

"""simple docstring"""





import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
__UpperCamelCase  :      Dict							      =		logging.get_logger(__name__)

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						Any  				=	WavaVecaForSequenceClassification.from_pretrained(A_						,     config=A_ )
	lowerCAmelCase__	:						List[Any]  				=	downstream_dict['''projector.weight''']
	lowerCAmelCase__	:						Tuple  				=	downstream_dict['''projector.bias''']
	lowerCAmelCase__	:						Dict  				=	downstream_dict['''model.post_net.linear.weight''']
	lowerCAmelCase__	:						Optional[int]  				=	downstream_dict['''model.post_net.linear.bias''']
	return model

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						List[str]  				=	WavaVecaForAudioFrameClassification.from_pretrained(A_						,     config=A_ )
	lowerCAmelCase__	:						Optional[int]  				=	downstream_dict['''model.linear.weight''']
	lowerCAmelCase__	:						Tuple  				=	downstream_dict['''model.linear.bias''']
	return model

def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						List[Any]  				=	WavaVecaForXVector.from_pretrained(A_						,     config=A_ )
	lowerCAmelCase__	:						int  				=	downstream_dict['''connector.weight''']
	lowerCAmelCase__	:						str  				=	downstream_dict['''connector.bias''']
	for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
		lowerCAmelCase__	:						List[str]  				=	downstream_dict[
		    f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
		]
		lowerCAmelCase__	:						List[str]  				=	downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

	lowerCAmelCase__	:						str  				=	downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
	lowerCAmelCase__	:						List[str]  				=	downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
	lowerCAmelCase__	:						Any  				=	downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
	lowerCAmelCase__	:						str  				=	downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
	lowerCAmelCase__	:						str  				=	downstream_dict['''objective.W''']
	return model

@torch.no_grad()
def   __SCREAMING_SNAKE_CASE  (      A_						,     A_						,     A_						,     A_ ):
	lowerCAmelCase__	:						str  				=	torch.load(A_						,     map_location='''cpu''' )

	lowerCAmelCase__	:						int  				=	checkpoint['''Downstream''']

	lowerCAmelCase__	:						Dict  				=	WavaVecaConfig.from_pretrained(A_ )
	lowerCAmelCase__	:						int  				=	WavaVecaFeatureExtractor.from_pretrained(
	    A_						,     return_attention_mask=A_						,     do_normalize=A_ )

	lowerCAmelCase__	:						Tuple  				=	hf_config.architectures[0]
	if arch.endswith('''ForSequenceClassification''' ):
		lowerCAmelCase__	:						List[Any]  				=	convert_classification(A_						,     A_						,     A_ )
	elif arch.endswith('''ForAudioFrameClassification''' ):
		lowerCAmelCase__	:						Any  				=	convert_diarization(A_						,     A_						,     A_ )
	elif arch.endswith('''ForXVector''' ):
		lowerCAmelCase__	:						Optional[Any]  				=	convert_xvector(A_						,     A_						,     A_ )
	else:
		raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}' )

	if hf_config.use_weighted_layer_sum:
		lowerCAmelCase__	:						Optional[Any]  				=	checkpoint['''Featurizer''']['''weights''']

	hf_feature_extractor.save_pretrained(A_ )
	hf_model.save_pretrained(A_ )


if __name__ == "__main__":
							__UpperCamelCase  :      Optional[Any]							      =		argparse.ArgumentParser()
							parser.add_argument(
							    '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
							)
							parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
							parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
							parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
							__UpperCamelCase  :      List[str]							      =		parser.parse_args()
							convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)







"""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




