





import torch

from transformers import AutoModel






class       __A					(					torch.nn.Module				):

			'''simple docstring'''







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



				'''simple docstring'''







				super(__lowerCAmelCase ,						self							).__init__()

				lowerCamelCase__              =	AutoModel.from_pretrained(__lowerCAmelCase ,						return_dict=__lowerCAmelCase							)
				lowerCamelCase__              =	torch.nn.CosineSimilarity(3 ,						1E-08							)
				lowerCamelCase__              =	torch.nn.Softmax(dim=1							)





			def 	__lowerCamelCase       (					self ,						**__lowerCAmelCase							):



				'''simple docstring'''







				return self.bert(**__lowerCAmelCase							).last_hidden_state





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				return token_embeddings.sum(2 ,						keepdim=__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase=1							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







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

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

				lowerCamelCase__              =	self.BERT(**__lowerCAmelCase							)
				lowerCamelCase__              =	self.BERT(**__lowerCAmelCase							)

				lowerCamelCase__              =	None
				lowerCamelCase__              =	None

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

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

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

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

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

				return p_starts, p_ends










import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


_a						=   logging.get_logger(__name__)






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			def __init__(					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







				warnings.warn(
				    '''The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
				    ''' Please use SegformerImageProcessor instead.''' ,						__lowerCAmelCase ,						)
				super().__init__(*__lowerCAmelCase ,						**__lowerCAmelCase							)



