






'''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       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     List[str]:
		UpperCamelCase__				:					Optional[int]					=					WavaVecaForSequenceClassification.from_pretrained(lowerCamelCase_     ,      config=lowerCamelCase_)
		UpperCamelCase__				:					str					=					downstream_dict['projector.weight']
		UpperCamelCase__				:					Dict					=					downstream_dict['projector.bias']
		UpperCamelCase__				:					Optional[Any]					=					downstream_dict['model.post_net.linear.weight']
		UpperCamelCase__				:					Optional[int]					=					downstream_dict['model.post_net.linear.bias']
		return model







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     List[str]:
		UpperCamelCase__				:					Optional[Any]					=					WavaVecaForAudioFrameClassification.from_pretrained(lowerCamelCase_     ,      config=lowerCamelCase_)
		UpperCamelCase__				:					Tuple					=					downstream_dict['model.linear.weight']
		UpperCamelCase__				:					List[str]					=					downstream_dict['model.linear.bias']
		return model







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     List[str]:
		UpperCamelCase__				:					Optional[Any]					=					WavaVecaForXVector.from_pretrained(lowerCamelCase_     ,      config=lowerCamelCase_)
		UpperCamelCase__				:					str					=					downstream_dict['connector.weight']
		UpperCamelCase__				:					Any					=					downstream_dict['connector.bias']
		for i, kernel_size in enumerate(hf_config.tdnn_kernel):
				UpperCamelCase__				:					int					=					downstream_dict[
				    f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
				]
				UpperCamelCase__				:					List[Any]					=					downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

		UpperCamelCase__				:					Any					=					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
		UpperCamelCase__				:					Union[str, Any]					=					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
		UpperCamelCase__				:					Dict					=					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
		UpperCamelCase__				:					List[str]					=					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
		UpperCamelCase__				:					str					=					downstream_dict['objective.W']
		return model







@torch.no_grad()
def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     Dict:
		UpperCamelCase__				:					Optional[int]					=					torch.load(lowerCamelCase_     ,      map_location='cpu')

		UpperCamelCase__				:					Optional[int]					=					checkpoint['Downstream']

		UpperCamelCase__				:					int					=					WavaVecaConfig.from_pretrained(lowerCamelCase_)
		UpperCamelCase__				:					str					=					WavaVecaFeatureExtractor.from_pretrained(
		    lowerCamelCase_     ,      return_attention_mask=lowerCamelCase_     ,      do_normalize=lowerCamelCase_)

		UpperCamelCase__				:					List[str]					=					hf_config.architectures[0]
		if arch.endswith('ForSequenceClassification'):
				UpperCamelCase__				:					int					=					convert_classification(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
		elif arch.endswith('ForAudioFrameClassification'):
				UpperCamelCase__				:					Union[str, Any]					=					convert_diarization(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
		elif arch.endswith('ForXVector'):
				UpperCamelCase__				:					Optional[int]					=					convert_xvector(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
		else:
				raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}')

		if hf_config.use_weighted_layer_sum:
				UpperCamelCase__				:					Optional[int]					=					checkpoint['Featurizer']['weights']

		hf_feature_extractor.save_pretrained(lowerCamelCase_)
		hf_model.save_pretrained(lowerCamelCase_)


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'''




from typing import Union

import fire
import torch
from tqdm import tqdm







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_ = "cpu"     ,      lowerCamelCase_ = None)							->     None:
		UpperCamelCase__				:					List[Any]					=					torch.load(lowerCamelCase_     ,      map_location=lowerCamelCase_)
		for k, v in tqdm(state_dict.items()):
				if not isinstance(lowerCamelCase_     ,      torch.Tensor):
						raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin')
				UpperCamelCase__				:					int					=					v.half()
		if save_path is None:  # overwrite src_path
				UpperCamelCase__				:					List[Any]					=					src_path
		torch.save(lowerCamelCase_     ,      lowerCamelCase_)


if __name__ == "__main__":
		fire.Fire(convert)




