
'''simple docstring'''





import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

a__						:				Any           =    ["UtteranceLevel"]





@torch.no_grad()
def     snake_case							(						UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase		)->     Dict:







      """simple docstring"""
      __A    		=     torch.load(UpperCAmelCase ,					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(UpperCAmelCase		)
      __A    		=     HubertForSequenceClassification.from_pretrained(UpperCAmelCase ,					config=UpperCAmelCase		)
      __A    		=     WavaVecaFeatureExtractor.from_pretrained(
          UpperCAmelCase ,					return_attention_mask=UpperCAmelCase ,					do_normalize=UpperCAmelCase		)

      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(UpperCAmelCase		)
      hf_model.save_pretrained(UpperCAmelCase		)


if __name__ == "__main__":
 a__						:				Optional[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.")
 a__						:				Optional[int]           =    parser.parse_args()
 convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)








'''simple docstring'''





import itertools
from pathlib import Path





def     snake_case							(						UpperCAmelCase		)->     int:







      """simple docstring"""
      __A    		=     [tuple(UpperCAmelCase		) for login in logins]

      __A    		=     {char for login in split_logins for char in login}

      for permutation in itertools.permutations(UpperCAmelCase		):
            __A    		=     True
            for login in logins:
                  if not (
                      permutation.index(login[0]		)
                      < permutation.index(login[1]		)
                      < permutation.index(login[2]		)
                  ):
                        __A    		=     False
                        break

            if satisfied:
                  return int(''.join(UpperCAmelCase		)		)

      raise Exception('Unable to find the secret passcode'		)





def     snake_case							(						UpperCAmelCase = "keylog.txt"		)->     int:







      """simple docstring"""
      __A    		=     Path(UpperCAmelCase		).parent.joinpath(UpperCAmelCase		).read_text().splitlines()

      return find_secret_passcode(UpperCAmelCase		)


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






