



"""simple docstring"""







def 				UpperCamelCase_    (    lowerCAmelCase__      :				Dict = 1    ,   lowerCAmelCase__      :				Any = 1000						)						->   Tuple:

  """simple docstring"""




  lowerCAmelCase_   :				Optional[int]								= 1
  lowerCAmelCase_   :				Union[str, Any]								= 0

  for divide_by_number in range(__lowerCAmelCase    ,   digit + 1						):
    lowerCAmelCase_   :				list[int]								= []
    lowerCAmelCase_   :				Dict								= numerator
    for _ in range(1    ,   digit + 1						):
      if now_divide in has_been_divided:
        if longest_list_length < len(__lowerCAmelCase						):
          lowerCAmelCase_   :				str								= len(__lowerCAmelCase						)
          lowerCAmelCase_   :				Tuple								= divide_by_number
      else:
        has_been_divided.append(__lowerCAmelCase						)
        lowerCAmelCase_   :				int								= now_divide * 10 % divide_by_number

  return the_digit


# Tests
if __name__ == "__main__":
     import doctest

     doctest.testmod()









'''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__					=   logging.getLogger()

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






class 		lowerCAmelCase__	(	UpperCAmelCase__		):




			def 							lowerCAmelCase__			(						self     :   List[str]		,     lowerCamelCase__     :   Optional[int]       )      ->Tuple:



					'''simple docstring'''







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




			def 							lowerCAmelCase__			(						self     :   int		,     lowerCamelCase__     :   int		,     lowerCamelCase__     :   str = "pytorch"       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase						:				Any	    =					self.get_auto_remove_tmp_dir()
					_UpperCAmelCase						:				int	    =					os.path.join(lowerCamelCase__		,     "output"       )
					_UpperCAmelCase						:				Tuple	    =					os.path.join(lowerCamelCase__		,     "data"       )
					self._create_dummy_data(data_dir=lowerCamelCase__       )

					_UpperCAmelCase						:				str	    =					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"       )

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

					_UpperCAmelCase						:				Optional[int]	    =					os.path.join(lowerCamelCase__		,     "metrics.json"       )
					with open(lowerCamelCase__       ) as f:
							_UpperCAmelCase						:				Dict	    =					json.load(lowerCamelCase__       )
					return result




			@require_torch_gpu
			def 							lowerCAmelCase__			(						self     :   Dict       )      ->Dict:



					'''simple docstring'''







					_UpperCAmelCase						:				Optional[Any]	    =					self._run_finetune(gpus=1       )
					self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,     0.2       )




			@require_torch_multi_gpu
			def 							lowerCAmelCase__			(						self     :   List[Any]       )      ->List[str]:



					'''simple docstring'''







					_UpperCAmelCase						:				List[str]	    =					self._run_finetune(gpus=2       )
					self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,     0.2       )




			@require_torch_gpu
			@require_ray
			def 							lowerCAmelCase__			(						self     :   int       )      ->str:



					'''simple docstring'''







					_UpperCAmelCase						:				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 							lowerCAmelCase__			(						self     :   int       )      ->Any:



					'''simple docstring'''







					_UpperCAmelCase						:				str	    =					self._run_finetune(gpus=1		,     distributed_retriever="ray"       )
					self.assertGreaterEqual(result["test"][0]["test_avg_em"]		,     0.2       )



