





from __future__ import annotations

from collections.abc import Iterator





class      lowercase__    :







  def __init__(	self    ,						_lowercase						):
       lowerCAmelCase_					:       List[Any]    =		value
       lowerCAmelCase_					:       Node | None    =		None
       lowerCAmelCase_					:       Node | None    =		None





class      lowercase__    :







  def __init__(	self    ,						_lowercase						):
       lowerCAmelCase_					:       Any    =		tree







  def 	UpperCAmelCase__		(	self    ,						_lowercase						):
       if node is None:
            return 0
       return node.value + (
           self.depth_first_search(node.left						) + self.depth_first_search(node.right						)
       )







  def __iter__(	self						):
       yield self.depth_first_search(self.tree						)


if __name__ == "__main__":
      import doctest

      doctest.testmod()







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
