






# 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 						UpperCAmelCase_  (		SCREAMING_SNAKE_CASE__    ):


						'''simple docstring'''




						@classmethod
						def 					_lowerCAmelCase				(		cls )       -> List[str]:
								return cls






						@slow
						@require_torch_gpu
						def 					_lowerCAmelCase				(		self )       -> Optional[Any]:
								snake_case_ :							Union[str, Any]         =				self.get_auto_remove_tmp_dir()
								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
								snake_case_ :							Dict         =				get_gpu_count()
								snake_case_ :							Tuple         =				f'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
								snake_case_ :							Any         =				[sys.executable] + distributed_args + args
								execute_subprocess_async(_SCREAMING_SNAKE_CASE						, env=self.get_env() )

								snake_case_ :							Optional[Any]         =				os.path.join(_SCREAMING_SNAKE_CASE						, "test_bleu.json" )
								snake_case_ :							int         =				load_json(_SCREAMING_SNAKE_CASE )
								# print(metrics)
								self.assertGreaterEqual(metrics["bleu"]						, 25 )








from __future__ import annotations

def    lowerCAmelCase__		(			_a				:	list[int]     ,					_a				:	int			):
		snake_case_       ,				snake_case_ :							Tuple         =				0, len(_a			) - 1
		while start_ind <= end_ind:
				if array[start_ind] == search_item:
						return start_ind
				elif array[end_ind] == search_item:
						return end_ind
				else:
						start_ind += 1
						end_ind -= 1
    # returns -1 if search_item is not found in array
		return -1


if __name__ == "__main__":
							print(double_linear_search(list(range(1_00)), 40))
