

"""simple docstring"""





from __future__ import annotations




def   _A  (  UpperCamelCase_     :       list[int | str])	->						None:
   '''simple docstring'''


   create_state_space_tree(UpperCamelCase_,			[],			0,			[0 for i in range(len(UpperCamelCase_))])




def   _A  (  UpperCamelCase_     :       list[int | str],			UpperCamelCase_     :       list[int | str],			UpperCamelCase_     :       int,			UpperCamelCase_     :       list[int],			)	->						None:
   '''simple docstring'''


   if index == len(UpperCamelCase_):
      print(UpperCamelCase_)
      return

   for i in range(len(UpperCamelCase_)):
      if not index_used[i]:
         current_sequence.append(sequence[i])
         __lowercase								=						True
         create_state_space_tree(UpperCamelCase_,			UpperCamelCase_,			index + 1,			UpperCamelCase_)
         current_sequence.pop()
         __lowercase								=						False
_a       =							[3, 1, 2, 4]
generate_all_permutations(sequence)

_a       =							["A", "B", "C"]

generate_all_permutations(sequence_a)







"""simple docstring"""





import torch

from transformers import AutoModel







class 				SCREAMING_SNAKE_CASE					(		torch.nn.Module       ):






					"""simple docstring"""



					def __init__(       self			:				Tuple   ,lowercase_			:				Dict="sayef/fsner-bert-base-uncased"		):
						super(lowercase_   ,self		).__init__()

						lowerCAmelCase__	:						int  				=	AutoModel.from_pretrained(lowercase_   ,return_dict=lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	torch.nn.CosineSimilarity(3   ,1E-08		)
						lowerCAmelCase__	:						List[str]  				=	torch.nn.Softmax(dim=1		)







					def        __lowerCAmelCase (       self			:				str   ,**lowercase_			:				int		):
						return self.bert(**lowercase_		).last_hidden_state







					def        __lowerCAmelCase (       self			:				List[Any]   ,lowercase_			:				Optional[int]		):
						return token_embeddings.sum(2   ,keepdim=lowercase_		)







					def        __lowerCAmelCase (       self			:				Dict   ,lowercase_			:				int   ,lowercase_			:				str   ,lowercase_			:				Tuple=1		):
						return self.softmax(T * self.cos(lowercase_   ,lowercase_		)		)







					def        __lowerCAmelCase (       self			:				Optional[Any]   ,lowercase_			:				str   ,lowercase_			:				Union[str, Any]		):
						lowerCAmelCase__	:						List[Any]  				=	W_supports['''sizes'''].tolist()
						lowerCAmelCase__	:						Dict  				=	W_supports['''start_token_id'''].item()
						lowerCAmelCase__	:						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"]

						lowerCAmelCase__	:						Optional[Any]  				=	self.BERT(**lowercase_		)
						lowerCAmelCase__	:						int  				=	self.BERT(**lowercase_		)

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

						lowerCAmelCase__	:						int  				=	W_supports['''input_ids'''] == start_token_id
						lowerCAmelCase__	:						Optional[Any]  				=	W_supports['''input_ids'''] == end_token_id

						for i, size in enumerate(lowercase_		):
							if i == 0:
								lowerCAmelCase__	:						str  				=	0
							else:
								lowerCAmelCase__	:						List[Any]  				=	support_sizes[i - 1]

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

							lowerCAmelCase__	:						Union[str, Any]  				=	torch.matmul(q[i]   ,s_start.T		).sum(1		).softmax(0		)
							lowerCAmelCase__	:						Any  				=	torch.matmul(q[i]   ,s_end.T		).sum(1		).softmax(0		)

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

						return p_starts, p_ends




