


'''simple docstring'''




import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
_lowerCAmelCase  :		Dict		     =	logging.getLogger()

_lowerCAmelCase  :		Union[str, Any]		     =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



class  lowerCAmelCase				(				a							):



	def 				lowercase     (					self ,					snake_case__    ):
		os.makedirs(snake_case__ ,					exist_ok=snake_case__    )
		lowerCAmelCase					:    Optional[Any]      							= {'source': 'What is love ?', 'target': 'life'}
		lowerCAmelCase					:    str      							= {'train': 12, 'val': 2, 'test': 2}
		for split in ["train", "test", "val"]:
			for field in ["source", "target"]:
				lowerCAmelCase					:    int      							= '\n'.join([contents[field]] * n_lines[split]    )
				with open(os.path.join(snake_case__ ,					f"{split}.{field}"    ) ,					'w'    ) as f:
					f.write(snake_case__    )



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ = "pytorch"    ):
		lowerCAmelCase					:    List[str]      							= self.get_auto_remove_tmp_dir()
		lowerCAmelCase					:    Any      							= os.path.join(snake_case__ ,					'output'    )
		lowerCAmelCase					:    Optional[Any]      							= os.path.join(snake_case__ ,					'data'    )
		self._create_dummy_data(data_dir=snake_case__    )

		lowerCAmelCase					:    List[str]      							= f"\n                --data_dir {data_dir} \\n                --output_dir {output_dir} \\n                --model_name_or_path facebook/rag-sequence-base \\n                --model_type rag_sequence \\n                --do_train \\n                --do_predict \\n                --n_val -1 \\n                --val_check_interval 1.0 \\n                --train_batch_size 2 \\n                --eval_batch_size 1 \\n                --max_source_length 25 \\n                --max_target_length 25 \\n                --val_max_target_length 25 \\n                --test_max_target_length 25 \\n                --label_smoothing 0.1 \\n                --dropout 0.1 \\n                --attention_dropout 0.1 \\n                --weight_decay 0.001 \\n                --adam_epsilon 1e-08 \\n                --max_grad_norm 0.1 \\n                --lr_scheduler polynomial \\n                --learning_rate 3e-04 \\n                --num_train_epochs 1 \\n                --warmup_steps 4 \\n                --gradient_accumulation_steps 1 \\n                --distributed-port 8787 \\n                --use_dummy_dataset 1 \\n                --distributed_retriever {distributed_retriever} \\n            ".split()

		if gpus > 0:
			testargs.append(f"--gpus={gpus}"    )
			if is_apex_available():
				testargs.append('--fp16'    )
		else:
			testargs.append('--gpus=0'    )
			testargs.append('--distributed_backend=ddp_cpu'    )
			testargs.append('--num_processes=2'    )

		lowerCAmelCase					:    Union[str, Any]      							= [sys.executable, str(Path(finetune_rag.__file__    ).resolve()    )] + testargs
		execute_subprocess_async(snake_case__ ,					env=self.get_env()    )

		lowerCAmelCase					:    Tuple      							= os.path.join(snake_case__ ,					'metrics.json'    )
		with open(snake_case__    ) as f:
			lowerCAmelCase					:    List[str]      							= json.load(snake_case__    )
		return result



	@require_torch_gpu
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= self._run_finetune(gpus=1    )
		self.assertGreaterEqual(result['test'][0]['test_avg_em'] ,					0.2    )



	@require_torch_multi_gpu
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= self._run_finetune(gpus=2    )
		self.assertGreaterEqual(result['test'][0]['test_avg_em'] ,					0.2    )



	@require_torch_gpu
	@require_ray
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= self._run_finetune(gpus=1 ,					distributed_retriever='ray'    )
		self.assertGreaterEqual(result['test'][0]['test_avg_em'] ,					0.2    )



	@require_torch_multi_gpu
	@require_ray
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= self._run_finetune(gpus=1 ,					distributed_retriever='ray'    )
		self.assertGreaterEqual(result['test'][0]['test_avg_em'] ,					0.2    )




'''simple docstring'''




def 			__UpperCamelCase    (    _A    :				int				)    ->      bool:




	"""simple docstring"""





	return number & 1 == 0


if __name__ == "__main__":
				import doctest

				doctest.testmod()
