

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


_lowerCamelCase      :					Optional[int]					    =      logging.get_logger(__name__)

_lowerCamelCase      :					List[str]					    =      {
    '''unc-nlp/lxmert-base-uncased''': '''https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json''',
}





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    """lxmert"""
       lowercase_						=    {}

       def __init__(							self ,   lowercase__=3_0_5_2_2 ,   lowercase__=7_6_8 ,   lowercase__=1_2 ,   lowercase__=9_5_0_0 ,   lowercase__=1_6_0_0 ,   lowercase__=4_0_0 ,   lowercase__=3_0_7_2 ,   lowercase__="gelu" ,   lowercase__=0.1 ,   lowercase__=0.1 ,   lowercase__=5_1_2 ,   lowercase__=2 ,   lowercase__=0.02 ,   lowercase__=1E-12 ,   lowercase__=9 ,   lowercase__=5 ,   lowercase__=5 ,   lowercase__=2_0_4_8 ,   lowercase__=4 ,   lowercase__=6.67 ,   lowercase__=True ,   lowercase__=True ,   lowercase__=True ,   lowercase__=True ,   lowercase__=True ,   lowercase__=True ,   lowercase__=True ,   **lowercase__ ,   ):







             '''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__(**lowercase__     )





from collections import deque





def      A__					(			__A  :						Optional[Any]			)					->Tuple:
      __A    =len(__A			)
      __A    =deque()
      __A    =[False for _ in range(__A			)]
      __A    =[-1 for _ in range(__A			)]
      __A    =index_of[:]

      def strong_connect(__A  :						Union[str, Any]     ,				__A  :						int     ,				__A  :						Optional[int]			):
            __A    =index  # the number when this node is seen
            __A    =index  # lowest rank node reachable from here
            index += 1
            stack.append(__A			)
            __A    =True

            for w in g[v]:
                  if index_of[w] == -1:
                        __A    =strong_connect(__A     ,				__A     ,				__A			)
                        __A    =(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )
                  elif on_stack[w]:
                        __A    =(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )

            if lowlink_of[v] == index_of[v]:
                  __A    =[]
                  __A    =stack.pop()
                  __A    =False
                  component.append(__A			)
                  while w != v:
                        __A    =stack.pop()
                        __A    =False
                        component.append(__A			)
                  components.append(__A			)
            return index

      __A    =[]
      for v in range(__A			):
            if index_of[v] == -1:
                  strong_connect(__A     ,				0     ,				__A			)

      return components





def      A__					(			__A  :						List[Any]     ,				__A  :						Optional[int]			)					->Tuple:
      __A    =[[] for _ in range(__A			)]
      for u, v in edges:
            g[u].append(__A			)
      return g


if __name__ == "__main__":
   # Test
   _lowerCamelCase      :					int					    =      7
   _lowerCamelCase      :					Tuple					    =      [0, 0, 1, 2, 3, 3, 4, 4, 6]
   _lowerCamelCase      :					Dict					    =      [1, 3, 2, 0, 1, 4, 5, 6, 5]
   _lowerCamelCase      :					Optional[Any]					    =      [(u, v) for u, v in zip(source, target)]
   _lowerCamelCase      :					Dict					    =      create_graph(n_vertices, edges)

   assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)


