






def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     list:
							lowerCAmelCase     		=		len(snake_case__							)

							# Set the gap value to be decreased by a factor of 1.3
							# after each iteration
							lowerCAmelCase     		=		1.3

							# Continue sorting until the gap is 1
							while gap > 1:
														# Decrease the gap value
														lowerCAmelCase     		=		int(gap / shrink							)

														# Sort the elements using insertion sort
														for i in range(snake_case__       ,  len(snake_case__							)							):
																					lowerCAmelCase     		=		collection[i]
																					lowerCAmelCase     		=		i
																					while j >= gap and collection[j - gap] > temp:
																												lowerCAmelCase     		=		collection[j - gap]
																												j -= gap
																					lowerCAmelCase     		=		temp

							return collection


if __name__ == "__main__":
	import doctest

	doctest.testmod()




# 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_  (  UpperCamelCase_						):



	"""simple docstring"""





	@classmethod
	def       SCREAMING_SNAKE_CASE_    (							cls    )						->Union[str, Any]:
								return cls




	@slow
	@require_torch_gpu
	def       SCREAMING_SNAKE_CASE_    (							self    )						->int:
								lowerCAmelCase     		=		self.get_auto_remove_tmp_dir()
								lowerCAmelCase     		=		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
								lowerCAmelCase     		=		get_gpu_count()
								lowerCAmelCase     		=		F"\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        ".split()
								lowerCAmelCase     		=		[sys.executable] + distributed_args + args
								execute_subprocess_async(__SCREAMING_SNAKE_CASE		,      env=self.get_env()    )

								lowerCAmelCase     		=		os.path.join(__SCREAMING_SNAKE_CASE		,      '''test_bleu.json'''    )
								lowerCAmelCase     		=		load_json(__SCREAMING_SNAKE_CASE    )
								# print(metrics)
								self.assertGreaterEqual(metrics['''bleu''']		,      25    )



