def A__	(      lowercase:  Optional[Any]       )					->		tuple[int, int]:
       try:
              A    :  int								=float(lowerCamelCase_       )
       except ValueError:
              raise ValueError('Please enter a valid number'       )
       A    :  Optional[int]								=decimal - int(lowerCamelCase_       )
       if fractional_part == 0:
              return int(lowerCamelCase_       ), 1
       else:
              A    :  Dict								=len(str(lowerCamelCase_       ).split('.'       )[1]       )
              A    :  Any								=int(decimal * (10**number_of_frac_digits)       )
              A    :  Tuple								=10**number_of_frac_digits
              A    :  Tuple								=denominator, numerator
              while True:
                     A    :  List[Any]								=dividend % divisor
                     if remainder == 0:
                            break
                     A    :  Optional[int]								=divisor, remainder
              A    :  Optional[Any]								=numerator / divisor, denominator / divisor
              return int(lowerCamelCase_       ), int(lowerCamelCase_       )


if __name__ == "__main__":
    print(f'''{decimal_to_fraction(2) = }''')
    print(f'''{decimal_to_fraction(8_9.0) = }''')
    print(f'''{decimal_to_fraction('67') = }''')
    print(f'''{decimal_to_fraction('45.0') = }''')
    print(f'''{decimal_to_fraction(1.5) = }''')
    print(f'''{decimal_to_fraction('6.25') = }''')
    print(f'''{decimal_to_fraction('78td') = }''')










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


SCREAMING_SNAKE_CASE			: str				   =					{
    "configuration_gpt_bigcode": ["GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTBigCodeConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE			: int				   =					[
     "GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPTBigCodeForSequenceClassification",
     "GPTBigCodeForTokenClassification",
     "GPTBigCodeForCausalLM",
     "GPTBigCodeModel",
     "GPTBigCodePreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_bigcode import (
      GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTBigCodeForCausalLM,
      GPTBigCodeForSequenceClassification,
      GPTBigCodeForTokenClassification,
      GPTBigCodeModel,
      GPTBigCodePreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE			: List[Any]				   =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


