
import random




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int])      ->					bool:






							'''simple docstring'''





							__UpperCamelCase			:     List[Any]         =      num - 1
							__UpperCamelCase			:     Union[str, Any]         =      0

							while s % 2 == 0:
														__UpperCamelCase			:     List[Any]         =      s // 2
														t += 1

							for _ in range(5):
														__UpperCamelCase			:     List[str]         =      random.randrange(2		,						num - 1)
														__UpperCamelCase			:     Optional[int]         =      pow(_lowerCAmelCase		,						_lowerCAmelCase		,						_lowerCAmelCase)
														if v != 1:
																					__UpperCamelCase			:     str         =      0
																					while v != (num - 1):
																												if i == t - 1:
																																			return False
																												else:
																																			__UpperCamelCase			:     Any         =      i + 1
																																			__UpperCamelCase			:     List[str]         =      (v**2) % num
							return True




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[Any])      ->					bool:






							'''simple docstring'''





							if num < 2:
														return False

							__UpperCamelCase			:     str         =      [
							    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 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int] = 1_024)      ->					int:






							'''simple docstring'''





							while True:
														__UpperCamelCase			:     Any         =      random.randrange(2 ** (keysize - 1)		,						2 ** (keysize))
														if is_prime_low_num(_lowerCAmelCase):
																					return num


if __name__ == "__main__":
		lowercase						:      List[Any]									= generate_large_prime()
		print(('Prime number:', num))
		print(('is_prime_low_num:', is_prime_low_num(num)))

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Union[str, Any])      ->					List[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase			:     List[Any]         =      BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)

							# Save pytorch-model
							print(F'Save PyTorch model to {pytorch_dump_path}')
							torch.save(model.state_dict()		,						_lowerCamelCase)


if __name__ == "__main__":
		lowercase						:      Any									= argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase						:      Tuple									= parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)