






import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase__			 =	logging.get_logger(__name__)







def 						a_      (__A							,					__A							,					__A			)							->	Optional[int]:



					"""simple docstring"""
					__a				:	int			=				WavLMForSequenceClassification.from_pretrained(__A							,					config=__A			)
					__a				:	List[str]			=				downstream_dict["projector.weight"]
					__a				:	Optional[Any]			=				downstream_dict["projector.bias"]
					__a				:	int			=				downstream_dict["model.post_net.linear.weight"]
					__a				:	Optional[Any]			=				downstream_dict["model.post_net.linear.bias"]
					return model







def 						a_      (__A							,					__A							,					__A			)							->	Tuple:



					"""simple docstring"""
					__a				:	str			=				WavLMForAudioFrameClassification.from_pretrained(__A							,					config=__A			)
					__a				:	Dict			=				downstream_dict["model.linear.weight"]
					__a				:	Any			=				downstream_dict["model.linear.bias"]
					return model







def 						a_      (__A							,					__A							,					__A			)							->	Tuple:



					"""simple docstring"""
					__a				:	str			=				WavLMForXVector.from_pretrained(__A							,					config=__A			)
					__a				:	Any			=				downstream_dict["connector.weight"]
					__a				:	Any			=				downstream_dict["connector.bias"]
					for i, kernel_size in enumerate(hf_config.tdnn_kernel			):
										__a				:	Union[str, Any]			=				downstream_dict[
										    f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
										]
										__a				:	int			=				downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

					__a				:	Dict			=				downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
					__a				:	Any			=				downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
					__a				:	List[Any]			=				downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
					__a				:	str			=				downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
					__a				:	Union[str, Any]			=				downstream_dict["objective.W"]
					return model







@torch.no_grad()
def 						a_      (__A							,					__A							,					__A							,					__A			)							->	Union[str, Any]:



					"""simple docstring"""
					__a				:	Tuple			=				torch.load(__A							,					map_location="cpu"			)

					__a				:	int			=				checkpoint["Downstream"]

					__a				:	Optional[int]			=				WavLMConfig.from_pretrained(__A			)
					__a				:	Optional[Any]			=				WavaVecaFeatureExtractor.from_pretrained(
					    __A							,					return_attention_mask=__A							,					do_normalize=__A			)

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

					if hf_config.use_weighted_layer_sum:
										__a				:	Tuple			=				checkpoint["Featurizer"]["weights"]

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


if __name__ == "__main__":
			UpperCAmelCase__			 =	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__			 =	parser.parse_args()
			convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)













def 						a_      (__A			)							->	list:



					"""simple docstring"""
					if len(__A			) <= 1:
										return data
					else:
										return [
										    *quick_sort([e for e in data[1:] if e <= data[0]]			),
										    data[0],
										    *quick_sort([e for e in data[1:] if e > data[0]]			),
										]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





