

'''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      :

    def __init__(					self,	lowercase_,	lowercase_=36000,	lowercase_=13,	lowercase_=7,	lowercase_=True,	lowercase_=32,	lowercase_=42,	lowercase_=5,	lowercase_=2,	lowercase_=4,	lowercase_=2,	lowercase_=32,	lowercase_=80,	lowercase_=33,	lowercase_=0.0,	lowercase_=1E-6,	lowercase_=100,	lowercase_=0.0,	)						->       Union[str, Any]:
     snake_case							 =				vocab_size
     snake_case							 =				parent
     snake_case							 =				batch_size
     snake_case							 =				num_contexts
     # For common tests
     snake_case							 =				self.num_contexts
     snake_case							 =				is_training
     snake_case							 =				hidden_size
     snake_case							 =				num_ext_layers
     snake_case							 =				ext_size
     snake_case							 =				num_hidden_layers
     snake_case							 =				num_attention_heads
     snake_case							 =				num_experts
     snake_case							 =				d_ff
     snake_case							 =				d_ext
     snake_case							 =				d_spout
     snake_case							 =				dropout_rate
     snake_case							 =				layer_norm_epsilon
     snake_case							 =				expert_capacity
     snake_case							 =				router_jitter_noise

    def 				_lowerCamelCase						(					self   )						->       int:
     return GPTSanJapaneseConfig.from_pretrained('Tanrei/GPTSAN-japanese'   )

    def 				_lowerCamelCase						(					self   )						->       Optional[int]:
     snake_case							 =				ids_tensor([self.batch_size, self.seq_length],	self.vocab_size   )

     snake_case							 =				self.get_config()

     return (config, input_ids)

    def 				_lowerCamelCase						(					self   )						->       Any:
     snake_case							 =				ids_tensor([self.batch_size, self.seq_length],	self.vocab_size   )

     snake_case							 =				self.get_config()

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

    def 				_lowerCamelCase						(					self   )						->       Any:
     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 				_lowerCamelCase						(					self,	lowercase_,	lowercase_,	)						->       List[str]:
     snake_case							 =				GPTSanJapaneseForConditionalGeneration(config=lowercase_   )
     model.to(lowercase_   )
     model.eval()
     snake_case							 =				model(
         input_ids=lowercase_,	)
     self.parent.assertIsNotNone(lowercase_   )







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

    def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_,	lowercase_,	lowercase_,	lowercase_   )						->       List[Any]:
     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 				_lowerCamelCase						(					self   )						->       List[Any]:
     snake_case							 =				GPTSanJapaneseTester(self   )
     snake_case							 =				ConfigTester(self,	config_class=lowercase_,	d_model=37   )

    def 				_lowerCamelCase						(					self   )						->       Any:
     GPTSanJapaneseConfig()

    def 				_lowerCamelCase						(					self   )						->       Optional[Any]:
     snake_case							 =				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 				_lowerCamelCase						(					self   )						->       Any:
     super().test_model_parallelism()






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







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

    def 				_lowerCamelCase						(					self   )						->       Optional[int]:
     snake_case							 =				GPTSanJapaneseTester(self   )
     snake_case							 =				ConfigTester(self,	config_class=lowercase_,	d_model=37   )

    def 				_lowerCamelCase						(					self   )						->       Dict:
     GPTSanJapaneseConfig()

    def 				_lowerCamelCase						(					self   )						->       Any:
     snake_case							 =				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 				_lowerCamelCase						(					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 				_lowerCamelCase						(					self   )						->       List[Any]:
     pass

