"""simple docstring"""







import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


__SCREAMING_SNAKE_CASE								={
    "debug": logging.DEBUG,
    "info": logging.INFO,
    "warning": logging.WARNING,
    "error": logging.ERROR,
    "critical": logging.CRITICAL,
}

__SCREAMING_SNAKE_CASE								=logging.WARNING



def     lowercase__( ):
     lowercase_			:       List[Any]									=			os.getenv('DATASETS_VERBOSITY'  ,    __SCREAMING_SNAKE_CASE   )
     if env_level_str:
          if env_level_str in log_levels:
               return log_levels[env_level_str]
          else:
               logging.getLogger().warning(
                   F'''Unknown option DATASETS_VERBOSITY={env_level_str}, '''
                   F'''has to be one of: { ", ".join(log_levels.keys()   ) }'''   )
     return _default_log_level



def     lowercase__( ):
     return __name__.split('.'   )[0]



def     lowercase__( ):
     return logging.getLogger(_get_library_name()   )



def     lowercase__( ):
     # Apply our default configuration to the library root logger.
     lowercase_			:       Optional[int]									=			_get_library_root_logger()
     library_root_logger.setLevel(_get_default_logging_level()   )



def     lowercase__( ):
     lowercase_			:       int									=			_get_library_root_logger()
     library_root_logger.setLevel(logging.NOTSET   )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[str] = None   ):
     if name is None:
          lowercase_			:       Union[str, Any]									=			_get_library_name()
     return logging.getLogger(__SCREAMING_SNAKE_CASE   )



def     lowercase__( ):
     return _get_library_root_logger().getEffectiveLevel()



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int   ):
     _get_library_root_logger().setLevel(__SCREAMING_SNAKE_CASE   )



def     lowercase__( ):
     return set_verbosity(__SCREAMING_SNAKE_CASE   )



def     lowercase__( ):
     return set_verbosity(__SCREAMING_SNAKE_CASE   )



def     lowercase__( ):
     return set_verbosity(__SCREAMING_SNAKE_CASE   )



def     lowercase__( ):
     return set_verbosity(__SCREAMING_SNAKE_CASE   )



def     lowercase__( ):
     lowercase_			:       Tuple									=			False



def     lowercase__( ):
     lowercase_			:       Any									=			True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()







class  UpperCamelCase	:
   def __init__(    self  ,*__UpperCamelCase  ,**__UpperCamelCase    )			->   int:  # pylint: disable=unused-argument


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			args[0] if args else None
   def __iter__(    self    )			->   Union[str, Any]:


        '''simple docstring'''


        return iter(self._iterator    )
   def __getattr__(    self  ,__UpperCamelCase    )			->   List[Any]:


        '''simple docstring'''


        def empty_fn(*__UpperCamelCase  ,**__UpperCamelCase    ):  # pylint: disable=unused-argument
             return

        return empty_fn
   def __enter__(    self    )			->   Optional[int]:


        '''simple docstring'''


        return self







   def __exit__(    self  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Dict:


        '''simple docstring'''


        return






__SCREAMING_SNAKE_CASE								=True







class  UpperCamelCase	:
   def __call__(    self  ,*__UpperCamelCase  ,__UpperCamelCase=False  ,**__UpperCamelCase    )			->   Tuple:


        '''simple docstring'''


        if _tqdm_active and not disable:
             return tqdm_lib.tqdm(*__UpperCamelCase  ,**__UpperCamelCase    )
        else:
             return EmptyTqdm(*__UpperCamelCase  ,**__UpperCamelCase    )
   def     _UpperCAmelCase     (    self  ,*__UpperCamelCase  ,**__UpperCamelCase    )			->   str:


        '''simple docstring'''


        lowercase_			:       Tuple									=			None
        if _tqdm_active:
             return tqdm_lib.tqdm.set_lock(*__UpperCamelCase  ,**__UpperCamelCase    )







   def     _UpperCAmelCase     (    self    )			->   Any:


        '''simple docstring'''


        if _tqdm_active:
             return tqdm_lib.tqdm.get_lock()






__SCREAMING_SNAKE_CASE								=_tqdm_cls()



def     lowercase__( ):
     global _tqdm_active
     return bool(_tqdm_active   )



def     lowercase__( ):
     global _tqdm_active
     lowercase_			:       Union[str, Any]									=			True







def     lowercase__( ):
     global _tqdm_active
     lowercase_			:       Optional[int]									=			False


"""simple docstring"""







import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  int   ):
     lowercase_			:       List[str]									=			UniSpeechSatForSequenceClassification.from_pretrained(__SCREAMING_SNAKE_CASE  ,    config=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       List[Any]									=			downstream_dict['projector.weight']
     lowercase_			:       Optional[Any]									=			downstream_dict['projector.bias']
     lowercase_			:       Optional[int]									=			downstream_dict['model.post_net.linear.weight']
     lowercase_			:       Tuple									=			downstream_dict['model.post_net.linear.bias']
     return model



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Tuple   ):
     lowercase_			:       List[Any]									=			UniSpeechSatForAudioFrameClassification.from_pretrained(__SCREAMING_SNAKE_CASE  ,    config=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Dict									=			downstream_dict['model.linear.weight']
     lowercase_			:       Union[str, Any]									=			downstream_dict['model.linear.bias']
     return model



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[Any]  ,    __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]   ):
     lowercase_			:       Any									=			UniSpeechSatForXVector.from_pretrained(__SCREAMING_SNAKE_CASE  ,    config=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       int									=			downstream_dict['connector.weight']
     lowercase_			:       List[str]									=			downstream_dict['connector.bias']
     for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
          lowercase_			:       Dict									=			downstream_dict[
              F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
          ]
          lowercase_			:       List[Any]									=			downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

     lowercase_			:       Optional[Any]									=			downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
     lowercase_			:       int									=			downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
     lowercase_			:       Dict									=			downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
     lowercase_			:       int									=			downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
     lowercase_			:       Dict									=			downstream_dict['objective.W']
     return model



@torch.no_grad()
def     lowercase__( __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]  ,    __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]   ):
     lowercase_			:       int									=			torch.load(__SCREAMING_SNAKE_CASE  ,    map_location='cpu'   )

     lowercase_			:       str									=			checkpoint['Downstream']

     lowercase_			:       str									=			UniSpeechSatConfig.from_pretrained(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Tuple									=			WavaVecaFeatureExtractor.from_pretrained(
         __SCREAMING_SNAKE_CASE  ,    return_attention_mask=__SCREAMING_SNAKE_CASE  ,    do_normalize=__SCREAMING_SNAKE_CASE   )

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

     if hf_config.use_weighted_layer_sum:
          lowercase_			:       Dict									=			checkpoint['Featurizer']['weights']

     hf_feature_extractor.save_pretrained(__SCREAMING_SNAKE_CASE   )
     hf_model.save_pretrained(__SCREAMING_SNAKE_CASE   )


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)

