



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


_lowercase: Dict      			=		logging.get_logger(__name__)

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



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							"lxmert"
	__A							=							{}




	def __init__(self		,       lowerCamelCase_=30522		,       lowerCamelCase_=768		,       lowerCamelCase_=12		,       lowerCamelCase_=9500		,       lowerCamelCase_=1600		,       lowerCamelCase_=400		,       lowerCamelCase_=3072		,       lowerCamelCase_="gelu"		,       lowerCamelCase_=0.1		,       lowerCamelCase_=0.1		,       lowerCamelCase_=512		,       lowerCamelCase_=2		,       lowerCamelCase_=0.02		,       lowerCamelCase_=1E-1_2		,       lowerCamelCase_=9		,       lowerCamelCase_=5		,       lowerCamelCase_=5		,       lowerCamelCase_=2048		,       lowerCamelCase_=4		,       lowerCamelCase_=6.67		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   vocab_size
						a					     =   hidden_size
						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					     =   num_qa_labels
						a					     =   num_object_labels
						a					     =   num_attr_labels
						a					     =   l_layers
						a					     =   x_layers
						a					     =   r_layers
						a					     =   visual_feat_dim
						a					     =   visual_pos_dim
						a					     =   visual_loss_normalizer
						a					     =   task_matched
						a					     =   task_mask_lm
						a					     =   task_obj_predict
						a					     =   task_qa
						a					     =   visual_obj_loss
						a					     =   visual_attr_loss
						a					     =   visual_feat_loss
						a					     =   {"vision": r_layers, "cross_encoder": x_layers, "language": l_layers}
						super().__init__(**lowerCamelCase_		)





_lowercase: Dict      			=		[
    (1000, "M"),
    (900, "CM"),
    (500, "D"),
    (400, "CD"),
    (100, "C"),
    (90, "XC"),
    (50, "L"),
    (40, "XL"),
    (10, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]






def     a(      A       :    str       )		->       int:
					"""simple docstring"""





					a					     =   {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}
					a					     =   0
					a					     =   0
					while place < len(A       ):
										if (place + 1 < len(A       )) and (vals[roman[place]] < vals[roman[place + 1]]):
															total += vals[roman[place + 1]] - vals[roman[place]]
															place += 2
										else:
															total += vals[roman[place]]
															place += 1
					return total






def     a(      A       :    int       )		->       str:
					"""simple docstring"""





					a					     =   []
					for arabic, roman in ROMAN:
										((a)     ,				(a))					     =   divmod(A ,     A       )
										result.append(roman * factor       )
										if number == 0:
															break
					return "".join(A       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
