



"""simple docstring"""
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   A_    (     _lowercase,   _lowercase,   _lowercase,   _lowercase      ):







						'''simple docstring'''


						snake_case_					:List[Any]		=     torch.load(_lowercase,   map_location="""cpu"""      )
						if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
												raise NotImplementedError(f"""The supported s3prl models are {SUPPORTED_MODELS}"""      )

						snake_case_					:Optional[Any]		=     checkpoint["""Downstream"""]

						snake_case_					:str		=     HubertConfig.from_pretrained(_lowercase      )
						snake_case_					:Union[str, Any]		=     HubertForSequenceClassification.from_pretrained(_lowercase,   config=_lowercase      )
						snake_case_					:Optional[int]		=     WavaVecaFeatureExtractor.from_pretrained(
						    _lowercase,   return_attention_mask=_lowercase,   do_normalize=_lowercase      )

						if hf_congfig.use_weighted_layer_sum:
												snake_case_					:str		=     checkpoint["""Featurizer"""]["""weights"""]

						snake_case_					:Union[str, Any]		=     downstream_dict["""projector.weight"""]
						snake_case_					:Optional[Any]		=     downstream_dict["""projector.bias"""]
						snake_case_					:str		=     downstream_dict["""model.post_net.linear.weight"""]
						snake_case_					:Optional[int]		=     downstream_dict["""model.post_net.linear.bias"""]

						hf_feature_extractor.save_pretrained(_lowercase      )
						hf_model.save_pretrained(_lowercase      )


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)








"""simple docstring"""





from itertools import combinations, product



def      __lowercase       (     a			:					int							,		a			:					int							,		a			:					int							,		a			:					int			)       ->       bool:
      if xa == ya == 0 or xa == ya == 0:
            return False
      __snake_case   :     Any	      =xa * xa + ya * ya
      __snake_case   :     Tuple	      =xa * xa + ya * ya
      __snake_case   :     List[Any]	      =(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
      return (
          a_square + b_square == c_square
          or a_square + c_square == b_square
          or b_square + c_square == a_square
      )



def      __lowercase       (     a			:					int = 50			)       ->       int:
      return sum(
          1
          for pta, pta in combinations(product(range(limit + 1			)							,		repeat=2			)							,		2			)
          if is_right(*a							,		*a			)			)


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


