




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:     Optional[Any]									= logging.getLogger()

SCREAMING_SNAKE_CASE:     str									= logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



class    lowercase_		(_a ):



						def 	__a					(			self	:   Tuple					,							snake_case__	:   Tuple  ):

								"""simple docstring"""

								os.makedirs(_SCREAMING_SNAKE_CASE					,							exist_ok=_SCREAMING_SNAKE_CASE  )
								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(_SCREAMING_SNAKE_CASE					,							f'''{split}.{field}'''  )					,							'w'  ) as f:
														f.write(_SCREAMING_SNAKE_CASE  )



						def 	__a					(			self	:   Tuple					,							snake_case__	:   Union[str, Any]					,							snake_case__	:   Dict = "pytorch"  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.get_auto_remove_tmp_dir()
								SCREAMING_SNAKE_CASE_					      =							os.path.join(_SCREAMING_SNAKE_CASE					,							'output'  )
								SCREAMING_SNAKE_CASE_					      =							os.path.join(_SCREAMING_SNAKE_CASE					,							'data'  )
								self._create_dummy_data(data_dir=_SCREAMING_SNAKE_CASE  )

								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(_SCREAMING_SNAKE_CASE					,							env=self.get_env()  )

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



						@require_torch_gpu
						def 	__a					(			self	:   Dict  ):

								"""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	:   str  ):

								"""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	:   Union[str, Any]  ):

								"""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	:   Tuple  ):

								"""simple docstring"""

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





def lowerCamelCase__  (     ):

     '''simple docstring'''


     UpperCAmelCase_     :				Dict  =     0
     for i in range(1					,		1001 ):
          total += i**i
     return str(_lowercase )[-10:]


if __name__ == "__main__":
     print(solution())