

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_       : Tuple         =			logging.get_logger(__name__)

A_       : int         =			{
    'SCUT-DLVCLab/lilt-roberta-en-base': (
        'https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json'
    ),
}



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   Tuple  							=       '''lilt'''






					def __init__( self    ,       A__=3_0522    ,       A__=768    ,       A__=12    ,       A__=12    ,       A__=3072    ,       A__="gelu"    ,       A__=0.1    ,       A__=0.1    ,       A__=512    ,       A__=2    ,       A__=0.0_2    ,       A__=1e-12    ,       A__=0    ,       A__="absolute"    ,       A__=None    ,       A__=4    ,       A__=1024    ,       **A__    ,       ):
						super().__init__(pad_token_id=A__    ,       **A__			)

						A__							:    Dict        =		vocab_size
						A__							:    Any        =		hidden_size
						A__							:    List[Any]        =		num_hidden_layers
						A__							:    str        =		num_attention_heads
						A__							:    Union[str, Any]        =		hidden_act
						A__							:    Union[str, Any]        =		intermediate_size
						A__							:    List[Any]        =		hidden_dropout_prob
						A__							:    str        =		attention_probs_dropout_prob
						A__							:    Optional[Any]        =		max_position_embeddings
						A__							:    str        =		type_vocab_size
						A__							:    List[Any]        =		initializer_range
						A__							:    List[str]        =		layer_norm_eps
						A__							:    str        =		position_embedding_type
						A__							:    Any        =		classifier_dropout
						A__							:    List[str]        =		channel_shrink_ratio
						A__							:    Optional[Any]        =		max_ad_position_embeddings






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


A_       : List[str]         =			{
    'configuration_gpt_neox_japanese': ['GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoXJapaneseConfig'],
    'tokenization_gpt_neox_japanese': ['GPTNeoXJapaneseTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_       : Optional[int]         =			[
	    'GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'GPTNeoXJapaneseForCausalLM',
	    'GPTNeoXJapaneseLayer',
	    'GPTNeoXJapaneseModel',
	    'GPTNeoXJapanesePreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
	from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox_japanese import (
		    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXJapaneseForCausalLM,
		    GPTNeoXJapaneseLayer,
		    GPTNeoXJapaneseModel,
		    GPTNeoXJapanesePreTrainedModel,
		)


else:
	import sys

	A_       : Any         =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



