

import math
from decimal import Decimal, getcontext

def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int = 4_000_000    ):



     '''simple docstring'''





     try:
          lowercase__					:     int   = int(SCREAMING_SNAKE_CASE_    )
     except (TypeError, ValueError):
          raise TypeError('Parameter n must be int or castable to int.'    )
     if n <= 0:
          raise ValueError('Parameter n must be greater than or equal to one.'    )
     lowercase__					:     Any   = 100
     lowercase__					:     List[str]   = (Decimal(5    ) ** Decimal(0.5    ) + 1) / Decimal(2    )

     lowercase__					:     Dict   = (math.floor(math.log(n * (phi + 2)			,  SCREAMING_SNAKE_CASE_    ) - 1    ) // 3) * 3 + 2
     lowercase__					:     Tuple   = Decimal(round(phi ** Decimal(index + 1    )    )    ) / (phi + 2)
     lowercase__					:     Optional[Any]   = num // 2
     return int(SCREAMING_SNAKE_CASE_    )


if __name__ == "__main__":
       print(F'''{solution() = }''')




# 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    SCREAMING_SNAKE_CASE__      (__snake_case  ):





    @classmethod
    def    snake_case_       (   cls):
         return cls





    @slow
    @require_torch_gpu
    def    snake_case_       (   self):
         lowercase__					:     List[Any]   = self.get_auto_remove_tmp_dir()
         lowercase__					:     List[str]   = 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
         lowercase__					:     int   = get_gpu_count()
         lowercase__					:     Tuple   = f"""
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        """.split()
         lowercase__					:     Optional[Any]   = [sys.executable] + distributed_args + args
         execute_subprocess_async(a		,     env=self.get_env())

         lowercase__					:     str   = os.path.join(a		,     'test_bleu.json')
         lowercase__					:     List[str]   = load_json(a)
         # print(metrics)
         self.assertGreaterEqual(metrics['bleu']		,     25)

