






"""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        lowerCAmelCase__       (  __magic_name__					):
   @classmethod
   def 	__a				(						cls	:     List[Any]				):


        '''simple docstring'''
        return cls




   @slow
   @require_torch_gpu
   def 	__a				(						self	:     Any				):


        '''simple docstring'''
        UpperCAmelCase__		:  Dict				=				self.get_auto_remove_tmp_dir()
        UpperCAmelCase__		:  List[str]				=				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
        UpperCAmelCase__		:  Union[str, Any]				=				get_gpu_count()
        UpperCAmelCase__		:  Any				=				f'\n            -m torch.distributed.launch\n            --nproc_per_node={n_gpu}\n            {self.test_file_dir}/run_distributed_eval.py\n        '.split()
        UpperCAmelCase__		:  Union[str, Any]				=				[sys.executable] + distributed_args + args
        execute_subprocess_async(snake_case__				,       env=self.get_env()				)

        UpperCAmelCase__		:  List[str]				=				os.path.join(snake_case__				,       "test_bleu.json"				)
        UpperCAmelCase__		:  int				=				load_json(snake_case__				)
        # print(metrics)
        self.assertGreaterEqual(metrics["bleu"]				,       2_5				)







from .chunk_utils import chunk_layer
from .data_transforms import make_atomaa_masks
from .feats import atomaa_to_atomaa, frames_and_literature_positions_to_atomaa_pos, torsion_angles_to_frames
from .loss import compute_predicted_aligned_error, compute_tm
from .protein import Protein as OFProtein
from .protein import to_pdb
from .rigid_utils import Rigid, Rotation
from .tensor_utils import dict_multimap, flatten_final_dims, permute_final_dims





