






"""simple docstring"""





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


__lowerCamelCase							=  logging.get_logger(__name__)

__lowerCamelCase							=  {
    'unc-nlp/lxmert-base-uncased': 'https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json',
}



class       __A		(   SCREAMING_SNAKE_CASE_			):
					UpperCAmelCase__								=				'''lxmert'''
					UpperCAmelCase__								=				{}

					def __init__(			self  :      Any     ,		__snake_case  :      Optional[int]=3_0_5_2_2     ,		__snake_case  :      List[Any]=7_6_8     ,		__snake_case  :      int=1_2     ,		__snake_case  :      List[str]=9_5_0_0     ,		__snake_case  :      str=1_6_0_0     ,		__snake_case  :      Optional[Any]=4_0_0     ,		__snake_case  :      List[Any]=3_0_7_2     ,		__snake_case  :      Dict="gelu"     ,		__snake_case  :      str=0.1     ,		__snake_case  :      List[Any]=0.1     ,		__snake_case  :      int=5_1_2     ,		__snake_case  :      str=2     ,		__snake_case  :      Optional[int]=0.02     ,		__snake_case  :      str=1E-12     ,		__snake_case  :      str=9     ,		__snake_case  :      Any=5     ,		__snake_case  :      Tuple=5     ,		__snake_case  :      List[str]=2_0_4_8     ,		__snake_case  :      Dict=4     ,		__snake_case  :      Optional[int]=6.67     ,		__snake_case  :      int=True     ,		__snake_case  :      str=True     ,		__snake_case  :      Dict=True     ,		__snake_case  :      str=True     ,		__snake_case  :      List[Any]=True     ,		__snake_case  :      List[Any]=True     ,		__snake_case  :      Optional[int]=True     ,		**__snake_case  :      Tuple     ,		)			->							str:
											__magic_name__:	str							=       vocab_size
											__magic_name__:	Tuple							=       hidden_size
											__magic_name__:	Optional[Any]							=       num_attention_heads
											__magic_name__:	int							=       hidden_act
											__magic_name__:	str							=       intermediate_size
											__magic_name__:	str							=       hidden_dropout_prob
											__magic_name__:	Dict							=       attention_probs_dropout_prob
											__magic_name__:	List[str]							=       max_position_embeddings
											__magic_name__:	Any							=       type_vocab_size
											__magic_name__:	List[str]							=       initializer_range
											__magic_name__:	int							=       layer_norm_eps
											__magic_name__:	Tuple							=       num_qa_labels
											__magic_name__:	Tuple							=       num_object_labels
											__magic_name__:	List[Any]							=       num_attr_labels
											__magic_name__:	Dict							=       l_layers
											__magic_name__:	Dict							=       x_layers
											__magic_name__:	int							=       r_layers
											__magic_name__:	str							=       visual_feat_dim
											__magic_name__:	int							=       visual_pos_dim
											__magic_name__:	Optional[int]							=       visual_loss_normalizer
											__magic_name__:	List[str]							=       task_matched
											__magic_name__:	Union[str, Any]							=       task_mask_lm
											__magic_name__:	List[str]							=       task_obj_predict
											__magic_name__:	List[Any]							=       task_qa
											__magic_name__:	Dict							=       visual_obj_loss
											__magic_name__:	str							=       visual_attr_loss
											__magic_name__:	Optional[int]							=       visual_feat_loss
											__magic_name__:	List[Any]							=       {"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
											super().__init__(**A__					)






A_			:					Union[str, Any]    			=     '\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'

A_			:					str    			=     [{'type': 'code', 'content': INSTALL_CONTENT}]
A_			:					Any    			=     {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}



