





import torch

from transformers import AutoModel




class        _a    (    torch.nn.Module):

      """simple docstring"""


      def __init__(		self:			str	,    __lowerCamelCase:			List[str]="sayef/fsner-bert-base-uncased"					):
          '''simple docstring'''


          super(__lowerCamelCase	,    self					).__init__()

          UpperCamelCase__:	Any					=     AutoModel.from_pretrained(__lowerCamelCase	,    return_dict=__lowerCamelCase					)
          UpperCamelCase__:	Optional[int]					=     torch.nn.CosineSimilarity(3	,    1e-08					)
          UpperCamelCase__:	Optional[int]					=     torch.nn.Softmax(dim=1					)






      def 			UpperCAmelCase_       (		self:			Optional[int]	,    **__lowerCamelCase:			int					):
          '''simple docstring'''


          return self.bert(**__lowerCamelCase					).last_hidden_state






      def 			UpperCAmelCase_       (		self:			Any	,    __lowerCamelCase:			List[str]					):
          '''simple docstring'''


          return token_embeddings.sum(2	,    keepdim=__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			str	,    __lowerCamelCase:			int	,    __lowerCamelCase:			Optional[Any]	,    __lowerCamelCase:			Union[str, Any]=1					):
          '''simple docstring'''


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






      def 			UpperCAmelCase_       (		self:			List[str]	,    __lowerCamelCase:			Dict	,    __lowerCamelCase:			List[Any]					):
          '''simple docstring'''


          UpperCamelCase__:	List[str]					=     W_supports["sizes"].tolist()
          UpperCamelCase__:	Tuple					=     W_supports["start_token_id"].item()
          UpperCamelCase__:	Tuple					=     W_supports["end_token_id"].item()

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

          UpperCamelCase__:	Optional[Any]					=     self.BERT(**__lowerCamelCase					)
          UpperCamelCase__:	Optional[int]					=     self.BERT(**__lowerCamelCase					)

          UpperCamelCase__:	List[Any]					=     None
          UpperCamelCase__:	Union[str, Any]					=     None

          UpperCamelCase__:	Any					=     W_supports["input_ids"] == start_token_id
          UpperCamelCase__:	Tuple					=     W_supports["input_ids"] == end_token_id

          for i, size in enumerate(__lowerCamelCase					):
              if i == 0:
                  UpperCamelCase__:	Optional[Any]					=     0
              else:
                  UpperCamelCase__:	Tuple					=     support_sizes[i - 1]

              UpperCamelCase__:	List[Any]					=     S[s : s + size][start_token_masks[s : s + size]]
              UpperCamelCase__:	Union[str, Any]					=     S[s : s + size][end_token_masks[s : s + size]]

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

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

          return p_starts, p_ends








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A__:    List[Any]  	=	{'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Dict  	=	[
       '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''OPTForCausalLM''',
       '''OPTModel''',
       '''OPTPreTrainedModel''',
       '''OPTForSequenceClassification''',
       '''OPTForQuestionAnswering''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    Optional[int]  	=	['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A__:    str  	=	[
       '''FlaxOPTForCausalLM''',
       '''FlaxOPTModel''',
       '''FlaxOPTPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_opt import (
          OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          OPTForCausalLM,
          OPTForQuestionAnswering,
          OPTForSequenceClassification,
          OPTModel,
          OPTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
   import sys

   A__:    Optional[Any]  	=	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

