





import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__A    :   str          =       logging.get_logger(__name__)

__A    :   List[str]          =       ["UtteranceLevel"]





@torch.no_grad()
def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__      ,      UpperCamelCase__			)  ->    List[str]:







			'''simple docstring'''

			UpperCAmelCase       		=    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}"""			)

			UpperCAmelCase       		=    checkpoint['''Downstream''']

			UpperCAmelCase       		=    HubertConfig.from_pretrained(UpperCamelCase__			)
			UpperCAmelCase       		=    HubertForSequenceClassification.from_pretrained(UpperCamelCase__      ,      config=UpperCamelCase__			)
			UpperCAmelCase       		=    WavaVecaFeatureExtractor.from_pretrained(
			    UpperCamelCase__      ,      return_attention_mask=UpperCamelCase__      ,      do_normalize=UpperCamelCase__			)

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

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

			hf_feature_extractor.save_pretrained(UpperCamelCase__			)
			hf_model.save_pretrained(UpperCamelCase__			)


if __name__ == "__main__":
				__A    :   Optional[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)










import math





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__			)  ->    bool:







			'''simple docstring'''

			if 1 < number < 4:
						# 2 and 3 are primes
						return True
			elif number < 2 or number % 2 == 0 or number % 3 == 0:
						# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
						return False

			# All primes number are in format of 6k +/- 1
			for i in range(5      ,      int(math.sqrt(UpperCamelCase__			) + 1			)      ,      6			):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True





def 						__SCREAMING_SNAKE_CASE  (    UpperCamelCase__ = 6008_5147_5143			)  ->    int:







			'''simple docstring'''

			try:
						UpperCAmelCase       		=    int(UpperCamelCase__			)
			except (TypeError, ValueError):
						raise TypeError('''Parameter n must be int or castable to int.'''			)
			if n <= 0:
						raise ValueError('''Parameter n must be greater than or equal to one.'''			)
			UpperCAmelCase       		=    0
			if is_prime(UpperCamelCase__			):
						return n
			while n % 2 == 0:
						n //= 2
			if is_prime(UpperCamelCase__			):
						return n
			for i in range(3      ,      int(math.sqrt(UpperCamelCase__			)			) + 1      ,      2			):
						if n % i == 0:
									if is_prime(n // i			):
												UpperCAmelCase       		=    n // i
												break
									elif is_prime(UpperCamelCase__			):
												UpperCAmelCase       		=    i
			return max_number


if __name__ == "__main__":
				print(F'{solution() = }')



