

"""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)






def   lowerCAmelCase_						(					_snake_case      :  list[list[int | float]]   )				->		int:







	'''simple docstring'''


	__magic_name__		:		Any	   =     len(_snake_case   )
	__magic_name__		:		Optional[Any]	   =     len(matrix[0]   )
	__magic_name__		:		Union[str, Any]	   =     min(_snake_case      ,   _snake_case   )

	for row in range(_snake_case   ):
		# Check if diagonal element is not zero
		if matrix[row][row] != 0:
			# Eliminate all the elements below the diagonal
			for col in range(row + 1      ,   _snake_case   ):
				__magic_name__		:		Optional[Any]	   =     matrix[col][row] / matrix[row][row]
				for i in range(_snake_case      ,   _snake_case   ):
					matrix[col][i] -= multiplier * matrix[row][i]
		else:
			# Find a non-zero diagonal element to swap rows
			__magic_name__		:		str	   =     True
			for i in range(row + 1      ,   _snake_case   ):
				if matrix[i][row] != 0:
					__magic_name__   ,    __magic_name__		:		List[str]	   =     matrix[i], matrix[row]
					__magic_name__		:		Union[str, Any]	   =     False
					break
			if reduce:
				rank -= 1
				for i in range(_snake_case   ):
					__magic_name__		:		Any	   =     matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
			row -= 1

	return rank


if __name__ == "__main__":
				import doctest

				doctest.testmod()
