





"""simple docstring"""




# 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  _a						(      _lowerCAmelCase				):






				@classmethod
				def 				snake_case	( cls		:    List[str]							)	->      Tuple:




										'''simple docstring'''



										return cls






				@slow
				@require_torch_gpu
				def 				snake_case	( self		:    Tuple							)	->      Tuple:




										'''simple docstring'''



										_UpperCamelCase			:					Optional[int]     =    self.get_auto_remove_tmp_dir()
										_UpperCamelCase			:					Tuple     =    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
										_UpperCamelCase			:					Tuple     =    get_gpu_count()
										_UpperCamelCase			:					Dict     =    f"""
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        """.split()
										_UpperCamelCase			:					Tuple     =    [sys.executable] + distributed_args + args
										execute_subprocess_async(lowerCAmelCase__, env=self.get_env()							)

										_UpperCamelCase			:					Union[str, Any]     =    os.path.join(lowerCAmelCase__, '''test_bleu.json'''							)
										_UpperCamelCase			:					Optional[Any]     =    load_json(lowerCAmelCase__							)
										# print(metrics)
										self.assertGreaterEqual(metrics['''bleu'''], 2_5							)










"""simple docstring"""




from functools import lru_cache





@lru_cache
def        a_				(							_lowercase   ):
						if not isinstance(_lowercase       ,						_lowercase   ):
												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        a_				(							_lowercase       ,						_lowercase   ):
						_UpperCamelCase			:					Any     =    len(_lowercase   )
						# Find m such that F_m >= n where F_i is the i_th fibonacci number.
						_UpperCamelCase			:					Optional[int]     =    0
						while True:
												if fibonacci(_lowercase   ) >= len_list:
																		_UpperCamelCase			:					List[Any]     =    i
																		break
												i += 1
						_UpperCamelCase			:					Tuple     =    0
						while fibb_k > 0:
												_UpperCamelCase			:					Union[str, Any]     =    min(
												    offset + fibonacci(fibb_k - 1   )       ,						len_list - 1   )  # Prevent out of range
												_UpperCamelCase			:					Any     =    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()



