




'''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							:   Optional[int]      ,		_lowerCamelCase							:   Optional[Any]      ,		_lowerCamelCase							:   Union[str, Any]		)->      List[str]:




     '''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							:   str      ,		_lowerCamelCase							:   Union[str, Any]      ,		_lowerCamelCase							:   Any		)->      List[str]:




     '''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[int]      ,		_lowerCamelCase							:   Union[str, Any]      ,		_lowerCamelCase							:   List[Any]		)->      Tuple:




     '''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							:   Union[str, Any]      ,		_lowerCamelCase							:   Any      ,		_lowerCamelCase							:   Optional[Any]      ,		_lowerCamelCase							:   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_				:     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.''')
     UpperCAmelCase_				:     int     			=						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_detr import DetrImageProcessor


UpperCAmelCase_				:     Dict     			=						logging.get_logger(__name__)





class 			lowerCAmelCase      (       __lowerCAmelCase):


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



            '''simple docstring'''




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






