
'''simple docstring'''







import argparse

import torch

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


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







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A			) ->			Optional[int]:
	_snake_case     		=	WavaVecaForSequenceClassification.from_pretrained(__A    ,       config=__A			)
	_snake_case     		=	downstream_dict['projector.weight']
	_snake_case     		=	downstream_dict['projector.bias']
	_snake_case     		=	downstream_dict['model.post_net.linear.weight']
	_snake_case     		=	downstream_dict['model.post_net.linear.bias']
	return model







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A			) ->			List[Any]:
	_snake_case     		=	WavaVecaForAudioFrameClassification.from_pretrained(__A    ,       config=__A			)
	_snake_case     		=	downstream_dict['model.linear.weight']
	_snake_case     		=	downstream_dict['model.linear.bias']
	return model







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A			) ->			Optional[Any]:
	_snake_case     		=	WavaVecaForXVector.from_pretrained(__A    ,       config=__A			)
	_snake_case     		=	downstream_dict['connector.weight']
	_snake_case     		=	downstream_dict['connector.bias']
	for i, kernel_size in enumerate(hf_config.tdnn_kernel			):
		_snake_case     		=	downstream_dict[
		    F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
		]
		_snake_case     		=	downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

	_snake_case     		=	downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
	_snake_case     		=	downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
	_snake_case     		=	downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
	_snake_case     		=	downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
	_snake_case     		=	downstream_dict['objective.W']
	return model







@torch.no_grad()
def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A    ,       __A			) ->			Optional[Any]:
	_snake_case     		=	torch.load(__A    ,       map_location='cpu'			)

	_snake_case     		=	checkpoint['Downstream']

	_snake_case     		=	WavaVecaConfig.from_pretrained(__A			)
	_snake_case     		=	WavaVecaFeatureExtractor.from_pretrained(
	    __A    ,       return_attention_mask=__A    ,       do_normalize=__A			)

	_snake_case     		=	hf_config.architectures[0]
	if arch.endswith('ForSequenceClassification'			):
		_snake_case     		=	convert_classification(__A    ,       __A    ,       __A			)
	elif arch.endswith('ForAudioFrameClassification'			):
		_snake_case     		=	convert_diarization(__A    ,       __A    ,       __A			)
	elif arch.endswith('ForXVector'			):
		_snake_case     		=	convert_xvector(__A    ,       __A    ,       __A			)
	else:
		raise NotImplementedError(F'S3PRL weights conversion is not supported for {arch}'			)

	if hf_config.use_weighted_layer_sum:
		_snake_case     		=	checkpoint['Featurizer']['weights']

	hf_feature_extractor.save_pretrained(__A			)
	hf_model.save_pretrained(__A			)


if __name__ == "__main__":
				lowercase					:							int   =      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.")
				lowercase					:							Any   =      parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



'''simple docstring'''







from typing import Any

import numpy as np







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			bool:
	return np.array_equal(__A    ,       matrix.conjugate().T			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			Any:
	_snake_case     		=	v.conjugate().T
	_snake_case     		=	v_star.dot(__A			)
	assert isinstance(__A    ,       np.ndarray			)
	return (v_star_dot.dot(__A			)) / (v_star.dot(__A			))







def        SCREAMING_SNAKE_CASE__			(     ) ->			None:
	_snake_case     		=	np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]			)
	_snake_case     		=	np.array([[1], [2], [3]]			)
	assert is_hermitian(__A			), F'{a} is not hermitian.'
	print(rayleigh_quotient(__A    ,       __A			)			)

	_snake_case     		=	np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]			)
	assert is_hermitian(__A			), F'{a} is not hermitian.'
	assert rayleigh_quotient(__A    ,       __A			) == float(3			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					tests()

