



import argparse

import torch

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


logging.set_verbosity_info()
_lowerCamelCase         =	logging.get_logger(__name__)






def 						_lowerCAmelCase    (					__lowerCamelCase :  List[str]		,						__lowerCamelCase :  int		,						__lowerCamelCase :  Dict  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     List[str]							=						WavLMForSequenceClassification.from_pretrained(__lowerCamelCase		,						config=__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Any							=						downstream_dict["projector.weight"]
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						downstream_dict["projector.bias"]
 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						downstream_dict["model.post_net.linear.weight"]
 __SCREAMING_SNAKE_CASE							:     Tuple							=						downstream_dict["model.post_net.linear.bias"]
 return model






def 						_lowerCAmelCase    (					__lowerCamelCase :  Any		,						__lowerCamelCase :  Dict		,						__lowerCamelCase :  List[Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						WavLMForAudioFrameClassification.from_pretrained(__lowerCamelCase		,						config=__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						downstream_dict["model.linear.weight"]
 __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						downstream_dict["model.linear.bias"]
 return model






def 						_lowerCAmelCase    (					__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  Any		,						__lowerCamelCase :  Union[str, Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						WavLMForXVector.from_pretrained(__lowerCamelCase		,						config=__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						downstream_dict["connector.weight"]
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						downstream_dict["connector.bias"]
 for i, kernel_size in enumerate(hf_config.tdnn_kernel  ):
  __SCREAMING_SNAKE_CASE							:     str							=						downstream_dict[
      F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
  ]
  __SCREAMING_SNAKE_CASE							:     int							=						downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
 __SCREAMING_SNAKE_CASE							:     List[str]							=						downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
 __SCREAMING_SNAKE_CASE							:     Tuple							=						downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
 __SCREAMING_SNAKE_CASE							:     Any							=						downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
 __SCREAMING_SNAKE_CASE							:     int							=						downstream_dict["objective.W"]
 return model






@torch.no_grad()
def 						_lowerCAmelCase    (					__lowerCamelCase :  Dict		,						__lowerCamelCase :  Tuple		,						__lowerCamelCase :  str		,						__lowerCamelCase :  int  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Any							=						torch.load(__lowerCamelCase		,						map_location="cpu"  )

 __SCREAMING_SNAKE_CASE							:     List[Any]							=						checkpoint["Downstream"]

 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						WavLMConfig.from_pretrained(__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Dict							=						WavaVecaFeatureExtractor.from_pretrained(
     __lowerCamelCase		,						return_attention_mask=__lowerCamelCase		,						do_normalize=__lowerCamelCase  )

 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						hf_config.architectures[0]
 if arch.endswith("ForSequenceClassification"  ):
  __SCREAMING_SNAKE_CASE							:     Tuple							=						convert_classification(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )
 elif arch.endswith("ForAudioFrameClassification"  ):
  __SCREAMING_SNAKE_CASE							:     int							=						convert_diarization(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )
 elif arch.endswith("ForXVector"  ):
  __SCREAMING_SNAKE_CASE							:     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:
  __SCREAMING_SNAKE_CASE							:     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)











import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


lowerCAmelCase					:				Union[str, Any]   	=2 << 20  # 2mb
lowerCAmelCase					:				Union[str, Any]   	=get_logger(__name__)






class 	_a			(      snake_case_  ):






			def 							_snake_case   (    self )	->					Dict:
									lowerCAmelCase	:   Optional[int]					     =					self._options.get_all_options().get("""runner""" )
									return runner in [None, "DirectRunner", "PortableRunner"]







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__=False  ):


						'''simple docstring'''




						lowerCAmelCase	:   Union[str, Any]					     =					FileSystems
						if fs.exists(SCREAMING_SNAKE_CASE__  ):
												if force_upload:
																		logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""  )
												else:
																		logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""  )
																		return
						with fs.create(SCREAMING_SNAKE_CASE__  ) as remote_file:
												with open(SCREAMING_SNAKE_CASE__			,"""rb"""  ) as local_file:
																		lowerCAmelCase	:   Any					     =					local_file.read(SCREAMING_SNAKE_CASE__  )
																		while chunk:
																								remote_file.write(SCREAMING_SNAKE_CASE__  )
																								lowerCAmelCase	:   List[str]					     =					local_file.read(SCREAMING_SNAKE_CASE__  )







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__=False  ):


						'''simple docstring'''




						lowerCAmelCase	:   Optional[int]					     =					FileSystems
						if os.path.exists(SCREAMING_SNAKE_CASE__  ):
												if force_download:
																		logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""  )
												else:
																		logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""  )
																		return
						with fs.open(SCREAMING_SNAKE_CASE__  ) as remote_file:
												with open(SCREAMING_SNAKE_CASE__			,"""wb"""  ) as local_file:
																		lowerCAmelCase	:   Optional[int]					     =					remote_file.read(SCREAMING_SNAKE_CASE__  )
																		while chunk:
																								local_file.write(SCREAMING_SNAKE_CASE__  )
																								lowerCAmelCase	:   Union[str, Any]					     =					remote_file.read(SCREAMING_SNAKE_CASE__  )

