




'''simple docstring'''



import argparse

import torch

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


logging.set_verbosity_info()
UpperCAmelCase_							:	List[Any]	     =		logging.get_logger(__name__)







def       _UpperCamelCase			(_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  Tuple ,						_lowerCamelCase				:  str						)-> Any:




     '''simple docstring'''





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







def       _UpperCamelCase			(_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  List[Any] ,						_lowerCamelCase				:  int						)-> Any:




     '''simple docstring'''





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







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




     '''simple docstring'''





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

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







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




     '''simple docstring'''





     __snake_case            =	torch.load(_lowerCamelCase ,						map_location='''cpu'''						)

     __snake_case            =	checkpoint['''Downstream''']

     __snake_case            =	WavaVecaConfig.from_pretrained(_lowerCamelCase						)
     __snake_case            =	WavaVecaFeatureExtractor.from_pretrained(
         _lowerCamelCase ,						return_attention_mask=_lowerCamelCase ,						do_normalize=_lowerCamelCase						)

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

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

     hf_feature_extractor.save_pretrained(_lowerCamelCase						)
     hf_model.save_pretrained(_lowerCamelCase						)


if __name__ == "__main__":
     UpperCAmelCase_							:	Any	     =		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_							:	Tuple	     =		parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)









from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


lowercase_   							=  datasets.utils.logging.get_logger(__name__)





class 					__lowerCAmelCase	(   folder_based_builder.FolderBasedBuilderConfig				):
  _a		     =    None
  _a		     =    None





class 					__lowerCAmelCase	(   folder_based_builder.FolderBasedBuilder				):
  _a		     =    datasets.Audio()
  _a		     =    """audio"""
  _a		     =    AudioFolderConfig
  _a		     =    42  # definition at the bottom of the script
  _a		     =    AudioClassification(audio_column="""audio"""     ,     label_column="""label"""				)
lowercase_   							=  [
    '.aiff',
    '.au',
    '.avr',
    '.caf',
    '.flac',
    '.htk',
    '.svx',
    '.mat4',
    '.mat5',
    '.mpc2k',
    '.ogg',
    '.paf',
    '.pvf',
    '.raw',
    '.rf64',
    '.sd2',
    '.sds',
    '.ircam',
    '.voc',
    '.w64',
    '.wav',
    '.nist',
    '.wavex',
    '.wve',
    '.xi',
    '.mp3',
    '.opus',
]
lowercase_   							=  AUDIO_EXTENSIONS


