



'''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 							_lowerCAmelCase	:


 """simple docstring"""

 def __init__(      self					,    _lowerCamelCase					,    _lowerCamelCase=3_6000					,    _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					,    )    ->    Union[str, Any]:
    A_  :		int						=					vocab_size
    A_  :		Union[str, Any]						=					parent
    A_  :		Optional[int]						=					batch_size
    A_  :		Optional[Any]						=					num_contexts
    # For common tests
    A_  :		int						=					self.num_contexts
    A_  :		Dict						=					is_training
    A_  :		Union[str, Any]						=					hidden_size
    A_  :		int						=					num_ext_layers
    A_  :		str						=					ext_size
    A_  :		List[Any]						=					num_hidden_layers
    A_  :		Tuple						=					num_attention_heads
    A_  :		Optional[Any]						=					num_experts
    A_  :		List[Any]						=					d_ff
    A_  :		Tuple						=					d_ext
    A_  :		Optional[Any]						=					d_spout
    A_  :		Union[str, Any]						=					dropout_rate
    A_  :		str						=					layer_norm_epsilon
    A_  :		List[str]						=					expert_capacity
    A_  :		Union[str, Any]						=					router_jitter_noise







 def       UpperCAmelCase_       (      self  )    ->    str:
    return GPTSanJapaneseConfig.from_pretrained("""Tanrei/GPTSAN-japanese"""  )







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    A_  :		Tuple						=					ids_tensor([self.batch_size, self.seq_length]					,    self.vocab_size  )

    A_  :		Dict						=					self.get_config()

    return (config, input_ids)







 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    A_  :		Any						=					ids_tensor([self.batch_size, self.seq_length]					,    self.vocab_size  )

    A_  :		List[Any]						=					self.get_config()

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







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    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					,    _lowerCamelCase					,    _lowerCamelCase					,    )    ->    str:
    A_  :		Tuple						=					GPTSanJapaneseForConditionalGeneration(config=lowerCAmelCase__  )
    model.to(lowerCAmelCase__  )
    model.eval()
    A_  :		Optional[Any]						=					model(
        input_ids=lowerCAmelCase__					,    )
    self.parent.assertIsNotNone(lowerCAmelCase__  )


@require_torch
class 							_lowerCAmelCase	(  _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       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )    ->    Optional[int]:
    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  )    ->    Tuple:
    A_  :		Union[str, Any]						=					GPTSanJapaneseTester(self  )
    A_  :		Dict						=					ConfigTester(self					,    config_class=lowerCAmelCase__					,    d_model=37  )







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    GPTSanJapaneseConfig()







 def       UpperCAmelCase_       (      self  )    ->    Any:
    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       UpperCAmelCase_       (      self  )    ->    List[str]:
    super().test_model_parallelism()







 @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    pass


@require_torch
class 							_lowerCAmelCase	(  _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       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    A_  :		Any						=					GPTSanJapaneseTester(self  )
    A_  :		Union[str, Any]						=					ConfigTester(self					,    config_class=lowerCAmelCase__					,    d_model=37  )







 def       UpperCAmelCase_       (      self  )    ->    str:
    GPTSanJapaneseConfig()







 def       UpperCAmelCase_       (      self  )    ->    str:
    A_  :		Union[str, Any]						=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowerCAmelCase__  )







 @unittest.skip(
     reason="""skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`"""  )
 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    super().test_model_parallelism()







 @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    pass







 @slow
 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    A_  :		Optional[Any]						=					GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
    A_  :		Optional[int]						=					GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
    A_  :		str						=					tokenizer.encode("""武田信玄は"""					,    return_tensors="""pt"""  )
    A_  :		Dict						=					model(lowerCAmelCase__  )
    A_  :		List[str]						=					outputs.logits.detach().cpu().numpy()
    # Output of original model created with mesh-tensoflow
    A_  :		str						=					[
        # 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
    ]
    A_  :		Union[str, Any]						=					np.array(lowerCAmelCase__  ).flatten()
    A_  :		List[str]						=					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       UpperCAmelCase_       (      self  )    ->    Tuple:
    A_  :		Union[str, Any]						=					GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
    A_  :		str						=					GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
    model.to(lowerCAmelCase__  )

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

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

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

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

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

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

    A_  :		Any						=					tokenizer(sentences[1]					,    return_tensors="""pt"""  ).input_ids.to(lowerCAmelCase__  )
    A_  :		Dict						=					model.generate(input_ids=lowerCAmelCase__					,    max_new_tokens=3					,    generation_config=lowerCAmelCase__  )

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

    A_  :		Union[str, Any]						=					tokenizer.batch_decode(lowerCAmelCase__					,    skip_special_tokens=lowerCAmelCase__  )
    A_  :		List[str]						=					tokenizer.decode(output_non_padded[0]					,    skip_special_tokens=lowerCAmelCase__  )
    A_  :		int						=					tokenizer.decode(output_padded[0]					,    skip_special_tokens=lowerCAmelCase__  )

    A_  :		List[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       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		int						=					GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
    A_  :		int						=					GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
    # Output of original model created with mesh-tensoflow
    A_  :		Dict						=					[
        ("""武田信玄は""", 3_5675),
        ("""武田信玄は、""", 45),
        ("""武田信玄は、この""", 29),
        ("""武田信玄は、このよう""", 3_0642),
        ("""武田信玄は、このような""", 3_5680),
        ("""武田信玄は、このような「""", 8640),
        ("""武田信玄は、このような「武田""", 3_1617),
        ("""武田信玄は、このような「武田家""", 3_0646),
        ("""武田信玄は、このような「武田家の""", 3_1617),
        ("""武田信玄は、このような「武田家の家""", 3_1381),
    ]
    for input, output in target:
       A_  :		Tuple						=					tokenizer.encode(lowerCAmelCase__					,    return_tensors="""pt"""  )
       A_  :		Optional[int]						=					model(lowerCAmelCase__  )
       A_  :		Union[str, Any]						=					outputs.logits.detach().cpu().numpy()[0]
       A_  :		List[Any]						=					np.argmax(output_logits[-1]  )
       self.assertEqual(lowerCAmelCase__					,    lowerCAmelCase__  )







