


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

__SCREAMING_SNAKE_CASE	:							Optional[int]           =  ["UtteranceLevel"]

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






				'''simple docstring'''






				_lowerCAmelCase  						=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}"     )

				_lowerCAmelCase  						=checkpoint['''Downstream''']

				_lowerCAmelCase  						=HubertConfig.from_pretrained(_lowercase     )
				_lowerCAmelCase  						=HubertForSequenceClassification.from_pretrained(_lowercase       ,     config=_lowercase     )
				_lowerCAmelCase  						=WavaVecaFeatureExtractor.from_pretrained(
				    _lowercase       ,     return_attention_mask=_lowercase       ,     do_normalize=_lowercase     )

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

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

				hf_feature_extractor.save_pretrained(_lowercase     )
				hf_model.save_pretrained(_lowercase     )


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


"""simple docstring"""




from collections.abc import Callable

UpperCAmelCase       :    Optional[int]									=			Callable[[float], float]  # type alias for a real -> real function



def 				__a       (			_lowercase							, _lowercase							, _lowercase							, ):







					"""simple docstring"""





					lowerCamelCase__		:   Tuple  				=					float(_lowercase						)
					while True:
										try:
															lowerCamelCase__		:   str  				=					prev_guess - function(_lowercase						) / derivative(_lowercase						)
										except ZeroDivisionError:
															raise ZeroDivisionError('''Could not find root'''						) from None
										if abs(prev_guess - next_guess						) < 10**-5:
															return next_guess
										lowerCamelCase__		:   List[str]  				=					next_guess



def 				__a       (			_lowercase						):







					"""simple docstring"""





					return (x**3) - (2 * x) - 5



def 				__a       (			_lowercase						):







					"""simple docstring"""





					return 3 * (x**2) - 2


if __name__ == "__main__":
					print(newton(f, fa, 3))






