code: 
  
  
  
  
  """simple docstring"""
  
  
  
  import copy
  import fnmatch
  import json
  import os
  import pickle as pkl
  import shutil
  import sys
  import tarfile
  import tempfile
  from collections import OrderedDict
  from contextlib import contextmanager
  from functools import partial
  from hashlib import shaaaa
  from io import BytesIO
  from pathlib import Path
  from urllib.parse import urlparse
  from zipfile import ZipFile, is_zipfile
  
  import cva
  import numpy as np
  import requests
  import wget
  from filelock import FileLock
  from PIL import Image
  from tqdm.auto import tqdm
  from yaml import Loader, dump, load
  
  
  try:
        import torch
  
        SCREAMING_SNAKE_CASE       = True
  except ImportError:
        SCREAMING_SNAKE_CASE       = False
  
  
  try:
        from torch.hub import _get_torch_home
  
        SCREAMING_SNAKE_CASE       = _get_torch_home()
  except ImportError:
        SCREAMING_SNAKE_CASE       = os.path.expanduser(
            os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
        )
  
  SCREAMING_SNAKE_CASE       = os.path.join(torch_cache_home, "transformers")
  
  SCREAMING_SNAKE_CASE       = "https://cdn.huggingface.co"
  SCREAMING_SNAKE_CASE       = "https://s3.amazonaws.com/models.huggingface.co/bert"
  SCREAMING_SNAKE_CASE       = "/".join(str(Path(__file__).resolve()).split("/")[:-1])
  SCREAMING_SNAKE_CASE       = os.path.join(PATH, "config.yaml")
  SCREAMING_SNAKE_CASE       = os.path.join(PATH, "attributes.txt")
  SCREAMING_SNAKE_CASE       = os.path.join(PATH, "objects.txt")
  SCREAMING_SNAKE_CASE       = os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path)
  SCREAMING_SNAKE_CASE       = os.getenv("PYTORCH_TRANSFORMERS_CACHE", PYTORCH_PRETRAINED_BERT_CACHE)
  SCREAMING_SNAKE_CASE       = os.getenv("TRANSFORMERS_CACHE", PYTORCH_TRANSFORMERS_CACHE)
  SCREAMING_SNAKE_CASE       = "pytorch_model.bin"
  SCREAMING_SNAKE_CASE       = "config.yaml"
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_=OBJECTS    ,				lowercase_=ATTRIBUTES		)		->	Optional[int]:
  
   '''simple docstring'''
  
  
  
  
  
   A__ =       []
   with open(__a		) as f:
    for object in f.readlines():
     vg_classes.append(object.split(","		)[0].lower().strip()		)
  
   A__ =       []
   with open(__a		) as f:
    for object in f.readlines():
     vg_attrs.append(object.split(","		)[0].lower().strip()		)
   return vg_classes, vg_attrs
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Union[str, Any]:
  
   '''simple docstring'''
  
  
  
  
  
   A__ =       OrderedDict()
   with open(__a    ,				"rb"		) as f:
    A__ =       pkl.load(__a		)['''model''']
   for k in copy.deepcopy(list(ckp.keys()		)		):
    A__ =       ckp.pop(__a		)
    if isinstance(__a    ,				np.ndarray		):
     A__ =       torch.tensor(__a		)
    else:
     assert isinstance(__a    ,				torch.tensor		), type(__a		)
    A__ =       v
   return r
  
  
  
  
  class 			UpperCAmelCase_						:
      lowercase__		      =   {}
  
  
  
  
      def __init__(					self		: Dict			,				snake_case_		: str			,				snake_case_		: Any = "root"			,				snake_case_		: str=0   )   ->							Optional[Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       name
       A__ =       level
       A__ =       {}
       for k, v in dictionary.items():
        if v is None:
         raise ValueError()
        A__ =       copy.deepcopy(snake_case_   )
        A__ =       copy.deepcopy(snake_case_   )
        if isinstance(snake_case_			,				snake_case_   ):
         A__ =       Config(snake_case_			,				name=snake_case_			,				level=level + 1   )
        A__ =       v
        setattr(self			,				snake_case_			,				snake_case_   )
  
       A__ =       d
  
  
  
  
      def __repr__(					self		: List[Any]   )   ->							Dict:
  
  
  
  
       '''simple docstring'''
  
       return str(list((self._pointer.keys())   )   )
  
  
  
  
      def __setattr__(					self		: List[Any]			,				snake_case_		: Dict			,				snake_case_		: str   )   ->							Any:
  
  
  
  
       '''simple docstring'''
  
