






def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int					,  snake_case_ :							int       ):
	return abs(snake_case_       ) if a == 0 else greatest_common_divisor(b % a					,  snake_case_       )







def 					SCREAMING_SNAKE_CASE    (			snake_case_ :							int					,  snake_case_ :							int       ):
	while y:  # --> when y=0 then loop will terminate and return x as final GCD.
		snake_case__,						snake_case__			:				List[str]       =  y, x % y
	return abs(snake_case_       )







def 					SCREAMING_SNAKE_CASE    (			):
	try:
		snake_case__			:				str       =  input("Enter two integers separated by comma (,): "       ).split(","       )
		snake_case__			:				Optional[int]       =  int(nums[0]       )
		snake_case__			:				Optional[Any]       =  int(nums[1]       )
		print(
		    F'''greatest_common_divisor({num_a}, {num_a}) = '''
		    F'''{greatest_common_divisor(snake_case_					,  snake_case_       )}'''       )
		print(F'''By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(snake_case_					,  snake_case_       )}'''       )
	except (IndexError, UnboundLocalError, ValueError):
		print("Wrong input"       )


if __name__ == "__main__":
	main()











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__lowerCamelCase :      Union[str, Any]										=				{
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase :      Tuple										=				[
	    """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimeSeriesTransformerForPrediction""",
	    """TimeSeriesTransformerModel""",
	    """TimeSeriesTransformerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase :      Tuple										=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



