



import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
A__       =			logging.get_logger(__name__)

A__       =			["""UtteranceLevel"""]


@torch.no_grad()
def 						_UpperCAmelCase							( snake_case      ,		snake_case      ,		snake_case      ,		snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       torch.load(snake_case      ,		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(snake_case     )
					_lowerCAmelCase         =       HubertForSequenceClassification.from_pretrained(snake_case      ,		config=snake_case     )
					_lowerCAmelCase         =       WavaVecaFeatureExtractor.from_pretrained(
					    snake_case      ,		return_attention_mask=snake_case      ,		do_normalize=snake_case     )

					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(snake_case     )
					hf_model.save_pretrained(snake_case     )


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








from .stack import Stack






def 			UpperCAmelCase__     (     lowercase__ )			->   bool:
						__lowercase											=					Stack()
						__lowercase											=					{"""(""": """)""", """[""": """]""", """{""": """}"""}
						for bracket in parentheses:
												if bracket in bracket_pairs:
																		stack.push(lowercase__ )
												elif bracket in (")", "]", "}"):
																		if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
																								return False
						return stack.is_empty()


if __name__ == "__main__":
			from doctest import testmod

			testmod()

			UpperCamelCase__			=    ["((()))", "((())", "(()))"]
			print("Balanced parentheses demonstration:\n")
			for example in examples:
						UpperCamelCase__			=    "" if balanced_parentheses(example) else "not "
						print(F"""{example} is {not_str}balanced""")





