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__=3_60_00				,       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__=1_00				,       lowerCAmelCase__=0.0				,       )					-> str:

							'''simple docstring'''


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





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Dict:

							'''simple docstring'''


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





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

							'''simple docstring'''


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

							__lowercase			      =							self.get_config()

							return (config, input_ids)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Tuple:

							'''simple docstring'''


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

							__lowercase			      =							self.get_config()

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





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

							'''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				,       lowerCAmelCase__				,       lowerCAmelCase__				,       )					-> Dict:

							'''simple docstring'''


							__lowercase			      =							GPTSanJapaneseForConditionalGeneration(config=lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()
							__lowercase			      =							model(
							    input_ids=lowerCAmelCase__				,       )
							self.parent.assertIsNotNone(lowerCAmelCase__						)




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


			"""simple docstring"""






			__a  :			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  :			Any        =						False
			__a  :			List[Any]        =						False
			__a  :			Any        =						False
			__a  :			Optional[Any]        =						False
			__a  :			str        =						False
			__a  :			List[str]        =						False
			__a  :			Optional[Any]        =						False
			__a  :			int        =						False
			# The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
			__a  :			Union[str, Any]        =						[0.8, 0.9]





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Tuple:

							'''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						)					-> Union[str, Any]:

							'''simple docstring'''


							__lowercase			      =							GPTSanJapaneseTester(self						)
							__lowercase			      =							ConfigTester(self				,       config_class=lowerCAmelCase__				,       d_model=37						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> str:

							'''simple docstring'''


							GPTSanJapaneseConfig()





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

							'''simple docstring'''


							__lowercase			      =							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						)					-> Dict:

							'''simple docstring'''


							super().test_model_parallelism()





			@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''						)
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							pass




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


			"""simple docstring"""






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





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

							'''simple docstring'''


							__lowercase			      =							GPTSanJapaneseTester(self						)
							__lowercase			      =							ConfigTester(self				,       config_class=lowerCAmelCase__				,       d_model=37						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							GPTSanJapaneseConfig()





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''simple docstring'''


							__lowercase			      =							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						)					-> str:

							'''simple docstring'''


							pass





			@slow
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[str]:

							'''simple docstring'''


