
"""simple docstring"""
import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

lowercase		:						Dict =					['UtteranceLevel']


@torch.no_grad()
def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			Dict:


					'''simple docstring'''
					__a:						Tuple		    =					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:						int		    =					checkpoint["""Downstream"""]

					__a:						Any		    =					HubertConfig.from_pretrained(lowerCamelCase_ )
					__a:						List[Any]		    =					HubertForSequenceClassification.from_pretrained(lowerCamelCase_	,				config=lowerCamelCase_ )
					__a:						Union[str, Any]		    =					WavaVecaFeatureExtractor.from_pretrained(
					    lowerCamelCase_	,				return_attention_mask=lowerCamelCase_	,				do_normalize=lowerCamelCase_ )

					if hf_congfig.use_weighted_layer_sum:
										__a:						Optional[int]		    =					checkpoint["""Featurizer"""]["""weights"""]

					__a:						Tuple		    =					downstream_dict["""projector.weight"""]
					__a:						Tuple		    =					downstream_dict["""projector.bias"""]
					__a:						Tuple		    =					downstream_dict["""model.post_net.linear.weight"""]
					__a:						Union[str, Any]		    =					downstream_dict["""model.post_net.linear.bias"""]

					hf_feature_extractor.save_pretrained(lowerCamelCase_ )
					hf_model.save_pretrained(lowerCamelCase_ )


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


"""simple docstring"""
lowercase		:						str =					{
    '0000': '0',
    '0001': '1',
    '0010': '2',
    '0011': '3',
    '0100': '4',
    '0101': '5',
    '0110': '6',
    '0111': '7',
    '1000': '8',
    '1001': '9',
    '1010': 'a',
    '1011': 'b',
    '1100': 'c',
    '1101': 'd',
    '1110': 'e',
    '1111': 'f',
}


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			str:


					'''simple docstring'''
					__a:						Optional[int]		    =					str(lowerCamelCase_ ).strip()

					# Exceptions
					if not binary_str:
										raise ValueError("""Empty string was passed to the function""" )
					__a:						List[Any]		    =					binary_str[0] == """-"""
					__a:						List[str]		    =					binary_str[1:] if is_negative else binary_str
					if not all(char in """01""" for char in binary_str ):
										raise ValueError("""Non-binary value was passed to the function""" )

					__a:						Any		    =					(
					    """0""" * (4 * (divmod(len(lowerCamelCase_ )	,				4 )[0] + 1) - len(lowerCamelCase_ )) + binary_str
					)

					__a:						int		    =					[]
					for x in range(0	,				len(lowerCamelCase_ )	,				4 ):
										hexadecimal.append(BITS_TO_HEX[binary_str[x : x + 4]] )
					__a:						Tuple		    =					"""0x""" + """""".join(lowerCamelCase_ )

					return "-" + hexadecimal_str if is_negative else hexadecimal_str


if __name__ == "__main__":
			from doctest import testmod

			testmod()
