



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__							=logging.getLogger()

lowerCAmelCase__							=logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)

class        UpperCAmelCase_  (       __UpperCamelCase	):

						'''simple docstring'''




						def 	_A     (   self				,							_A		):







													'''simple docstring'''

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






						def 	_A     (   self				,							_A				,							_A = "pytorch"		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.get_auto_remove_tmp_dir()
													__SCREAMING_SNAKE_CASE									= os.path.join(_A				,							'output'		)
													__SCREAMING_SNAKE_CASE									= os.path.join(_A				,							'data'		)
													self._create_dummy_data(data_dir=_A		)

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

													__SCREAMING_SNAKE_CASE									= [sys.executable, str(Path(finetune_rag.__file__		).resolve()		)] + testargs
													execute_subprocess_async(_A				,							env=self.get_env()		)

													__SCREAMING_SNAKE_CASE									= os.path.join(_A				,							'metrics.json'		)
													with open(_A		) as f:
																				__SCREAMING_SNAKE_CASE									= json.load(_A		)
													return result






						@require_torch_gpu
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self._run_finetune(gpus=1		)
													self.assertGreaterEqual(result['test'][0]['test_avg_em']				,							0.2		)






						@require_torch_multi_gpu
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self._run_finetune(gpus=2		)
													self.assertGreaterEqual(result['test'][0]['test_avg_em']				,							0.2		)






						@require_torch_gpu
						@require_ray
						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 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 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self._run_finetune(gpus=1				,							distributed_retriever='ray'		)
													self.assertGreaterEqual(result['test'][0]['test_avg_em']				,							0.2		)











lowerCAmelCase__		:					Optional[int]							=9.80_665






def      __lowercase       (			a__							,       a__							,       a__ = g       )    ->			float:

							if fluid_density <= 0:
														raise ValueError('Impossible fluid density'       )
							if volume < 0:
														raise ValueError('Impossible Object volume'       )
							if gravity <= 0:
														raise ValueError('Impossible Gravity'       )

							return fluid_density * gravity * volume


if __name__ == "__main__":
			import doctest

			# run doctest
			doctest.testmod()






