"""simple docstring"""







import argparse

import torch

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


logging.set_verbosity_info()
lowerCamelCase      : Any       =logging.get_logger(__name__)




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				str  ,       _SCREAMING_SNAKE_CASE				:				List[str]  ,       _SCREAMING_SNAKE_CASE				:				Any						)    ->     Optional[Any]:







     '''simple docstring'''


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




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Any  ,       _SCREAMING_SNAKE_CASE				:				List[str]  ,       _SCREAMING_SNAKE_CASE				:				Any						)    ->     List[str]:







     '''simple docstring'''


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




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Optional[int]  ,       _SCREAMING_SNAKE_CASE				:				str  ,       _SCREAMING_SNAKE_CASE				:				int						)    ->     Tuple:







     '''simple docstring'''


     __A					:					str								=		WavLMForXVector.from_pretrained(_SCREAMING_SNAKE_CASE  ,       config=_SCREAMING_SNAKE_CASE						)
     __A					:					int								=		downstream_dict['connector.weight']
     __A					:					Tuple								=		downstream_dict['connector.bias']
     for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
          __A					:					int								=		downstream_dict[
              F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
          ]
          __A					:					str								=		downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']

     __A					:					Dict								=		downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
     __A					:					str								=		downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
     __A					:					str								=		downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
     __A					:					List[str]								=		downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
     __A					:					str								=		downstream_dict['objective.W']
     return model




@torch.no_grad()
def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Union[str, Any]  ,       _SCREAMING_SNAKE_CASE				:				int  ,       _SCREAMING_SNAKE_CASE				:				Dict  ,       _SCREAMING_SNAKE_CASE				:				int						)    ->     Union[str, Any]:







     '''simple docstring'''


     __A					:					Tuple								=		torch.load(_SCREAMING_SNAKE_CASE  ,       map_location='cpu'						)

     __A					:					Dict								=		checkpoint['Downstream']

     __A					:					Tuple								=		WavLMConfig.from_pretrained(_SCREAMING_SNAKE_CASE						)
     __A					:					Dict								=		WavaVecaFeatureExtractor.from_pretrained(
         _SCREAMING_SNAKE_CASE  ,       return_attention_mask=_SCREAMING_SNAKE_CASE  ,       do_normalize=_SCREAMING_SNAKE_CASE						)

     __A					:					Union[str, Any]								=		hf_config.architectures[0]
     if arch.endswith('ForSequenceClassification'						):
          __A					:					Union[str, Any]								=		convert_classification(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						)
     elif arch.endswith('ForAudioFrameClassification'						):
          __A					:					int								=		convert_diarization(_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE						)
     elif arch.endswith('ForXVector'						):
          __A					:					Union[str, 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:
          __A					:					int								=		checkpoint['Featurizer']['weights']

     hf_feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE						)
     hf_model.save_pretrained(_SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
       lowerCamelCase      : Dict       =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.''')
       lowerCamelCase      : Optional[int]       =parser.parse_args()
       convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




"""simple docstring"""







import pytest

from datasets.utils.version import Version




@pytest.mark.parametrize(
    'other, expected_equality'  ,       [
        (Version('1.0.0'						), True),
        ('1.0.0', True),
        (Version('2.0.0'						), False),
        ('2.0.0', False),
        ('1', False),
        ('a', False),
        (1, False),
        (None, False),
    ]  ,       )
def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Tuple  ,       _SCREAMING_SNAKE_CASE				:				Any						)    ->     List[str]:







     '''simple docstring'''


     __A					:					Tuple								=		Version('1.0.0'						)
     assert (version == other) is expected_equality
     assert (version != other) is not expected_equality
     assert (hash(_SCREAMING_SNAKE_CASE						) == hash(_SCREAMING_SNAKE_CASE						)) is expected_equality



