


import torch

from transformers import AutoModel







class 			_lowercase (							torch.nn.Module):


       """simple docstring"""




       def __init__(   self							:			Union[str, Any]					,      __lowerCamelCase							:			Tuple="sayef/fsner-bert-base-uncased"       ):


             '''simple docstring'''



             super(__lowerCamelCase					,      self       ).__init__()

             lowerCamelCase__		:				Any    =  AutoModel.from_pretrained(__lowerCamelCase					,      return_dict=__lowerCamelCase       )
             lowerCamelCase__		:				Dict    =  torch.nn.CosineSimilarity(3					,      1E-0_8       )
             lowerCamelCase__		:				Any    =  torch.nn.Softmax(dim=1       )





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      **__lowerCamelCase							:			Dict       ):


             '''simple docstring'''



             return self.bert(**__lowerCamelCase       ).last_hidden_state





       def 						lowerCAmelCase     (   self							:			Optional[Any]					,      __lowerCamelCase							:			int       ):


             '''simple docstring'''



             return token_embeddings.sum(2					,      keepdim=__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			List[str]					,      __lowerCamelCase							:			List[str]					,      __lowerCamelCase							:			int					,      __lowerCamelCase							:			Optional[Any]=1       ):


             '''simple docstring'''



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





       def 						lowerCAmelCase     (   self							:			int					,      __lowerCamelCase							:			Optional[Any]					,      __lowerCamelCase							:			Union[str, Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  W_supports["sizes"].tolist()
             lowerCamelCase__		:				Optional[int]    =  W_supports["start_token_id"].item()
             lowerCamelCase__		:				Dict    =  W_supports["end_token_id"].item()

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

             lowerCamelCase__		:				Any    =  self.BERT(**__lowerCamelCase       )
             lowerCamelCase__		:				Union[str, Any]    =  self.BERT(**__lowerCamelCase       )

             lowerCamelCase__		:				Any    =  None
             lowerCamelCase__		:				int    =  None

             lowerCamelCase__		:				List[Any]    =  W_supports["input_ids"] == start_token_id
             lowerCamelCase__		:				Optional[int]    =  W_supports["input_ids"] == end_token_id

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

                   lowerCamelCase__		:				int    =  S[s : s + size][start_token_masks[s : s + size]]
                   lowerCamelCase__		:				List[str]    =  S[s : s + size][end_token_masks[s : s + size]]

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

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

             return p_starts, p_ends






import warnings

from ...utils import logging
from .image_processing_beit import BeitImageProcessor


A      :    List[str]		=					logging.get_logger(__name__)







class 			_lowercase (							lowercase__):


       """simple docstring"""




       def __init__(   self							:			Optional[int]					,      *__lowerCamelCase							:			List[str]					,      **__lowerCamelCase							:			str       ):


             '''simple docstring'''



             warnings.warn(
                 "The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
                 " use BeitImageProcessor instead."					,      __lowerCamelCase					,      )
             super().__init__(*__lowerCamelCase					,      **__lowerCamelCase       )


