




'''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       _a  :
       def __init__(     self						:     int       ,    lowercase						:     Optional[int]       ,    lowercase						:     List[Any]=36_000       ,    lowercase						:     Optional[Any]=13       ,    lowercase						:     Tuple=7       ,    lowercase						:     List[str]=True       ,    lowercase						:     str=32       ,    lowercase						:     Optional[int]=42       ,    lowercase						:     Dict=5       ,    lowercase						:     List[str]=2       ,    lowercase						:     int=4       ,    lowercase						:     int=2       ,    lowercase						:     Dict=32       ,    lowercase						:     List[Any]=80       ,    lowercase						:     Tuple=33       ,    lowercase						:     List[Any]=0.0       ,    lowercase						:     Optional[Any]=1E-6       ,    lowercase						:     Optional[int]=100       ,    lowercase						:     str=0.0       ,    ):



           '''simple docstring'''

           UpperCAmelCase      	=		vocab_size
           UpperCAmelCase      	=		parent
           UpperCAmelCase      	=		batch_size
           UpperCAmelCase      	=		num_contexts
           # For common tests
           UpperCAmelCase      	=		self.num_contexts
           UpperCAmelCase      	=		is_training
           UpperCAmelCase      	=		hidden_size
           UpperCAmelCase      	=		num_ext_layers
           UpperCAmelCase      	=		ext_size
           UpperCAmelCase      	=		num_hidden_layers
           UpperCAmelCase      	=		num_attention_heads
           UpperCAmelCase      	=		num_experts
           UpperCAmelCase      	=		d_ff
           UpperCAmelCase      	=		d_ext
           UpperCAmelCase      	=		d_spout
           UpperCAmelCase      	=		dropout_rate
           UpperCAmelCase      	=		layer_norm_epsilon
           UpperCAmelCase      	=		expert_capacity
           UpperCAmelCase      	=		router_jitter_noise
       def    A				(     self						:     List[str]    ):



           '''simple docstring'''

           return GPTSanJapaneseConfig.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
       def    A				(     self						:     Dict    ):



           '''simple docstring'''

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

           UpperCAmelCase      	=		self.get_config()

           return (config, input_ids)
       def    A				(     self						:     Optional[int]    ):



           '''simple docstring'''

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

           UpperCAmelCase      	=		self.get_config()

           return (config, {"input_ids": input_ids})
       def    A				(     self						:     Any    ):



           '''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						:     Dict       ,    lowercase						:     Optional[int]       ,    lowercase						:     List[Any]       ,    ):



           '''simple docstring'''

           UpperCAmelCase      	=		GPTSanJapaneseForConditionalGeneration(config=lowercase    )
           model.to(lowercase    )
           model.eval()
           UpperCAmelCase      	=		model(
               input_ids=lowercase       ,    )
           self.parent.assertIsNotNone(lowercase    )






@require_torch
class       _a  (     __a							,							__a							,							unittest.TestCase			):
       __a    :					Optional[Any]           = (GPTSanJapaneseModel,) if is_torch_available() else ()
       __a    :					Any           = (
           {
               """conversational""": GPTSanJapaneseForConditionalGeneration,
               """feature-extraction""": GPTSanJapaneseForConditionalGeneration,
               """summarization""": GPTSanJapaneseForConditionalGeneration,
               """text2text-generation""": GPTSanJapaneseForConditionalGeneration,
               """translation""": GPTSanJapaneseForConditionalGeneration,
           }
           if is_torch_available()
           else {}
       )
       __a    :					str           = False
       __a    :					Union[str, Any]           = False
       __a    :					Tuple           = False
       __a    :					Any           = False
       __a    :					Dict           = False
       __a    :					List[str]           = False
       __a    :					Any           = False
       __a    :					List[str]           = False
       # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
       __a    :					Optional[int]           = [0.8, 0.9]
       def    A				(     self						:     Dict       ,    lowercase						:     int       ,    lowercase						:     Tuple       ,    lowercase						:     List[Any]       ,    lowercase						:     Optional[Any]       ,    lowercase						:     Union[str, Any]    ):



           '''simple docstring'''

           if pipeline_test_casse_name == "SummarizationPipelineTests":
               # TODO: fix `_reorder_cache` is not implemented for this model
               return True
           elif pipeline_test_casse_name == "Text2TextGenerationPipelineTests":
               # TODO: check this.
               return True

           return False
       def    A				(     self						:     List[str]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		GPTSanJapaneseTester(self    )
           UpperCAmelCase      	=		ConfigTester(self       ,    config_class=lowercase       ,    d_model=37    )
       def    A				(     self						:     List[Any]    ):



