import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict



@dataclass
class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  ComputeEnvironment.AMAZON_SAGEMAKER
     A_										=  True
     A_										=  'ml.p3.2xlarge'
     A_										=  'accelerate_sagemaker_execution_role'
     A_										=  'hf-sm'
     A_										=  'us-east-1'
     A_										=  1
     A_										=  'accelerate-sagemaker-1'
     A_										=  '1.6'
     A_										=  '4.4'
     A_										=  'train.py'
     A_										=  [
         '--model_name_or_path',
         'bert',
         '--do_train',
         'False',
         '--epochs',
         '3',
         '--learning_rate',
         '5e-5',
         '--max_steps',
         '50.5',
     ]
     A_										=  [
         '--model_name_or_path',
         'bert',
         '--do_train',
         '--do_test',
         'False',
         '--do_predict',
         '--epochs',
         '3',
         '--learning_rate',
         '5e-5',
         '--max_steps',
         '50.5',
     ]





class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           # If no defaults are changed, `to_kwargs` returns an empty dict.
           _lowerCamelCase			    =				_convert_nargs_to_dict(MockLaunchConfig.success_training_script_args						)
           assert isinstance(converted_args['''model_name_or_path''']				,				A_						)
           assert isinstance(converted_args['''do_train''']				,				A_						)
           assert isinstance(converted_args['''epochs''']				,				A_						)
           assert isinstance(converted_args['''learning_rate''']				,				A_						)
           assert isinstance(converted_args['''max_steps''']				,				A_						)

           with pytest.raises(A_						):
                 _convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args						)
import argparse
import json

from tqdm import tqdm
def       __magic_name__(  )						->    List[str]:



      '''simple docstring'''

      _lowerCamelCase			    =				argparse.ArgumentParser()

      # Required parameters
      parser.add_argument(
          '''--src_path''' ,						type=__UpperCAmelCase ,						default='''biencoder-nq-dev.json''' ,						help='''Path to raw DPR training data''' ,						)
      parser.add_argument(
          '''--evaluation_set''' ,						type=__UpperCAmelCase ,						help='''where to store parsed evaluation_set file''' ,						)
      parser.add_argument(
          '''--gold_data_path''' ,						type=__UpperCAmelCase ,						help='''where to store parsed gold_data_path file''' ,						)
      _lowerCamelCase			    =				parser.parse_args()

      with open(args.src_path ,						'''r'''		) as src_file, open(args.evaluation_set ,						'''w'''		) as eval_file, open(
          args.gold_data_path ,						'''w'''		) as gold_file:
            _lowerCamelCase			    =				json.load(__UpperCAmelCase		)
            for dpr_record in tqdm(__UpperCAmelCase		):
                  _lowerCamelCase			    =				dpr_record['''question''']
                  _lowerCamelCase			    =				[context['''title'''] for context in dpr_record['''positive_ctxs''']]
                  eval_file.write(question + '''\n'''		)
                  gold_file.write('''\t'''.join(__UpperCAmelCase		) + '''\n'''		)


if __name__ == "__main__":
     main()