       A__ =       val
       A__ =       val
       A__ =       key.split("."   )
       A__ =       len(snake_case_   ) - 1
       A__ =       self._pointer
       if len(snake_case_   ) > 1:
        for i, l in enumerate(snake_case_   ):
         if hasattr(self			,				snake_case_   ) and isinstance(getattr(self			,				snake_case_   )			,				snake_case_   ):
          setattr(getattr(self			,				snake_case_   )			,				".".join(levels[i:]   )			,				snake_case_   )
         if l == last_level:
          A__ =       val
         else:
          A__ =       pointer[l]
  
  
  
  
      def 							__magic_name__							(					self		: Dict   )   ->							Tuple:
  
  
  
  
       '''simple docstring'''
  
       return self._pointer
  
  
  
  
      def 							__magic_name__							(					self		: Optional[int]			,				snake_case_		: Union[str, Any]			,				snake_case_		: Tuple   )   ->							List[Any]:
  
  
  
  
       '''simple docstring'''
  
       with open(F"""{file_name}"""			,				"w"   ) as stream:
        dump(snake_case_			,				snake_case_   )
  
  
  
  
      def 							__magic_name__							(					self		: List[Any]			,				snake_case_		: Dict			,				snake_case_		: int   )   ->							List[Any]:
  
  
  
  
       '''simple docstring'''
  
       with open(F"""{file_name}"""			,				"w"   ) as stream:
        json.dump(snake_case_			,				snake_case_   )
  
  
  
  
      @staticmethod
      def 							__magic_name__							(					snake_case_		: Optional[Any]   )   ->							Optional[Any]:
  
  
  
  
       '''simple docstring'''
  
       with open(snake_case_   ) as stream:
        A__ =       load(snake_case_			,				Loader=snake_case_   )
       return data
  
  
  
  
      def __str__(					self		: Any   )   ->							Any:
  
  
  
  
       '''simple docstring'''
  
       A__ =       '''    '''
       if self._name != "root":
        A__ =       F"""{t * (self._level-1)}{self._name}:\n"""
       else:
        A__ =       ''''''
       A__ =       self._level
       for i, (k, v) in enumerate(self._pointer.items()   ):
        if isinstance(snake_case_			,				snake_case_   ):
         r += F"""{t * (self._level)}{v}\n"""
         self._level += 1
        else:
         r += F"""{t * (self._level)}{k}: {v} ({type(snake_case_   ).__name__})\n"""
        A__ =       level
       return r[:-1]
  
  
  
  
      @classmethod
      def 							__magic_name__							(					cls		: str			,				snake_case_		: Dict			,				**snake_case_		: Optional[Any]   )   ->							Optional[Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       cls.get_config_dict(snake_case_			,				**snake_case_   )
       return cls(snake_case_   )
  
  
  
  
      @classmethod
      def 							__magic_name__							(					cls		: Any			,				snake_case_		: str			,				**snake_case_		: Union[str, Any]   )   ->							Tuple:
  
  
  
  
       '''simple docstring'''
  
       A__ =       kwargs.pop("cache_dir"			,				snake_case_   )
       A__ =       kwargs.pop("force_download"			,				snake_case_   )
       A__ =       kwargs.pop("resume_download"			,				snake_case_   )
       A__ =       kwargs.pop("proxies"			,				snake_case_   )
       A__ =       kwargs.pop("local_files_only"			,				snake_case_   )
  
       if os.path.isdir(snake_case_   ):
        A__ =       os.path.join(snake_case_			,				snake_case_   )
       elif os.path.isfile(snake_case_   ) or is_remote_url(snake_case_   ):
        A__ =       pretrained_model_name_or_path
       else:
        A__ =       hf_bucket_url(snake_case_			,				filename=snake_case_			,				use_cdn=snake_case_   )
  
       try:
        # Load from URL or cache if already cached
        A__ =       cached_path(
            snake_case_			,				cache_dir=snake_case_			,				force_download=snake_case_			,				proxies=snake_case_			,				resume_download=snake_case_			,				local_files_only=snake_case_			,				)
        # Load config dict
        if resolved_config_file is None:
         raise EnvironmentError
  
        A__ =       Config.load_yaml(snake_case_   )
  
       except EnvironmentError:
        A__ =       '''Can\'t load config for'''
        raise EnvironmentError(snake_case_   )
  
       if resolved_config_file == config_file:
        print("loading configuration file from path"   )
       else:
        print("loading configuration file cache"   )
  
       return Config.load_yaml(snake_case_   ), kwargs
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Optional[int]:
  
   '''simple docstring'''
  
  
  
