






"""simple docstring"""



import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
_UpperCamelCase						:    Optional[int]		=						logging.get_logger(__name__)
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Any						,   __snake_case     :    Union[str, Any]						,   __snake_case     :    str       ):





  '''simple docstring'''





  lowercase				       =			WavLMForSequenceClassification.from_pretrained(__snake_case						,   config=__snake_case       )
  lowercase				       =			downstream_dict['projector.weight']
  lowercase				       =			downstream_dict['projector.bias']
  lowercase				       =			downstream_dict['model.post_net.linear.weight']
  lowercase				       =			downstream_dict['model.post_net.linear.bias']
  return model
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Union[str, Any]						,   __snake_case     :    Optional[int]						,   __snake_case     :    Optional[Any]       ):





  '''simple docstring'''





  lowercase				       =			WavLMForAudioFrameClassification.from_pretrained(__snake_case						,   config=__snake_case       )
  lowercase				       =			downstream_dict['model.linear.weight']
  lowercase				       =			downstream_dict['model.linear.bias']
  return model
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Union[str, Any]						,   __snake_case     :    List[str]						,   __snake_case     :    str       ):





  '''simple docstring'''





  lowercase				       =			WavLMForXVector.from_pretrained(__snake_case						,   config=__snake_case       )
  lowercase				       =			downstream_dict['connector.weight']
  lowercase				       =			downstream_dict['connector.bias']
  for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
    lowercase				       =			downstream_dict[
        f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
    ]
    lowercase				       =			downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']

  lowercase				       =			downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
  lowercase				       =			downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
  lowercase				       =			downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
  lowercase				       =			downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
  lowercase				       =			downstream_dict['objective.W']
  return model
@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[Any]						,   __snake_case     :    Optional[int]						,   __snake_case     :    List[Any]						,   __snake_case     :    Optional[int]       ):





  '''simple docstring'''





  lowercase				       =			torch.load(__snake_case						,   map_location='cpu'       )

  lowercase				       =			checkpoint['Downstream']

  lowercase				       =			WavLMConfig.from_pretrained(__snake_case       )
  lowercase				       =			WavaVecaFeatureExtractor.from_pretrained(
      __snake_case						,   return_attention_mask=__snake_case						,   do_normalize=__snake_case       )

  lowercase				       =			hf_config.architectures[0]
  if arch.endswith('ForSequenceClassification'       ):
    lowercase				       =			convert_classification(__snake_case						,   __snake_case						,   __snake_case       )
  elif arch.endswith('ForAudioFrameClassification'       ):
    lowercase				       =			convert_diarization(__snake_case						,   __snake_case						,   __snake_case       )
  elif arch.endswith('ForXVector'       ):
    lowercase				       =			convert_xvector(__snake_case						,   __snake_case						,   __snake_case       )
  else:
    raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}'       )

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

  hf_feature_extractor.save_pretrained(__snake_case       )
  hf_model.save_pretrained(__snake_case       )


if __name__ == "__main__":
    _UpperCamelCase						:    int		=						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.')
    _UpperCamelCase						:    List[str]		=						parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
snake_case__						      =			abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def    __magic_name__(	__UpperCAmelCase		)    ->     int:



      '''simple docstring'''

      from transformers.testing_utils import pytest_addoption_shared

      pytest_addoption_shared(__UpperCAmelCase		)




def    __magic_name__(	__UpperCAmelCase		)    ->     List[str]:



      '''simple docstring'''

      from transformers.testing_utils import pytest_terminal_summary_main

      _lowerCamelCase           =					terminalreporter.config.getoption('''--make-reports'''		)
      if make_reports:
            pytest_terminal_summary_main(__UpperCAmelCase			,    id=__UpperCAmelCase		)