 @slow
 def       UpperCAmelCase_       (      self  )    ->    int:
    A_  :		Dict						=					GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
    A_  :		int						=					GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
    model.to(lowerCAmelCase__  )

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

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

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

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

    A_  :		Union[str, Any]						=					model.generate(
        input_ids=lowerCAmelCase__					,    spout=spouts[1]					,    max_new_tokens=20					,    generation_config=lowerCAmelCase__					,    )

    A_  :		int						=					model.generate(
        input_ids=lowerCAmelCase__					,    spout=lowerCAmelCase__					,    max_new_tokens=20					,    generation_config=lowerCAmelCase__					,    )

    A_  :		Dict						=					tokenizer.decode(outputa[0]  )
    A_  :		Any						=					tokenizer.decode(outputa[0]  )
    A_  :		int						=					tokenizer.batch_decode(lowerCAmelCase__  )

    A_  :		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(lowerCAmelCase__					,    lowerCAmelCase__  )
    self.assertListEqual(lowerCAmelCase__					,    [outa_sentence, outa_sentence]  )







 @slow
 def       UpperCAmelCase_       (      self  )    ->    Any:
    A_  :		int						=					GPTSanJapaneseForConditionalGeneration.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
    A_  :		int						=					GPTSanJapaneseTokenizer.from_pretrained("""Tanrei/GPTSAN-japanese"""  )
    model.to(lowerCAmelCase__  )

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

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

    A_  :		Dict						=					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_  :		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__  )					,    attention_mask=input_tok_a.attention_mask.to(lowerCAmelCase__  )					,    max_new_tokens=20					,    generation_config=lowerCAmelCase__					,    )

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

    A_  :		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(lowerCAmelCase__					,    lowerCAmelCase__  )
    self.assertListEqual(lowerCAmelCase__					,    [outa_sentence, outa_sentence]  )

import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
      print('''#################### AVAILABLE DEVICES ####################''')
      print(jax.devices())
      print('''###########################################################''')

      # setup for wandb sweep
      UpperCAmelCase_    		=				Args()
      UpperCAmelCase_    		=				wandb.init(project='''bigbird-natural-questions''', config=args.__dict__)
      UpperCAmelCase_    		=				dict(logger.config)
      del wandb_args["batch_size"]
      UpperCAmelCase_    		=				replace(args, **wandb_args)
      UpperCAmelCase_    		=				args.base_dir + '''-''' + wandb.run.id
      UpperCAmelCase_    		=				replace(args, base_dir=base_dir)
      print(args)

      UpperCAmelCase_    		=				load_dataset('''json''', data_files=args.tr_data_path)['''train''']
      UpperCAmelCase_    		=				load_dataset('''json''', data_files=args.val_data_path)['''train''']

      # drop extra batch for now
      UpperCAmelCase_    		=				range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
      UpperCAmelCase_    		=				tr_dataset.shuffle().select(indices)
      UpperCAmelCase_    		=				range(len(val_dataset) - len(val_dataset) % args.batch_size)
      UpperCAmelCase_    		=				val_dataset.shuffle().select(indices)

      if os.environ.get('''TRAIN_ON_SMALL''', '''false''') == "true":
            UpperCAmelCase_    		=				tr_dataset.shuffle().select(range(80_000))
            UpperCAmelCase_    		=				val_dataset.shuffle().select(range(8_000))

      print(tr_dataset)
      print(val_dataset)

      UpperCAmelCase_    		=				FlaxBigBirdForNaturalQuestions.from_pretrained(
          args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
      )
      UpperCAmelCase_    		=				BigBirdTokenizerFast.from_pretrained(args.model_id)
      UpperCAmelCase_    		=				DataCollator(pad_id=tokenizer.pad_token_id, max_length=4_096)

      UpperCAmelCase_    		=				{
          '''lr''': args.lr,
          '''init_lr''': args.init_lr,
          '''warmup_steps''': args.warmup_steps,
          '''num_train_steps''': args.max_epochs * (len(tr_dataset) // args.batch_size),
          '''weight_decay''': args.weight_decay,
      }
      UpperCAmelCase_		,     UpperCAmelCase_    		=				build_tx(**tx_args)

      UpperCAmelCase_    		=				Trainer(
          args=args,
          data_collator=data_collator,
          model_save_fn=model.save_pretrained,
          train_step_fn=train_step,
          val_step_fn=val_step,
          logger=logger,
          scheduler_fn=lr,
      )

      UpperCAmelCase_    		=				None
      UpperCAmelCase_    		=				trainer.create_state(model, tx, num_train_steps=tx_args['''num_train_steps'''], ckpt_dir=ckpt_dir)
      try:
            trainer.train(state, tr_dataset, val_dataset)
      except KeyboardInterrupt:
            print('''Oooops; TRAINING STOPPED UNFORTUNATELY''')

      print('''SAVING WEIGHTS IN `final-weights`''')
      UpperCAmelCase_    		=				jax_utils.unreplicate(state.params)
      model.save_pretrained(os.path.join(args.base_dir, '''final-weights'''), params=params)