




import argparse
import json

from tqdm import tqdm

def  SCREAMING_SNAKE_CASE							(       )      ->					Tuple:
     a     =				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"		, )
     a     =				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:
          a     =				json.load(__UpperCamelCase)
          for dpr_record in tqdm(__UpperCamelCase):
               a     =				dpr_record["question"]
               a     =				[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()







import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property




@unittest.skipUnless(os.path.exists(UpperCamelCase__				)       , """Tatoeba directory does not exist."""				)
class       a__	( unittest.TestCase				):







     @cached_property
     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a     =				tempfile.mkdtemp()
          return TatoebaConverter(save_dir=A			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          self.resolver.convert_models(["heb-eng"]			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          a  ,				a     =				self.resolver.write_model_card("opus-mt-he-en"  ,		dry_run=A			)
          assert mmeta["long_pair"] == "heb-eng"

