






"""simple docstring"""
import argparse

import torch

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


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

def 				snake_case      (   A__	,A__	,A__						):
			UpperCAmelCase_     : Any			     =					WavaVecaForSequenceClassification.from_pretrained(A__	,config=A__						)
			UpperCAmelCase_     : str			     =					downstream_dict["projector.weight"]
			UpperCAmelCase_     : Dict			     =					downstream_dict["projector.bias"]
			UpperCAmelCase_     : str			     =					downstream_dict["model.post_net.linear.weight"]
			UpperCAmelCase_     : List[Any]			     =					downstream_dict["model.post_net.linear.bias"]
			return model

def 				snake_case      (   A__	,A__	,A__						):
			UpperCAmelCase_     : Optional[Any]			     =					WavaVecaForAudioFrameClassification.from_pretrained(A__	,config=A__						)
			UpperCAmelCase_     : Dict			     =					downstream_dict["model.linear.weight"]
			UpperCAmelCase_     : Optional[Any]			     =					downstream_dict["model.linear.bias"]
			return model

def 				snake_case      (   A__	,A__	,A__						):
			UpperCAmelCase_     : List[Any]			     =					WavaVecaForXVector.from_pretrained(A__	,config=A__						)
			UpperCAmelCase_     : Tuple			     =					downstream_dict["connector.weight"]
			UpperCAmelCase_     : Dict			     =					downstream_dict["connector.bias"]
			for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
						UpperCAmelCase_     : Union[str, Any]			     =					downstream_dict[
						    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
						]
						UpperCAmelCase_     : Tuple			     =					downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

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

@torch.no_grad()
def 				snake_case      (   A__	,A__	,A__	,A__						):
			UpperCAmelCase_     : List[str]			     =					torch.load(A__	,map_location="cpu"						)

			UpperCAmelCase_     : Tuple			     =					checkpoint["Downstream"]

			UpperCAmelCase_     : Tuple			     =					WavaVecaConfig.from_pretrained(A__						)
			UpperCAmelCase_     : str			     =					WavaVecaFeatureExtractor.from_pretrained(
			    A__	,return_attention_mask=A__	,do_normalize=A__						)

			UpperCAmelCase_     : Union[str, Any]			     =					hf_config.architectures[0]
			if arch.endswith("ForSequenceClassification"						):
						UpperCAmelCase_     : int			     =					convert_classification(A__	,A__	,A__						)
			elif arch.endswith("ForAudioFrameClassification"						):
						UpperCAmelCase_     : int			     =					convert_diarization(A__	,A__	,A__						)
			elif arch.endswith("ForXVector"						):
						UpperCAmelCase_     : 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:
						UpperCAmelCase_     : Tuple			     =					checkpoint["Featurizer"]["weights"]

			hf_feature_extractor.save_pretrained(A__						)
			hf_model.save_pretrained(A__						)


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











"""simple docstring"""
def 				snake_case      (   A__	,A__	,A__	,A__	,A__						):
			if index == number_of_items:
						return 0
			UpperCAmelCase_     : str			     =					0
			UpperCAmelCase_     : Dict			     =					0
			UpperCAmelCase_     : Tuple			     =					knapsack(A__	,A__	,A__	,A__	,index + 1						)
			if weights[index] <= max_weight:
						UpperCAmelCase_     : Union[str, Any]			     =					values[index] + knapsack(
						    A__	,A__	,A__	,max_weight - weights[index]	,index + 1						)
			return max(A__	,A__						)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