							__lowercase			      =							GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
							__lowercase			      =							GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
							__lowercase			      =							tokenizer.encode('''武田信玄は'''				,       return_tensors='''pt'''						)
							__lowercase			      =							model(lowerCAmelCase__						)
							__lowercase			      =							outputs.logits.detach().cpu().numpy()
							# Output of original model created with mesh-tensoflow
							__lowercase			      =							[
							    # fmt: off
							    [-12.037_8398_8952_6367, -12.433_0615_9973_1445, -14.333_8403_7017_8223, -12.450_3459_9304_1992, -11.1_6613_7695_3125,
							    -11.930_1376_3427_7344, -10.659_7404_4799_8047, -12.909_5745_0866_6992, -13.241_0430_9082_0312, -13.398_5795_9747_3145,
							    -11.107_5248_7182_6172, -12.3_6859_4169_6167, -22.97_9431_1523_4375, -10.481_0676_5747_0703, -12.484_0307_2357_1777,
							    -12.807_3606_4910_8887, -14.769_7000_5035_4004, -12.233_5796_3562_0117, -13.428_1454_0863_0371, -22.624_1779_3273_9258],
							    [-7.511_1498_8327_0264, -8.281_8517_6849_3652, -7.943_1271_5530_3955, -7.55_0213_3369_4458, -6.49_8699_6650_6958,
							    -7.586_7962_8372_1924, -6.978_0859_9472_0459, -7.839_1451_8356_3232, -8.21_9641_6854_8584, -8.695_0912_4755_8594,
							    -6.706_9106_1019_8975, -6.6585_7982_6354_9805, -19.565_6986_2365_7227, -5.353_8427_3529_0527, -8.350_6860_7330_3223,
							    -8.039_3886_5661_6211, -10.856_5692_9016_1133, -7.75_1544_4755_5542, -8.819_0221_7864_9902, -19.51_5327_4536_1328],
							    [-9.73_0662_3458_8623, -10.223_9227_2949_2188, -9.932_9814_9108_8867, -11.857_8367_2332_7637, -7.662_6262_6647_9492,
							    -11.13_5291_0995_4834, -7.765_0976_1810_3027, -11.472_9232_7880_8594, -9.543_1499_4812_0117, -11.905_6339_2639_1602,
							    -9.366_1642_0745_8496, -11.5_7342_8153_9917, -23.699_0032_1960_4492, -9.429_5902_2521_9727, -10.42_8392_4102_7832,
							    -10.585_2403_6407_4707, -10.94_7719_5739_7461, -11.095_4160_6903_0762, -10.390_2406_6925_0488, -23.769_3729_4006_3477],
							    [-9.728_2657_6232_9102, -9.859_7126_0070_8008, -10.09_7292_9000_8545, -9.678_5221_0998_5352, -6.879_5199_3942_2607,
							    -9.68_4875_4882_8125, -4.2803_4257_8887_9395, -10.018_9142_2271_7285, -9.308_4459_3048_0957, -10.63_3945_4650_8789,
							    -8.083_6467_7429_1992, -9.06_3014_9841_3086, -21.904_2663_5742_1875, -8.90_1608_4671_0205, -8.841_8760_2996_8262,
							    -11.856_7199_7070_3125, -12.079_3981_5521_2402, -11.233_7532_0434_5703, -10.177_3386_0015_8691, -21.87_2566_2231_4453],
							    [-9.669_7645_1873_7793, -9.614_1986_8469_2383, -9.814_5103_4545_8984, -9.996_5019_2260_7422, -11.375_6904_6020_5078,
							    -10.113_4052_2766_1133, -10.546_8673_7060_5469, -10.04_3690_6814_5752, -10.907_8092_5750_7324, -10.504_2161_9415_2832,
							    -11.129_1990_2801_5137, -10.151_1240_0054_9316, -21.96_5867_9962_1582, -9.086_3494_8730_4688, -11.730_3390_5029_2969,
							    -10.460_6676_1016_8457, -10.298_0499_2675_7812, -10.784_1482_1624_7559, -10.840_6934_7381_5918, -22.03_1528_4729_0039],
							    # fmt: on
							]
							__lowercase			      =							np.array(lowerCAmelCase__						).flatten()
							__lowercase			      =							output_logits[0, :, :20].flatten()

							def check(lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__=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						)					-> Dict:

							'''simple docstring'''


							__lowercase			      =							GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
							__lowercase			      =							GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
							model.to(lowerCAmelCase__						)

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

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

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

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

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

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

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

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

							__lowercase			      =							tokenizer.batch_decode(lowerCAmelCase__				,       skip_special_tokens=lowerCAmelCase__						)
							__lowercase			      =							tokenizer.decode(output_non_padded[0]				,       skip_special_tokens=lowerCAmelCase__						)
							__lowercase			      =							tokenizer.decode(output_padded[0]				,       skip_special_tokens=lowerCAmelCase__						)

							__lowercase			      =							[
							    "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						)					-> str:

							'''simple docstring'''


							__lowercase			      =							GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
							__lowercase			      =							GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
							# Output of original model created with mesh-tensoflow
							__lowercase			      =							[
							    ("武田信玄は", 3_56_75),
							    ("武田信玄は、", 45),
							    ("武田信玄は、この", 29),
							    ("武田信玄は、このよう", 3_06_42),
							    ("武田信玄は、このような", 3_56_80),
							    ("武田信玄は、このような「", 86_40),
							    ("武田信玄は、このような「武田", 3_16_17),
							    ("武田信玄は、このような「武田家", 3_06_46),
							    ("武田信玄は、このような「武田家の", 3_16_17),
							    ("武田信玄は、このような「武田家の家", 3_13_81),
							]
							for input, output in target:
											__lowercase			      =							tokenizer.encode(lowerCAmelCase__				,       return_tensors='''pt'''						)
											__lowercase			      =							model(lowerCAmelCase__						)
											__lowercase			      =							outputs.logits.detach().cpu().numpy()[0]
											__lowercase			      =							np.argmax(output_logits[-1]						)
											self.assertEqual(lowerCAmelCase__				,       lowerCAmelCase__						)





