



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  lowerCamelCase_	(      lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	Optional[Any]			,	lowerCAmelCase__	:	Optional[Any]				)       ->							str:

							'''simple docstring'''






							A      						=    WavaVecaForSequenceClassification.from_pretrained(lowerCAmelCase__			,	config=lowerCAmelCase__				)
							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  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[Any]			,	lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	List[str]				)       ->							Tuple:

							'''simple docstring'''






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






def  lowerCamelCase_	(      lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	Optional[int]			,	lowerCAmelCase__	:	Dict				)       ->							Optional[Any]:

							'''simple docstring'''






							A      						=    WavaVecaForXVector.from_pretrained(lowerCAmelCase__			,	config=lowerCAmelCase__				)
							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  lowerCamelCase_	(      lowerCAmelCase__	:	List[str]			,	lowerCAmelCase__	:	List[Any]			,	lowerCAmelCase__	:	Optional[int]			,	lowerCAmelCase__	:	str				)       ->							Tuple:

							'''simple docstring'''






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

							A      						=    checkpoint['Downstream']

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

							A      						=    hf_config.architectures[0]
							if arch.endswith('ForSequenceClassification'				):
														A      						=    convert_classification(lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				)
							elif arch.endswith('ForAudioFrameClassification'				):
														A      						=    convert_diarization(lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				)
							elif arch.endswith('ForXVector'				):
														A      						=    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      						=    checkpoint['Featurizer']['weights']

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


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




from bisect import bisect
from itertools import accumulate






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

							'''simple docstring'''






							A      						=    sorted(zip(lowerCAmelCase__			,	lowerCAmelCase__				)			,	key=lambda lowerCAmelCase__				: x[0] / x[1]			,	reverse=lowerCAmelCase__				)
							A						,   A      						=    [i[0] for i in r], [i[1] for i in r]
							A      						=    list(accumulate(lowerCAmelCase__				)				)
							A      						=    bisect(lowerCAmelCase__			,	lowerCAmelCase__				)
							return (
							    0
							    if k == 0
							    else sum(vl[:k]				) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
							    if k != n
							    else sum(vl[:k]				)
							)


if __name__ == "__main__":
				import doctest

				doctest.testmod()