





"""simple docstring"""
import unittest

import numpy as np

from transformers import (
    GPTSanJapaneseConfig,
    GPTSanJapaneseForConditionalGeneration,
    GPTSanJapaneseModel,
    GPTSanJapaneseTokenizer,
    is_torch_available,
)
from transformers.generation import GenerationConfig
from transformers.testing_utils import require_torch, slow, tooslow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin






class  _UpperCAmelCase      :


					def __init__( self      :str					,	__UpperCamelCase      :str					,	__UpperCamelCase      :Dict=3_60_00					,	__UpperCamelCase      :Any=13					,	__UpperCamelCase      :List[str]=7					,	__UpperCamelCase      :List[Any]=True					,	__UpperCamelCase      :int=32					,	__UpperCamelCase      :Dict=42					,	__UpperCamelCase      :int=5					,	__UpperCamelCase      :Dict=2					,	__UpperCamelCase      :Optional[int]=4					,	__UpperCamelCase      :Any=2					,	__UpperCamelCase      :Union[str, Any]=32					,	__UpperCamelCase      :Dict=80					,	__UpperCamelCase      :Optional[int]=33					,	__UpperCamelCase      :Tuple=0.0					,	__UpperCamelCase      :List[str]=1e-6					,	__UpperCamelCase      :str=1_00					,	__UpperCamelCase      :str=0.0					,	):
												A		=  vocab_size
												A		=  parent
												A		=  batch_size
												A		=  num_contexts
												# For common tests
												A		=  self.num_contexts
												A		=  is_training
												A		=  hidden_size
												A		=  num_ext_layers
												A		=  ext_size
												A		=  num_hidden_layers
												A		=  num_attention_heads
												A		=  num_experts
												A		=  d_ff
												A		=  d_ext
												A		=  d_spout
												A		=  dropout_rate
												A		=  layer_norm_epsilon
												A		=  expert_capacity
												A		=  router_jitter_noise


					def      lowerCamelCase	( self      :Union[str, Any]		):
												return GPTSanJapaneseConfig.from_pretrained("Tanrei/GPTSAN-japanese"		)


					def      lowerCamelCase	( self      :List[Any]		):
												A		=  ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size		)

												A		=  self.get_config()

												return (config, input_ids)


					def      lowerCamelCase	( self      :Dict		):
												A		=  ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size		)

												A		=  self.get_config()

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


					def      lowerCamelCase	( self      :Union[str, 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      :int					,	__UpperCamelCase      :List[Any]					,	__UpperCamelCase      :Any					,	):
												A		=  GPTSanJapaneseForConditionalGeneration(config=__UpperCamelCase		)
												model.to(__UpperCamelCase		)
												model.eval()
												A		=  model(
												    input_ids=__UpperCamelCase					,	)
												self.parent.assertIsNotNone(__UpperCamelCase		)







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


					def      lowerCamelCase	( self      :Dict					,	__UpperCamelCase      :List[str]					,	__UpperCamelCase      :Optional[int]					,	__UpperCamelCase      :str					,	__UpperCamelCase      :Union[str, Any]					,	__UpperCamelCase      :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      lowerCamelCase	( self      :Any		):
												A		=  GPTSanJapaneseTester(self		)
												A		=  ConfigTester(self					,	config_class=__UpperCamelCase					,	d_model=37		)


					def      lowerCamelCase	( self      :int		):
												GPTSanJapaneseConfig()


					def      lowerCamelCase	( self      :Any		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase		)


					@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[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      :Dict		):
												pass







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


					def      lowerCamelCase	( self      :Union[str, Any]		):
												A		=  GPTSanJapaneseTester(self		)
												A		=  ConfigTester(self					,	config_class=__UpperCamelCase					,	d_model=37		)


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


					def      lowerCamelCase	( self      :Dict		):
												A		=  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__UpperCamelCase		)


					@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      :int		):
												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


