




def       _SCREAMING_SNAKE_CASE      (							):


      '''simple docstring'''



      lowerCamelCase_							=   0
      for i in range(1     ,							10_01					):
            total += i**i
      return str(lowercase					)[-10:]


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









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    :		int			   =       logging.getLogger()

lowerCamelCase    :		Tuple			   =       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 def     a__			(    self :  List[str]						,							A_ :  Optional[Any]  )       ->     int:

       """simple docstring"""






       os.makedirs(A_						,							exist_ok=A_  )
       lowerCamelCase_							=   {'source': 'What is love ?', 'target': 'life'}
       lowerCamelCase_							=   {'train': 12, 'val': 2, 'test': 2}
       for split in ["train", "test", "val"]:
             for field in ["source", "target"]:
                   lowerCamelCase_							=   '\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 :  Optional[Any]						,							A_ :  int						,							A_ :  str = "pytorch"  )       ->     Any:

       """simple docstring"""






       lowerCamelCase_							=   self.get_auto_remove_tmp_dir()
       lowerCamelCase_							=   os.path.join(A_						,							'output'  )
       lowerCamelCase_							=   os.path.join(A_						,							'data'  )
       self._create_dummy_data(data_dir=A_  )

       lowerCamelCase_							=   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'  )

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

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

 @require_torch_gpu
 def     a__			(    self :  List[Any]  )       ->     Any:

       """simple docstring"""






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

 @require_torch_multi_gpu
 def     a__			(    self :  List[Any]  )       ->     Union[str, Any]:

       """simple docstring"""






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

 @require_torch_gpu
 @require_ray
 def     a__			(    self :  Tuple  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   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 :  Dict  )       ->     Dict:

       """simple docstring"""






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



