



class 			_lowercase							:





	"""simple docstring"""




	def __init__(self	, lowerCamelCase_	):
						"""simple docstring"""

						a        =     data
						a        =     42
						a        =     42






def 					a(   A :					Node )	-> None:
					"""simple docstring"""





					a        =     0
					a        =     x






def 					a(   A :					Node				,  A :					Node )	-> None:
					"""simple docstring"""





					a  ,       a        =     find_set(A ), find_set(A )
					if x == y:
										return

					elif x.rank > y.rank:
										a        =     x
					else:
										a        =     y
										if x.rank == y.rank:
															y.rank += 1






def 					a(   A :					Node )	-> Node:
					"""simple docstring"""





					if x != x.parent:
										a        =     find_set(x.parent )
					return x.parent






def 					a(   A :					Node )	-> set:
					"""simple docstring"""





					a        =     ({0, 1, 2}, {3, 4, 5})
					for s in sets:
										if node.data in s:
															return s
					a        =     f'''{node.data} is not in {sets}'''
					raise ValueError(A )






def 					a(   )	-> None:
					"""simple docstring"""





					a        =     [Node(A ) for i in range(6 )]
					for v in vertex:
										make_set(A )

					union_set(vertex[0]				,  vertex[1] )
					union_set(vertex[1]				,  vertex[2] )
					union_set(vertex[3]				,  vertex[4] )
					union_set(vertex[3]				,  vertex[5] )

					for nodea in vertex:
										for nodea in vertex:
															if find_python_set(A ).isdisjoint(find_python_set(A ) ):
																				assert find_set(A ) != find_set(A )
															else:
																				assert find_set(A ) == find_set(A )


if __name__ == "__main__":
				test_disjoint_set()





# 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 			_lowercase							(     lowerCAmelCase			):





	"""simple docstring"""




	@classmethod
	def   UpperCamelCase_ (cls	):
						"""simple docstring"""

						return cls




	@slow
	@require_torch_gpu
	def   UpperCamelCase_ (self	):
						"""simple docstring"""

						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(lowerCamelCase_	, env=self.get_env()	)

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