






import argparse

import torch

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


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







def 		_UpperCamelCase				(      UpperCamelCase_    :   str      ,    UpperCamelCase_    :   int      ,    UpperCamelCase_    :   int						)					->				Any:

						"""simple docstring"""



						lowerCAmelCase__        =  WavaVecaForSequenceClassification.from_pretrained(UpperCamelCase_      ,    config=UpperCamelCase_						)
						lowerCAmelCase__        =  downstream_dict['projector.weight']
						lowerCAmelCase__        =  downstream_dict['projector.bias']
						lowerCAmelCase__        =  downstream_dict['model.post_net.linear.weight']
						lowerCAmelCase__        =  downstream_dict['model.post_net.linear.bias']
						return model







def 		_UpperCamelCase				(      UpperCamelCase_    :   Tuple      ,    UpperCamelCase_    :   Union[str, Any]      ,    UpperCamelCase_    :   int						)					->				Optional[int]:

						"""simple docstring"""



						lowerCAmelCase__        =  WavaVecaForAudioFrameClassification.from_pretrained(UpperCamelCase_      ,    config=UpperCamelCase_						)
						lowerCAmelCase__        =  downstream_dict['model.linear.weight']
						lowerCAmelCase__        =  downstream_dict['model.linear.bias']
						return model







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[int]      ,    UpperCamelCase_    :   int      ,    UpperCamelCase_    :   Optional[int]						)					->				Any:

						"""simple docstring"""



						lowerCAmelCase__        =  WavaVecaForXVector.from_pretrained(UpperCamelCase_      ,    config=UpperCamelCase_						)
						lowerCAmelCase__        =  downstream_dict['connector.weight']
						lowerCAmelCase__        =  downstream_dict['connector.bias']
						for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
												lowerCAmelCase__        =  downstream_dict[
												    F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
												]
												lowerCAmelCase__        =  downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

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







@torch.no_grad()
def 		_UpperCamelCase				(      UpperCamelCase_    :   List[Any]      ,    UpperCamelCase_    :   Optional[Any]      ,    UpperCamelCase_    :   List[Any]      ,    UpperCamelCase_    :   Optional[int]						)					->				Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__        =  torch.load(UpperCamelCase_      ,    map_location='cpu'						)

						lowerCAmelCase__        =  checkpoint['Downstream']

						lowerCAmelCase__        =  WavaVecaConfig.from_pretrained(UpperCamelCase_						)
						lowerCAmelCase__        =  WavaVecaFeatureExtractor.from_pretrained(
						    UpperCamelCase_      ,    return_attention_mask=UpperCamelCase_      ,    do_normalize=UpperCamelCase_						)

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

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

						hf_feature_extractor.save_pretrained(UpperCamelCase_						)
						hf_model.save_pretrained(UpperCamelCase_						)


if __name__ == "__main__":
					__snake_case      :      List[str]             =       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.""")
					__snake_case      :      List[Any]             =       parser.parse_args()
					convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)














def 		_UpperCamelCase				(      UpperCamelCase_    :   str						)					->				str:

						"""simple docstring"""



						return " ".join(input_str.split()[::-1]						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()





