



from __future__ import annotations






def 					a(   A :					int )	-> set[int]:
					"""simple docstring"""





					a        =     {1}
					a        =     [1]
					for _ in range(2				,  depth + 1 ):
										a        =     [*previous_coefficients, 0]
										a        =     [0, *previous_coefficients]
										a        =     []
										for x, y in zip(A				,  A ):
															coefficients.add(x + y )
															previous_coefficients.append(x + y )
					return coefficients






def 					a(   A :					set[int] )	-> set[int]:
					"""simple docstring"""





					a        =     set()
					for number in unique_coefficients:
										a        =     2
										a        =     number
										while divisor**2 <= copy_number:
															a        =     0
															while copy_number % divisor == 0:
																				copy_number //= divisor
																				multiplicity += 1
															if multiplicity >= 2:
																				non_squarefrees.add(A )
																				break
															divisor += 1

					return unique_coefficients.difference(A )






def 					a(   A :					int = 51 )	-> int:
					"""simple docstring"""





					a        =     get_pascal_triangle_unique_coefficients(A )
					a        =     get_squarefrees(A )
					return sum(A )


if __name__ == "__main__":
				print(F"""{solution() = }""")





# 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	)
