

# 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 _UpperCamelCase							(							__A				):
			'''simple docstring'''







			@classmethod
			def 							__UpperCamelCase	(     cls							:	str		)							->    Any:

				"""simple docstring"""

				return cls






			@slow
			@require_torch_gpu
			def 							__UpperCamelCase	(     self							:	Optional[Any]		)							->    Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: int			       =    self.get_auto_remove_tmp_dir()
				SCREAMING_SNAKE_CASE				: List[Any]			       =    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
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    get_gpu_count()
				SCREAMING_SNAKE_CASE				: List[Any]			       =    F"\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        ".split()
				SCREAMING_SNAKE_CASE				: Tuple			       =    [sys.executable] + distributed_args + args
				execute_subprocess_async(a , env=self.get_env()		)

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


from __future__ import annotations

def 	lowerCamelCase__	(   _a							,     _a							,     _a):
	if len(_a) == 0:
		raise ValueError("find_min() arg is an empty sequence")
	if (
	    left >= len(_a)
	    or left < -len(_a)
	    or right >= len(_a)
	    or right < -len(_a)
	):
		raise IndexError("list index out of range")
	if left == right:
		return nums[left]
	SCREAMING_SNAKE_CASE				: List[str]			       =    (left + right) >> 1  # the middle
	SCREAMING_SNAKE_CASE				: Tuple			       =    find_min(_a							,     _a							,     _a)  # find min in range[left, mid]
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    find_min(_a							,     mid + 1							,     _a)  # find min in range[mid + 1, right]

	return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
			import doctest

			doctest.testmod(verbose=True)