code: 
  
  
  
  
  """simple docstring"""
  
  
  
  import subprocess
  from argparse import ArgumentParser
  from typing import List, Union
  
  from huggingface_hub.hf_api import HfFolder, create_repo, whoami
  from requests.exceptions import HTTPError
  
  from . import BaseTransformersCLICommand
  
  
  
  
  class 			UpperCAmelCase_						(		SCREAMING_SNAKE_CASE__							):
  
  
  
  
      @staticmethod
      def 							__magic_name__							(					snake_case_		: Optional[int]   )   ->							Dict:
  
  
  
  
       '''simple docstring'''
  
       A__ =       parser.add_parser("login"			,				help="Log in using the same credentials as on huggingface.co"   )
       login_parser.set_defaults(func=lambda snake_case_   : LoginCommand(snake_case_   )   )
       A__ =       parser.add_parser("whoami"			,				help="Find out which huggingface.co account you are logged in as."   )
       whoami_parser.set_defaults(func=lambda snake_case_   : WhoamiCommand(snake_case_   )   )
       A__ =       parser.add_parser("logout"			,				help="Log out"   )
       logout_parser.set_defaults(func=lambda snake_case_   : LogoutCommand(snake_case_   )   )
  
       # new system: git-based repo system
       A__ =       parser.add_parser(
           "repo"			,				help="Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos."			,				)
       A__ =       repo_parser.add_subparsers(
           help="Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands"   )
       A__ =       repo_subparsers.add_parser(
           "create"			,				help="Deprecated: use `huggingface-cli` instead. Create a new repo on huggingface.co"   )
       repo_create_parser.add_argument(
           "name"			,				type=snake_case_			,				help="Name for your model\'s repo. Will be namespaced under your username to build the model id."			,				)
       repo_create_parser.add_argument("--organization"			,				type=snake_case_			,				help="Optional: organization namespace."   )
       repo_create_parser.add_argument("-y"			,				"--yes"			,				action="store_true"			,				help="Optional: answer Yes to the prompt"   )
       repo_create_parser.set_defaults(func=lambda snake_case_   : RepoCreateCommand(snake_case_   )   )
  
  
  
  
  
  
  class 			UpperCAmelCase_						:
      lowercase__		      =   '''\u001b[1m'''
      lowercase__		      =   '''\u001b[31m'''
      lowercase__		      =   '''\u001b[90m'''
      lowercase__		      =   '''\u001b[0m'''
  
  
  
  
      @classmethod
      def 							__magic_name__							(					cls		: Any			,				snake_case_		: str   )   ->							Union[str, Any]:
  
  
  
  
       '''simple docstring'''
  
       return F"""{cls._bold}{s}{cls._reset}"""
  
  
  
  
      @classmethod
      def 							__magic_name__							(					cls		: Union[str, Any]			,				snake_case_		: Tuple   )   ->							List[Any]:
  
  
  
  
       '''simple docstring'''
  
       return F"""{cls._bold}{cls._red}{s}{cls._reset}"""
  
  
  
  
      @classmethod
      def 							__magic_name__							(					cls		: Optional[int]			,				snake_case_		: Optional[int]   )   ->							List[str]:
  
  
  
  
       '''simple docstring'''
  
       return F"""{cls._gray}{s}{cls._reset}"""
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_		)		->	str:
  
   '''simple docstring'''
  
  
  
  
  
   A__ =       [max(len(str(lowercase_		)		) for x in col		) for col in zip(*lowercase_    ,				lowercase_		)]
   A__ =       ("{{:{}}} " * len(lowercase_		)).format(*lowercase_		)
   A__ =       []
   lines.append(row_format.format(*lowercase_		)		)
   lines.append(row_format.format(*["-" * w for w in col_widths]		)		)
   for row in rows:
    lines.append(row_format.format(*lowercase_		)		)
   return "\n".join(lowercase_		)
  
  
  
  
  class 			UpperCAmelCase_						:
  
  
  
  
      def __init__(					self		: Dict			,				snake_case_		: int   )   ->							Optional[Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       args
  
  
  
