

"""simple docstring"""




import argparse

import torch

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


logging.set_verbosity_info()
__A  :					int												=	logging.get_logger(__name__)
def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__   ):
					"""simple docstring"""
					A    =      WavLMForSequenceClassification.from_pretrained(lowercase__    ,							config=lowercase__   )
					A    =      downstream_dict["projector.weight"]
					A    =      downstream_dict["projector.bias"]
					A    =      downstream_dict["model.post_net.linear.weight"]
					A    =      downstream_dict["model.post_net.linear.bias"]
					return model
def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__   ):
					"""simple docstring"""
					A    =      WavLMForAudioFrameClassification.from_pretrained(lowercase__    ,							config=lowercase__   )
					A    =      downstream_dict["model.linear.weight"]
					A    =      downstream_dict["model.linear.bias"]
					return model
def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__   ):
					"""simple docstring"""
					A    =      WavLMForXVector.from_pretrained(lowercase__    ,							config=lowercase__   )
					A    =      downstream_dict["connector.weight"]
					A    =      downstream_dict["connector.bias"]
					for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
										A    =      downstream_dict[
										    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
										]
										A    =      downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

					A    =      downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
					A    =      downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
					A    =      downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
					A    =      downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
					A    =      downstream_dict["objective.W"]
					return model
@torch.no_grad()
def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__   ):
					"""simple docstring"""
					A    =      torch.load(lowercase__    ,							map_location="cpu"   )

					A    =      checkpoint["Downstream"]

					A    =      WavLMConfig.from_pretrained(lowercase__   )
					A    =      WavaVecaFeatureExtractor.from_pretrained(
					    lowercase__    ,							return_attention_mask=lowercase__    ,							do_normalize=lowercase__   )

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

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

					hf_feature_extractor.save_pretrained(lowercase__   )
					hf_model.save_pretrained(lowercase__   )


if __name__ == "__main__":
	__A  :					Dict												=	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.')
	__A  :					Dict												=	parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)








"""simple docstring"""




def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__   ):
					"""simple docstring"""
					A    =      1
					while b > 0:
										if b & 1:
															res *= a

										a *= a
										b >>= 1

					return res
def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__   ):
					"""simple docstring"""
					A    =      1
					while b > 0:
										if b & 1:
															A    =      ((res % c) * (a % c)) % c

										a *= a
										b >>= 1

					return res





