


"""simple docstring"""






from collections import namedtuple

SCREAMING_SNAKE_CASE__				:  Dict						= namedtuple("from_to", "from_ to")

SCREAMING_SNAKE_CASE__				:  Optional[int]						= {
    "atm": from_to(1, 1),
    "pascal": from_to(0.0_0_0_0_0_9_8, 10_1325),
    "bar": from_to(0.9_8_6_9_2_3, 1.0_1_3_2_5),
    "kilopascal": from_to(0.0_0_9_8_6_9_2_3, 1_0_1.3_2_5),
    "megapascal": from_to(9.8_6_9_2_3, 0.1_0_1_3_2_5),
    "psi": from_to(0.0_6_8_0_4_6, 1_4.6_9_5_9),
    "inHg": from_to(0.0_3_3_4_2_1_1, 2_9.9_2_1_3),
    "torr": from_to(0.0_0_1_3_1_5_7_9, 760),
}




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     float:




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


if __name__ == "__main__":
 import doctest

 doctest.testmod()






import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_SCREAMING_SNAKE_CASE     =     ["""UtteranceLevel"""]
@torch.no_grad()
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->int:

  '''simple docstring'''




  a__  		=  torch.load(lowerCamelCase__			,					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(lowerCamelCase__       )
  a__  		=  HubertForSequenceClassification.from_pretrained(lowerCamelCase__			,					config=lowerCamelCase__       )
  a__  		=  WavaVecaFeatureExtractor.from_pretrained(
      lowerCamelCase__			,					return_attention_mask=lowerCamelCase__			,					do_normalize=lowerCamelCase__       )

  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(lowerCamelCase__       )
  hf_model.save_pretrained(lowerCamelCase__       )


if __name__ == "__main__":
     _SCREAMING_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.""")
     _SCREAMING_SNAKE_CASE     =     parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

