




'''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      :       Union[str, Any]							, _lowerCamelCase      :       Optional[Any]							, _lowerCamelCase      :       str=36000							, _lowerCamelCase      :       Optional[Any]=13							, _lowerCamelCase      :       List[str]=7							, _lowerCamelCase      :       Dict=True							, _lowerCamelCase      :       Any=32							, _lowerCamelCase      :       Union[str, Any]=42							, _lowerCamelCase      :       Optional[Any]=5							, _lowerCamelCase      :       Dict=2							, _lowerCamelCase      :       Optional[int]=4							, _lowerCamelCase      :       str=2							, _lowerCamelCase      :       int=32							, _lowerCamelCase      :       Optional[Any]=80							, _lowerCamelCase      :       str=33							, _lowerCamelCase      :       Any=0.0							, _lowerCamelCase      :       Optional[Any]=1E-6							, _lowerCamelCase      :       Tuple=100							, _lowerCamelCase      :       Tuple=0.0							, ):





											"""simple docstring"""







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







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





											"""simple docstring"""







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







						def       _a    (     self      :       Dict    ):





											"""simple docstring"""







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

											A_ :     Any =							self.get_config()

											return (config, input_ids)







						def       _a    (     self      :       Dict    ):





											"""simple docstring"""







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

											A_ :     int =							self.get_config()

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







						def       _a    (     self      :       Tuple    ):





											"""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      :       Union[str, Any]							, _lowerCamelCase      :       Any							, _lowerCamelCase      :       Optional[Any]							, ):





											"""simple docstring"""







											A_ :     Optional[Any] =							GPTSanJapaneseForConditionalGeneration(config=_lowerCamelCase    )
											model.to(_lowerCamelCase    )
											model.eval()
											A_ :     List[Any] =							model(
											    input_ids=_lowerCamelCase							, )
											self.parent.assertIsNotNone(_lowerCamelCase    )






@require_torch
class     UpperCamelCase_     (a__,      a__,      unittest.TestCase     ):







						"""simple docstring"""


						_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      :       Union[str, Any]							, _lowerCamelCase      :       Any							, _lowerCamelCase      :       int							, _lowerCamelCase      :       Any							, _lowerCamelCase      :       Optional[int]							, _lowerCamelCase      :       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       _a    (     self      :       List[str]    ):





											"""simple docstring"""







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







						def       _a    (     self      :       Optional[Any]    ):





											"""simple docstring"""







											GPTSanJapaneseConfig()







						def       _a    (     self      :       Any    ):





											"""simple docstring"""







											A_ :     Tuple =							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      :       Optional[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      :       int    ):





											"""simple docstring"""







											pass






@require_torch
class     UpperCamelCase_     (a__,      a__,      unittest.TestCase     ):







						"""simple docstring"""


						_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      :       List[str]    ):





											"""simple docstring"""







											A_ :     List[Any] =							GPTSanJapaneseTester(self    )
											A_ :     Union[str, Any] =							ConfigTester(self							, config_class=_lowerCamelCase							, d_model=37    )







						def       _a    (     self      :       Dict    ):





											"""simple docstring"""







											GPTSanJapaneseConfig()







						def       _a    (     self      :       List[str]    ):





											"""simple docstring"""







											A_ :     Optional[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       _a    (     self      :       Optional[int]    ):





											"""simple docstring"""







											super().test_model_parallelism()







						@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''    )
						def       _a    (     self      :       List[str]    ):





											"""simple docstring"""







											pass







						@slow
						def       _a    (     self      :       Optional[Any]    ):





											"""simple docstring"""







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

											def check(_lowerCamelCase      :       Union[str, Any]							, _lowerCamelCase      :       Any							, _lowerCamelCase      :       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[Any]    ):





											"""simple docstring"""







											A_ :     Union[str, Any] =							GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
											A_ :     Optional[int] =							GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
											model.to(_lowerCamelCase    )

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

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

											A_ :     Tuple =							'''left'''
											A_ :     Tuple =							tokenizer(_lowerCamelCase							, return_tensors='''pt'''							, padding=_lowerCamelCase    )
											A_ :     List[Any] =							inputs['''input_ids'''].to(_lowerCamelCase    )

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

											A_ :     Optional[int] =							model.generate(
											    input_ids=_lowerCamelCase							, attention_mask=inputs['''attention_mask'''].to(_lowerCamelCase    )							, max_new_tokens=3							, generation_config=_lowerCamelCase							, )

											A_ :     str =							tokenizer(sentences[0]							, return_tensors='''pt'''    ).input_ids.to(_lowerCamelCase    )
											A_ :     int =							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_ :     List[Any] =							model.generate(input_ids=_lowerCamelCase							, max_new_tokens=3							, generation_config=_lowerCamelCase    )

