"""simple docstring"""






def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				while b:
								UpperCAmelCase__      ,	UpperCAmelCase__             =							b, a % b
				return a



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				return a if b == 0 else euclidean_gcd_recursive(lowerCAmelCase_					,			a % b       )



def   a_						(					):
				print(f'''euclidean_gcd(3, 5) = {euclidean_gcd(3					,			5       )}'''       )
				print(f'''euclidean_gcd(5, 3) = {euclidean_gcd(5					,			3       )}'''       )
				print(f'''euclidean_gcd(1, 3) = {euclidean_gcd(1					,			3       )}'''       )
				print(f'''euclidean_gcd(3, 6) = {euclidean_gcd(3					,			6       )}'''       )
				print(f'''euclidean_gcd(6, 3) = {euclidean_gcd(6					,			3       )}'''       )

				print(f'''euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3					,			5       )}'''       )
				print(f'''euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5					,			3       )}'''       )
				print(f'''euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1					,			3       )}'''       )
				print(f'''euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3					,			6       )}'''       )
				print(f'''euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6					,			3       )}'''       )


if __name__ == "__main__":
						main()










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case  :    List[Any]						    =  {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Optional[int]						    =  [
         'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'BioGptForCausalLM',
         'BioGptForTokenClassification',
         'BioGptForSequenceClassification',
         'BioGptModel',
         'BioGptPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

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

