


'''simple docstring'''






import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin
class 						__UpperCamelCase      (						lowercase__							):
							lowercase			:		Dict   		=  'MCTCTFeatureExtractor'
							lowercase			:		Any   		=  'AutoTokenizer'





							def __init__(  self		:int  ,_UpperCamelCase		:Any  ,_UpperCamelCase		:int			):
									super().__init__(_UpperCamelCase  ,_UpperCamelCase			)
									snake_case_		:							Any			       =						self.feature_extractor
									snake_case_		:							List[str]			       =						False





							def __call__(  self		:Optional[Any]  ,*_UpperCamelCase		:Optional[Any]  ,**_UpperCamelCase		:Union[str, Any]			):
									# For backward compatibility
									if self._in_target_context_manager:
											return self.current_processor(*_UpperCamelCase  ,**_UpperCamelCase			)

									if "raw_speech" in kwargs:
											warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""			)
											snake_case_		:							Tuple			       =						kwargs.pop("""raw_speech"""			)
									else:
											snake_case_		:							Optional[Any]			       =						kwargs.pop("""audio"""  ,_UpperCamelCase			)
									snake_case_		:							str			       =						kwargs.pop("""sampling_rate"""  ,_UpperCamelCase			)
									snake_case_		:							int			       =						kwargs.pop("""text"""  ,_UpperCamelCase			)
									if len(_UpperCamelCase			) > 0:
											snake_case_		:							List[Any]			       =						args[0]
											snake_case_		:							int			       =						args[1:]

									if audio is None and text is None:
											raise ValueError("""You need to specify either an `audio` or `text` input to process."""			)

									if audio is not None:
											snake_case_		:							Union[str, Any]			       =						self.feature_extractor(_UpperCamelCase  ,*_UpperCamelCase  ,sampling_rate=_UpperCamelCase  ,**_UpperCamelCase			)
									if text is not None:
											snake_case_		:							str			       =						self.tokenizer(_UpperCamelCase  ,**_UpperCamelCase			)

									if text is None:
											return inputs
									elif audio is None:
											return encodings
									else:
											snake_case_		:							int			       =						encodings["""input_ids"""]
											return inputs





							def 		a__   (  self		:int  ,*_UpperCamelCase		:Dict  ,**_UpperCamelCase		:Union[str, Any]			):
									return self.tokenizer.batch_decode(*_UpperCamelCase  ,**_UpperCamelCase			)





							def 		a__   (  self		:Union[str, Any]  ,*_UpperCamelCase		:List[str]  ,**_UpperCamelCase		:int			):
									# For backward compatibility
									if self._in_target_context_manager:
											return self.current_processor.pad(*_UpperCamelCase  ,**_UpperCamelCase			)

									snake_case_		:							Tuple			       =						kwargs.pop("""input_features"""  ,_UpperCamelCase			)
									snake_case_		:							Optional[int]			       =						kwargs.pop("""labels"""  ,_UpperCamelCase			)
									if len(_UpperCamelCase			) > 0:
											snake_case_		:							str			       =						args[0]
											snake_case_		:							Dict			       =						args[1:]

									if input_features is not None:
											snake_case_		:							Tuple			       =						self.feature_extractor.pad(_UpperCamelCase  ,*_UpperCamelCase  ,**_UpperCamelCase			)
									if labels is not None:
											snake_case_		:							Optional[int]			       =						self.tokenizer.pad(_UpperCamelCase  ,**_UpperCamelCase			)

									if labels is None:
											return input_features
									elif input_features is None:
											return labels
									else:
											snake_case_		:							List[Any]			       =						labels["""input_ids"""]
											return input_features





							def 		a__   (  self		:Union[str, Any]  ,*_UpperCamelCase		:List[str]  ,**_UpperCamelCase		:Dict			):
									return self.tokenizer.decode(*_UpperCamelCase  ,**_UpperCamelCase			)





							@contextmanager
							def 		a__   (  self		:List[Any]			):
									warnings.warn(
									    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
									    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
									    """your audio inputs, or in a separate call."""			)
									snake_case_		:							str			       =						True
									snake_case_		:							str			       =						self.tokenizer
									yield
									snake_case_		:							Tuple			       =						self.feature_extractor
									snake_case_		:							Dict			       =						False



'''simple docstring'''






import torch

from transformers import AutoModel
class 						__UpperCamelCase	(						torch.nn.Module							):





							def __init__(  self		:Union[str, Any]  ,_UpperCamelCase		:Tuple="sayef/fsner-bert-base-uncased"			):
									super(_UpperCamelCase  ,self			).__init__()

									snake_case_		:							Dict			       =						AutoModel.from_pretrained(_UpperCamelCase  ,return_dict=_UpperCamelCase			)
									snake_case_		:							Dict			       =						torch.nn.CosineSimilarity(3  ,1E-0_8			)
									snake_case_		:							int			       =						torch.nn.Softmax(dim=1			)





							def 		a__   (  self		:Optional[Any]  ,**_UpperCamelCase		:Optional[Any]			):
									return self.bert(**_UpperCamelCase			).last_hidden_state





							def 		a__   (  self		:List[Any]  ,_UpperCamelCase		:Dict			):
									return token_embeddings.sum(2  ,keepdim=_UpperCamelCase			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[str]=1			):
									return self.softmax(T * self.cos(_UpperCamelCase  ,_UpperCamelCase			)			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:int			):
									snake_case_		:							Union[str, Any]			       =						W_supports["""sizes"""].tolist()
									snake_case_		:							Dict			       =						W_supports["""start_token_id"""].item()
									snake_case_		:							int			       =						W_supports["""end_token_id"""].item()

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

									snake_case_		:							int			       =						self.BERT(**_UpperCamelCase			)
									snake_case_		:							int			       =						self.BERT(**_UpperCamelCase			)

									snake_case_		:							Any			       =						None
									snake_case_		:							Any			       =						None

									snake_case_		:							Union[str, Any]			       =						W_supports["""input_ids"""] == start_token_id
									snake_case_		:							Tuple			       =						W_supports["""input_ids"""] == end_token_id

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

											snake_case_		:							Dict			       =						S[s : s + size][start_token_masks[s : s + size]]
											snake_case_		:							Optional[Any]			       =						S[s : s + size][end_token_masks[s : s + size]]

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

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

									return p_starts, p_ends