






import torch

from transformers import AutoModel




class       snake_case__ (				torch.nn.Module):

       '''simple docstring'''



       def __init__(				self      ,    a__="sayef/fsner-bert-base-uncased"     )			->   str:






            '''simple docstring'''





            super(a__      ,    self     ).__init__()

            __snake_case      :Union[str, Any] 							=    AutoModel.from_pretrained(a__      ,    return_dict=a__     )
            __snake_case      :List[str] 							=    torch.nn.CosineSimilarity(3      ,    1e-08     )
            __snake_case      :Optional[int] 							=    torch.nn.Softmax(dim=1     )



       def        __lowercase    (				self      ,    **a__     )			->   str:






            '''simple docstring'''





            return self.bert(**a__     ).last_hidden_state



       def        __lowercase    (				self      ,    a__     )			->   Optional[Any]:






            '''simple docstring'''





            return token_embeddings.sum(2      ,    keepdim=a__     )



       def        __lowercase    (				self      ,    a__      ,    a__      ,    a__=1     )			->   Union[str, Any]:






            '''simple docstring'''





            return self.softmax(T * self.cos(a__      ,    a__     )     )



       def        __lowercase    (				self      ,    a__      ,    a__     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :str 							=    W_supports["""sizes"""].tolist()
            __snake_case      :int 							=    W_supports["""start_token_id"""].item()
            __snake_case      :str 							=    W_supports["""end_token_id"""].item()

            del W_supports["sizes"]
            del W_supports["start_token_id"]
            del W_supports["end_token_id"]

            __snake_case      :List[str] 							=    self.BERT(**a__     )
            __snake_case      :Optional[Any] 							=    self.BERT(**a__     )

            __snake_case      :Any 							=    None
            __snake_case      :List[Any] 							=    None

            __snake_case      :Any 							=    W_supports["""input_ids"""] == start_token_id
            __snake_case      :Any 							=    W_supports["""input_ids"""] == end_token_id

            for i, size in enumerate(a__     ):
                 if i == 0:
                      __snake_case      :List[str] 							=    0
                 else:
                      __snake_case      :Dict 							=    support_sizes[i - 1]

                 __snake_case      :Union[str, Any] 							=    S[s : s + size][start_token_masks[s : s + size]]
                 __snake_case      :Any 							=    S[s : s + size][end_token_masks[s : s + size]]

                 __snake_case      :Any 							=    torch.matmul(q[i]      ,    s_start.T     ).sum(1     ).softmax(0     )
                 __snake_case      :List[str] 							=    torch.matmul(q[i]      ,    s_end.T     ).sum(1     ).softmax(0     )

                 if p_starts is not None:
                      __snake_case      :str 							=    torch.vstack((p_starts, p_start)     )
                      __snake_case      :Optional[Any] 							=    torch.vstack((p_ends, p_end)     )
                 else:
                      __snake_case      :Optional[Any] 							=    p_start
                      __snake_case      :int 							=    p_end

            return p_starts, p_ends






"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


UpperCAmelCase				=						logging.get_logger(__name__)




class 	lowercase__				(					A_				):





   def __init__(							self      ,   *SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  None:
      warnings.warn(
          """The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
          """ Please use ImageGPTImageProcessor instead."""      ,   SCREAMING_SNAKE_CASE      ,   )
      super().__init__(*SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)

