





import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
UpperCamelCase__ :			List[str]       	=			logging.get_logger(__name__)

UpperCamelCase__ :			Any       	=			["""UtteranceLevel"""]




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

	UpperCAmelCase_		    =  checkpoint["""Downstream"""]

	UpperCAmelCase_		    =  HubertConfig.from_pretrained(A )
	UpperCAmelCase_		    =  HubertForSequenceClassification.from_pretrained(A					,   config=A )
	UpperCAmelCase_		    =  WavaVecaFeatureExtractor.from_pretrained(
	    A					,   return_attention_mask=A					,   do_normalize=A )

	if hf_congfig.use_weighted_layer_sum:
		UpperCAmelCase_		    =  checkpoint["""Featurizer"""]["""weights"""]

	UpperCAmelCase_		    =  downstream_dict["""projector.weight"""]
	UpperCAmelCase_		    =  downstream_dict["""projector.bias"""]
	UpperCAmelCase_		    =  downstream_dict["""model.post_net.linear.weight"""]
	UpperCAmelCase_		    =  downstream_dict["""model.post_net.linear.bias"""]

	hf_feature_extractor.save_pretrained(A )
	hf_model.save_pretrained(A )


if __name__ == "__main__":
				UpperCamelCase__ :			List[Any]       	=			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.""")
				UpperCamelCase__ :			List[str]       	=			parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)




"""simple docstring"""






from collections import namedtuple

UpperCamelCase_						   =   namedtuple('from_to', 'from_ to')

UpperCamelCase_						   =   {
    'atm': from_to(1, 1),
    'pascal': from_to(0.0_00_00_98, 101325),
    'bar': from_to(0.98_69_23, 1.0_13_25),
    'kilopascal': from_to(0.00_98_69_23, 1_01.3_25),
    'megapascal': from_to(9.8_69_23, 0.10_13_25),
    'psi': from_to(0.06_80_46, 14.69_59),
    'inHg': from_to(0.0_33_42_11, 29.92_13),
    'torr': from_to(0.00_13_15_79, 760),
}
def    UpperCamelCase						(					UpperCAmelCase ,   UpperCAmelCase ,   UpperCAmelCase     )						->float:

   """simple docstring"""



   if from_type not in PRESSURE_CONVERSION:
      raise ValueError(
          F'''Invalid \'from_type\' value: {from_type!r}  Supported values are:\n'''
          + ", ".join(UpperCAmelCase     )     )
   if to_type not in PRESSURE_CONVERSION:
      raise ValueError(
          F'''Invalid \'to_type\' value: {to_type!r}.  Supported values are:\n'''
          + ", ".join(UpperCAmelCase     )     )
   return (
       value * PRESSURE_CONVERSION[from_type].from_ * PRESSURE_CONVERSION[to_type].to
   )


if __name__ == "__main__":
      import doctest

      doctest.testmod()