'''simple docstring'''




import argparse

import torch

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


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

def      UpperCamelCase_							( _UpperCAmelCase  :				Optional[Any]      ,     _UpperCAmelCase  :				int      ,     _UpperCAmelCase  :				List[str]   )   ->   str:


       """simple docstring"""







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

def      UpperCamelCase_							( _UpperCAmelCase  :				Dict      ,     _UpperCAmelCase  :				List[Any]      ,     _UpperCAmelCase  :				Optional[Any]   )   ->   Dict:


       """simple docstring"""







       _UpperCAmelCase					:  List[Any]											=   WavLMForAudioFrameClassification.from_pretrained(_UpperCAmelCase      ,     config=_UpperCAmelCase   )
       _UpperCAmelCase					:  List[str]											=   downstream_dict["model.linear.weight"]
       _UpperCAmelCase					:  Dict											=   downstream_dict["model.linear.bias"]
       return model

def      UpperCamelCase_							( _UpperCAmelCase  :				Tuple      ,     _UpperCAmelCase  :				int      ,     _UpperCAmelCase  :				Union[str, Any]   )   ->   int:


       """simple docstring"""







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

       _UpperCAmelCase					:  Any											=   downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
       _UpperCAmelCase					:  Any											=   downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
       _UpperCAmelCase					:  Any											=   downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
       _UpperCAmelCase					:  str											=   downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
       _UpperCAmelCase					:  List[Any]											=   downstream_dict["objective.W"]
       return model

@torch.no_grad()
def      UpperCamelCase_							( _UpperCAmelCase  :				Any      ,     _UpperCAmelCase  :				Union[str, Any]      ,     _UpperCAmelCase  :				Dict      ,     _UpperCAmelCase  :				Optional[Any]   )   ->   Union[str, Any]:


       """simple docstring"""







       _UpperCAmelCase					:  Union[str, Any]											=   torch.load(_UpperCAmelCase      ,     map_location="cpu"   )

       _UpperCAmelCase					:  Tuple											=   checkpoint["Downstream"]

       _UpperCAmelCase					:  Dict											=   WavLMConfig.from_pretrained(_UpperCAmelCase   )
       _UpperCAmelCase					:  Any											=   WavaVecaFeatureExtractor.from_pretrained(
           _UpperCAmelCase      ,     return_attention_mask=_UpperCAmelCase      ,     do_normalize=_UpperCAmelCase   )

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

       if hf_config.use_weighted_layer_sum:
              _UpperCAmelCase					:  Any											=   checkpoint["Featurizer"]["weights"]

       hf_feature_extractor.save_pretrained(_UpperCAmelCase   )
       hf_model.save_pretrained(_UpperCAmelCase   )


if __name__ == "__main__":
  __SCREAMING_SNAKE_CASE	:					Tuple					=  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	:					int					=  parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


'''simple docstring'''




import warnings

from .generation import FlaxGenerationMixin

class      lowerCamelCase_     (snake_case__   ):
      '''simple docstring'''

      warnings.warn(
          "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
          "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."							,			snake_case__							,			)

