class    lowercase_    :



							def __init__(						self				):
													_snake_case						:     List[str]				=    [0, 1]



							def        UpperCamelCase       (						self      ,				lowercase_				):
													if (difference := index - (len(self.sequence				) - 2)) >= 1:
																			for _ in range(lowercase_				):
																									self.sequence.append(self.sequence[-1] + self.sequence[-2]				)
													return self.sequence[:index]




def 					snake_case						()       ->       None:

						'''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=""					,			)
						_snake_case						:     Tuple				=    Fibonacci()

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

												try:
																		_snake_case						:     int				=    int(__lowercase						)
												except ValueError:
																		print("Enter a number or 'exit'"						)
																		continue

												print(fibonacci.get(__lowercase						)						)


if __name__ == "__main__":
				main()
import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE						:    Dict     = logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:    List[Any]     = ['UtteranceLevel']
@torch.no_grad()
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase						)       ->       Tuple:

						'''simple docstring'''



						_snake_case						:     Optional[int]				=    torch.load(__lowercase					,			map_location="cpu"						)
						if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
												raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""						)

						_snake_case						:     List[str]				=    checkpoint["Downstream"]

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

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

						_snake_case						:     str				=    downstream_dict["projector.weight"]
						_snake_case						:     Tuple				=    downstream_dict["projector.bias"]
						_snake_case						:     List[str]				=    downstream_dict["model.post_net.linear.weight"]
						_snake_case						:     str				=    downstream_dict["model.post_net.linear.bias"]

						hf_feature_extractor.save_pretrained(__lowercase						)
						hf_model.save_pretrained(__lowercase						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    Optional[int]     = 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						:    str     = parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)