# 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__							(   __snake_case		):



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls    ) ->    Optional[int]:
								return cls







			@slow
			@require_torch_gpu
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								__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(UpperCAmelCase      ,						env=self.get_env()    )

								__a							=   os.path.join(UpperCAmelCase      ,						'test_bleu.json'    )
								__a							=   load_json(UpperCAmelCase    )
								# print(metrics)
								self.assertGreaterEqual(metrics['bleu']      ,						2_5    )





"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


a							=logging.get_logger(__name__)

a							={
    'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json',
}






class    __UpperCAmelCase						(	__lowerCAmelCase	):
				A__      :   Optional[int]  							=       '''mpnet'''

				def __init__(      self	, _lowerCamelCase=30527	, _lowerCamelCase=768	, _lowerCamelCase=12	, _lowerCamelCase=12	, _lowerCamelCase=3072	, _lowerCamelCase="gelu"	, _lowerCamelCase=0.1	, _lowerCamelCase=0.1	, _lowerCamelCase=512	, _lowerCamelCase=0.0_2	, _lowerCamelCase=1E-12	, _lowerCamelCase=32	, _lowerCamelCase=1	, _lowerCamelCase=0	, _lowerCamelCase=2	, **_lowerCamelCase	, ):
										super().__init__(pad_token_id=_lowerCamelCase	, bos_token_id=_lowerCamelCase	, eos_token_id=_lowerCamelCase	, **_lowerCamelCase      )

										lowerCamelCase__					=vocab_size
										lowerCamelCase__					=hidden_size
										lowerCamelCase__					=num_hidden_layers
										lowerCamelCase__					=num_attention_heads
										lowerCamelCase__					=hidden_act
										lowerCamelCase__					=intermediate_size
										lowerCamelCase__					=hidden_dropout_prob
										lowerCamelCase__					=attention_probs_dropout_prob
										lowerCamelCase__					=max_position_embeddings
										lowerCamelCase__					=initializer_range
										lowerCamelCase__					=layer_norm_eps
										lowerCamelCase__					=relative_attention_num_buckets