  
  
   A__ =       torch.load("dump.pt"    ,				map_location=in_tensor.device		)
   A__ =       in_tensor.numpy()
   A__ =       out_tensor.numpy()[0]
   print(na.shape    ,				na[0, 0, :5]		)
   print(na.shape    ,				na[0, 0, :5]		)
   assert np.allclose(__a    ,				__a    ,				rtol=0.0_1    ,				atol=0.1		), (
       f"""{sum([1 for x in np.isclose(__a    ,				__a    ,				rtol=0.0_1    ,				atol=0.1		).flatten() if x is False]		)/len(na.flatten()		)*1_00:.4f} %"""
       " element-wise mismatch"
   )
   raise Exception("tensors are all good"		)
  
   # Hugging face functions below
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Optional[Any]:
  
   '''simple docstring'''
  
  
  
  
  
   A__ =       urlparse(__a		)
   return parsed.scheme in ("http", "https")
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_=True		)		->	str:
  
   '''simple docstring'''
  
  
  
  
  
   A__ =       CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
   A__ =       '''/''' not in model_id
   if legacy_format:
    return f"""{endpoint}/{model_id}-{filename}"""
   else:
    return f"""{endpoint}/{model_id}/{filename}"""
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_=None    ,				lowercase_=0    ,				lowercase_=None    ,				)		->	Tuple:
  
   '''simple docstring'''
  
  
  
  
  
   A__ =       '''python/{}'''.format(sys.version.split()[0]		)
   if _torch_available:
    ua += "; torch/{}".format(torch.__version__		)
   if isinstance(__a    ,				__a		):
    ua += "; " + "; ".join("{}/{}".format(__a    ,				__a		) for k, v in user_agent.items()		)
   elif isinstance(__a    ,				__a		):
    ua += "; " + user_agent
   A__ =       {'''user-agent''': ua}
   if resume_size > 0:
    A__ =       '''bytes=%d-''' % (resume_size,)
   A__ =       requests.get(__a    ,				stream=__a    ,				proxies=__a    ,				headers=__a		)
   if response.status_code == 4_16:  # Range not satisfiable
    return
   A__ =       response.headers.get("Content-Length"		)
   A__ =       resume_size + int(__a		) if content_length is not None else None
   A__ =       tqdm(
       unit="B"    ,				unit_scale=__a    ,				total=__a    ,				initial=__a    ,				desc="Downloading"    ,				)
   for chunk in response.iter_content(chunk_size=10_24		):
    if chunk:  # filter out keep-alive new chunks
     progress.update(len(__a		)		)
     temp_file.write(__a		)
   progress.close()
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_=None    ,				lowercase_=False    ,				lowercase_=None    ,				lowercase_=10    ,				lowercase_=False    ,				lowercase_=None    ,				lowercase_=False    ,				)		->	int:
  
   '''simple docstring'''
  
  
  
  
  
   if cache_dir is None:
    A__ =       TRANSFORMERS_CACHE
   if isinstance(__a    ,				__a		):
    A__ =       str(__a		)
  
   os.makedirs(__a    ,				exist_ok=__a		)
  
   A__ =       None
   if not local_files_only:
    try:
     A__ =       requests.head(__a    ,				allow_redirects=__a    ,				proxies=__a    ,				timeout=__a		)
     if response.status_code == 2_00:
      A__ =       response.headers.get("ETag"		)
    except (EnvironmentError, requests.exceptions.Timeout):
     # etag is already None
     pass
  
   A__ =       url_to_filename(__a    ,				__a		)
  
   # get cache path to put the file
   A__ =       os.path.join(__a    ,				__a		)
  
   # etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
   # try to get the last downloaded one
   if etag is None:
    if os.path.exists(__a		):
     return cache_path
    else:
     A__ =       [
         file
         for file in fnmatch.filter(os.listdir(__a		)    ,				filename + ".*"		)
         if not file.endswith(".json"		) and not file.endswith(".lock"		)
     ]
     if len(__a		) > 0:
      return os.path.join(__a    ,				matching_files[-1]		)
     else:
      # If files cannot be found and local_files_only=True,
      # the models might've been found if local_files_only=False
      # Notify the user about that
      if local_files_only:
       raise ValueError(
           "Cannot find the requested files in the cached path and outgoing traffic has been"
           " disabled. To enable model look-ups and downloads online, set \'local_files_only\'"
           " to False."		)
      return None
  
