






"""simple docstring"""



def 	SCREAMING_SNAKE_CASE__       (  snake_case       : int			,					snake_case       : int				)->		int:






     '''simple docstring'''



     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				)->		int:






     '''simple docstring'''



     while y:  # --> when y=0 then loop will terminate and return x as final GCD.
          UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[int]							=							y, x % y
     return abs(snake_case				)


def 	SCREAMING_SNAKE_CASE__       (  )->		Tuple:






     '''simple docstring'''



     try:
          UpperCAmelCase__						:    Optional[int]							=							input("Enter two integers separated by comma (,): "				).split(","				)
          UpperCAmelCase__						:    int							=							int(nums[0]				)
          UpperCAmelCase__						:    Optional[int]							=							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()










"""simple docstring"""



import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
 import optax
 from flax.training.common_utils import onehot

 from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
 from transformers.models.ta.modeling_flax_ta import shift_tokens_right




@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   @slow
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"							)
        UpperCAmelCase__						:    str							=							AutoTokenizer.from_pretrained("google/mt5-small"							)

        UpperCAmelCase__						:    str							=							tokenizer("Hello there"							,			return_tensors="np"							).input_ids
        UpperCAmelCase__						:    Optional[Any]							=							tokenizer("Hi I am"							,			return_tensors="np"							).input_ids

        UpperCAmelCase__						:    Any							=							shift_tokens_right(snake_case__							,			model.config.pad_token_id							,			model.config.decoder_start_token_id							)

        UpperCAmelCase__						:    Any							=							model(snake_case__							,			decoder_input_ids=snake_case__							).logits
        UpperCAmelCase__						:    Any							=							optax.softmax_cross_entropy(snake_case__							,			onehot(snake_case__							,			logits.shape[-1]							)							).mean()

        UpperCAmelCase__						:    List[str]							=							-(labels.shape[-1] * loss.item())

        UpperCAmelCase__						:    Dict							=							-84.9127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1e-4							)


