

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







			lowerCamelCase__          ='mpnet'
			def __init__(     self							:	Optional[int] , a							:	Dict=3_0527 , a							:	Optional[int]=768 , a							:	List[str]=12 , a							:	List[str]=12 , a							:	str=3072 , a							:	Optional[int]="gelu" , a							:	Tuple=0.1 , a							:	Optional[int]=0.1 , a							:	Optional[Any]=512 , a							:	List[Any]=0.02 , a							:	str=1e-12 , a							:	Optional[Any]=32 , a							:	int=1 , a							:	Union[str, Any]=0 , a							:	List[str]=2 , **a							:	Optional[int] , )							->    Union[str, Any]:

				"""simple docstring"""

				super().__init__(pad_token_id=a , bos_token_id=a , eos_token_id=a , **a		)

				SCREAMING_SNAKE_CASE				: Tuple			       =    vocab_size
				SCREAMING_SNAKE_CASE				: Any			       =    hidden_size
				SCREAMING_SNAKE_CASE				: int			       =    num_hidden_layers
				SCREAMING_SNAKE_CASE				: Any			       =    num_attention_heads
				SCREAMING_SNAKE_CASE				: Tuple			       =    hidden_act
				SCREAMING_SNAKE_CASE				: Tuple			       =    intermediate_size
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    hidden_dropout_prob
				SCREAMING_SNAKE_CASE				: List[str]			       =    attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE				: Dict			       =    max_position_embeddings
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    initializer_range
				SCREAMING_SNAKE_CASE				: Any			       =    layer_norm_eps
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    relative_attention_num_buckets