


from __future__ import annotations






def 		a__       (							A__							):
			for r in range(1,	A__							):
						SCREAMING_SNAKE_CASE_						:			Optional[int]       						= []
						for x in range(n - 1							):
									SCREAMING_SNAKE_CASE_						:			Tuple       						= pow(A__,	A__,	A__							)
									if val in li:
												break
									li.append(A__							)
						else:
									return r
			return None


if __name__ == "__main__":
				lowerCAmelCase__       :	Optional[Any]     				=int(input('Enter a prime number q: '))
				lowerCAmelCase__       :	int     				=find_primitive(q)
				if a is None:
								print(F"""Cannot find the primitive for the value: {a!r}""")
				else:
								lowerCAmelCase__       :	Union[str, Any]     				=int(input('Enter private key of A: '))
								lowerCAmelCase__       :	str     				=pow(a, a_private, q)
								lowerCAmelCase__       :	str     				=int(input('Enter private key of B: '))
								lowerCAmelCase__       :	Union[str, Any]     				=pow(a, b_private, q)

								lowerCAmelCase__       :	str     				=pow(b_public, a_private, q)
								lowerCAmelCase__       :	Any     				=pow(a_public, b_private, q)

								print('The key value generated by A is: ', a_secret)
								print('The key value generated by B is: ', b_secret)









# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							@classmethod
							def  UpperCamelCase__			(	cls    ):





										"""simple docstring"""




										return cls






							@slow
							@require_torch_gpu
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= self.get_auto_remove_tmp_dir()
										SCREAMING_SNAKE_CASE_						:			Tuple       						= F'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

										# we want this test to run even if there is only one GPU, but if there are more we use them all
										SCREAMING_SNAKE_CASE_						:			Dict       						= get_gpu_count()
										SCREAMING_SNAKE_CASE_						:			int       						= F'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= [sys.executable] + distributed_args + args
										execute_subprocess_async(lowerCAmelCase__       ,		env=self.get_env()    )

										SCREAMING_SNAKE_CASE_						:			List[str]       						= os.path.join(lowerCAmelCase__       ,		'test_bleu.json'    )
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= load_json(lowerCAmelCase__    )
										# print(metrics)
										self.assertGreaterEqual(metrics['bleu']       ,		2_5    )





