





from __future__ import annotations






def 							_lowerCAmelCase (  _a   :					list[float]					,   _a   :					list[float]			)	->			float:
     lowerCAmelCase_					:       List[str]    =		sorted(numsa + numsa			)
     lowerCAmelCase_					:       Tuple    =		divmod(len(_a			)					,   2			)
     if mod == 1:
          return all_numbers[div]
     else:
          return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      UpperCAmelCase_					:   str 							=						[float(x) for x in input("""Enter the elements of first array: """).split()]
      UpperCAmelCase_					:   Tuple 							=						[float(x) for x in input("""Enter the elements of second array: """).split()]
      print(F"""The median of two arrays is: {median_of_two_arrays(array_a, array_a)}""")







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
