
"""simple docstring"""




import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

__a        = ['UtteranceLevel']




@torch.no_grad()
def     a						(							snake_case__: Union[str, Any]						,       snake_case__: Optional[int]						,       snake_case__: List[str]						,       snake_case__: str ):


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

				lowercase_					  = checkpoint['Downstream']

				lowercase_					  = HubertConfig.from_pretrained(snake_case__ )
				lowercase_					  = HubertForSequenceClassification.from_pretrained(snake_case__						,       config=snake_case__ )
				lowercase_					  = WavaVecaFeatureExtractor.from_pretrained(
				    snake_case__						,       return_attention_mask=snake_case__						,       do_normalize=snake_case__ )

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

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

				hf_feature_extractor.save_pretrained(snake_case__ )
				hf_model.save_pretrained(snake_case__ )


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






class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      str	)	->					None:
					lowercase_					  = [0, 1]




	def 						_lowercase  (	self  :      str	,							SCREAMING_SNAKE_CASE_  :      int	)	->					list:
					if (difference := index - (len(self.sequence	) - 2)) >= 1:
									for _ in range(SCREAMING_SNAKE_CASE_	):
													self.sequence.append(self.sequence[-1] + self.sequence[-2]	)
					return self.sequence[:index]




def     a						(							):


				'''simple docstring'''
				print(
				    '''Fibonacci Series Using Dynamic Programming\n'''						,       '''Enter the index of the Fibonacci number you want to calculate '''						,       '''in the prompt below. (To exit enter exit or Ctrl-C)\n'''						,       sep=''''''						,       )
				lowercase_					  = Fibonacci()

				while True:
								lowercase_					  = input('''>> ''' )
								if prompt in {"exit", "quit"}:
												break

								try:
												lowercase_					  = int(snake_case__ )
								except ValueError:
												print('''Enter a number or \'exit\'''' )
												continue

								print(fibonacci.get(snake_case__ ) )


if __name__ == "__main__":
						main()




