


import enum
import shutil
import sys


__snake_case      ,__snake_case            =   shutil.get_terminal_size()

__snake_case            =   {"UP": "A", "DOWN": "B", "RIGHT": "C", "LEFT": "D"}




class    UpperCAmelCase					(       enum.Enum    ):
			lowercase						= 0
			lowercase						= 1






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Any       ,  SCREAMING_SNAKE_CASE_   :		List[Any]=""	):





			"""simple docstring"""




			sys.stdout.write(str(SCREAMING_SNAKE_CASE_	) + end	)
			sys.stdout.flush()






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[str]       ,  SCREAMING_SNAKE_CASE_   :		Union[str, Any]       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]=""	):





			"""simple docstring"""




			forceWrite(f'\u001b[{color}m{content}\u001b[0m'       ,  SCREAMING_SNAKE_CASE_	)






def 							_lowercase    (		):





			"""simple docstring"""




			forceWrite("""\r"""	)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		str	):





			"""simple docstring"""




			forceWrite(f'\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}'	)






def 							_lowercase    (		):





			"""simple docstring"""




			forceWrite(""" """ * TERMINAL_WIDTH	)
			reset_cursor()






def 							_lowercase    (		):





			"""simple docstring"""




			reset_cursor()
			forceWrite("""-""" * TERMINAL_WIDTH	)








import torch

from transformers import AutoModel




class    UpperCAmelCase					(       torch.nn.Module    ):




			def __init__(						self							:		int       ,       __magic_name__							:		List[Any]="sayef/fsner-bert-base-uncased"						):







						"""simple docstring"""







						super(__magic_name__       ,       self						).__init__()

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




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       **__magic_name__							:		List[Any]						):







						"""simple docstring"""







						return self.bert(**__magic_name__						).last_hidden_state




			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		int						):







						"""simple docstring"""







						return token_embeddings.sum(2       ,       keepdim=__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		Optional[Any]=1						):







						"""simple docstring"""







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





			def   lowerCamelCase_					(						self							:		Union[str, Any]       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					W_supports["""sizes"""].tolist()
						UpperCamelCase         =					W_supports["""start_token_id"""].item()
						UpperCamelCase         =					W_supports["""end_token_id"""].item()

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

						UpperCamelCase         =					self.BERT(**__magic_name__						)
						UpperCamelCase         =					self.BERT(**__magic_name__						)

						UpperCamelCase         =					None
						UpperCamelCase         =					None

						UpperCamelCase         =					W_supports["""input_ids"""] == start_token_id
						UpperCamelCase         =					W_supports["""input_ids"""] == end_token_id

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

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

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

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

						return p_starts, p_ends




