






import torch

from transformers import AutoModel


class 					_snake_case						(			torch.nn.Module ):






	def __init__(		self					,UpperCamelCase="sayef/fsner-bert-base-uncased"       )						->    str:
					super(lowerCAmelCase_					,self       ).__init__()

					snake_case__      :str         =   AutoModel.from_pretrained(lowerCAmelCase_					,return_dict=lowerCAmelCase_       )
					snake_case__      :int         =   torch.nn.CosineSimilarity(3					,1E-08       )
					snake_case__      :Union[str, Any]         =   torch.nn.Softmax(dim=1       )






	def    lowerCAmelCase_							(		self					,**UpperCamelCase       )						->    Any:
					return self.bert(**lowerCAmelCase_       ).last_hidden_state






	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    int:
					return token_embeddings.sum(2					,keepdim=lowerCAmelCase_       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase=1       )						->    Union[str, Any]:
					return self.softmax(T * self.cos(lowerCAmelCase_					,lowerCAmelCase_       )       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase       )						->    Optional[int]:
					snake_case__      :List[Any]         =   W_supports["sizes"].tolist()
					snake_case__      :Union[str, Any]         =   W_supports["start_token_id"].item()
					snake_case__      :List[str]         =   W_supports["end_token_id"].item()

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

					snake_case__      :Dict         =   self.BERT(**lowerCAmelCase_       )
					snake_case__      :Tuple         =   self.BERT(**lowerCAmelCase_       )

					snake_case__      :Any         =   None
					snake_case__      :Tuple         =   None

					snake_case__      :List[str]         =   W_supports["input_ids"] == start_token_id
					snake_case__      :Dict         =   W_supports["input_ids"] == end_token_id

					for i, size in enumerate(lowerCAmelCase_       ):
									if i == 0:
													snake_case__      :Union[str, Any]         =   0
									else:
													snake_case__      :int         =   support_sizes[i - 1]

									snake_case__      :List[str]         =   S[s : s + size][start_token_masks[s : s + size]]
									snake_case__      :str         =   S[s : s + size][end_token_masks[s : s + size]]

									snake_case__      :List[str]         =   torch.matmul(q[i]					,s_start.T       ).sum(1       ).softmax(0       )
									snake_case__      :int         =   torch.matmul(q[i]					,s_end.T       ).sum(1       ).softmax(0       )

									if p_starts is not None:
													snake_case__      :Tuple         =   torch.vstack((p_starts, p_start)       )
													snake_case__      :Tuple         =   torch.vstack((p_ends, p_end)       )
									else:
													snake_case__      :Optional[Any]         =   p_start
													snake_case__      :Any         =   p_end

					return p_starts, p_ends







import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class 					_snake_case						(			_A       ,    _A       ,    _A ):






	@register_to_config
	def __init__(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = False					,)						->    int:
					super().__init__()

					snake_case__      :Union[str, Any]         =   nn.Embedding(UpperCamelCase					,UpperCamelCase       )

					snake_case__      :int         =   nn.Embedding(UpperCamelCase					,UpperCamelCase       )
					snake_case__      :Any         =   False

					snake_case__      :List[Any]         =   nn.Dropout(p=UpperCamelCase       )

					snake_case__      :Tuple         =   TaConfig(
					    vocab_size=UpperCamelCase					,d_model=UpperCamelCase					,num_heads=UpperCamelCase					,d_kv=UpperCamelCase					,d_ff=UpperCamelCase					,dropout_rate=UpperCamelCase					,feed_forward_proj=UpperCamelCase					,is_decoder=UpperCamelCase					,is_encoder_decoder=UpperCamelCase					,)

					snake_case__      :List[str]         =   nn.ModuleList()
					for lyr_num in range(UpperCamelCase       ):
									snake_case__      :List[Any]         =   TaBlock(UpperCamelCase       )
									self.encoders.append(UpperCamelCase       )

					snake_case__      :Optional[Any]         =   TaLayerNorm(UpperCamelCase       )
					snake_case__      :Any         =   nn.Dropout(p=UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase       )						->    int:
					snake_case__      :str         =   self.token_embedder(UpperCamelCase       )

					snake_case__      :int         =   encoder_input_tokens.shape[1]
					snake_case__      :List[Any]         =   torch.arange(UpperCamelCase					,device=encoder_input_tokens.device       )
					x += self.position_encoding(UpperCamelCase       )

					snake_case__      :Optional[int]         =   self.dropout_pre(UpperCamelCase       )

					# inverted the attention mask
					snake_case__      :Optional[Any]         =   encoder_input_tokens.size()
					snake_case__      :Dict         =   self.get_extended_attention_mask(UpperCamelCase					,UpperCamelCase       )

					for lyr in self.encoders:
									snake_case__      :str         =   lyr(UpperCamelCase					,UpperCamelCase       )[0]
					snake_case__      :List[Any]         =   self.layer_norm(UpperCamelCase       )

					return self.dropout_post(UpperCamelCase       ), encoder_inputs_mask