



'''simple docstring'''





from math import sqrt
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase			):



    """simple docstring"""





    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5							,    int(sqrt(UpperCamelCase			) + 1			)							,    6			):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True
def    _SCREAMING_SNAKE_CASE  (       UpperCamelCase = 10001			):



    """simple docstring"""





    lowerCAmelCase__ :  Dict							      =					0
    lowerCAmelCase__ :  Optional[int]							      =					1
    while count != nth and number < 3:
        number += 1
        if is_prime(UpperCamelCase			):
            count += 1
    while count != nth:
        number += 2
        if is_prime(UpperCamelCase			):
            count += 1
    return number


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



import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def  snake_case  (       snake_case__			:int   ,	snake_case__			:List[str]   ,	snake_case__			:Union[str, Any])						->   str:
       # Initialise PyTorch model
       _A        =  AlbertConfig.from_json_file(snake_case__)
       print(F'''Building PyTorch model from configuration: {config}''')
       _A        =  AlbertForPreTraining(snake_case__)

       # Load weights from tf checkpoint
       load_tf_weights_in_albert(snake_case__   ,	snake_case__   ,	snake_case__)

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


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




