





"""simple docstring"""




from __future__ import annotations

from fractions import Fraction





def 	a_    (							_lowercase	,						_lowercase       ):
						return (
						    num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
						)





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : Dict         =   []
						_UpperCamelCase      : str         =   11
						_UpperCamelCase      : List[Any]         =   int('''1''' + '''0''' * digit_len       )
						for num in range(_lowercase	,						_lowercase       ):
												while den <= 99:
																		if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
																								if is_digit_cancelling(_lowercase	,						_lowercase       ):
																														solutions.append(F"""{num}/{den}"""       )
																		den += 1
												num += 1
												_UpperCamelCase      : Optional[Any]         =   10
						return solutions





def 	a_    (							_lowercase = 2       ):
						_UpperCamelCase      : List[str]         =   1.0
						for fraction in fraction_list(_lowercase       ):
												_UpperCamelCase      : Optional[int]         =   Fraction(_lowercase       )
												result *= frac.denominator / frac.numerator
						return int(_lowercase       )


if __name__ == "__main__":
		print(solution())










"""simple docstring"""




import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						# Initialise PyTorch model
						_UpperCamelCase      : List[Any]         =   MobileBertConfig.from_json_file(_lowercase       )
						print(F"""Building PyTorch model from configuration: {config}"""       )
						_UpperCamelCase      : List[str]         =   MobileBertForPreTraining(_lowercase       )
						# Load weights from tf checkpoint
						_UpperCamelCase      : Union[str, Any]         =   load_tf_weights_in_mobilebert(_lowercase	,						_lowercase	,						_lowercase       )
						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}"""       )
						torch.save(model.state_dict()	,						_lowercase       )


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



