





"""simple docstring"""






import argparse

import torch

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


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






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			Dict			)    ->			int:



			'''simple docstring'''
			a__  :	Optional[int]		 =			WavaVecaForSequenceClassification.from_pretrained(lowerCAmelCase__							,			config=lowerCAmelCase__			)
			a__  :	Dict		 =			downstream_dict["projector.weight"]
			a__  :	Tuple		 =			downstream_dict["projector.bias"]
			a__  :	List[str]		 =			downstream_dict["model.post_net.linear.weight"]
			a__  :	Optional[int]		 =			downstream_dict["model.post_net.linear.bias"]
			return model






def      lowercase__ (					lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			List[str]			)    ->			Any:



			'''simple docstring'''
			a__  :	int		 =			WavaVecaForAudioFrameClassification.from_pretrained(lowerCAmelCase__							,			config=lowerCAmelCase__			)
			a__  :	Optional[int]		 =			downstream_dict["model.linear.weight"]
			a__  :	Tuple		 =			downstream_dict["model.linear.bias"]
			return model






def      lowercase__ (					lowerCAmelCase__   :			Any							,			lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			Dict			)    ->			List[str]:



			'''simple docstring'''
			a__  :	Dict		 =			WavaVecaForXVector.from_pretrained(lowerCAmelCase__							,			config=lowerCAmelCase__			)
			a__  :	Tuple		 =			downstream_dict["connector.weight"]
			a__  :	Tuple		 =			downstream_dict["connector.bias"]
			for i, kernel_size in enumerate(hf_config.tdnn_kernel			):
						a__  :	List[str]		 =			downstream_dict[
						    F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
						]
						a__  :	Optional[Any]		 =			downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

			a__  :	Optional[int]		 =			downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
			a__  :	Dict		 =			downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
			a__  :	Any		 =			downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
			a__  :	List[Any]		 =			downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
			a__  :	Dict		 =			downstream_dict["objective.W"]
			return model






@torch.no_grad()
def      lowercase__ (					lowerCAmelCase__   :			Union[str, Any]							,			lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			Optional[int]							,			lowerCAmelCase__   :			Optional[int]			)    ->			str:



			'''simple docstring'''
			a__  :	Dict		 =			torch.load(lowerCAmelCase__							,			map_location="cpu"			)

			a__  :	List[Any]		 =			checkpoint["Downstream"]

			a__  :	int		 =			WavaVecaConfig.from_pretrained(lowerCAmelCase__			)
			a__  :	Optional[Any]		 =			WavaVecaFeatureExtractor.from_pretrained(
			    lowerCAmelCase__							,			return_attention_mask=lowerCAmelCase__							,			do_normalize=lowerCAmelCase__			)

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

			if hf_config.use_weighted_layer_sum:
						a__  :	Optional[int]		 =			checkpoint["Featurizer"]["weights"]

			hf_feature_extractor.save_pretrained(lowerCAmelCase__			)
			hf_model.save_pretrained(lowerCAmelCase__			)


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)






"""simple docstring"""






import functools






def      lowercase__ (					lowerCAmelCase__   :			str							,			lowerCAmelCase__   :			str			)    ->			int:



			'''simple docstring'''
			a__  :	Any		 =			len(lowerCAmelCase__			)
			a__  :	Optional[int]		 =			len(lowerCAmelCase__			)

			@functools.cache
			def min_distance(lowerCAmelCase__   :			int							,			lowerCAmelCase__   :			int			) -> int:
						# if first word index is overflow - delete all from the second word
						if indexa >= len_worda:
									return len_worda - indexa
						# if second word index is overflow - delete all from the first word
						if indexa >= len_worda:
									return len_worda - indexa
						a__  :	List[Any]		 =			int(worda[indexa] != worda[indexa]			)  # current letters not identical
						return min(
						    1 + min_distance(indexa + 1							,			lowerCAmelCase__			)							,			1 + min_distance(lowerCAmelCase__							,			indexa + 1			)							,			diff + min_distance(indexa + 1							,			indexa + 1			)							,			)

			return min_distance(0							,			0			)


if __name__ == "__main__":
		import doctest

		doctest.testmod()