



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


if __name__ == "__main__":
							print(f'{decimal_to_fraction(2) = }')
							print(f'{decimal_to_fraction(89.0) = }')
							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,
)


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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase							:       str					 =	[
							    """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

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



