


import argparse

import torch

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


logging.set_verbosity_info()
snake_case				:    int       				=     logging.get_logger(__name__)
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		Optional[int]						):




      """simple docstring"""





      a						:Any 							=       UniSpeechSatForSequenceClassification.from_pretrained(UpperCAmelCase_ ,  config=UpperCAmelCase_						)
      a						:Tuple 							=       downstream_dict['''projector.weight''']
      a						:List[Any] 							=       downstream_dict['''projector.bias''']
      a						:Optional[int] 							=       downstream_dict['''model.post_net.linear.weight''']
      a						:Union[str, Any] 							=       downstream_dict['''model.post_net.linear.bias''']
      return model
def 				__lowerCamelCase						( UpperCAmelCase_						:		Any ,  UpperCAmelCase_						:		str ,  UpperCAmelCase_						:		str						):




      """simple docstring"""





      a						:List[str] 							=       UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCAmelCase_ ,  config=UpperCAmelCase_						)
      a						:Dict 							=       downstream_dict['''model.linear.weight''']
      a						:Any 							=       downstream_dict['''model.linear.bias''']
      return model
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		Optional[Any] ,  UpperCAmelCase_						:		Union[str, Any]						):




      """simple docstring"""





      a						:Optional[int] 							=       UniSpeechSatForXVector.from_pretrained(UpperCAmelCase_ ,  config=UpperCAmelCase_						)
      a						:Tuple 							=       downstream_dict['''connector.weight''']
      a						:int 							=       downstream_dict['''connector.bias''']
      for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
            a						:Optional[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						:List[Any] 							=       downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
      a						:str 							=       downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
      a						:int 							=       downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
      a						:List[Any] 							=       downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
      a						:str 							=       downstream_dict['''objective.W''']
      return model
@torch.no_grad()
def 				__lowerCamelCase						( UpperCAmelCase_						:		Optional[int] ,  UpperCAmelCase_						:		Dict ,  UpperCAmelCase_						:		List[str] ,  UpperCAmelCase_						:		Any						):




      """simple docstring"""





      a						:Dict 							=       torch.load(UpperCAmelCase_ ,  map_location='''cpu'''						)

      a						:List[str] 							=       checkpoint['''Downstream''']

      a						:Optional[Any] 							=       UniSpeechSatConfig.from_pretrained(UpperCAmelCase_						)
      a						:Optional[Any] 							=       WavaVecaFeatureExtractor.from_pretrained(
          UpperCAmelCase_ ,  return_attention_mask=UpperCAmelCase_ ,  do_normalize=UpperCAmelCase_						)

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

      if hf_config.use_weighted_layer_sum:
            a						:Union[str, Any] 							=       checkpoint['''Featurizer''']['''weights''']

      hf_feature_extractor.save_pretrained(UpperCAmelCase_						)
      hf_model.save_pretrained(UpperCAmelCase_						)


if __name__ == "__main__":
     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.''')
     snake_case				:    Optional[int]       				=     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_tokenizers_available, is_torch_available


_snake_case  :    List[Any]						    =  {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Optional[int]						    =  [
         'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'BioGptForCausalLM',
         'BioGptForTokenClassification',
         'BioGptForSequenceClassification',
         'BioGptModel',
         'BioGptPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

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

