


def   lowerCamelCase(      a__):
				if not isinstance(a__		,a__):
								raise ValueError('''Input series is not valid, valid series - [2, 4, 8]''')
				if len(a__) == 0:
								raise ValueError('''Input list must be a non empty list''')
				if len(a__) == 1:
								return True
				try:
								_SCREAMING_SNAKE_CASE	     =series[1] / series[0]
								for index in range(len(a__) - 1):
												if series[index + 1] / series[index] != common_ratio:
																return False
				except ZeroDivisionError:
								return False
				return True







def   lowerCamelCase(      a__):
				if not isinstance(a__		,a__):
								raise ValueError('''Input series is not valid, valid series - [2, 4, 8]''')
				if len(a__) == 0:
								raise ValueError('''Input list must be a non empty list''')
				_SCREAMING_SNAKE_CASE	     =1
				for value in series:
								answer *= value
				return pow(a__		,1 / len(a__))


if __name__ == "__main__":
							import doctest

							doctest.testmod()
# 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 						a__							(   __snake_case		):



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls    ) ->    Optional[int]:
								return cls







			@slow
			@require_torch_gpu
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								__a							=   self.get_auto_remove_tmp_dir()
								__a							=   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
								__a							=   get_gpu_count()
								__a							=   f'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
								__a							=   [sys.executable] + distributed_args + args
								execute_subprocess_async(UpperCAmelCase      ,						env=self.get_env()    )

								__a							=   os.path.join(UpperCAmelCase      ,						'test_bleu.json'    )
								__a							=   load_json(UpperCAmelCase    )
								# print(metrics)
								self.assertGreaterEqual(metrics['bleu']      ,						2_5    )




