



import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
A__  :  Any       = logging.get_logger(__name__)

A__  :  str       = ['''UtteranceLevel''']


@torch.no_grad()
def       UpperCamelCase(  __UpperCamelCase							:		Dict					,__UpperCamelCase							:		Optional[int]					,__UpperCamelCase							:		Tuple					,__UpperCamelCase							:		int			):
		lowerCAmelCase_      :					Optional[Any]     =			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}"""			)

		lowerCAmelCase_      :					int     =			checkpoint['''Downstream''']

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

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

		lowerCAmelCase_      :					Dict     =			downstream_dict['''projector.weight''']
		lowerCAmelCase_      :					str     =			downstream_dict['''projector.bias''']
		lowerCAmelCase_      :					int     =			downstream_dict['''model.post_net.linear.weight''']
		lowerCAmelCase_      :					Any     =			downstream_dict['''model.post_net.linear.bias''']

		hf_feature_extractor.save_pretrained(__UpperCamelCase			)
		hf_model.save_pretrained(__UpperCamelCase			)


if __name__ == "__main__":
						A__  :  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.''')
						A__  :  Tuple       = parser.parse_args()
						convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)










def       UpperCamelCase(  __UpperCamelCase							:		list			):
		for i, _ in enumerate(__UpperCamelCase			):
				if (i % 2 == 1) == (nums[i - 1] > nums[i]):
						lowerCAmelCase_ ,  lowerCAmelCase_      :					Union[str, Any]     =			nums[i], nums[i - 1]

		return nums


if __name__ == "__main__":
						print('''Enter the array elements:''')
						A__  :  str       = list(map(int, input().split()))
						print('''The unsorted array is:''')
						print(array)
						print('''Array after Wiggle sort:''')
						print(wiggle_sort(array))





