

'''simple docstring'''
import torch

from transformers import AutoModel







class 							A__    (       torch.nn.Module  ):





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





       '''simple docstring'''






       super(_a							,					self ).__init__()

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





  def 						A  (		self      :	int							,					**_a      :	str )	->    Union[str, Any]:





       '''simple docstring'''






       return self.bert(**_a ).last_hidden_state





  def 						A  (		self      :	str							,					_a      :	int )	->    Optional[Any]:





       '''simple docstring'''






       return token_embeddings.sum(2							,					keepdim=_a )





  def 						A  (		self      :	Dict							,					_a      :	List[str]							,					_a      :	Dict							,					_a      :	Dict=1 )	->    Optional[int]:





       '''simple docstring'''






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





  def 						A  (		self      :	Optional[Any]							,					_a      :	List[str]							,					_a      :	List[Any] )	->    Union[str, Any]:





       '''simple docstring'''






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

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

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

       _SCREAMING_SNAKE_CASE							    =None
       _SCREAMING_SNAKE_CASE							    =None

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

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

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

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

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

       return p_starts, p_ends






"""simple docstring"""


from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


A_						=				logging.get_logger(__name__)






class 					lowercase:







 '''simple docstring'''





 lowercase__ 					=					42
 lowercase__ 					=					None


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   raise NotImplementedError


 def  UpperCamelCase_   (				self:		Tuple,      a_:		int,      a_:		int,      a_:		str,      **a_:		Dict		):




   '''simple docstring'''



   raise NotImplementedError


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		List[str]		):




   '''simple docstring'''



   raise NotImplementedError


 def  UpperCamelCase_   (				self:		Union[str, Any]		):




   '''simple docstring'''



   if not self.is_available():
     raise RuntimeError(
         f"You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}."		)


 @classmethod
 def  UpperCamelCase_   (				cls:		Tuple		):




   '''simple docstring'''



   return f"`pip install {cls.pip_package or cls.name}`"







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"optuna"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_optuna_available()


 def  UpperCamelCase_   (				self:		Union[str, Any],      a_:		List[Any],      a_:		int,      a_:		str,      **a_:		List[str]		):




   '''simple docstring'''



   return run_hp_search_optuna(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		Any		):




   '''simple docstring'''



   return default_hp_space_optuna(a_		)







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"ray"
 lowercase__ 					=					"'ray[tune]'"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_ray_available()


 def  UpperCamelCase_   (				self:		int,      a_:		Optional[Any],      a_:		int,      a_:		str,      **a_:		List[Any]		):




   '''simple docstring'''



   return run_hp_search_ray(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		Tuple		):




   '''simple docstring'''



   return default_hp_space_ray(a_		)







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"sigopt"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_sigopt_available()


 def  UpperCamelCase_   (				self:		Dict,      a_:		str,      a_:		int,      a_:		str,      **a_:		int		):




   '''simple docstring'''



   return run_hp_search_sigopt(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		List[str]		):




   '''simple docstring'''



   return default_hp_space_sigopt(a_		)







class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"wandb"


 @staticmethod
 def  UpperCamelCase_   (				):




   '''simple docstring'''



   return is_wandb_available()


 def  UpperCamelCase_   (				self:		Optional[Any],      a_:		str,      a_:		int,      a_:		str,      **a_:		Union[str, Any]		):




   '''simple docstring'''



   return run_hp_search_wandb(a_,      a_,      a_,      **a_		)


 def  UpperCamelCase_   (				self:		str,      a_:		Any		):




   '''simple docstring'''



   return default_hp_space_wandb(a_		)







A_						=				{
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}


def 					UpperCAmelCase__       ():
  """simple docstring"""

  _snake_case							:		Optional[int]								=  [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
  if len(snake_case__    ) > 0:
    _snake_case							:		Any								=  available_backends[0].name
    if len(snake_case__    ) > 1:
      logger.info(
          F"{len(snake_case__    )} hyperparameter search backends available. Using {name} as the default."    )
    return name
  raise RuntimeError(
      """No hyperparameter search backend available.\n"""
      + """\n""".join(
          F" - To install {backend.name} run {backend.pip_install()}"
          for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()    )    )


