






"""simple docstring"""







import random






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			num - 1
       UpperCAmelCase	    =			0

       while s % 2 == 0:
              UpperCAmelCase	    =			s // 2
              t += 1

       for _ in range(5		):
              UpperCAmelCase	    =			random.randrange(2	,      num - 1		)
              UpperCAmelCase	    =			pow(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
              if v != 1:
                     UpperCAmelCase	    =			0
                     while v != (num - 1):
                            if i == t - 1:
                                   return False
                            else:
                                   UpperCAmelCase	    =			i + 1
                                   UpperCAmelCase	    =			(v**2) % num
       return True






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       if num < 2:
              return False

       UpperCAmelCase	    =			[
           2,
           3,
           5,
           7,
           11,
           13,
           17,
           19,
           23,
           29,
           31,
           37,
           41,
           43,
           47,
           53,
           59,
           61,
           67,
           71,
           73,
           79,
           83,
           89,
           97,
           101,
           103,
           107,
           109,
           113,
           127,
           131,
           137,
           139,
           149,
           151,
           157,
           163,
           167,
           173,
           179,
           181,
           191,
           193,
           197,
           199,
           211,
           223,
           227,
           229,
           233,
           239,
           241,
           251,
           257,
           263,
           269,
           271,
           277,
           281,
           283,
           293,
           307,
           311,
           313,
           317,
           331,
           337,
           347,
           349,
           353,
           359,
           367,
           373,
           379,
           383,
           389,
           397,
           401,
           409,
           419,
           421,
           431,
           433,
           439,
           443,
           449,
           457,
           461,
           463,
           467,
           479,
           487,
           491,
           499,
           503,
           509,
           521,
           523,
           541,
           547,
           557,
           563,
           569,
           571,
           577,
           587,
           593,
           599,
           601,
           607,
           613,
           617,
           619,
           631,
           641,
           643,
           647,
           653,
           659,
           661,
           673,
           677,
           683,
           691,
           701,
           709,
           719,
           727,
           733,
           739,
           743,
           751,
           757,
           761,
           769,
           773,
           787,
           797,
           809,
           811,
           821,
           823,
           827,
           829,
           839,
           853,
           857,
           859,
           863,
           877,
           881,
           883,
           887,
           907,
           911,
           919,
           929,
           937,
           941,
           947,
           953,
           967,
           971,
           977,
           983,
           991,
           997,
       ]

       if num in low_primes:
              return True

       for prime in low_primes:
              if (num % prime) == 0:
                     return False

       return rabin_miller(lowerCAmelCase		)






def       _lowerCAmelCase       (      lowerCAmelCase = 1024		):







       '''simple docstring'''
       while True:
              UpperCAmelCase	    =			random.randrange(2 ** (keysize - 1)	,      2 ** (keysize)		)
              if is_prime_low_num(lowerCAmelCase		):
                     return num


if __name__ == "__main__":
 lowerCAmelCase_	:    str            =      generate_large_prime()
 print(('''Prime number:''', num))
 print(('''is_prime_low_num:''', is_prime_low_num(num)))








"""simple docstring"""







import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase_	:    Any            =      logging.get_logger(__name__)






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			UniSpeechSatForSequenceClassification.from_pretrained(lowerCAmelCase	,      config=lowerCAmelCase		)
       UpperCAmelCase	    =			downstream_dict["""projector.weight"""]
       UpperCAmelCase	    =			downstream_dict["""projector.bias"""]
       UpperCAmelCase	    =			downstream_dict["""model.post_net.linear.weight"""]
       UpperCAmelCase	    =			downstream_dict["""model.post_net.linear.bias"""]
       return model






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCAmelCase	,      config=lowerCAmelCase		)
       UpperCAmelCase	    =			downstream_dict["""model.linear.weight"""]
       UpperCAmelCase	    =			downstream_dict["""model.linear.bias"""]
       return model






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			UniSpeechSatForXVector.from_pretrained(lowerCAmelCase	,      config=lowerCAmelCase		)
       UpperCAmelCase	    =			downstream_dict["""connector.weight"""]
       UpperCAmelCase	    =			downstream_dict["""connector.bias"""]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
              UpperCAmelCase	    =			downstream_dict[
                  F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
              ]
              UpperCAmelCase	    =			downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

       UpperCAmelCase	    =			downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
       UpperCAmelCase	    =			downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
       UpperCAmelCase	    =			downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
       UpperCAmelCase	    =			downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
       UpperCAmelCase	    =			downstream_dict["""objective.W"""]
       return model






@torch.no_grad()
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			torch.load(lowerCAmelCase	,      map_location="""cpu"""		)

       UpperCAmelCase	    =			checkpoint["""Downstream"""]

       UpperCAmelCase	    =			UniSpeechSatConfig.from_pretrained(lowerCAmelCase		)
       UpperCAmelCase	    =			WavaVecaFeatureExtractor.from_pretrained(
           lowerCAmelCase	,      return_attention_mask=lowerCAmelCase	,      do_normalize=lowerCAmelCase		)

       UpperCAmelCase	    =			hf_config.architectures[0]
       if arch.endswith("""ForSequenceClassification"""		):
              UpperCAmelCase	    =			convert_classification(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
       elif arch.endswith("""ForAudioFrameClassification"""		):
              UpperCAmelCase	    =			convert_diarization(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
       elif arch.endswith("""ForXVector"""		):
              UpperCAmelCase	    =			convert_xvector(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
       else:
              raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''		)

       if hf_config.use_weighted_layer_sum:
              UpperCAmelCase	    =			checkpoint["""Featurizer"""]["""weights"""]

       hf_feature_extractor.save_pretrained(lowerCAmelCase		)
       hf_model.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
 lowerCAmelCase_	:    List[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_	:    str            =      parser.parse_args()
 convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
