

import torch

from transformers import AutoModel







class 						__magic_name__			(							torch.nn.Module							):

   '''simple docstring'''




   def __init__(				self,    lowercase_="sayef/fsner-bert-base-uncased"       )							->    List[Any]:






     """simple docstring"""






     super(lowercase_,    self       ).__init__()

     a__  =AutoModel.from_pretrained(lowercase_,    return_dict=lowercase_       )
     a__  =torch.nn.CosineSimilarity(3,    1E-08       )
     a__  =torch.nn.Softmax(dim=1       )



   def  _UpperCAmelCase (				self,    **lowercase_       )							->    List[Any]:






     """simple docstring"""






     return self.bert(**lowercase_       ).last_hidden_state



   def  _UpperCAmelCase (				self,    lowercase_       )							->    str:






     """simple docstring"""






     return token_embeddings.sum(2,    keepdim=lowercase_       )



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_,    lowercase_=1       )							->    Optional[Any]:






     """simple docstring"""






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



   def  _UpperCAmelCase (				self,    lowercase_,    lowercase_       )							->    int:






     """simple docstring"""






     a__  =W_supports['''sizes'''].tolist()
     a__  =W_supports['''start_token_id'''].item()
     a__  =W_supports['''end_token_id'''].item()

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

     a__  =self.BERT(**lowercase_       )
     a__  =self.BERT(**lowercase_       )

     a__  =None
     a__  =None

     a__  =W_supports['''input_ids'''] == start_token_id
     a__  =W_supports['''input_ids'''] == end_token_id

     for i, size in enumerate(lowercase_       ):
       if i == 0:
         a__  =0
       else:
         a__  =support_sizes[i - 1]

       a__  =S[s : s + size][start_token_masks[s : s + size]]
       a__  =S[s : s + size][end_token_masks[s : s + size]]

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

       if p_starts is not None:
         a__  =torch.vstack((p_starts, p_start)       )
         a__  =torch.vstack((p_ends, p_end)       )
       else:
         a__  =p_start
         a__  =p_end

     return p_starts, p_ends



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase           =					{
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''CanineForMultipleChoice''',
           '''CanineForQuestionAnswering''',
           '''CanineForSequenceClassification''',
           '''CanineForTokenClassification''',
           '''CanineLayer''',
           '''CanineModel''',
           '''CaninePreTrainedModel''',
           '''load_tf_weights_in_canine''',
       ]


if TYPE_CHECKING:
       from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
       from .tokenization_canine import CanineTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_canine import (
                  CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  CanineForMultipleChoice,
                  CanineForQuestionAnswering,
                  CanineForSequenceClassification,
                  CanineForTokenClassification,
                  CanineLayer,
                  CanineModel,
                  CaninePreTrainedModel,
                  load_tf_weights_in_canine,
              )


else:
       import sys

       lowerCamelCase           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
