


'''simple docstring'''






from importlib import import_module

from .logging import get_logger


__A   :    Any											= get_logger(__name__)






class   __snake_case			:







    """simple docstring"""



    def __init__(  self       :    Dict       ,		lowerCamelCase       :    List[str]       ,		lowerCamelCase       :    Tuple=None						)  ->    int:
      lowerCAmelCase_     : Tuple     =  attrs or []
      if module is not None:
        for key in module.__dict__:
          if key in attrs or not key.startswith("""__"""						):
            setattr(self       ,		lowerCamelCase       ,		getattr(lowerCamelCase       ,		lowerCamelCase						)						)
      lowerCAmelCase_     : int     =  module._original_module if isinstance(lowerCamelCase       ,		_PatchedModuleObj						) else module







class   __snake_case			:







    """simple docstring"""



    lowercase  					=   []

    def __init__(  self       :    Optional[Any]       ,		lowerCamelCase       :    Tuple       ,		lowerCamelCase       :    str       ,		lowerCamelCase       :    List[str]       ,		lowerCamelCase       :    Any=None						)  ->    List[str]:
      lowerCAmelCase_     : Tuple     =  obj
      lowerCAmelCase_     : List[Any]     =  target
      lowerCAmelCase_     : str     =  new
      lowerCAmelCase_     : int     =  target.split("""."""						)[0]
      lowerCAmelCase_     : List[Any]     =  {}
      lowerCAmelCase_     : List[Any]     =  attrs or []

    def __enter__(  self       :    List[str]						)  ->    Dict:
      *lowerCAmelCase_,				lowerCAmelCase_     : List[str]     =  self.target.split("""."""						)

      # Patch modules:
      # it's used to patch attributes of submodules like "os.path.join";
      # in this case we need to patch "os" and "os.path"

      for i in range(len(lowerCamelCase						)						):
        try:
          lowerCAmelCase_     : Optional[Any]     =  import_module(""".""".join(submodules[: i + 1]						)						)
        except ModuleNotFoundError:
          continue
        # We iterate over all the globals in self.obj in case we find "os" or "os.path"
        for attr in self.obj.__dir__():
          lowerCAmelCase_     : Union[str, Any]     =  getattr(self.obj       ,		lowerCamelCase						)
          # We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
          # This allows to patch renamed modules like "from os import path as ospath".
          if obj_attr is submodule or (
              (isinstance(lowerCamelCase       ,		_PatchedModuleObj						) and obj_attr._original_module is submodule)
          ):
            lowerCAmelCase_     : List[str]     =  obj_attr
            # patch at top level
            setattr(self.obj       ,		lowerCamelCase       ,		_PatchedModuleObj(lowerCamelCase       ,		attrs=self.attrs						)						)
            lowerCAmelCase_     : str     =  getattr(self.obj       ,		lowerCamelCase						)
            # construct lower levels patches
            for key in submodules[i + 1 :]:
              setattr(lowerCamelCase       ,		lowerCamelCase       ,		_PatchedModuleObj(getattr(lowerCamelCase       ,		lowerCamelCase       ,		lowerCamelCase						)       ,		attrs=self.attrs						)						)
              lowerCAmelCase_     : Tuple     =  getattr(lowerCamelCase       ,		lowerCamelCase						)
            # finally set the target attribute
            setattr(lowerCamelCase       ,		lowerCamelCase       ,		self.new						)

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

      if submodules:  # if it's an attribute of a submodule like "os.path.join"
        try:
          lowerCAmelCase_     : List[Any]     =  getattr(import_module(""".""".join(lowerCamelCase						)						)       ,		lowerCamelCase						)
        except (AttributeError, ModuleNotFoundError):
          return
        # We iterate over all the globals in self.obj in case we find "os.path.join"
        for attr in self.obj.__dir__():
          # We don't check for the name of the global, but rather if its value *is* "os.path.join".
          # This allows to patch renamed attributes like "from os.path import join as pjoin".
          if getattr(self.obj       ,		lowerCamelCase						) is attr_value:
            lowerCAmelCase_     : List[str]     =  getattr(self.obj       ,		lowerCamelCase						)
            setattr(self.obj       ,		lowerCamelCase       ,		self.new						)
      elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
        lowerCAmelCase_     : str     =  globals()["""__builtins__"""][target_attr]
        setattr(self.obj       ,		lowerCamelCase       ,		self.new						)
      else:
        raise RuntimeError(F'Tried to patch attribute {target_attr} instead of a submodule.'						)

