




'''simple docstring'''



import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase_       :  Union[str, Any]      =		logging.get_logger(__name__)







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




     '''simple docstring'''





     __snake_case    					=  WavLMForSequenceClassification.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							:     Tuple   ,	_lowerCamelCase							:     Dict	)->   Optional[int]:




     '''simple docstring'''





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







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




     '''simple docstring'''





     __snake_case    					=  WavLMForXVector.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							:     Dict   ,	_lowerCamelCase							:     Dict   ,	_lowerCamelCase							:     Union[str, Any]   ,	_lowerCamelCase							:     Optional[int]	)->   Optional[Any]:




     '''simple docstring'''





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

     __snake_case    					=  checkpoint["""Downstream"""]

     __snake_case    					=  WavLMConfig.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_       :  Optional[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_       :  Optional[Any]      =		parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)












'''simple docstring'''



import warnings

from ...utils import logging
from .image_processing_maskformer import MaskFormerImageProcessor


UpperCAmelCase_       :  int      =		logging.get_logger(__name__)





class     lowerCAmelCase   ( __lowerCAmelCase):


       def __init__(   self    ,  *__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )      ->  None:



            '''simple docstring'''




            warnings.warn(
                '''The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
                ''' Please use MaskFormerImageProcessor instead.'''    ,  __SCREAMING_SNAKE_CASE    ,  )
            super().__init__(*__SCREAMING_SNAKE_CASE    ,  **__SCREAMING_SNAKE_CASE  )






