
import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowerCamelCase_  :       Dict					      =     logging.get_logger(__name__)

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







@torch.no_grad()
def 				A__    (  lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase   ,			lowerCamelCase  )     ->		Optional[Any]:
				UpperCamelCase_:							List[Any]		=  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}'''  )

				UpperCamelCase_:							int		=  checkpoint["""Downstream"""]

				UpperCamelCase_:							str		=  HubertConfig.from_pretrained(lowerCamelCase  )
				UpperCamelCase_:							int		=  HubertForSequenceClassification.from_pretrained(lowerCamelCase   ,			config=lowerCamelCase  )
				UpperCamelCase_:							Optional[Any]		=  WavaVecaFeatureExtractor.from_pretrained(
				    lowerCamelCase   ,			return_attention_mask=lowerCamelCase   ,			do_normalize=lowerCamelCase  )

				if hf_congfig.use_weighted_layer_sum:
								UpperCamelCase_:							Union[str, Any]		=  checkpoint["""Featurizer"""]["""weights"""]

				UpperCamelCase_:							Any		=  downstream_dict["""projector.weight"""]
				UpperCamelCase_:							Optional[Any]		=  downstream_dict["""projector.bias"""]
				UpperCamelCase_:							Tuple		=  downstream_dict["""model.post_net.linear.weight"""]
				UpperCamelCase_:							Optional[Any]		=  downstream_dict["""model.post_net.linear.bias"""]

				hf_feature_extractor.save_pretrained(lowerCamelCase  )
				hf_model.save_pretrained(lowerCamelCase  )


if __name__ == "__main__":
							lowerCamelCase_  :       Union[str, 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.""")
							lowerCamelCase_  :       Dict					      =     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 					_snake_case   (     A ,    A       )						->    list[int]:
							lowerCAmelCase__          =	{}
							for index, val in enumerate(A       ):
														lowerCAmelCase__          =	target - val
														if compl in chk_map:
																					return [chk_map[compl], index]
														lowerCAmelCase__          =	index
							return []


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(f"""{two_sum([2, 7, 11, 15], 9) = }""")