           '''simple docstring'''

           GPTSanJapaneseConfig()
       def    A				(     self						:     List[str]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase    )
       @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						:     List[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						:     List[str]    ):



           '''simple docstring'''

           pass






@require_torch
class       _a  (     __a							,							__a							,							unittest.TestCase			):
       __a    :					Union[str, Any]           = (GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
       __a    :					Union[str, Any]           = False
       __a    :					str           = False
       __a    :					int           = False
       __a    :					Union[str, Any]           = False
       __a    :					List[str]           = False
       __a    :					Optional[int]           = False
       # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
       __a    :					Any           = [0.8, 0.9]
       def    A				(     self						:     Optional[int]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		GPTSanJapaneseTester(self    )
           UpperCAmelCase      	=		ConfigTester(self       ,    config_class=lowercase       ,    d_model=37    )
       def    A				(     self						:     int    ):



           '''simple docstring'''

           GPTSanJapaneseConfig()
       def    A				(     self						:     Dict    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase    )
       @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						:     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    A				(     self						:     Optional[int]    ):



           '''simple docstring'''

           pass
       @slow
       def    A				(     self						:     Union[str, Any]    ):



           '''simple docstring'''

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

           def check(lowercase						:     Any       ,    lowercase						:     str       ,    lowercase						:     List[str]=5E-4    ):
               return abs(a - b    ) < epsilon * max(abs(lowercase    )       ,    abs(lowercase    )    )

           self.assertTrue(np.all([check(target[i]       ,    predict[i]    ) for i in range(len(lowercase    )    )]    )    )
       @slow
       def    A				(     self						:     List[str]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
           UpperCAmelCase      	=		GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
           model.to(lowercase    )

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

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

           UpperCAmelCase      	=		'''left'''
           UpperCAmelCase      	=		tokenizer(lowercase       ,    return_tensors='''pt'''       ,    padding=lowercase    )
           UpperCAmelCase      	=		inputs['''input_ids'''].to(lowercase    )

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

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

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

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

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

           UpperCAmelCase      	=		tokenizer.batch_decode(lowercase       ,    skip_special_tokens=lowercase    )
           UpperCAmelCase      	=		tokenizer.decode(output_non_padded[0]       ,    skip_special_tokens=lowercase    )
           UpperCAmelCase      	=		tokenizer.decode(output_padded[0]       ,    skip_special_tokens=lowercase    )

           UpperCAmelCase      	=		[
               '''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(lowercase       ,    lowercase    )
           self.assertListEqual(lowercase       ,    [non_padded_sentence, padded_sentence]    )
       @tooslow
       def    A				(     self						:     Dict    ):



           '''simple docstring'''

           UpperCAmelCase      	=		GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
           UpperCAmelCase      	=		GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
           # Output of original model created with mesh-tensoflow
           UpperCAmelCase      	=		[
               ('''武田信玄は''', 35_675),
               ('''武田信玄は、''', 45),
               ('''武田信玄は、この''', 29),
               ('''武田信玄は、このよう''', 30_642),
               ('''武田信玄は、このような''', 35_680),
               ('''武田信玄は、このような「''', 8_640),
               ('''武田信玄は、このような「武田''', 31_617),
               ('''武田信玄は、このような「武田家''', 30_646),
               ('''武田信玄は、このような「武田家の''', 31_617),
               ('''武田信玄は、このような「武田家の家''', 31_381),
           ]
           for input, output in target:
               UpperCAmelCase      	=		tokenizer.encode(lowercase       ,    return_tensors='''pt'''    )
               UpperCAmelCase      	=		model(lowercase    )
               UpperCAmelCase      	=		outputs.logits.detach().cpu().numpy()[0]
               UpperCAmelCase      	=		np.argmax(output_logits[-1]    )
               self.assertEqual(lowercase       ,    lowercase    )
       @slow
       def    A				(     self						:     Union[str, Any]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
           UpperCAmelCase      	=		GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
           model.to(lowercase    )

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

           UpperCAmelCase      	=		'''武田信玄は、'''
           UpperCAmelCase      	=		tokenizer(lowercase       ,    return_tensors='''pt'''    ).input_ids.to(lowercase    )
           UpperCAmelCase      	=		tokenizer([input_text, input_text]       ,    return_tensors='''pt'''    ).input_ids.to(lowercase    )