      # From now on, etag is not None.
   if os.path.exists(__a		) and not force_download:
    return cache_path
  
   # Prevent parallel downloads of the same file with a lock.
   A__ =       cache_path + '''.lock'''
   with FileLock(__a		):
    # If the download just completed while the lock was activated.
    if os.path.exists(__a		) and not force_download:
     # Even if returning early like here, the lock will be released.
     return cache_path
  
    if resume_download:
     A__ =       cache_path + '''.incomplete'''
  
     @contextmanager
     def _resumable_file_manager():
      with open(__a    ,				"a+b"		) as f:
       yield f
  
     A__ =       _resumable_file_manager
     if os.path.exists(__a		):
      A__ =       os.stat(__a		).st_size
     else:
      A__ =       0
    else:
     A__ =       partial(tempfile.NamedTemporaryFile    ,				dir=__a    ,				delete=__a		)
     A__ =       0
  
    # Download to temporary file, then copy to cache dir once finished.
    # Otherwise you get corrupt cache entries if the download gets interrupted.
    with temp_file_manager() as temp_file:
     print(
         "%s not found in cache or force_download set to True, downloading to %s"    ,				__a    ,				temp_file.name    ,				)
  
     http_get(
         __a    ,				__a    ,				proxies=__a    ,				resume_size=__a    ,				user_agent=__a    ,				)
  
    os.replace(temp_file.name    ,				__a		)
  
    A__ =       {'''url''': url, '''etag''': etag}
    A__ =       cache_path + '''.json'''
    with open(__a    ,				"w"		) as meta_file:
     json.dump(__a    ,				__a		)
  
   return cache_path
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_=None		)		->	Tuple:
  
   '''simple docstring'''
  
  
  
  
  
   A__ =       url.encode("utf-8"		)
   A__ =       shaaaa(__a		)
   A__ =       url_hash.hexdigest()
  
   if etag:
    A__ =       etag.encode("utf-8"		)
    A__ =       shaaaa(__a		)
    filename += "." + etag_hash.hexdigest()
  
   if url.endswith(".h5"		):
    filename += ".h5"
  
   return filename
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_=None    ,				lowercase_=False    ,				lowercase_=None    ,				lowercase_=False    ,				lowercase_=None    ,				lowercase_=False    ,				lowercase_=False    ,				lowercase_=False    ,				)		->	str:
  
   '''simple docstring'''
  
  
  
  
  
   if cache_dir is None:
    A__ =       TRANSFORMERS_CACHE
   if isinstance(__a    ,				__a		):
    A__ =       str(__a		)
   if isinstance(__a    ,				__a		):
    A__ =       str(__a		)
  
   if is_remote_url(__a		):
    # URL, so get it from the cache (downloading if necessary)
    A__ =       get_from_cache(
        __a    ,				cache_dir=__a    ,				force_download=__a    ,				proxies=__a    ,				resume_download=__a    ,				user_agent=__a    ,				local_files_only=__a    ,				)
   elif os.path.exists(__a		):
    # File, and it exists.
    A__ =       url_or_filename
   elif urlparse(__a		).scheme == "":
    # File, but it doesn't exist.
    raise EnvironmentError("file {} not found".format(__a		)		)
   else:
    # Something unknown
    raise ValueError("unable to parse {} as a URL or as a local path".format(__a		)		)
  
   if extract_compressed_file:
    if not is_zipfile(__a		) and not tarfile.is_tarfile(__a		):
     return output_path
  
    # Path where we extract compressed archives
    # We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
    A__ =       os.path.split(__a		)
    A__ =       output_file.replace("."    ,				"-"		) + '''-extracted'''
    A__ =       os.path.join(__a    ,				__a		)
  
    if os.path.isdir(__a		) and os.listdir(__a		) and not force_extract:
     return output_path_extracted
  
    # Prevent parallel extractions
    A__ =       output_path + '''.lock'''
    with FileLock(__a		):
     shutil.rmtree(__a    ,				ignore_errors=__a		)
     os.makedirs(__a		)
     if is_zipfile(__a		):
      with ZipFile(__a    ,				"r"		) as zip_file:
       zip_file.extractall(__a		)
       zip_file.close()
     elif tarfile.is_tarfile(__a		):
      A__ =       tarfile.open(__a		)
      tar_file.extractall(__a		)
      tar_file.close()
     else:
      raise EnvironmentError("Archive format of {} could not be identified".format(__a		)		)
  
    return output_path_extracted
  
   return output_path
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_=","		)		->	Dict:
  
   '''simple docstring'''
  
  
  