					@slow
					def      lowerCamelCase	( self      :Union[str, Any]		):
												A		=  GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"		)
												A		=  GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"		)
												A		=  tokenizer.encode("武田信玄は"					,	return_tensors="pt"		)
												A		=  model(__UpperCamelCase		)
												A		=  outputs.logits.detach().cpu().numpy()
												# Output of original model created with mesh-tensoflow
												A		=  [
												    # 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
												]
												A		=  np.array(__UpperCamelCase		).flatten()
												A		=  output_logits[0, :, :20].flatten()

												def check(__UpperCamelCase      :str					,	__UpperCamelCase      :Union[str, Any]					,	__UpperCamelCase      :Optional[Any]=5e-4		):
																			return abs(a - b		) < epsilon * max(abs(__UpperCamelCase		)					,	abs(__UpperCamelCase		)		)

												self.assertTrue(np.all([check(target[i]					,	predict[i]		) for i in range(len(__UpperCamelCase		)		)]		)		)


					@slow
					def      lowerCamelCase	( self      :Optional[int]		):
												A		=  GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"		)
												A		=  GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"		)
												model.to(__UpperCamelCase		)

												# set deterministically
												A		=  GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"		)
												A		=  1

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

												A		=  "left"
												A		=  tokenizer(__UpperCamelCase					,	return_tensors="pt"					,	padding=__UpperCamelCase		)
												A		=  inputs["input_ids"].to(__UpperCamelCase		)

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

												A		=  model.generate(
												    input_ids=__UpperCamelCase					,	attention_mask=inputs["attention_mask"].to(__UpperCamelCase		)					,	max_new_tokens=3					,	generation_config=__UpperCamelCase					,	)

												A		=  tokenizer(sentences[0]					,	return_tensors="pt"		).input_ids.to(__UpperCamelCase		)
												A		=  model.generate(
												    input_ids=__UpperCamelCase					,	max_new_tokens=3					,	generation_config=__UpperCamelCase		)

												A		=  tokenizer(sentences[1]					,	return_tensors="pt"		).input_ids.to(__UpperCamelCase		)
												A		=  model.generate(input_ids=__UpperCamelCase					,	max_new_tokens=3					,	generation_config=__UpperCamelCase		)

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

												A		=  tokenizer.batch_decode(__UpperCamelCase					,	skip_special_tokens=__UpperCamelCase		)
												A		=  tokenizer.decode(output_non_padded[0]					,	skip_special_tokens=__UpperCamelCase		)
												A		=  tokenizer.decode(output_padded[0]					,	skip_special_tokens=__UpperCamelCase		)

												A		=  [
												    "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(__UpperCamelCase					,	__UpperCamelCase		)
												self.assertListEqual(__UpperCamelCase					,	[non_padded_sentence, padded_sentence]		)


					@tooslow
					def      lowerCamelCase	( self      :Union[str, Any]		):
												A		=  GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"		)
												A		=  GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"		)
												# Output of original model created with mesh-tensoflow
												A		=  [
												    ("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:
																			A		=  tokenizer.encode(__UpperCamelCase					,	return_tensors="pt"		)
																			A		=  model(__UpperCamelCase		)
																			A		=  outputs.logits.detach().cpu().numpy()[0]
																			A		=  np.argmax(output_logits[-1]		)
																			self.assertEqual(__UpperCamelCase					,	__UpperCamelCase		)


					@slow
					def      lowerCamelCase	( self      :Dict		):
												A		=  GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"		)
												A		=  GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"		)
												model.to(__UpperCamelCase		)

												# set deterministically
												A		=  GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"		)
												A		=  1

												A		=  "武田信玄は、"
												A		=  tokenizer(__UpperCamelCase					,	return_tensors="pt"		).input_ids.to(__UpperCamelCase		)
												A		=  tokenizer([input_text, input_text]					,	return_tensors="pt"		).input_ids.to(__UpperCamelCase		)

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

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

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

												A		=  model.generate(
												    input_ids=__UpperCamelCase					,	spout=__UpperCamelCase					,	max_new_tokens=20					,	generation_config=__UpperCamelCase					,	)

												A		=  tokenizer.decode(outputa[0]		)
												A		=  tokenizer.decode(outputa[0]		)
												A		=  tokenizer.batch_decode(__UpperCamelCase		)

												A		=  [
												    "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(__UpperCamelCase					,	__UpperCamelCase		)
												self.assertListEqual(__UpperCamelCase					,	[outa_sentence, outa_sentence]		)


					@slow
					def      lowerCamelCase	( self      :Optional[int]		):
												A		=  GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"		)
												A		=  GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"		)
												model.to(__UpperCamelCase		)