           # spout from uniform and one-hot
           UpperCAmelCase      	=		[
               # 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
           ]

           UpperCAmelCase      	=		model.generate(
               input_ids=lowercase       ,    spout=spouts[0]       ,    max_new_tokens=20       ,    generation_config=lowercase       ,    )

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

           UpperCAmelCase      	=		model.generate(
               input_ids=lowercase       ,    spout=lowercase       ,    max_new_tokens=20       ,    generation_config=lowercase       ,    )

           UpperCAmelCase      	=		tokenizer.decode(outputa[0]    )
           UpperCAmelCase      	=		tokenizer.decode(outputa[0]    )
           UpperCAmelCase      	=		tokenizer.batch_decode(lowercase    )

           UpperCAmelCase      	=		[
               '''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(lowercase       ,    lowercase    )
           self.assertListEqual(lowercase       ,    [outa_sentence, outa_sentence]    )







       @slow
       def    A				(     self						:     List[str]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		GPTSanJapaneseForConditionalGeneration.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
           UpperCAmelCase      	=		GPTSanJapaneseTokenizer.from_pretrained('''Tanrei/GPTSAN-japanese'''    )
           model.to(lowercase    )

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

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

           UpperCAmelCase      	=		model.generate(
               input_ids=input_tok_a.input_ids.to(lowercase    )       ,    token_type_ids=input_tok_a.token_type_ids.to(lowercase    )       ,    max_new_tokens=20       ,    generation_config=lowercase       ,    )

           UpperCAmelCase      	=		model.generate(
               input_ids=input_tok_a.input_ids.to(lowercase    )       ,    token_type_ids=input_tok_a.token_type_ids.to(lowercase    )       ,    max_new_tokens=20       ,    generation_config=lowercase       ,    )

           UpperCAmelCase      	=		model.generate(
               input_ids=input_tok_a.input_ids.to(lowercase    )       ,    token_type_ids=input_tok_a.token_type_ids.to(lowercase    )       ,    attention_mask=input_tok_a.attention_mask.to(lowercase    )       ,    max_new_tokens=20       ,    generation_config=lowercase       ,    )

           UpperCAmelCase      	=		tokenizer.decode(outputa[0]    )
           UpperCAmelCase      	=		tokenizer.decode(outputa[0]    )
           UpperCAmelCase      	=		tokenizer.batch_decode(lowercase    )

           UpperCAmelCase      	=		[
               '''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(lowercase       ,    lowercase    )
           self.assertListEqual(lowercase       ,    [outa_sentence, outa_sentence]    )










'''simple docstring'''







import subprocess
from typing import Union

import numpy as np
import requests

from ..utils import add_end_docstrings, is_torch_available, is_torchaudio_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_torch_available():
   from ..models.auto.modeling_auto import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING

A			   =logging.get_logger(__name__)





def 						snake_case_							(_a     :							bytes   ,       _a     :							int					):
    UpperCAmelCase      	=		F"{sampling_rate}"
    UpperCAmelCase      	=		'''1'''
    UpperCAmelCase      	=		'''f32le'''
    UpperCAmelCase      	=		[
        '''ffmpeg''',
        '''-i''',
        '''pipe:0''',
        '''-ac''',
        ac,
        '''-ar''',
        ar,
        '''-f''',
        format_for_conversion,
        '''-hide_banner''',
        '''-loglevel''',
        '''quiet''',
        '''pipe:1''',
    ]

    try:
        UpperCAmelCase      	=		subprocess.Popen(_a   ,       stdin=subprocess.PIPE   ,       stdout=subprocess.PIPE					)
    except FileNotFoundError:
        raise ValueError('''ffmpeg was not found but is required to load audio files from filename'''					)
    UpperCAmelCase      	=		ffmpeg_process.communicate(_a					)
    UpperCAmelCase      	=		output_stream[0]

    UpperCAmelCase      	=		np.frombuffer(_a   ,       np.floataa					)
    if audio.shape[0] == 0:
        raise ValueError('''Malformed soundfile'''					)
    return audio







@add_end_docstrings(__a			)
class       _a  (     __a			):
       def __init__(     self						:     Dict       ,    *lowercase						:     Dict       ,    **lowercase						:     Any    ):



           '''simple docstring'''