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

											A_ :     List[Any] =							tokenizer.batch_decode(_lowerCamelCase							, skip_special_tokens=_lowerCamelCase    )
											A_ :     Dict =							tokenizer.decode(output_non_padded[0]							, skip_special_tokens=_lowerCamelCase    )
											A_ :     Optional[int] =							tokenizer.decode(output_padded[0]							, skip_special_tokens=_lowerCamelCase    )

											A_ :     Optional[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       _a    (     self      :       str    ):





											"""simple docstring"""







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







						@slow
						def       _a    (     self      :       Optional[Any]    ):





											"""simple docstring"""







											A_ :     Optional[int] =							GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
											A_ :     Optional[Any] =							GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
											model.to(_lowerCamelCase    )

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

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

											# spout from uniform and one-hot
											A_ :     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
											]

											A_ :     Union[str, Any] =							model.generate(
											    input_ids=_lowerCamelCase							, spout=spouts[0]							, max_new_tokens=20							, generation_config=_lowerCamelCase							, )

											A_ :     List[Any] =							model.generate(
											    input_ids=_lowerCamelCase							, spout=spouts[1]							, max_new_tokens=20							, generation_config=_lowerCamelCase							, )

											A_ :     Tuple =							model.generate(
											    input_ids=_lowerCamelCase							, spout=_lowerCamelCase							, max_new_tokens=20							, generation_config=_lowerCamelCase							, )

											A_ :     Optional[Any] =							tokenizer.decode(outputa[0]    )
											A_ :     Any =							tokenizer.decode(outputa[0]    )
											A_ :     int =							tokenizer.batch_decode(_lowerCamelCase    )

											A_ :     List[str] =							[
											    '''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      :       Optional[Any]    ):





											"""simple docstring"""







											A_ :     List[str] =							GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
											A_ :     Union[str, Any] =							GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
											model.to(_lowerCamelCase    )

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

											A_ :     int =							'''武田信玄'''
											A_ :     List[Any] =							'''織田信長'''
											A_ :     Any =							'''は、'''
											A_ :     Union[str, Any] =							'''が、'''
											A_ :     List[str] =							tokenizer(_lowerCamelCase							, prefix_text=_lowerCamelCase							, return_tensors='''pt'''    )
											A_ :     Optional[Any] =							tokenizer(_lowerCamelCase							, prefix_text=_lowerCamelCase							, return_tensors='''pt'''    )
											A_ :     Union[str, Any] =							tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]							, return_tensors='''pt'''    )

											A_ :     str =							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							, )

											A_ :     Union[str, 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=20							, generation_config=_lowerCamelCase							, )

											A_ :     List[Any] =							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							, )

											A_ :     str =							tokenizer.decode(outputa[0]    )
											A_ :     Dict =							tokenizer.decode(outputa[0]    )
											A_ :     Tuple =							tokenizer.batch_decode(_lowerCamelCase    )

											A_ :     Optional[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(_lowerCamelCase							, _lowerCamelCase    )
											self.assertListEqual(_lowerCamelCase							, [outa_sentence, outa_sentence]    )









'''simple docstring'''




import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


snake_case__          =						logging.getLogger(__name__)






class     UpperCamelCase_     :







						"""simple docstring"""


						def __init__(     self      :       Tuple    ):





											"""simple docstring"""







											A_ :     Optional[int] =							False







						def       _a    (     self      :       Any							, _lowerCamelCase      :       List[str]							, _lowerCamelCase      :       Any							, _lowerCamelCase      :       str							, _lowerCamelCase      :       int    ):





											"""simple docstring"""







											if not self.initialized:
																A_ :     Optional[Any] =							RagRetriever(
																    _lowerCamelCase							, question_encoder_tokenizer=_lowerCamelCase							, generator_tokenizer=_lowerCamelCase							, index=_lowerCamelCase							, init_retrieval=_lowerCamelCase							, )
																A_ :     Optional[Any] =							True







						def       _a    (     self      :       int    ):





											"""simple docstring"""







											self.retriever.index.init_index()







						def       _a    (     self      :       Any    ):





											"""simple docstring"""







											del self.retriever
											A_ :     List[Any] =							False







						def       _a    (     self      :       Optional[int]							, _lowerCamelCase      :       Union[str, Any]							, _lowerCamelCase      :       Union[str, Any]    ):





											"""simple docstring"""







											A_ ,A_ :     Dict =							self.retriever._main_retrieve(_lowerCamelCase							, _lowerCamelCase    )
											A_ :     int =							self.retriever.index.get_doc_dicts(_lowerCamelCase    )
											return doc_ids, retrieved_doc_embeds, doc_dicts






class     UpperCamelCase_     (a__     ):