  
  
   assert isinstance(__a    ,				__a		)
   if os.path.isfile(__a		):
    with open(__a		) as f:
     A__ =       eval(f.read()		)
   else:
    A__ =       requests.get(__a		)
    try:
     A__ =       requests.json()
    except Exception:
     A__ =       req.content.decode()
     assert data is not None, "could not connect"
     try:
      A__ =       eval(__a		)
     except Exception:
      A__ =       data.split("\n"		)
    req.close()
   return data
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Optional[int]:
  
   '''simple docstring'''
  
  
  
  
  
   A__ =       requests.get(__a		)
   A__ =       np.array(Image.open(BytesIO(response.content		)		)		)
   return img
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_		)		->	Tuple:
  
   '''simple docstring'''
  
  
  
  
  
   A__ =       url.split("/"		)[-1]
   if fn not in os.listdir(os.getcwd()		):
    wget.download(__a		)
   with open(__a    ,				"rb"		) as stream:
    A__ =       pkl.load(__a		)
   A__ =       weights.pop("model"		)
   A__ =       {}
   for k, v in model.items():
    A__ =       torch.from_numpy(__a		)
    if "running_var" in k:
     A__ =       torch.tensor([0]		)
     A__ =       k.replace("running_var"    ,				"num_batches_tracked"		)
     A__ =       zero
   return new
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							)		->	int:
  
   '''simple docstring'''
  
  
  
  
  
   print(f"""{os.path.abspath(os.path.join(__a    ,				os.pardir		)		)}/demo.ipynb"""		)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_="RGB"		)		->	Any:
  
   '''simple docstring'''
  
  
  
  
  
   assert isinstance(__a    ,				__a		)
   if os.path.isfile(__a		):
    A__ =       cva.imread(__a		)
   else:
    A__ =       get_image_from_url(__a		)
    assert img is not None, f"""could not connect to: {im}"""
   A__ =       cva.cvtColor(__a    ,				cva.COLOR_BGR2RGB		)
   if input_format == "RGB":
    A__ =       img[:, :, ::-1]
   return img
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_=1		)		->	int:
  
   '''simple docstring'''
  
  
  
  
  
   return (images[i : i + batch] for i in range(0    ,				len(__a		)    ,				__a		))
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  import argparse
  
  import torch
  
  from transformers import (
      WavaVecaFeatureExtractor,
      WavLMConfig,
      WavLMForAudioFrameClassification,
      WavLMForSequenceClassification,
      WavLMForXVector,
      logging,
  )
  
  
  logging.set_verbosity_info()
  SCREAMING_SNAKE_CASE       = logging.get_logger(__name__)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	int:
   A__ =       WavLMForSequenceClassification.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["projector.weight"]
   A__ =       downstream_dict["projector.bias"]
   A__ =       downstream_dict["model.post_net.linear.weight"]
   A__ =       downstream_dict["model.post_net.linear.bias"]
   return model
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Optional[Any]:
   A__ =       WavLMForAudioFrameClassification.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["model.linear.weight"]
   A__ =       downstream_dict["model.linear.bias"]
   return model
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	int:
   A__ =       WavLMForXVector.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["connector.weight"]
   A__ =       downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
    A__ =       downstream_dict[
        f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
    ]
    A__ =       downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]
  
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   A__ =       downstream_dict["objective.W"]
   return model
  
  
  
  
  
  
  
  @torch.no_grad()
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)		->	Union[str, Any]:
   A__ =       torch.load(lowercase_    ,				map_location="cpu"		)
  
   A__ =       checkpoint["Downstream"]
  
   A__ =       WavLMConfig.from_pretrained(lowercase_		)
   A__ =       WavaVecaFeatureExtractor.from_pretrained(
       lowercase_    ,				return_attention_mask=lowercase_    ,				do_normalize=lowercase_		)
  
   A__ =       hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"		):
    A__ =       convert_classification(lowercase_    ,				lowercase_    ,				lowercase_		)
   elif arch.endswith("ForAudioFrameClassification"		):
    A__ =       convert_diarization(lowercase_    ,				lowercase_    ,				lowercase_		)
   elif arch.endswith("ForXVector"		):
    A__ =       convert_xvector(lowercase_    ,				lowercase_    ,				lowercase_		)
   else:
    raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""		)
  
   if hf_config.use_weighted_layer_sum:
    A__ =       checkpoint["Featurizer"]["weights"]
  
   hf_feature_extractor.save_pretrained(lowercase_		)
   hf_model.save_pretrained(lowercase_		)
  
  
  if __name__ == "__main__":
        SCREAMING_SNAKE_CASE       = 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.")
        SCREAMING_SNAKE_CASE       = parser.parse_args()
        convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  
  
  
  
  
  