			@slow
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
							__lowercase			      =							GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
							model.to(lowerCAmelCase__						)

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

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

							# spout from uniform and one-hot
							__lowercase			      =							[
							    # fmt: off
							    [0.8788_2208, 0.3842_6396, 0.3322_0248, 0.4389_0406, 0.1656_2252,
							    0.0480_3985, 0.21_1572  , 0.2318_8473, 0.3715_3068, 0.783_6377 ,
							    0.0216_0172, 0.3876_1719, 0.7529_0772, 0.9019_8857, 0.3436_5777,
							    0.6416_8169, 0.4431_8471, 0.1457_5746, 0.9256_2881, 0.4081_2148,
							    0.2901_9122, 0.8886_1599, 0.6552_4846, 0.4356_3456, 0.3817_7187,
							    0.7083_2965, 0.8152_7892, 0.6883_2812, 0.3883_3192, 0.456_1522 ,
							    0.1482_8817, 0.4724_8213, 0.5435_7335, 0.8200_9566, 0.133_8884 ,
							    0.0275_5417, 0.1976_4677, 0.242_2084 , 0.0475_7674, 0.6540_9606,
							    0.082_4589 , 0.0330_4383, 0.9438_7689, 0.9876_4509, 0.8243_3901,
							    0.2764_6741, 0.6490_7493, 0.7600_9406, 0.3008_7915, 0.1790_4689,
							    0.4160_1714, 0.6704_6398, 0.1042_2822, 0.0844_7374, 0.0735_4344,
							    0.6142_3565, 0.7028_4866, 0.753_2333 , 0.197_2038 , 0.2957_5659,
							    0.9058_3886, 0.2926_5307, 0.5000_0175, 0.7040_7655, 0.88_9363  ,
							    0.8190_4418, 0.6682_9128, 0.6446_8815, 0.5656_3723, 0.8560_1875,
							    0.9492_4672, 0.0016_6762, 0.2522_0643, 0.7454_0219, 0.6799_3247,
							    0.154_9675 , 0.3938_5352, 0.9215_3607, 0.6374_5931, 0.2775_9043,
							    0.8470_2295, 0.6590_4271, 0.5867_6614, 0.866_6936 , 0.3960_7438,
							    0.7995_4983, 0.4222_0697, 0.3965_0381, 0.784_9864 , 0.5615_0201,
							    0.1567_8925, 0.1474_6032, 0.3454_2114, 0.4702_6783, 0.1195_6489,
							    0.2542_1435, 0.3378_8901, 0.6893_4842, 0.3642_4685, 0.7173_7898,
							    0.3898_3449, 0.9439_3779, 0.3957_5588, 0.3661_6553, 0.8710_4665,
							    0.6463_0203, 0.2251_6905, 0.8827_0804, 0.1503_1338, 0.7514_4345,
							    0.4645_9025, 0.8539_6454, 0.8635_5643, 0.6513_9851, 0.7026_6061,
							    0.3024_1389, 0.8105_6497, 0.8886_5969, 0.3877_3807, 0.7063_5849,
							    0.9071_8459, 0.4324_5789, 0.2800_0654, 0.4593_5562, 0.0877_3519,
							    0.955_2151 , 0.9390_1511, 0.2248_9288], # 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			      =							model.generate(
							    input_ids=lowerCAmelCase__				,       spout=spouts[0]				,       max_new_tokens=20				,       generation_config=lowerCAmelCase__				,       )

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

							__lowercase			      =							model.generate(
							    input_ids=lowerCAmelCase__				,       spout=lowerCAmelCase__				,       max_new_tokens=20				,       generation_config=lowerCAmelCase__				,       )

