






"""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)
_SCREAMING_SNAKE_CASE					:				int					    =						logging.getLogger()

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






class      a						(					__snake_case		):






    def    UpperCamelCase       (       self     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]					)     ->		Dict:
      os.makedirs(__SCREAMING_SNAKE_CASE       ,						exist_ok=__SCREAMING_SNAKE_CASE					)
      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(__SCREAMING_SNAKE_CASE       ,						F'''{split}.{field}'''					)       ,						'w'					) as f:
            f.write(__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]       ,						__SCREAMING_SNAKE_CASE     :							int = "pytorch"					)     ->		int:
      lowerCamelCase_         =						self.get_auto_remove_tmp_dir()
      lowerCamelCase_         =						os.path.join(__SCREAMING_SNAKE_CASE       ,						'output'					)
      lowerCamelCase_         =						os.path.join(__SCREAMING_SNAKE_CASE       ,						'data'					)
      self._create_dummy_data(data_dir=__SCREAMING_SNAKE_CASE					)

      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(__SCREAMING_SNAKE_CASE       ,						env=self.get_env()					)

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






    @require_torch_gpu
    def    UpperCamelCase       (       self     :							str					)     ->		List[str]:
      lowerCamelCase_         =						self._run_finetune(gpus=1					)
      self.assertGreaterEqual(result['test'][0]['test_avg_em']       ,						0.2					)






    @require_torch_multi_gpu
    def    UpperCamelCase       (       self     :							List[str]					)     ->		Tuple:
      lowerCamelCase_         =						self._run_finetune(gpus=2					)
      self.assertGreaterEqual(result['test'][0]['test_avg_em']       ,						0.2					)






    @require_torch_gpu
    @require_ray
    def    UpperCamelCase       (       self     :							str					)     ->		Tuple:
      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    UpperCamelCase       (       self     :							Optional[int]					)     ->		List[str]:
      lowerCamelCase_         =						self._run_finetune(gpus=1       ,						distributed_retriever='ray'					)
      self.assertGreaterEqual(result['test'][0]['test_avg_em']       ,						0.2					)









'''simple docstring'''





def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise ValueError('''multiplicative_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''multiplicative_persistence() does not accept negative values''')

     _a      =  0
     _a      =  str(__A)

     while len(__A) != 1:
          _a      =  [int(__A) for i in num_string]

          _a      =  1
          for i in range(0  ,	len(__A)):
               total *= numbers[i]

          _a      =  str(__A)

          steps += 1
     return steps




def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise ValueError('''additive_persistence() only accepts integral values''')
     if num < 0:
          raise ValueError('''additive_persistence() does not accept negative values''')

     _a      =  0
     _a      =  str(__A)

     while len(__A) != 1:
          _a      =  [int(__A) for i in num_string]

          _a      =  0
          for i in range(0  ,	len(__A)):
               total += numbers[i]

          _a      =  str(__A)

          steps += 1
     return steps


if __name__ == "__main__":
  import doctest

  doctest.testmod()