												# set deterministically
												A		=  GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"		)
												A		=  1

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

												A		=  model.generate(
												    input_ids=input_tok_a.input_ids.to(__UpperCamelCase		)					,	token_type_ids=input_tok_a.token_type_ids.to(__UpperCamelCase		)					,	max_new_tokens=20					,	generation_config=__UpperCamelCase					,	)

												A		=  model.generate(
												    input_ids=input_tok_a.input_ids.to(__UpperCamelCase		)					,	token_type_ids=input_tok_a.token_type_ids.to(__UpperCamelCase		)					,	max_new_tokens=20					,	generation_config=__UpperCamelCase					,	)

												A		=  model.generate(
												    input_ids=input_tok_a.input_ids.to(__UpperCamelCase		)					,	token_type_ids=input_tok_a.token_type_ids.to(__UpperCamelCase		)					,	attention_mask=input_tok_a.attention_mask.to(__UpperCamelCase		)					,	max_new_tokens=20					,	generation_config=__UpperCamelCase					,	)

												A		=  tokenizer.decode(outputa[0]		)
												A		=  tokenizer.decode(outputa[0]		)
												A		=  tokenizer.batch_decode(__UpperCamelCase		)

												A		=  [
												    "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(__UpperCamelCase					,	__UpperCamelCase		)
												self.assertListEqual(__UpperCamelCase					,	[outa_sentence, outa_sentence]		)









"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case					:		Optional[int]							=			logging.get_logger(__name__)

_snake_case					:		List[Any]							=			{
    'ZinengTang/tvlt-base': 'https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json',
}






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''tvlt'''


					def __init__( self      :Dict					,	__UpperCamelCase      :Tuple=2_24					,	__UpperCamelCase      :Dict=20_48					,	__UpperCamelCase      :Optional[Any]=1_28					,	__UpperCamelCase      :Optional[int]=[16, 16]					,	__UpperCamelCase      :Any=[16, 16]					,	__UpperCamelCase      :List[str]=3					,	__UpperCamelCase      :str=1					,	__UpperCamelCase      :List[str]=8					,	__UpperCamelCase      :str=7_68					,	__UpperCamelCase      :List[str]=12					,	__UpperCamelCase      :Tuple=12					,	__UpperCamelCase      :Union[str, Any]=30_72					,	__UpperCamelCase      :List[Any]="gelu"					,	__UpperCamelCase      :List[str]=0.0					,	__UpperCamelCase      :Union[str, Any]=0.0					,	__UpperCamelCase      :List[str]=0.02					,	__UpperCamelCase      :Dict=1e-6					,	__UpperCamelCase      :Any=True					,	__UpperCamelCase      :List[str]=False					,	__UpperCamelCase      :str=16					,	__UpperCamelCase      :List[str]=5_12					,	__UpperCamelCase      :int=8					,	__UpperCamelCase      :int=20_48					,	__UpperCamelCase      :int=0.75					,	__UpperCamelCase      :Tuple=0.15					,	__UpperCamelCase      :Optional[Any]="frame-level"					,	__UpperCamelCase      :int=True					,	__UpperCamelCase      :str=True					,	__UpperCamelCase      :Tuple="classification"					,	**__UpperCamelCase      :Any					,	):
												super().__init__(**__UpperCamelCase		)

												if audio_mask_type not in ("frame-level", "patch_level"):
																			raise ValueError(
																			    "audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') "
																			    f"got {audio_mask_type}"		)

												A		=  image_size
												A		=  spectrogram_length
												A		=  frequency_length
												A		=  image_patch_size
												A		=  audio_patch_size
												A		=  num_image_channels
												A		=  num_audio_channels
												A		=  num_frames

												A		=  hidden_size
												A		=  num_hidden_layers
												A		=  num_attention_heads
												A		=  intermediate_size
												A		=  hidden_act
												A		=  hidden_dropout_prob
												A		=  attention_probs_dropout_prob
												A		=  initializer_range
												A		=  layer_norm_eps
												A		=  qkv_bias
												A		=  use_mean_pooling

												A		=  decoder_num_attention_heads
												A		=  decoder_hidden_size
												A		=  decoder_num_hidden_layers
												A		=  decoder_intermediate_size
												A		=  pixel_mask_ratio
												A		=  audio_mask_ratio
												A		=  audio_mask_type

												A		=  task_matching
												A		=  task_mae
												A		=  loss_type


