






import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
A_			:List[Any]	    =		logging.get_logger(__name__)

A_			:List[str]	    =		['''UtteranceLevel''']






@torch.no_grad()
def 				A	(			a_				,a_				,a_				,a_   )	->		Dict:
					__UpperCamelCase :   int									=torch.load(a_				,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 :   Optional[int]									=checkpoint['Downstream']

					__UpperCamelCase :   List[Any]									=HubertConfig.from_pretrained(a_   )
					__UpperCamelCase :   Optional[int]									=HubertForSequenceClassification.from_pretrained(a_				,config=a_   )
					__UpperCamelCase :   Dict									=WavaVecaFeatureExtractor.from_pretrained(
					    a_				,return_attention_mask=a_				,do_normalize=a_   )

					if hf_congfig.use_weighted_layer_sum:
										__UpperCamelCase :   Optional[Any]									=checkpoint['Featurizer']['weights']

					__UpperCamelCase :   Union[str, Any]									=downstream_dict['projector.weight']
					__UpperCamelCase :   Tuple									=downstream_dict['projector.bias']
					__UpperCamelCase :   int									=downstream_dict['model.post_net.linear.weight']
					__UpperCamelCase :   List[str]									=downstream_dict['model.post_net.linear.bias']

					hf_feature_extractor.save_pretrained(a_   )
					hf_model.save_pretrained(a_   )


if __name__ == "__main__":
							A_			: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.''')
							A_			:Union[str, Any]	    =		parser.parse_args()
							convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)









from __future__ import annotations




class 							__A	:





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__ = 0       ):




									"""simple docstring"""

									__UpperCamelCase :   str									=value
									__UpperCamelCase :   Node | None									=None
									__UpperCamelCase :   Node | None									=None






def 				A	(			a_				,a_   )	->		Node | None:
					if treea is None:
										return treea
					if treea is None:
										return treea

					__UpperCamelCase :   Dict									=treea.value + treea.value
					__UpperCamelCase :   Any									=merge_two_binary_trees(treea.left				,treea.left   )
					__UpperCamelCase :   Union[str, Any]									=merge_two_binary_trees(treea.right				,treea.right   )
					return treea






def 				A	(			a_   )	->		None:
					if root:
										print(root.value   )
										print_preorder(root.left   )
										print_preorder(root.right   )


if __name__ == "__main__":
							A_			:List[str]	    =		Node(1)
							A_			:Dict	    =		Node(2)
							A_			:str	    =		Node(3)
							A_			:Union[str, Any]	    =		Node(4)

							A_			:Any	    =		Node(2)
							A_			:Tuple	    =		Node(4)
							A_			:Optional[Any]	    =		Node(6)
							A_			:Optional[Any]	    =		Node(9)
							A_			:str	    =		Node(5)

							print('''Tree1 is: ''')
							print_preorder(treea)
							print('''Tree2 is: ''')
							print_preorder(treea)
							A_			:Optional[Any]	    =		merge_two_binary_trees(treea, treea)
							print('''Merged Tree is: ''')
							print_preorder(merged_tree)

