



"""simple docstring"""
import torch

from transformers import AutoModel



class       SCREAMING_SNAKE_CASE__  (					torch.nn.Module ):

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased"						)  ->      Tuple:





    '''simple docstring'''




    super(_SCREAMING_SNAKE_CASE      ,     self						).__init__()

    UpperCAmelCase	:					Any	=	AutoModel.from_pretrained(_SCREAMING_SNAKE_CASE      ,     return_dict=_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Tuple	=	torch.nn.CosineSimilarity(3      ,     1E-08						)
    UpperCAmelCase	:					List[str]	=	torch.nn.Softmax(dim=1						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     **_SCREAMING_SNAKE_CASE						)  ->      Dict:





    '''simple docstring'''




    return self.bert(**_SCREAMING_SNAKE_CASE						).last_hidden_state

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE						)  ->      Union[str, Any]:





    '''simple docstring'''




    return token_embeddings.sum(2      ,     keepdim=_SCREAMING_SNAKE_CASE						)

   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE=1						)  ->      Union[str, Any]:





    '''simple docstring'''




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




   def 	SCREAMING_SNAKE_CASE     (   self      ,     _SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						)  ->      List[Any]:





    '''simple docstring'''




    UpperCAmelCase	:					Dict	=	W_supports["""sizes"""].tolist()
    UpperCAmelCase	:					Optional[Any]	=	W_supports["""start_token_id"""].item()
    UpperCAmelCase	:					Any	=	W_supports["""end_token_id"""].item()

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

    UpperCAmelCase	:					str	=	self.BERT(**_SCREAMING_SNAKE_CASE						)
    UpperCAmelCase	:					Union[str, Any]	=	self.BERT(**_SCREAMING_SNAKE_CASE						)

    UpperCAmelCase	:					Dict	=	None
    UpperCAmelCase	:					Dict	=	None

    UpperCAmelCase	:					Optional[int]	=	W_supports["""input_ids"""] == start_token_id
    UpperCAmelCase	:					List[str]	=	W_supports["""input_ids"""] == end_token_id

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

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

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

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

    return p_starts, p_ends


import logging

from transformers.configuration_utils import PretrainedConfig


__a  			=					logging.getLogger(__name__)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Optional[int]         =	'masked_bert'
	def __init__(       self  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=3_0_5_2_2       ,							SCREAMING_SNAKE_CASE_  :			List[str]=7_6_8       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=1_2       ,							SCREAMING_SNAKE_CASE_  :			Any=1_2       ,							SCREAMING_SNAKE_CASE_  :			str=3_0_7_2       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]="gelu"       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=0.1       ,							SCREAMING_SNAKE_CASE_  :			List[str]=0.1       ,							SCREAMING_SNAKE_CASE_  :			Tuple=5_1_2       ,							SCREAMING_SNAKE_CASE_  :			str=2       ,							SCREAMING_SNAKE_CASE_  :			Dict=0.02       ,							SCREAMING_SNAKE_CASE_  :			Any=1e-12       ,							SCREAMING_SNAKE_CASE_  :			Any=0       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]="topK"       ,							SCREAMING_SNAKE_CASE_  :			Dict="constant"       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=0.0       ,							**SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							)       ->     Optional[Any]:
					super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )

					lowercase_       =					vocab_size
					lowercase_       =					hidden_size
					lowercase_       =					num_hidden_layers
					lowercase_       =					num_attention_heads
					lowercase_       =					hidden_act
					lowercase_       =					intermediate_size
					lowercase_       =					hidden_dropout_prob
					lowercase_       =					attention_probs_dropout_prob
					lowercase_       =					max_position_embeddings
					lowercase_       =					type_vocab_size
					lowercase_       =					initializer_range
					lowercase_       =					layer_norm_eps
					lowercase_       =					pruning_method
					lowercase_       =					mask_init
					lowercase_       =					mask_scale




