





from __future__ import annotations

from random import random





class      lowercase__    :







  def __init__(	self    ,						_lowercase = None						):
       lowerCAmelCase_					:       Optional[int]    =		value
       lowerCAmelCase_					:       Union[str, Any]    =		random()
       lowerCAmelCase_					:       Node | None    =		None
       lowerCAmelCase_					:       Node | None    =		None







  def __repr__(	self						):
       from pprint import pformat

       if self.left is None and self.right is None:
            return F'\'{self.value}: {self.prior:.5}\''
       else:
            return pformat(
                {F'{self.value}: {self.prior:.5}': (self.left, self.right)}    ,						indent=1						)







  def __str__(	self						):
       lowerCAmelCase_					:       str    =		str(self.value						) + """ """
       lowerCAmelCase_					:       Tuple    =		str(self.left or """"""						)
       lowerCAmelCase_					:       int    =		str(self.right or """"""						)
       return value + left + right






def 							_lowerCAmelCase (  _a   :					Node | None					,   _a   :					int			)	->			tuple[Node | None, Node | None]:
     if root is None:  # None tree is split into 2 Nones
          return None, None
     elif root.value is None:
          return None, None
     else:
          if value < root.value:
               lowerCAmelCase_					:       Optional[Any]    =		split(root.left					,   _a			)
               return left, root
          else:
               lowerCAmelCase_					:       List[str]    =		split(root.right					,   _a			)
               return root, right






def 							_lowerCAmelCase (  _a   :					Node | None					,   _a   :					Node | None			)	->			Node | None:
     if (not left) or (not right):  # If one node is None, return the other
          return left or right
     elif left.prior < right.prior:
          lowerCAmelCase_					:       Dict    =		merge(left.right					,   _a			)
          return left
     else:
          lowerCAmelCase_					:       Optional[int]    =		merge(_a					,   right.left			)
          return right






def 							_lowerCAmelCase (  _a   :					Node | None					,   _a   :					int			)	->			Node | None:
     lowerCAmelCase_					:       Union[str, Any]    =		Node(_a			)
     lowerCAmelCase_					:       List[Any]    =		split(_a					,   _a			)
     return merge(merge(_a					,   _a			)					,   _a			)






def 							_lowerCAmelCase (  _a   :					Node | None					,   _a   :					int			)	->			Node | None:
     lowerCAmelCase_					:       Union[str, Any]    =		split(_a					,   value - 1			)
     lowerCAmelCase_					:       str    =		split(_a					,   _a			)
     return merge(_a					,   _a			)






def 							_lowerCAmelCase (  _a   :					Node | None			)	->			None:
     if not root:  # None
          return
     else:
          inorder(root.left			)
          print(root.value					,   end=""","""			)
          inorder(root.right			)






def 							_lowerCAmelCase (  _a   :					Node | None					,   _a   :					str			)	->			Node | None:
     for arg in args.split():
          if arg[0] == "+":
               lowerCAmelCase_					:       Optional[Any]    =		insert(_a					,   int(arg[1:]			)			)

          elif arg[0] == "-":
               lowerCAmelCase_					:       List[str]    =		erase(_a					,   int(arg[1:]			)			)

          else:
               print("""Unknown command"""			)

     return root






def 							_lowerCAmelCase (  )	->			None:
     lowerCAmelCase_					:       Any    =		None
     print(
         """enter numbers to create a tree, + value to add value into treap, """
         """- value to erase all nodes with value. 'q' to quit. """			)

     lowerCAmelCase_					:       Any    =		input()
     while args != "q":
          lowerCAmelCase_					:       str    =		interact_treap(_a					,   _a			)
          print(_a			)
          lowerCAmelCase_					:       Any    =		input()

     print("""good by!"""			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()







import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  """M-CLIP"""







  def __init__(	self    ,						_lowercase=1_024    ,						_lowercase=768    ,						**_lowercase						):
       lowerCAmelCase_					:       Tuple    =		transformerDimSize
       lowerCAmelCase_					:       Optional[Any]    =		imageDimSize
       super().__init__(**_lowercase						)





class      lowercase__    (							__A  ):
  __UpperCamelCase	     =  MCLIPConfig







  def __init__(	self    ,						_lowercase    ,						*_lowercase    ,						**_lowercase						):
       super().__init__(_lowercase    ,						*_lowercase    ,						**_lowercase						)
       lowerCAmelCase_					:       str    =		XLMRobertaModel(_lowercase						)
       lowerCAmelCase_					:       str    =		torch.nn.Linear(
           in_features=config.transformerDimensions    ,						out_features=config.numDims						)







  def 	UpperCAmelCase__		(	self    ,						_lowercase    ,						_lowercase						):
       lowerCAmelCase_					:       Optional[int]    =		self.transformer(input_ids=_lowercase    ,						attention_mask=_lowercase						)[0]
       lowerCAmelCase_					:       Any    =		(embs * attention_mask.unsqueeze(2						)).sum(dim=1						) / attention_mask.sum(dim=1						)[:, None]
       return self.LinearTransformation(_lowercase						), embs
