


import argparse

import torch

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


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






def 		lowercase_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

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






def 		lowercase_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  lowerCamelCase			: str   =    WavLMForXVector.from_pretrained(SCREAMING_SNAKE_CASE_				,						config=SCREAMING_SNAKE_CASE_     )
  lowerCamelCase			: Union[str, Any]   =    downstream_dict["connector.weight"]
  lowerCamelCase			: Optional[Any]   =    downstream_dict["connector.bias"]
  for i, kernel_size in enumerate(hf_config.tdnn_kernel     ):
    lowerCamelCase			: Any   =    downstream_dict[
        f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
    ]
    lowerCamelCase			: Dict   =    downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

  lowerCamelCase			: Dict   =    downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
  lowerCamelCase			: Optional[Any]   =    downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
  lowerCamelCase			: Union[str, Any]   =    downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
  lowerCamelCase			: int   =    downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
  lowerCamelCase			: Tuple   =    downstream_dict["objective.W"]
  return model






@torch.no_grad()
def 		lowercase_(  SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     ):


  '''simple docstring'''

  lowerCamelCase			: Optional[int]   =    torch.load(SCREAMING_SNAKE_CASE_				,						map_location="cpu"     )

  lowerCamelCase			: List[str]   =    checkpoint["Downstream"]

  lowerCamelCase			: Any   =    WavLMConfig.from_pretrained(SCREAMING_SNAKE_CASE_     )
  lowerCamelCase			: Any   =    WavaVecaFeatureExtractor.from_pretrained(
      SCREAMING_SNAKE_CASE_				,						return_attention_mask=SCREAMING_SNAKE_CASE_				,						do_normalize=SCREAMING_SNAKE_CASE_     )

  lowerCamelCase			: Any   =    hf_config.architectures[0]
  if arch.endswith("ForSequenceClassification"     ):
    lowerCamelCase			: Union[str, Any]   =    convert_classification(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     )
  elif arch.endswith("ForAudioFrameClassification"     ):
    lowerCamelCase			: List[Any]   =    convert_diarization(SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_				,						SCREAMING_SNAKE_CASE_     )
  elif arch.endswith("ForXVector"     ):
    lowerCamelCase			: Optional[int]   =    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:
    lowerCamelCase			: Union[str, Any]   =    checkpoint["Featurizer"]["weights"]

  hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_     )
  hf_model.save_pretrained(SCREAMING_SNAKE_CASE_     )


if __name__ == "__main__":
   _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.''')
   _snake_case					=					parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_snake_case					=					{
    '''configuration_rwkv''': ['''RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RwkvConfig''', '''RwkvOnnxConfig'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case					=					[
       '''RWKV_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RwkvForCausalLM''',
       '''RwkvModel''',
       '''RwkvPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_rwkv import (
          RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
          RwkvForCausalLM,
          RwkvModel,
          RwkvPreTrainedModel,
      )
else:
   import sys

   _snake_case					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



