






from functools import lru_cache

@lru_cache
def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     Any:

							if not isinstance(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_							):
														raise TypeError('''k must be an integer.'''							)
							if k < 0:
														raise ValueError('''k integer must be greater or equal to zero.'''							)
							if k == 0:
														return 0
							elif k == 1:
														return 1
							else:
														return fibonacci(k - 1							) + fibonacci(k - 2							)

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__							)					->     List[Any]:
							lowerCAmelCase     		=		len(SCREAMING_SNAKE_CASE_							)
							# Find m such that F_m >= n where F_i is the i_th fibonacci number.
							lowerCAmelCase     		=		0
							while True:
														if fibonacci(SCREAMING_SNAKE_CASE_							) >= len_list:
																					lowerCAmelCase     		=		i
																					break
														i += 1
							lowerCAmelCase     		=		0
							while fibb_k > 0:
														lowerCAmelCase     		=		min(
														    offset + fibonacci(fibb_k - 1							)       ,  len_list - 1							)  # Prevent out of range
														lowerCAmelCase     		=		arr[index_k]
														if item_k_1 == val:
																					return index_k
														elif val < item_k_1:
																					fibb_k -= 1
														elif val > item_k_1:
																					offset += fibonacci(fibb_k - 1							)
																					fibb_k -= 2
							else:
														return -1


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    )



