




"""simple docstring"""


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


A_      			=       logging.get_logger(__name__)

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






class       lowercase(  _snake_case				):







 '''simple docstring'''





 lowercase__				      =      "lxmert"
 lowercase__				      =      {}


 def __init__(       self:	int,   a_:	Any=30_522,   a_:	List[str]=768,   a_:	Union[str, Any]=12,   a_:	List[Any]=9_500,   a_:	Any=1_600,   a_:	Union[str, Any]=400,   a_:	Tuple=3_072,   a_:	Dict="gelu",   a_:	Tuple=0.1,   a_:	Tuple=0.1,   a_:	int=512,   a_:	List[str]=2,   a_:	List[str]=0.02,   a_:	str=1E-12,   a_:	str=9,   a_:	int=5,   a_:	Optional[int]=5,   a_:	List[Any]=2_048,   a_:	Any=4,   a_:	Dict=6.67,   a_:	Any=True,   a_:	Union[str, Any]=True,   a_:	Any=True,   a_:	Tuple=True,   a_:	Optional[Any]=True,   a_:	Optional[int]=True,   a_:	Tuple=True,   **a_:	List[Any],   ):




   '''simple docstring'''



   _snake_case							:	List[str]			=						vocab_size
   _snake_case							:	int			=						hidden_size
   _snake_case							:	Dict			=						num_attention_heads
   _snake_case							:	List[str]			=						hidden_act
   _snake_case							:	Optional[int]			=						intermediate_size
   _snake_case							:	List[str]			=						hidden_dropout_prob
   _snake_case							:	str			=						attention_probs_dropout_prob
   _snake_case							:	Any			=						max_position_embeddings
   _snake_case							:	Tuple			=						type_vocab_size
   _snake_case							:	Tuple			=						initializer_range
   _snake_case							:	Any			=						layer_norm_eps
   _snake_case							:	Optional[int]			=						num_qa_labels
   _snake_case							:	List[Any]			=						num_object_labels
   _snake_case							:	str			=						num_attr_labels
   _snake_case							:	Any			=						l_layers
   _snake_case							:	Optional[int]			=						x_layers
   _snake_case							:	List[str]			=						r_layers
   _snake_case							:	str			=						visual_feat_dim
   _snake_case							:	List[Any]			=						visual_pos_dim
   _snake_case							:	List[str]			=						visual_loss_normalizer
   _snake_case							:	int			=						task_matched
   _snake_case							:	List[Any]			=						task_mask_lm
   _snake_case							:	Optional[int]			=						task_obj_predict
   _snake_case							:	Tuple			=						task_qa
   _snake_case							:	Any			=						visual_obj_loss
   _snake_case							:	Optional[int]			=						visual_attr_loss
   _snake_case							:	List[Any]			=						visual_feat_loss
   _snake_case							:	Optional[Any]			=						{"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
   super().__init__(**lowerCAmelCase__	)








"""simple docstring"""


from typing import Any


def       UpperCAmelCase__ (snake_case__							:      list					):
  """simple docstring"""

  if not input_list:
    return []
  _snake_case							:	List[Any]			=						[input_list.count(snake_case__					) for value in input_list]
  _snake_case							:	Optional[int]			=						max(snake_case__					)  # Gets the maximum count in the input list.
  # Gets values of modes
  return sorted({input_list[i] for i, value in enumerate(snake_case__					) if value == y}					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()


