
# 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 (	__a	):




				@classmethod
				def 					__a						( cls					)    ->				List[Any]:
							return cls




				@slow
				@require_torch_gpu
				def 					__a						( self					)    ->				Optional[Any]:
							lowerCAmelCase_      =     self.get_auto_remove_tmp_dir()
							lowerCAmelCase_      =     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
							lowerCAmelCase_      =     get_gpu_count()
							lowerCAmelCase_      =     f"""
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        """.split()
							lowerCAmelCase_      =     [sys.executable] + distributed_args + args
							execute_subprocess_async(_UpperCamelCase    ,    env=self.get_env()					)

							lowerCAmelCase_      =     os.path.join(_UpperCamelCase    ,    "test_bleu.json"					)
							lowerCAmelCase_      =     load_json(_UpperCamelCase					)
							# print(metrics)
							self.assertGreaterEqual(metrics["bleu"]    ,    25					)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A       =					{"configuration_nat": ["NAT_PRETRAINED_CONFIG_ARCHIVE_MAP", "NatConfig"]}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A       =					[
			    "NAT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "NatForImageClassification",
			    "NatModel",
			    "NatPreTrainedModel",
			    "NatBackbone",
			]

if TYPE_CHECKING:
			from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_nat import (
						    NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    NatBackbone,
						    NatForImageClassification,
						    NatModel,
						    NatPreTrainedModel,
						)

else:
			import sys

			_A       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



