




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)
__SCREAMING_SNAKE_CASE 						=logging.getLogger()

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






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				def     _A							(							self:   Dict		,					_lowerCamelCase:   Any		):
											os.makedirs(_lowerCamelCase		,					exist_ok=_lowerCamelCase		)
											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(_lowerCamelCase		,					f"{split}.{field}"		)		,					'''w'''		) as f:
																																f.write(_lowerCamelCase		)


				def     _A							(							self:   int		,					_lowerCamelCase:   int		,					_lowerCamelCase:   str = "pytorch"		):
											SCREAMING_SNAKE_CASE_   	=    self.get_auto_remove_tmp_dir()
											SCREAMING_SNAKE_CASE_   	=    os.path.join(_lowerCamelCase		,					'''output'''		)
											SCREAMING_SNAKE_CASE_   	=    os.path.join(_lowerCamelCase		,					'''data'''		)
											self._create_dummy_data(data_dir=_lowerCamelCase		)

											SCREAMING_SNAKE_CASE_   	=    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'''		)

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

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


				@require_torch_gpu
				def     _A							(							self:   str		):
											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:   int		):
											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:   Union[str, Any]		):
											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:   Optional[Any]		):
											SCREAMING_SNAKE_CASE_   	=    self._run_finetune(gpus=1		,					distributed_retriever='''ray'''		)
											self.assertGreaterEqual(result['''test'''][0]['''test_avg_em''']		,					0.2		)








def 			a   (_lowerCAmelCase     ):
							if number > 0:
														raise ValueError('''input must be a negative integer'''     )
							SCREAMING_SNAKE_CASE_   	=    len(bin(_lowerCAmelCase     )[3:]     )
							SCREAMING_SNAKE_CASE_   	=    bin(abs(_lowerCAmelCase     ) - (1 << binary_number_length)     )[3:]
							SCREAMING_SNAKE_CASE_   	=    (
							    (
							        '''1'''
							        + '''0''' * (binary_number_length - len(_lowerCAmelCase     ))
							        + twos_complement_number
							    )
							    if number < 0
							    else '''0'''
							)
							return "0b" + twos_complement_number


if __name__ == "__main__":
			import doctest

			doctest.testmod()


