




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


lowercase__				:      List[Any]	   = logging.get_logger(__name__)

lowercase__				:      Optional[Any]	   = {
    'studio-ousia/luke-base': 'https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json',
    'studio-ousia/luke-large': 'https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json',
}




class       a__	( _A				):
					a		:      Optional[int]		 =     """luke"""







					def __init__(     self  ,		A=50267  ,		A=500000  ,		A=768  ,		A=256  ,		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=True  ,		A=None  ,		A=1  ,		A=0  ,		A=2  ,		**A  ,		)  ->   Optional[Any]:



										'''simple docstring'''



										super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE  ,		bos_token_id=__SCREAMING_SNAKE_CASE  ,		eos_token_id=__SCREAMING_SNAKE_CASE  ,		**__SCREAMING_SNAKE_CASE			)

										a     =				vocab_size
										a     =				entity_vocab_size
										a     =				hidden_size
										a     =				entity_emb_size
										a     =				num_hidden_layers
										a     =				num_attention_heads
										a     =				hidden_act
										a     =				intermediate_size
										a     =				hidden_dropout_prob
										a     =				attention_probs_dropout_prob
										a     =				max_position_embeddings
										a     =				type_vocab_size
										a     =				initializer_range
										a     =				layer_norm_eps
										a     =				use_entity_aware_attention
										a     =				classifier_dropout







lowercase__				:      str	   = "\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowercase__				:      Any	   = [{"type": "code", "content": INSTALL_CONTENT}]
lowercase__				:      Any	   = {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}