  
  
  
  class 			UpperCAmelCase_						(		SCREAMING_SNAKE_CASE__							):
  
  
  
  
      def 							__magic_name__							(					self		: Optional[Any]   )   ->							List[str]:
  
  
  
  
       '''simple docstring'''
  
       print(
           ANSI.red(
               "ERROR! `huggingface-cli login` uses an outdated login mechanism "
               "that is not compatible with the Hugging Face Hub backend anymore. "
               "Please use `huggingface-cli login instead."   )   )
  
  
  
  
  
  
  class 			UpperCAmelCase_						(		SCREAMING_SNAKE_CASE__							):
  
  
  
  
      def 							__magic_name__							(					self		: Dict   )   ->							int:
  
  
  
  
       '''simple docstring'''
  
       print(
           ANSI.red(
               "WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use "
               "`huggingface-cli whoami` instead."   )   )
       A__ =       HfFolder.get_token()
       if token is None:
        print("Not logged in"   )
        exit()
       try:
        A__,   A__ =       whoami(snake_case_   )
        print(snake_case_   )
        if orgs:
         print(ANSI.bold("orgs: "   )			,				",".join(snake_case_   )   )
       except HTTPError as e:
        print(snake_case_   )
        print(ANSI.red(e.response.text   )   )
        exit(1   )
  
  
  
  
  
  
  class 			UpperCAmelCase_						(		SCREAMING_SNAKE_CASE__							):
  
  
  
  
      def 							__magic_name__							(					self		: List[str]   )   ->							Any:
  
  
  
  
       '''simple docstring'''
  
       print(
           ANSI.red(
               "ERROR! `transformers-cli logout` uses an outdated logout mechanism "
               "that is not compatible with the Hugging Face Hub backend anymore. "
               "Please use `huggingface-cli logout instead."   )   )
  
  
  
  
  
  
  class 			UpperCAmelCase_						(		SCREAMING_SNAKE_CASE__							):
  
  
  
  
      def 							__magic_name__							(					self		: Union[str, Any]   )   ->							int:
  
  
  
  
       '''simple docstring'''
  
       print(
           ANSI.red(
               "WARNING! Managing repositories through transformers-cli is deprecated. "
               "Please use `huggingface-cli` instead."   )   )
       A__ =       HfFolder.get_token()
       if token is None:
        print("Not logged in"   )
        exit(1   )
       try:
        A__ =       subprocess.check_output(["git", "--version"]   ).decode("utf-8"   )
        print(ANSI.gray(stdout.strip()   )   )
       except FileNotFoundError:
        print("Looks like you do not have git installed, please install."   )
  
       try:
        A__ =       subprocess.check_output(["git-lfs", "--version"]   ).decode("utf-8"   )
        print(ANSI.gray(stdout.strip()   )   )
       except FileNotFoundError:
        print(
            ANSI.red(
                "Looks like you do not have git-lfs installed, please install."
                " You can install from https://git-lfs.github.com/."
                " Then run `git lfs install` (you only have to do this once)."   )   )
       print(""   )
  
       A__,   A__ =       whoami(snake_case_   )
       A__ =       self.args.organization if self.args.organization is not None else user
       A__ =       F"""{namespace}/{self.args.name}"""
       print(F"""You are about to create {ANSI.bold(snake_case_   )}"""   )
  
       if not self.args.yes:
        A__ =       input("Proceed? [Y/n] "   ).lower()
        if not (choice == "" or choice == "y" or choice == "yes"):
         print("Abort"   )
         exit()
       try:
        A__ =       create_repo(snake_case_			,				name=self.args.name			,				organization=self.args.organization   )
       except HTTPError as e:
        print(snake_case_   )
        print(ANSI.red(e.response.text   )   )
        exit(1   )
       print("\nYour repo now lives at:"   )
       print(F"""  {ANSI.bold(snake_case_   )}"""   )
       print("\nYou can clone it locally with the command below, and commit/push as usual."   )
       print(F"""\n  git clone {url}"""   )
       print(""   )
  
  
  
  
  
  
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)
  
  
  
  
  
  