    @slow
    def 				_lowerCamelCase						(					self   )						->       List[Any]:
     snake_case							 =				GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'   )
     snake_case							 =				GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'   )
     snake_case							 =				tokenizer.encode('武田信玄は',	return_tensors='pt'   )
     snake_case							 =				model(lowercase_   )
     snake_case							 =				outputs.logits.detach().cpu().numpy()
     # Output of original model created with mesh-tensoflow
     snake_case							 =				[
         # fmt: off
         [-12.037_839_889_526_367, -12.433_061_599_731_445, -14.333_840_370_178_223, -12.450_345_993_041_992, -11.1_661_376_953_125,
         -11.930_137_634_277_344, -10.659_740_447_998_047, -12.909_574_508_666_992, -13.241_043_090_820_312, -13.398_579_597_473_145,
         -11.107_524_871_826_172, -12.3_685_941_696_167, -22.97_943_115_234_375, -10.481_067_657_470_703, -12.484_030_723_571_777,
         -12.807_360_649_108_887, -14.769_700_050_354_004, -12.233_579_635_620_117, -13.428_145_408_630_371, -22.624_177_932_739_258],
         [-7.511_149_883_270_264, -8.281_851_768_493_652, -7.943_127_155_303_955, -7.55_021_333_694_458, -6.49_869_966_506_958,
         -7.586_796_283_721_924, -6.978_085_994_720_459, -7.839_145_183_563_232, -8.21_964_168_548_584, -8.695_091_247_558_594,
         -6.706_910_610_198_975, -6.6_585_798_263_549_805, -19.565_698_623_657_227, -5.353_842_735_290_527, -8.350_686_073_303_223,
         -8.039_388_656_616_211, -10.856_569_290_161_133, -7.75_154_447_555_542, -8.819_022_178_649_902, -19.51_532_745_361_328],
         [-9.73_066_234_588_623, -10.223_922_729_492_188, -9.932_981_491_088_867, -11.857_836_723_327_637, -7.662_626_266_479_492,
         -11.13_529_109_954_834, -7.765_097_618_103_027, -11.472_923_278_808_594, -9.543_149_948_120_117, -11.905_633_926_391_602,
         -9.366_164_207_458_496, -11.5_734_281_539_917, -23.699_003_219_604_492, -9.429_590_225_219_727, -10.42_839_241_027_832,
         -10.585_240_364_074_707, -10.94_771_957_397_461, -11.095_416_069_030_762, -10.390_240_669_250_488, -23.769_372_940_063_477],
         [-9.728_265_762_329_102, -9.859_712_600_708_008, -10.09_729_290_008_545, -9.678_522_109_985_352, -6.879_519_939_422_607,
         -9.68_487_548_828_125, -4.2_803_425_788_879_395, -10.018_914_222_717_285, -9.308_445_930_480_957, -10.63_394_546_508_789,
         -8.083_646_774_291_992, -9.06_301_498_413_086, -21.904_266_357_421_875, -8.90_160_846_710_205, -8.841_876_029_968_262,
         -11.856_719_970_703_125, -12.079_398_155_212_402, -11.233_753_204_345_703, -10.177_338_600_158_691, -21.87_256_622_314_453],
         [-9.669_764_518_737_793, -9.614_198_684_692_383, -9.814_510_345_458_984, -9.996_501_922_607_422, -11.375_690_460_205_078,
         -10.113_405_227_661_133, -10.546_867_370_605_469, -10.04_369_068_145_752, -10.907_809_257_507_324, -10.504_216_194_152_832,
         -11.129_199_028_015_137, -10.151_124_000_549_316, -21.96_586_799_621_582, -9.086_349_487_304_688, -11.730_339_050_292_969,
         -10.460_667_610_168_457, -10.298_049_926_757_812, -10.784_148_216_247_559, -10.840_693_473_815_918, -22.03_152_847_290_039],
         # fmt: on
     ]
     snake_case							 =				np.array(lowercase_   ).flatten()
     snake_case							 =				output_logits[0, :, :20].flatten()

     def check(lowercase_,	lowercase_,	lowercase_=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 				_lowerCamelCase						(					self   )						->       int:
     snake_case							 =				GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'   )
     snake_case							 =				GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'   )
     model.to(lowercase_   )

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

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

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

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

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

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

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

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

     snake_case							 =				tokenizer.batch_decode(lowercase_,	skip_special_tokens=lowercase_   )
     snake_case							 =				tokenizer.decode(output_non_padded[0],	skip_special_tokens=lowercase_   )
     snake_case							 =				tokenizer.decode(output_padded[0],	skip_special_tokens=lowercase_   )

