





import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
a__			:    Optional[int]			      =		logging.get_logger(__name__)

a__			:    Union[str, Any]			      =		['UtteranceLevel']

@torch.no_grad()
def     UpperCAmelCase_						(						_UpperCAmelCase       :int     ,	_UpperCAmelCase       :Tuple     ,	_UpperCAmelCase       :int     ,	_UpperCAmelCase       :str							)  ->							List[str]:
     '''simple docstring'''


     A_					      =					torch.load(_UpperCAmelCase     ,	map_location='''cpu'''							)
     if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
          raise NotImplementedError(f'The supported s3prl models are {SUPPORTED_MODELS}'							)

     A_					      =					checkpoint['''Downstream''']

     A_					      =					HubertConfig.from_pretrained(_UpperCAmelCase							)
     A_					      =					HubertForSequenceClassification.from_pretrained(_UpperCAmelCase     ,	config=_UpperCAmelCase							)
     A_					      =					WavaVecaFeatureExtractor.from_pretrained(
         _UpperCAmelCase     ,	return_attention_mask=_UpperCAmelCase     ,	do_normalize=_UpperCAmelCase							)

     if hf_congfig.use_weighted_layer_sum:
          A_					      =					checkpoint['''Featurizer''']['''weights''']

     A_					      =					downstream_dict['''projector.weight''']
     A_					      =					downstream_dict['''projector.bias''']
     A_					      =					downstream_dict['''model.post_net.linear.weight''']
     A_					      =					downstream_dict['''model.post_net.linear.bias''']

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


if __name__ == "__main__":
      a__			:    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.')
      a__			:    List[str]			      =		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


a__			:    Tuple			      =		{'configuration_yoso': ['YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YosoConfig']}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      a__			:    Optional[int]			      =		[
          'YOSO_PRETRAINED_MODEL_ARCHIVE_LIST',
          'YosoForMaskedLM',
          'YosoForMultipleChoice',
          'YosoForQuestionAnswering',
          'YosoForSequenceClassification',
          'YosoForTokenClassification',
          'YosoLayer',
          'YosoModel',
          'YosoPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_yoso import YOSO_PRETRAINED_CONFIG_ARCHIVE_MAP, YosoConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_yoso import (
                YOSO_PRETRAINED_MODEL_ARCHIVE_LIST,
                YosoForMaskedLM,
                YosoForMultipleChoice,
                YosoForQuestionAnswering,
                YosoForSequenceClassification,
                YosoForTokenClassification,
                YosoLayer,
                YosoModel,
                YosoPreTrainedModel,
            )


else:
      import sys

      a__			:    Union[str, Any]			      =		_LazyModule(__name__, globals()['__file__'], _import_structure)



