




"""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(""   )











"""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)





