





'''simple docstring'''







import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	Tuple       ,  lowerCAmelCase__     :	Optional[Any]       ,  lowerCAmelCase__     :	Optional[Any])			->						Dict:

     '''simple docstring'''




     _lowercase							:      Optional[Any]  						=							ConvBertConfig.from_json_file(lowerCAmelCase__)
     _lowercase							:      str  						=							ConvBertModel(lowerCAmelCase__)

     _lowercase							:      List[Any]  						=							load_tf_weights_in_convbert(lowerCAmelCase__       ,  lowerCAmelCase__       ,  lowerCAmelCase__)
     model.save_pretrained(lowerCAmelCase__)

     _lowercase							:      Optional[int]  						=							TFConvBertModel.from_pretrained(lowerCAmelCase__       ,  from_pt=lowerCAmelCase__)
     tf_model.save_pretrained(lowerCAmelCase__)


if __name__ == "__main__":
    A						    =							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(
        '''--convbert_config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained ConvBERT 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						    =							parser.parse_args()
    convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)






'''simple docstring'''







from __future__ import annotations

import math
import random

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	int       ,  lowerCAmelCase__     :	int       ,  lowerCAmelCase__     :	int)			->						list[int]:

     '''simple docstring'''




     _lowercase							:      Optional[Any]  						=							d * e - 1
     _lowercase							:      Optional[Any]  						=							0
     _lowercase							:      Dict  						=							0
     while p == 0:
          _lowercase							:      Optional[Any]  						=							random.randint(2       ,  n - 1)
          _lowercase							:      int  						=							k
          while True:
               if t % 2 == 0:
                    _lowercase							:      int  						=							t // 2
                    _lowercase							:      Any  						=							(g**t) % n
                    _lowercase							:      Tuple  						=							math.gcd(x - 1       ,  lowerCAmelCase__)
                    if x > 1 and y > 1:
                         _lowercase							:      Union[str, Any]  						=							y
                         _lowercase							:      Dict  						=							n // y
                         break  # find the correct factors
               else:
                    break  # t is not divisible by 2, break and choose another g
     return sorted([p, q])


if __name__ == "__main__":
    import doctest

    doctest.testmod()