
"""simple docstring"""






import torch

from transformers import AutoModel





class 				a__					(						torch.nn.Module       ):



     def __init__(							self :Dict      ,  _lowerCamelCase :int="sayef/fsner-bert-base-uncased"   ):







      '''simple docstring'''



      super(__lowerCamelCase      ,  self   ).__init__()

      UpperCamelCase_     :					List[str]   	=AutoModel.from_pretrained(__lowerCamelCase      ,  return_dict=__lowerCamelCase   )
      UpperCamelCase_     :					Optional[Any]   	=torch.nn.CosineSimilarity(3      ,  1E-0_8   )
      UpperCamelCase_     :					Optional[Any]   	=torch.nn.Softmax(dim=1   )



     def  lowerCamelCase_						(							self :int      ,  **_lowerCamelCase :Optional[int]   ):







      '''simple docstring'''



      return self.bert(**__lowerCamelCase   ).last_hidden_state



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :Dict   ):







      '''simple docstring'''



      return token_embeddings.sum(2      ,  keepdim=__lowerCamelCase   )



     def  lowerCamelCase_						(							self :str      ,  _lowerCamelCase :str      ,  _lowerCamelCase :int      ,  _lowerCamelCase :int=1   ):







      '''simple docstring'''



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




     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :Tuple      ,  _lowerCamelCase :Tuple   ):







      '''simple docstring'''



      UpperCamelCase_     :					Any   	=W_supports['sizes'].tolist()
      UpperCamelCase_     :					List[Any]   	=W_supports['start_token_id'].item()
      UpperCamelCase_     :					Union[str, Any]   	=W_supports['end_token_id'].item()

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

      UpperCamelCase_     :					Union[str, Any]   	=self.BERT(**__lowerCamelCase   )
      UpperCamelCase_     :					Optional[int]   	=self.BERT(**__lowerCamelCase   )

      UpperCamelCase_     :					int   	=None
      UpperCamelCase_     :					List[Any]   	=None

      UpperCamelCase_     :					Dict   	=W_supports['input_ids'] == start_token_id
      UpperCamelCase_     :					Optional[Any]   	=W_supports['input_ids'] == end_token_id

      for i, size in enumerate(__lowerCamelCase   ):
       if i == 0:
        UpperCamelCase_     :					Dict   	=0
       else:
        UpperCamelCase_     :					Optional[int]   	=support_sizes[i - 1]

       UpperCamelCase_     :					Optional[Any]   	=S[s : s + size][start_token_masks[s : s + size]]
       UpperCamelCase_     :					Any   	=S[s : s + size][end_token_masks[s : s + size]]

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

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

      return p_starts, p_ends






from typing import TYPE_CHECKING

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


__magic_name__  :  Optional[int]					=       {
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  int					=       [
          """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BioGptForCausalLM""",
          """BioGptForTokenClassification""",
          """BioGptForSequenceClassification""",
          """BioGptModel""",
          """BioGptPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
      from .tokenization_biogpt import BioGptTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_biogpt import (
                BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BioGptForCausalLM,
                BioGptForSequenceClassification,
                BioGptForTokenClassification,
                BioGptModel,
                BioGptPreTrainedModel,
            )


else:
      import sys

      __magic_name__  :  int					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

