






"""simple docstring"""







import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

__a							:						Dict   							=			['UtteranceLevel']





@torch.no_grad()
def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_):
					a__ 							=	torch.load(lowerCamelCase_			,  map_location='''cpu''')
					if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
										raise NotImplementedError(f'The supported s3prl models are {SUPPORTED_MODELS}')

					a__ 							=	checkpoint['''Downstream''']

					a__ 							=	HubertConfig.from_pretrained(lowerCamelCase_)
					a__ 							=	HubertForSequenceClassification.from_pretrained(lowerCamelCase_			,  config=lowerCamelCase_)
					a__ 							=	WavaVecaFeatureExtractor.from_pretrained(
					    lowerCamelCase_			,  return_attention_mask=lowerCamelCase_			,  do_normalize=lowerCamelCase_)

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

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

					hf_feature_extractor.save_pretrained(lowerCamelCase_)
					hf_model.save_pretrained(lowerCamelCase_)


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










"""simple docstring"""







from __future__ import annotations





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_):

					# Base Case
					if counter == 0 or capacity == 0:
										return 0

					# If weight of the nth item is more than Knapsack of capacity,
					#   then this item cannot be included in the optimal solution,
					# else return the maximum of two cases:
					#   (1) nth item included
					#   (2) not included
					if weights[counter - 1] > capacity:
										return knapsack(lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  counter - 1)
					else:
										a__ 							=	capacity - weights[counter - 1]
										a__ 							=	values[counter - 1] + knapsack(
										    lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  counter - 1)
										a__ 							=	knapsack(lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  counter - 1)
										return max(lowerCamelCase_			,  lowerCamelCase_)


if __name__ == "__main__":
	import doctest

	doctest.testmod()


