






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


lowercase			  =    logging.get_logger(__name__)

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




class 		UpperCAmelCase_						(	_UpperCAmelCase					):


						'''simple docstring'''




						A     :   List[Any]						=					'lilt'






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=3_0522		,				_SCREAMING_SNAKE_CASE=768		,				_SCREAMING_SNAKE_CASE=12		,				_SCREAMING_SNAKE_CASE=12		,				_SCREAMING_SNAKE_CASE=3072		,				_SCREAMING_SNAKE_CASE="gelu"		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=512		,				_SCREAMING_SNAKE_CASE=2		,				_SCREAMING_SNAKE_CASE=0.02		,				_SCREAMING_SNAKE_CASE=1e-12		,				_SCREAMING_SNAKE_CASE=0		,				_SCREAMING_SNAKE_CASE="absolute"		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=4		,				_SCREAMING_SNAKE_CASE=1024		,				**_SCREAMING_SNAKE_CASE		,				) ->				Optional[int]:
								super().__init__(pad_token_id=lowerCamelCase_		,				**lowerCamelCase_				)

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








import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			str  ,      **_a       :			Optional[int]    ):
		snake_case_				:							Any						 =       AutoConfig.from_pretrained(_a  ,      **_a    )
		snake_case_				:							str						 =       AutoModelForSeqaSeqLM.from_config(_a    )
		model.save_pretrained(_a    )
		AutoTokenizer.from_pretrained(_a    ).save_pretrained(_a    )
		return model


if __name__ == "__main__":
							fire.Fire(save_randomly_initialized_version)