    def __exit__(  self       :    int       ,		*lowerCamelCase       :    Union[str, Any]						)  ->    Optional[int]:
      for attr in list(self.original						):
        setattr(self.obj       ,		lowerCamelCase       ,		self.original.pop(lowerCamelCase						)						)

    def __lowercase  (  self       :    int						)  ->    int:
      self.__enter__()
      self._active_patches.append(self						)





    def __lowercase  (  self       :    Any						)  ->    Tuple:
      try:
        self._active_patches.remove(self						)
      except ValueError:
        # If the patch hasn't been started this will fail
        return None

      return self.__exit__()







'''simple docstring'''






import argparse

import torch

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


logging.set_verbosity_info()
__A   :    str											= logging.get_logger(__name__)







def 							UpperCamelCase_     (       A__   :  int				,       A__   :  List[Any]				,       A__   :  List[str]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  WavaVecaForSequenceClassification.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : str     =  downstream_dict["""projector.weight"""]
  lowerCAmelCase_     : Tuple     =  downstream_dict["""projector.bias"""]
  lowerCAmelCase_     : Tuple     =  downstream_dict["""model.post_net.linear.weight"""]
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.post_net.linear.bias"""]
  return model







def 							UpperCamelCase_     (       A__   :  Dict				,       A__   :  Any				,       A__   :  List[Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[int]     =  WavaVecaForAudioFrameClassification.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.linear.weight"""]
  lowerCAmelCase_     : List[str]     =  downstream_dict["""model.linear.bias"""]
  return model







def 							UpperCamelCase_     (       A__   :  List[str]				,       A__   :  Any				,       A__   :  int   ):
  '''simple docstring'''





  lowerCAmelCase_     : Optional[Any]     =  WavaVecaForXVector.from_pretrained(A__				,       config=A__   )
  lowerCAmelCase_     : List[Any]     =  downstream_dict["""connector.weight"""]
  lowerCAmelCase_     : Union[str, Any]     =  downstream_dict["""connector.bias"""]
  for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
    lowerCAmelCase_     : List[str]     =  downstream_dict[
        f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
    ]
    lowerCAmelCase_     : List[str]     =  downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

  lowerCAmelCase_     : Optional[int]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
  lowerCAmelCase_     : Optional[int]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
  lowerCAmelCase_     : Union[str, Any]     =  downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
  lowerCAmelCase_     : Dict     =  downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
  lowerCAmelCase_     : int     =  downstream_dict["""objective.W"""]
  return model







@torch.no_grad()
def 							UpperCamelCase_     (       A__   :  int				,       A__   :  int				,       A__   :  str				,       A__   :  Union[str, Any]   ):
  '''simple docstring'''





  lowerCAmelCase_     : Tuple     =  torch.load(A__				,       map_location="""cpu"""   )

  lowerCAmelCase_     : Optional[int]     =  checkpoint["""Downstream"""]

  lowerCAmelCase_     : int     =  WavaVecaConfig.from_pretrained(A__   )
  lowerCAmelCase_     : Union[str, Any]     =  WavaVecaFeatureExtractor.from_pretrained(
      A__				,       return_attention_mask=A__				,       do_normalize=A__   )

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

  if hf_config.use_weighted_layer_sum:
    lowerCAmelCase_     : Optional[int]     =  checkpoint["""Featurizer"""]["""weights"""]

  hf_feature_extractor.save_pretrained(A__   )
  hf_model.save_pretrained(A__   )


if __name__ == "__main__":
  __A   :    List[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.")
  __A   :    Tuple											= parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



