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							(     lowerCAmelCase_							):






		"""simple docstring"""







		@classmethod
		def 				_a				(			cls    :				str   ):






					"""simple docstring"""
					return cls







		@slow
		@require_torch_gpu
		def 				_a				(			self    :				str   ):






					"""simple docstring"""
					_snake_case              =      self.get_auto_remove_tmp_dir()
					_snake_case              =      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
					_snake_case              =      get_gpu_count()
					_snake_case              =      F'\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        '.split()
					_snake_case              =      [sys.executable] + distributed_args + args
					execute_subprocess_async(lowerCamelCase_  ,					env=self.get_env()   )

					_snake_case              =      os.path.join(lowerCamelCase_  ,					"test_bleu.json"   )
					_snake_case              =      load_json(lowerCamelCase_   )
					# print(metrics)
					self.assertGreaterEqual(metrics["bleu"]  ,					25   )


def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      sorted(lowerCamelCase_							,	key=lambda lowerCamelCase_  : value[2]							,	reverse=lowerCamelCase_  )

			# Create a list of size equal to the maximum deadline
			# and initialize it with -1
			_snake_case              =      max(lowerCamelCase_							,	key=lambda lowerCamelCase_  : value[1]  )[1]
			_snake_case              =      [-1] * max_deadline

			# Finding the maximum profit and the count of jobs
			_snake_case              =      0
			_snake_case              =      0
			for job in jobs:
						# Find a free time slot for this job
						# (Note that we start from the last possible slot)
						for i in range(job[1] - 1							,	-1							,	-1  ):
									if time_slots[i] == -1:
												_snake_case              =      job[0]
												count += 1
												max_profit += job[2]
												break
			return [count, max_profit]


if __name__ == "__main__":
			import doctest

			doctest.testmod()