           UpperCAmelCase      	=		5
           super().__init__(*lowercase       ,    **lowercase    )

           if self.framework != "pt":
               raise ValueError(f"The {self.__class__} is only available in PyTorch."    )

           self.check_model_type(lowercase    )
       def __call__(     self						:     List[Any]       ,    lowercase						:     Union[np.ndarray, bytes, str]       ,    **lowercase						:     Dict       ,    ):



           '''simple docstring'''

           return super().__call__(lowercase       ,    **lowercase    )
       def    A				(     self						:     Tuple       ,    lowercase						:     Dict=None       ,    **lowercase						:     List[str]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		{}
           if top_k is not None:
               if top_k > self.model.config.num_labels:
                   UpperCAmelCase      	=		self.model.config.num_labels
               UpperCAmelCase      	=		top_k
           return {}, {}, postprocess_params
       def    A				(     self						:     List[Any]       ,    lowercase						:     Union[str, Any]    ):



           '''simple docstring'''

           if isinstance(lowercase       ,    lowercase    ):
               if inputs.startswith('''http://'''    ) or inputs.startswith('''https://'''    ):
                   # We need to actually check for a real protocol, otherwise it's impossible to use a local file
                   # like http_huggingface_co.png
                   UpperCAmelCase      	=		requests.get(lowercase    ).content
               else:
                   with open(lowercase       ,    '''rb'''    ) as f:
                       UpperCAmelCase      	=		f.read()

           if isinstance(lowercase       ,    lowercase    ):
               UpperCAmelCase      	=		ffmpeg_read(lowercase       ,    self.feature_extractor.sampling_rate    )

           if isinstance(lowercase       ,    lowercase    ):
               # Accepting `"array"` which is the key defined in `datasets` for
               # better integration
               if not ("sampling_rate" in inputs and ("raw" in inputs or "array" in inputs)):
                   raise ValueError(
                       '''When passing a dictionary to AudioClassificationPipeline, the dict needs to contain a '''
                       '''"raw" key containing the numpy array representing the audio and a "sampling_rate" key, '''
                       '''containing the sampling_rate associated with that array'''    )

               UpperCAmelCase      	=		inputs.pop('''raw'''       ,    lowercase    )
               if _inputs is None:
                   # Remove path which will not be used from `datasets`.
                   inputs.pop('''path'''       ,    lowercase    )
                   UpperCAmelCase      	=		inputs.pop('''array'''       ,    lowercase    )
               UpperCAmelCase      	=		inputs.pop('''sampling_rate'''    )
               UpperCAmelCase      	=		_inputs
               if in_sampling_rate != self.feature_extractor.sampling_rate:
                   import torch

                   if is_torchaudio_available():
                       from torchaudio import functional as F
                   else:
                       raise ImportError(
                           '''torchaudio is required to resample audio samples in AudioClassificationPipeline. '''
                           '''The torchaudio package can be installed through: `pip install torchaudio`.'''    )

                   UpperCAmelCase      	=		F.resample(
                       torch.from_numpy(lowercase    )       ,    lowercase       ,    self.feature_extractor.sampling_rate    ).numpy()

           if not isinstance(lowercase       ,    np.ndarray    ):
               raise ValueError('''We expect a numpy ndarray as input'''    )
           if len(inputs.shape    ) != 1:
               raise ValueError('''We expect a single channel audio input for AudioClassificationPipeline'''    )

           UpperCAmelCase      	=		self.feature_extractor(
               lowercase       ,    sampling_rate=self.feature_extractor.sampling_rate       ,    return_tensors='''pt'''    )
           return processed
       def    A				(     self						:     Optional[Any]       ,    lowercase						:     List[str]    ):



           '''simple docstring'''

           UpperCAmelCase      	=		self.model(**lowercase    )
           return model_outputs







       def    A				(     self						:     Union[str, Any]       ,    lowercase						:     Optional[Any]       ,    lowercase						:     List[str]=5    ):



           '''simple docstring'''

           UpperCAmelCase      	=		model_outputs.logits[0].softmax(-1    )
           UpperCAmelCase	,					UpperCAmelCase      	=		probs.topk(lowercase    )

           UpperCAmelCase      	=		scores.tolist()
           UpperCAmelCase      	=		ids.tolist()

           UpperCAmelCase      	=		[{'''score''': score, '''label''': self.model.config.idalabel[_id]} for score, _id in zip(lowercase       ,    lowercase    )]

           return labels




