

# 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__					(		A__   ):







						"""simple docstring"""




						@classmethod
						def __lowerCamelCase					(				cls    )							->	Tuple:




									'''simple docstring'''


									return cls




						@slow
						@require_torch_gpu
						def __lowerCamelCase					(				self    )							->	int:




									'''simple docstring'''


									__UpperCamelCase    :    Optional[Any]								=      self.get_auto_remove_tmp_dir()
									__UpperCamelCase    :    Tuple								=      f'''\n            --model_name Helsinki-NLP/opus-mt-en-ro\n            --save_dir {output_dir}\n            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro\n            --num_beams 2\n            --task translation\n        '''.split()

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

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


from __future__ import annotations







def  UpperCAmelCase_   (_lowerCAmelCase :		int     ,			_lowerCAmelCase :		list[int]     ,			_lowerCAmelCase :		list[int]     ,			_lowerCAmelCase :		int		):

			# Base Case
			if counter == 0 or capacity == 0:
						return 0

			# If weight of the nth item is more than Knapsack of capacity,
			#   then this item cannot be included in the optimal solution,
			# else return the maximum of two cases:
			#   (1) nth item included
			#   (2) not included
			if weights[counter - 1] > capacity:
						return knapsack(_lowerCAmelCase     ,			_lowerCAmelCase     ,			_lowerCAmelCase     ,			counter - 1		)
			else:
						__UpperCamelCase    :    Tuple								=      capacity - weights[counter - 1]
						__UpperCamelCase    :    List[str]								=      values[counter - 1] + knapsack(
						    _lowerCAmelCase     ,			_lowerCAmelCase     ,			_lowerCAmelCase     ,			counter - 1		)
						__UpperCamelCase    :    Dict								=      knapsack(_lowerCAmelCase     ,			_lowerCAmelCase     ,			_lowerCAmelCase     ,			counter - 1		)
						return max(_lowerCAmelCase     ,			_lowerCAmelCase		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()