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(A_  ,    A_		)
										self.assertListEqual(A_  ,    [non_padded_sentence, padded_sentence]		)


						@tooslow
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


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


						@slow
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Tuple       						=			GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"		)
										_UpperCAmelCase				:						Optional[Any]       						=			GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"		)
										model.to(A_		)

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

										_UpperCAmelCase				:						List[Any]       						=			"武田信玄は、"
										_UpperCAmelCase				:						Optional[Any]       						=			tokenizer(A_  ,    return_tensors="pt"		).input_ids.to(A_		)
										_UpperCAmelCase				:						Any       						=			tokenizer([input_text, input_text]  ,    return_tensors="pt"		).input_ids.to(A_		)

										# spout from uniform and one-hot
										_UpperCAmelCase				:						Union[str, 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
										]

										_UpperCAmelCase				:						int       						=			model.generate(
										    input_ids=A_  ,    spout=spouts[0]  ,    max_new_tokens=20  ,    generation_config=A_  ,    )

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

										_UpperCAmelCase				:						Union[str, Any]       						=			model.generate(
										    input_ids=A_  ,    spout=A_  ,    max_new_tokens=20  ,    generation_config=A_  ,    )

										_UpperCAmelCase				:						str       						=			tokenizer.decode(outputa[0]		)
										_UpperCAmelCase				:						int       						=			tokenizer.decode(outputa[0]		)
										_UpperCAmelCase				:						Any       						=			tokenizer.batch_decode(A_		)

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





						@slow
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						str       						=			GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"		)
										_UpperCAmelCase				:						Dict       						=			GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"		)
										model.to(A_		)

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

										_UpperCAmelCase				:						Tuple       						=			"武田信玄"
										_UpperCAmelCase				:						Dict       						=			"織田信長"
										_UpperCAmelCase				:						Optional[Any]       						=			"は、"
										_UpperCAmelCase				:						int       						=			"が、"
										_UpperCAmelCase				:						Optional[int]       						=			tokenizer(A_  ,    prefix_text=A_  ,    return_tensors="pt"		)
										_UpperCAmelCase				:						Tuple       						=			tokenizer(A_  ,    prefix_text=A_  ,    return_tensors="pt"		)
										_UpperCAmelCase				:						Optional[int]       						=			tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]  ,    return_tensors="pt"		)

										_UpperCAmelCase				:						Dict       						=			model.generate(
										    input_ids=input_tok_a.input_ids.to(A_		)  ,    token_type_ids=input_tok_a.token_type_ids.to(A_		)  ,    max_new_tokens=20  ,    generation_config=A_  ,    )

										_UpperCAmelCase				:						str       						=			model.generate(
										    input_ids=input_tok_a.input_ids.to(A_		)  ,    token_type_ids=input_tok_a.token_type_ids.to(A_		)  ,    max_new_tokens=20  ,    generation_config=A_  ,    )

										_UpperCAmelCase				:						str       						=			model.generate(
										    input_ids=input_tok_a.input_ids.to(A_		)  ,    token_type_ids=input_tok_a.token_type_ids.to(A_		)  ,    attention_mask=input_tok_a.attention_mask.to(A_		)  ,    max_new_tokens=20  ,    generation_config=A_  ,    )

										_UpperCAmelCase				:						List[str]       						=			tokenizer.decode(outputa[0]		)
										_UpperCAmelCase				:						Dict       						=			tokenizer.decode(outputa[0]		)
										_UpperCAmelCase				:						Any       						=			tokenizer.batch_decode(A_		)

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





from hashlib import mda, shaaaa

SCREAMING_SNAKE_CASE_						=					(shaaaa, mda)

class  a     :


						def __init__(				self  ,    A_ = 8		):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			0B0
										_UpperCAmelCase				:						Optional[Any]       						=			size


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[Any]       						=			self.hash_(A_		)
										self.bitarray |= h


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						int       						=			self.hash_(A_		)
										return (h & self.bitarray) == h


						def __contains__(				self  ,    A_		):







										'''simple docstring'''


										return self.exists(A_		)


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Optional[int]       						=			bin(A_		)[2:]
										return res.zfill(self.size		)


						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										return self.format_bin(self.bitarray		)


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			0B0
										for func in HASH_FUNCTIONS:
														_UpperCAmelCase				:						str       						=			(
														    int.from_bytes(func(value.encode()		).digest()  ,    "little"		) % self.size
														)
														res |= 2**position
										return res


						def 				_UpperCAmelCase (				self  ,    A_		):







										'''simple docstring'''


										return self.format_bin(self.hash_(A_		)		)





						@property
						def 				_UpperCAmelCase (				self		):







										'''simple docstring'''


										_UpperCAmelCase				:						Union[str, Any]       						=			bin(self.bitarray		).count("1"		)
										return (n_ones / self.size) ** len(A_		)