						"""simple docstring"""


						def __init__(     self      :       int							, _lowerCamelCase      :       Optional[Any]							, _lowerCamelCase      :       Dict							, _lowerCamelCase      :       Optional[int]							, _lowerCamelCase      :       Dict							, _lowerCamelCase      :       Tuple=None    ):





											"""simple docstring"""







											if index is not None and index.is_initialized() and len(_lowerCamelCase    ) > 0:
																raise ValueError(
																    '''When using Ray for distributed fine-tuning, '''
																    '''you\'ll need to provide the paths instead, '''
																    '''as the dataset and the index are loaded '''
																    '''separately. More info in examples/rag/use_own_knowledge_dataset.py '''    )

											super().__init__(
											    _lowerCamelCase							, question_encoder_tokenizer=_lowerCamelCase							, generator_tokenizer=_lowerCamelCase							, index=_lowerCamelCase							, init_retrieval=_lowerCamelCase							, )

											A_ :     Tuple =							retrieval_workers
											A_ :     Any =							question_encoder_tokenizer
											A_ :     Dict =							generator_tokenizer
											if len(self.retrieval_workers    ) > 0:
																ray.get(
																    [
																        worker.create_rag_retriever.remote(_lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase							, _lowerCamelCase    )
																        for worker in self.retrieval_workers
																    ]    )







						def       _a    (     self      :       Dict    ):





											"""simple docstring"""







											logger.info('''initializing retrieval'''    )

											if len(self.retrieval_workers    ) > 0:
																ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]    )
											else:
																# Non-distributed training. Load index into this same process.
																self.index.init_index()







						def       _a    (     self      :       Dict							, _lowerCamelCase      :       Tuple							, _lowerCamelCase      :       int    ):





											"""simple docstring"""







											if len(self.retrieval_workers    ) > 0:
																# Select a random retrieval actor.
																A_ :     Optional[int] =							self.retrieval_workers[random.randint(0							, len(self.retrieval_workers    ) - 1    )]
																A_ ,A_ ,A_ :     Union[str, Any] =							ray.get(
																    random_worker.retrieve.remote(_lowerCamelCase							, _lowerCamelCase    )    )
											else:
																A_ ,A_ :     List[str] =							self._main_retrieve(_lowerCamelCase							, _lowerCamelCase    )
																A_ :     Union[str, Any] =							self.index.get_doc_dicts(_lowerCamelCase    )
											return retrieved_doc_embeds, doc_ids, doc_dicts







						@classmethod
						def       _a    (     cls      :       List[str]							, _lowerCamelCase      :       Union[str, Any]							, _lowerCamelCase      :       Optional[Any]=None							, **_lowerCamelCase      :       str    ):





											"""simple docstring"""







											return super(_lowerCamelCase							, cls    ).get_tokenizers(_lowerCamelCase							, _lowerCamelCase							, **_lowerCamelCase    )







						@classmethod
						def       _a    (     cls      :       Optional[Any]							, _lowerCamelCase      :       Any							, _lowerCamelCase      :       List[Any]							, _lowerCamelCase      :       str=None							, **_lowerCamelCase      :       Tuple    ):





											"""simple docstring"""







											A_ :     Tuple =							kwargs.pop('''config'''							, _lowerCamelCase    ) or RagConfig.from_pretrained(_lowerCamelCase							, **_lowerCamelCase    )
											A_ :     List[Any] =							RagTokenizer.from_pretrained(_lowerCamelCase							, config=_lowerCamelCase    )
											A_ :     Union[str, Any] =							rag_tokenizer.question_encoder
											A_ :     Dict =							rag_tokenizer.generator

											if indexed_dataset is not None:
																A_ :     Any =							'''custom'''
																A_ :     int =							CustomHFIndex(config.retrieval_vector_size							, _lowerCamelCase    )
											else:
																A_ :     Optional[int] =							cls._build_index(_lowerCamelCase    )

											return cls(
											    _lowerCamelCase							, question_encoder_tokenizer=_lowerCamelCase							, generator_tokenizer=_lowerCamelCase							, retrieval_workers=_lowerCamelCase							, index=_lowerCamelCase							, )







						def       _a    (     self      :       Optional[Any]    ):





											"""simple docstring"""







											logger.info('''re-loading the new dataset with embeddings'''    )
											# access from the training loop

											ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]    )

											# build the index object again
											A_ :     Dict =							self._build_index(self.config    )

											ray.get(
											    [
											        worker.create_rag_retriever.remote(
											            self.config							, self.question_encoder_tokenizer							, self.generator_tokenizer							, _lowerCamelCase    )
											        for worker in self.retrieval_workers
											    ]    )