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

    @slow
    def 				_lowerCamelCase						(					self   )						->       Optional[int]:
     snake_case							 =				GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'   )
     snake_case							 =				GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'   )
     model.to(lowercase_   )

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

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

     # spout from uniform and one-hot
     snake_case							 =				[
         # fmt: off
         [0.87_882_208, 0.38_426_396, 0.33_220_248, 0.43_890_406, 0.16_562_252,
         0.04_803_985, 0.211_572  , 0.23_188_473, 0.37_153_068, 0.7_836_377 ,
         0.02_160_172, 0.38_761_719, 0.75_290_772, 0.90_198_857, 0.34_365_777,
         0.64_168_169, 0.44_318_471, 0.14_575_746, 0.92_562_881, 0.40_812_148,
         0.29_019_122, 0.88_861_599, 0.65_524_846, 0.43_563_456, 0.38_177_187,
         0.70_832_965, 0.81_527_892, 0.68_832_812, 0.38_833_192, 0.4_561_522 ,
         0.14_828_817, 0.47_248_213, 0.54_357_335, 0.82_009_566, 0.1_338_884 ,
         0.02_755_417, 0.19_764_677, 0.2_422_084 , 0.04_757_674, 0.65_409_606,
         0.0_824_589 , 0.03_304_383, 0.94_387_689, 0.98_764_509, 0.82_433_901,
         0.27_646_741, 0.64_907_493, 0.76_009_406, 0.30_087_915, 0.17_904_689,
         0.41_601_714, 0.67_046_398, 0.10_422_822, 0.08_447_374, 0.07_354_344,
         0.61_423_565, 0.70_284_866, 0.7_532_333 , 0.1_972_038 , 0.29_575_659,
         0.90_583_886, 0.29_265_307, 0.50_000_175, 0.70_407_655, 0.889_363  ,
         0.81_904_418, 0.66_829_128, 0.64_468_815, 0.56_563_723, 0.85_601_875,
         0.94_924_672, 0.00_166_762, 0.25_220_643, 0.74_540_219, 0.67_993_247,
         0.1_549_675 , 0.39_385_352, 0.92_153_607, 0.63_745_931, 0.27_759_043,
         0.84_702_295, 0.65_904_271, 0.58_676_614, 0.8_666_936 , 0.39_607_438,
         0.79_954_983, 0.42_220_697, 0.39_650_381, 0.7_849_864 , 0.56_150_201,
         0.15_678_925, 0.14_746_032, 0.34_542_114, 0.47_026_783, 0.11_956_489,
         0.25_421_435, 0.33_788_901, 0.68_934_842, 0.36_424_685, 0.71_737_898,
         0.38_983_449, 0.94_393_779, 0.39_575_588, 0.36_616_553, 0.87_104_665,
         0.64_630_203, 0.22_516_905, 0.88_270_804, 0.15_031_338, 0.75_144_345,
         0.46_459_025, 0.85_396_454, 0.86_355_643, 0.65_139_851, 0.70_266_061,
         0.30_241_389, 0.81_056_497, 0.88_865_969, 0.38_773_807, 0.70_635_849,
         0.90_718_459, 0.43_245_789, 0.28_000_654, 0.45_935_562, 0.08_773_519,
         0.9_552_151 , 0.93_901_511, 0.22_489_288], # 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
     ]

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

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

     snake_case							 =				model.generate(
         input_ids=lowercase_,	spout=lowercase_,	max_new_tokens=20,	generation_config=lowercase_,	)

     snake_case							 =				tokenizer.decode(outputa[0]   )
     snake_case							 =				tokenizer.decode(outputa[0]   )
     snake_case							 =				tokenizer.batch_decode(lowercase_   )

     snake_case							 =				[
         '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 				_lowerCamelCase						(					self   )						->       Union[str, Any]:
     snake_case							 =				GPTSanJapaneseForConditionalGeneration.from_pretrained('Tanrei/GPTSAN-japanese'   )
     snake_case							 =				GPTSanJapaneseTokenizer.from_pretrained('Tanrei/GPTSAN-japanese'   )
     model.to(lowercase_   )

