
import argparse

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


logging.set_verbosity_info()

def a__   (	A_,					A_,					A_ ):






  '''simple docstring'''





  __magic_name__    					=	ConvBertConfig.from_json_file(A_ )
  __magic_name__    					=	ConvBertModel(A_ )

  __magic_name__    					=	load_tf_weights_in_convbert(A_,					A_,					A_ )
  model.save_pretrained(A_ )

  __magic_name__    					=	TFConvBertModel.from_pretrained(A_,					from_pt=A_ )
  tf_model.save_pretrained(A_ )


if __name__ == "__main__":
    __lowerCAmelCase      :					List[str]   	=       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.'
    )
    __lowerCAmelCase      :					str   	=       parser.parse_args()
    convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)



import sys

sys.set_int_max_str_digits(0)  # type: ignore

def a__   (	A_ ):






  '''simple docstring'''





  __magic_name__    					=	[0] * 11
  __magic_name__    					=	[0] * 11

  # mark last 9 numbers
  for _ in range(9 ):
    __magic_name__    					=	1
    __magic_name__    					=	number // 10
  # flag
  __magic_name__    					=	True

  # check last 9 numbers for pandigitality

  for x in range(9 ):
    if not check_last[x + 1]:
      __magic_name__    					=	False
  if not f:
    return f

  # mark first 9 numbers
  __magic_name__    					=	int(str(A_ )[:9] )

  for _ in range(9 ):
    __magic_name__    					=	1
    __magic_name__    					=	number // 10

  # check first 9 numbers for pandigitality

  for x in range(9 ):
    if not check_front[x + 1]:
      __magic_name__    					=	False
  return f

def a__   (	A_ ):






  '''simple docstring'''





  __magic_name__    					=	[0] * 11

  # mark last 9 numbers
  for _ in range(9 ):
    __magic_name__    					=	1
    __magic_name__    					=	number // 10
  # flag
  __magic_name__    					=	True

  # check last 9 numbers for pandigitality

  for x in range(9 ):
    if not check_last[x + 1]:
      __magic_name__    					=	False
  return f

def a__   (	):






  '''simple docstring'''





  __magic_name__    					=	1
  __magic_name__    					=	1
  __magic_name__    					=	2
  # temporary Fibonacci numbers

  __magic_name__    					=	1
  __magic_name__    					=	1
  __magic_name__    					=	2
  # temporary Fibonacci numbers mod 1e9

  # mod m=1e9, done for fast optimisation
  __magic_name__    					=	[0] * 1000000
  __magic_name__    					=	1000000000

  for x in range(1000000 ):
    __magic_name__    					=	(aa + ba) % m
    __magic_name__    					=	ba % m
    __magic_name__    					=	ca % m
    if checka(A_ ):
      __magic_name__    					=	1

  for x in range(1000000 ):
    __magic_name__    					=	a + b
    __magic_name__    					=	b
    __magic_name__    					=	c
    # perform check only if in tocheck
    if tocheck[x + 3] and check(A_ ):
      return x + 3  # first 2 already done
  return -1


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

