



'''simple docstring'''







import argparse

import torch

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


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__     : Optional[Any]				=   logging.get_logger(__name__)







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:List[Any]										=      WavLMForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE ,		config=SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      downstream_dict['projector.weight']
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      downstream_dict['projector.bias']
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      downstream_dict['model.post_net.linear.weight']
		SCREAMING_SNAKE_CASE_						:int										=      downstream_dict['model.post_net.linear.bias']
		return model







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      WavLMForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE ,		config=SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:List[Any]										=      downstream_dict['model.linear.weight']
		SCREAMING_SNAKE_CASE_						:str										=      downstream_dict['model.linear.bias']
		return model







def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      WavLMForXVector.from_pretrained(SCREAMING_SNAKE_CASE ,		config=SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:Any										=      downstream_dict['connector.weight']
		SCREAMING_SNAKE_CASE_						:int										=      downstream_dict['connector.bias']
		for i, kernel_size in enumerate(hf_config.tdnn_kernel     ):
				SCREAMING_SNAKE_CASE_						:Dict										=      downstream_dict[
				    F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
				]
				SCREAMING_SNAKE_CASE_						:Tuple										=      downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
		SCREAMING_SNAKE_CASE_						:Optional[int]										=      downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
		SCREAMING_SNAKE_CASE_						:str										=      downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
		SCREAMING_SNAKE_CASE_						:str										=      downstream_dict['objective.W']
		return model







@torch.no_grad()
def  SCREAMING_SNAKE_CASE__ (				SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE ,		SCREAMING_SNAKE_CASE     ):
		SCREAMING_SNAKE_CASE_						:str										=      torch.load(SCREAMING_SNAKE_CASE ,		map_location='cpu'     )

		SCREAMING_SNAKE_CASE_						:Any										=      checkpoint['Downstream']

		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      WavLMConfig.from_pretrained(SCREAMING_SNAKE_CASE     )
		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      WavaVecaFeatureExtractor.from_pretrained(
		    SCREAMING_SNAKE_CASE ,		return_attention_mask=SCREAMING_SNAKE_CASE ,		do_normalize=SCREAMING_SNAKE_CASE     )

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

		if hf_config.use_weighted_layer_sum:
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      checkpoint['Featurizer']['weights']

		hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE     )
		hf_model.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE__     : Tuple				=   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.""")
							SCREAMING_SNAKE_CASE__     : 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__ (				SCREAMING_SNAKE_CASE = 1000     ):
		return sum(i for i in range(SCREAMING_SNAKE_CASE     ) if i % 3 == 0 or i % 5 == 0     )


if __name__ == "__main__":
							print(f"""{solution() = }""")
