






import random

from .binary_exp_mod import bin_exp_mod






def 							A     (       a_							,a_=1_000					)     ->						Optional[Any]:
					if n < 2:
										return False

					if n % 2 == 0:
										return n == 2

					# this means n is odd
					__UpperCamelCase      :					List[Any]       		=n - 1
					__UpperCamelCase      :					Dict       		=0
					while d % 2 == 0:
										d /= 2
										exp += 1

					# n - 1=d*(2**exp)
					__UpperCamelCase      :					Optional[Any]       		=0
					while count < prec:
										__UpperCamelCase      :					Dict       		=random.randint(2							,n - 1					)
										__UpperCamelCase      :					Optional[Any]       		=bin_exp_mod(a_							,a_							,a_					)
										if b != 1:
															__UpperCamelCase      :					List[str]       		=True
															for _ in range(a_					):
																				if b == n - 1:
																									__UpperCamelCase      :					Tuple       		=False
																									break
																				__UpperCamelCase      :					Dict       		=b * b
																				b %= n
															if flag:
																				return False
															count += 1
					return True


if __name__ == "__main__":
							A_       :str       =						abs(int(input('''Enter bound : ''').strip()))
							print('''Here\'s the list of primes:''')
							print(''', '''.join(str(i) for i in range(n + 1) if is_prime_big(i)))






'''simple docstring'''






import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Union[str, Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = RemBertConfig.from_json_file(lowerCAmelCase_		)
       print("""Building PyTorch model from configuration: {}""".format(str(lowerCAmelCase_		)		)		)
       _UpperCAmelCase  :						Any			     = RemBertModel(lowerCAmelCase_		)

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)

       # Save pytorch-model
       print("""Save PyTorch model to {}""".format(lowerCAmelCase_		)		)
       torch.save(model.state_dict()       ,   lowerCAmelCase_		)


if __name__ == "__main__":
   A_							: Optional[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(
       """--rembert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained RemBERT 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."""
   )
   A_							: Any						 =	parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)



