

"""simple docstring"""





import logging
import os

from .state import PartialState







class 				SCREAMING_SNAKE_CASE					(		logging.LoggerAdapter       ):






					"""simple docstring"""



					@staticmethod
					def        __lowerCAmelCase (       lowercase_			:				Tuple		):
						lowerCAmelCase__	:						str  				=	PartialState()
						return not main_process_only or (main_process_only and state.is_main_process)







					def        __lowerCAmelCase (       self			:				Union[str, Any]   ,lowercase_			:				Optional[int]   ,lowercase_			:				Any   ,*lowercase_			:				List[str]   ,**lowercase_			:				str		):
						if PartialState._shared_state == {}:
							raise RuntimeError(
							    '''You must initialize the accelerate state by calling either `PartialState()` or `Accelerator()` before using the logging utility.'''		)
						lowerCAmelCase__	:						List[str]  				=	kwargs.pop('''main_process_only'''   ,lowercase_		)
						lowerCAmelCase__	:						Dict  				=	kwargs.pop('''in_order'''   ,lowercase_		)

						if self.isEnabledFor(lowercase_		):
							if self._should_log(lowercase_		):
								lowerCAmelCase__				,lowerCAmelCase__	:						Tuple  				=	self.process(lowercase_   ,lowercase_		)
								self.logger.log(lowercase_   ,lowercase_   ,*lowercase_   ,**lowercase_		)

							elif in_order:
								lowerCAmelCase__	:						List[str]  				=	PartialState()
								for i in range(state.num_processes		):
									if i == state.process_index:
										lowerCAmelCase__				,lowerCAmelCase__	:						str  				=	self.process(lowercase_   ,lowercase_		)
										self.logger.log(lowercase_   ,lowercase_   ,*lowercase_   ,**lowercase_		)
									state.wait_for_everyone()

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_ = None ):
	if log_level is None:
		lowerCAmelCase__	:						Optional[int]  				=	os.environ.get('''ACCELERATE_LOG_LEVEL'''						,     A_ )
	lowerCAmelCase__	:						Optional[Any]  				=	logging.getLogger(A_ )
	if log_level is not None:
		logger.setLevel(log_level.upper() )
		logger.root.setLevel(log_level.upper() )
	return MultiProcessAdapter(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