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

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

     snake_case							 =				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_,	)

     snake_case							 =				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_,	)

     snake_case							 =				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_,	)

     snake_case							 =				tokenizer.decode(outputa[0]   )
     snake_case							 =				tokenizer.decode(outputa[0]   )
     snake_case							 =				tokenizer.batch_decode(lowercase_   )

     snake_case							 =				[
         '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'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_						   =			logging.get_logger(__name__)

lowerCAmelCase_						   =			{
    "mnaylor/mega-base-wikitext": "https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json",
}







class lowerCamelCase      (						__lowerCAmelCase							):
				snake_case_           =   '''mega'''

				def __init__(					self,	lowercase_=30522,	lowercase_=128,	lowercase_=4,	lowercase_=256,	lowercase_=16,	lowercase_=True,	lowercase_=64,	lowercase_=False,	lowercase_=-1,	lowercase_=None,	lowercase_=True,	lowercase_="scalenorm",	lowercase_=True,	lowercase_="silu",	lowercase_="softmax",	lowercase_=0.1,	lowercase_=0.1,	lowercase_=0.1,	lowercase_=False,	lowercase_=True,	lowercase_=256,	lowercase_=True,	lowercase_=0.1,	lowercase_=2048,	lowercase_=False,	lowercase_=2,	lowercase_=0.02,	lowercase_=0.2,	lowercase_=0.02,	lowercase_=1.0,	lowercase_=1,	lowercase_=0,	lowercase_=2,	lowercase_="rotary",	lowercase_=None,	lowercase_=True,	lowercase_=True,	**lowercase_,	)						->       List[Any]:
					super().__init__(pad_token_id=lowercase_,	bos_token_id=lowercase_,	eos_token_id=lowercase_,	**lowercase_   )

					snake_case							 =				vocab_size
					snake_case							 =				hidden_size
					snake_case							 =				num_hidden_layers
					snake_case							 =				activation
					snake_case							 =				attention_activation
					snake_case							 =				intermediate_size
					snake_case							 =				ema_projection_size
					snake_case							 =				bidirectional
					snake_case							 =				shared_representation_size
					snake_case							 =				use_chunking
					snake_case							 =				chunk_size
					snake_case							 =				truncation
					snake_case							 =				normalize_before_mega
					snake_case							 =				normalization_type
					snake_case							 =				norm_affine
					snake_case							 =				dropout_prob
					snake_case							 =				hidden_dropout_prob
					snake_case							 =				attention_probs_dropout_prob
					snake_case							 =				use_feature_dropout
					snake_case							 =				use_normalized_ffn
					snake_case							 =				nffn_hidden_size
					snake_case							 =				normalize_before_ffn
					snake_case							 =				nffn_activation_dropout_prob
					snake_case							 =				max_positions
					snake_case							 =				add_token_type_embeddings
					snake_case							 =				type_vocab_size
					snake_case							 =				initializer_range
					snake_case							 =				ema_delta_alpha_range
					snake_case							 =				ema_beta_range
					snake_case							 =				ema_gamma_omega_range
					snake_case							 =				relative_positional_bias
					snake_case							 =				use_cache
					snake_case							 =				classifier_dropout
					snake_case							 =				add_lm_hidden_dense_layer
					snake_case							 =				1  # not used but required by Hugging Face







class lowerCamelCase      (						__lowerCAmelCase							):

				@property
				def 				_lowerCamelCase						(					self   )						->       Mapping[str, Mapping[int, str]]:
					if self.task == "multiple-choice":
						snake_case							 =				{0: 'batch', 1: 'choice', 2: 'sequence'}
					else:
						snake_case							 =				{0: 'batch', 1: 'sequence'}
					return OrderedDict(
					    [
					        ('input_ids', dynamic_axis),
					        ('attention_mask', dynamic_axis),
					    ]   )





