import torch

from transformers import AutoModel


class 			UpperCAmelCase__    (		torch.nn.Module					):



  """simple docstring"""






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




    '''simple docstring'''




    super(__lowerCAmelCase				,				self   ).__init__()

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






  def 	_UpperCAmelCase				(				self:			Any				,				**__lowerCAmelCase:			Tuple   ) -> Any:




    '''simple docstring'''




    return self.bert(**__lowerCAmelCase   ).last_hidden_state






  def 	_UpperCAmelCase				(				self:			Union[str, Any]				,				__lowerCAmelCase:			Any   ) -> Dict:




    '''simple docstring'''




    return token_embeddings.sum(2				,				keepdim=__lowerCAmelCase   )






  def 	_UpperCAmelCase				(				self:			int				,				__lowerCAmelCase:			List[str]				,				__lowerCAmelCase:			str				,				__lowerCAmelCase:			Dict=1   ) -> Dict:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			List[Any]				,				__lowerCAmelCase:			str				,				__lowerCAmelCase:			Optional[int]   ) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       W_supports["sizes"].tolist()
    __UpperCAmelCase						    =       W_supports["start_token_id"].item()
    __UpperCAmelCase						    =       W_supports["end_token_id"].item()

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

    __UpperCAmelCase						    =       self.BERT(**__lowerCAmelCase   )
    __UpperCAmelCase						    =       self.BERT(**__lowerCAmelCase   )

    __UpperCAmelCase						    =       None
    __UpperCAmelCase						    =       None

    __UpperCAmelCase						    =       W_supports["input_ids"] == start_token_id
    __UpperCAmelCase						    =       W_supports["input_ids"] == end_token_id

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

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

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

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

    return p_starts, p_ends




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a_							=      {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      ["""LayoutXLMTokenizer"""]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_							=      ["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
       from .processing_layoutxlm import LayoutXLMProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm import LayoutXLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
       import sys

       a_							=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



