




'''simple docstring'''





import torch

from transformers import AutoModel

class        _a    (						torch.nn.Module							):






     def __init__(			self ,_SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased"   )							->      List[str]:
          super(_SCREAMING_SNAKE_CASE ,self   ).__init__()

          _snake_case							=				AutoModel.from_pretrained(_SCREAMING_SNAKE_CASE ,return_dict=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				torch.nn.CosineSimilarity(3 ,1e-08   )
          _snake_case							=				torch.nn.Softmax(dim=1   )






     def     _lowercase     (			self ,**_SCREAMING_SNAKE_CASE   )							->      Optional[int]:
          return self.bert(**_SCREAMING_SNAKE_CASE   ).last_hidden_state






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      str:
          return token_embeddings.sum(2 ,keepdim=_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=1   )							->      Dict:
          return self.softmax(T * self.cos(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Union[str, Any]:
          _snake_case							=				W_supports["sizes"].tolist()
          _snake_case							=				W_supports["start_token_id"].item()
          _snake_case							=				W_supports["end_token_id"].item()

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

          _snake_case							=				self.BERT(**_SCREAMING_SNAKE_CASE   )
          _snake_case							=				self.BERT(**_SCREAMING_SNAKE_CASE   )

          _snake_case							=				None
          _snake_case							=				None

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

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

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

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

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

          return p_starts, p_ends








'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCamelCase_						:      Union[str, Any]       =						{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      Optional[int]       =						['''PLBartTokenizer''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      int       =						[
       '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''PLBartForCausalLM''',
       '''PLBartForConditionalGeneration''',
       '''PLBartForSequenceClassification''',
       '''PLBartModel''',
       '''PLBartPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_plbart import PLBartTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_plbart import (
          PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
          PLBartForCausalLM,
          PLBartForConditionalGeneration,
          PLBartForSequenceClassification,
          PLBartModel,
          PLBartPreTrainedModel,
      )


else:
   import sys

   UpperCamelCase_						:      str       =						_LazyModule(__name__, globals()['''__file__'''], _import_structure)