							__lowercase			      =							tokenizer.decode(outputa[0]						)
							__lowercase			      =							tokenizer.decode(outputa[0]						)
							__lowercase			      =							tokenizer.batch_decode(lowerCAmelCase__						)

							__lowercase			      =							[
							    "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'''


							__lowercase			      =							GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
							__lowercase			      =							GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''						)
							model.to(lowerCAmelCase__						)

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

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

							__lowercase			      =							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__				,       )

							__lowercase			      =							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__				,       )

							__lowercase			      =							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__				,       )

							__lowercase			      =							tokenizer.decode(outputa[0]						)
							__lowercase			      =							tokenizer.decode(outputa[0]						)
							__lowercase			      =							tokenizer.batch_decode(lowerCAmelCase__						)

							__lowercase			      =							[
							    "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]						)
import logging
import random

import ray

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


__a  :					Optional[Any]          =  logging.getLogger(__name__)




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self						)					-> Optional[int]:

							'''simple docstring'''


							__lowercase			      =							False





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Tuple:

							'''simple docstring'''


							if not self.initialized:
											__lowercase			      =							RagRetriever(
											    lowerCAmelCase__				,       question_encoder_tokenizer=lowerCAmelCase__				,       generator_tokenizer=lowerCAmelCase__				,       index=lowerCAmelCase__				,       init_retrieval=lowerCAmelCase__				,       )
											__lowercase			      =							True





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''simple docstring'''


							self.retriever.index.init_index()





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

							'''simple docstring'''


							del self.retriever
							__lowercase			      =							False





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


							__lowercase      ,					__lowercase			      =							self.retriever._main_retrieve(lowerCAmelCase__				,       lowerCAmelCase__						)
							__lowercase			      =							self.retriever.index.get_doc_dicts(lowerCAmelCase__						)
							return doc_ids, retrieved_doc_embeds, doc_dicts




class        _UpperCamelCase				(						_UpperCAmelCase			):


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__=None						)					-> int:

							'''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__				,       )

							__lowercase			      =							retrieval_workers
							__lowercase			      =							question_encoder_tokenizer
							__lowercase			      =							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 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[Any]:

							'''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 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Optional[int]:

							'''simple docstring'''


							if len(self.retrieval_workers						) > 0:
											# Select a random retrieval actor.
											__lowercase			      =							self.retrieval_workers[random.randint(0				,       len(self.retrieval_workers						) - 1						)]
											__lowercase      ,					__lowercase      ,					__lowercase			      =							ray.get(
											    random_worker.retrieve.remote(lowerCAmelCase__				,       lowerCAmelCase__						)						)
							else:
											__lowercase      ,					__lowercase			      =							self._main_retrieve(lowerCAmelCase__				,       lowerCAmelCase__						)
											__lowercase			      =							self.index.get_doc_dicts(lowerCAmelCase__						)
							return retrieved_doc_embeds, doc_ids, doc_dicts





			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (       cls				,       lowerCAmelCase__				,       lowerCAmelCase__=None				,       **lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


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





			@classmethod
			def 	_SCREAMING_SNAKE_CASE      (       cls				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__=None				,       **lowerCAmelCase__						)					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							kwargs.pop('''config'''				,       lowerCAmelCase__						) or RagConfig.from_pretrained(lowerCAmelCase__				,       **lowerCAmelCase__						)
							__lowercase			      =							RagTokenizer.from_pretrained(lowerCAmelCase__				,       config=lowerCAmelCase__						)
							__lowercase			      =							rag_tokenizer.question_encoder
							__lowercase			      =							rag_tokenizer.generator

							if indexed_dataset is not None:
											__lowercase			      =							'''custom'''
											__lowercase			      =							CustomHFIndex(config.retrieval_vector_size				,       lowerCAmelCase__						)
							else:
											__lowercase			      =							cls._build_index(lowerCAmelCase__						)

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





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> 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
							__lowercase			      =							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
							    ]						)