






import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
     from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__				   =		get_tests_dir("fixtures/test_sentencepiece.model")

UpperCAmelCase__				   =		{"target_lang": "fi", "source_lang": "en"}
UpperCAmelCase__				   =		">>zh<<"
UpperCAmelCase__				   =		"Helsinki-NLP/"

if is_torch_available():
     UpperCAmelCase__				   =		"pt"
elif is_tf_available():
     UpperCAmelCase__				   =		"tf"
else:
     UpperCAmelCase__				   =		"jax"






@require_sentencepiece
class 		lowercase_					(	lowercase ,  unittest.TestCase   ):






      '''simple docstring'''




      __snake_case       =  MarianTokenizer
      __snake_case       =  False
      __snake_case       =  True







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->List[Any]:




       """simple docstring"""


       super().setUp()
       a		  =      ['''</s>''', '''<unk>''', '''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''', '''\u0120''', '''<pad>''']
       a		  =      dict(zip(__UpperCAmelCase ,					range(len(__UpperCAmelCase  )  )  )  )
       a		  =      Path(self.tmpdirname  )
       save_json(__UpperCAmelCase ,					save_dir / VOCAB_FILES_NAMES['''vocab''']  )
       save_json(__UpperCAmelCase ,					save_dir / VOCAB_FILES_NAMES['''tokenizer_config_file''']  )
       if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
        copyfile(__UpperCAmelCase ,					save_dir / VOCAB_FILES_NAMES['''source_spm''']  )
        copyfile(__UpperCAmelCase ,					save_dir / VOCAB_FILES_NAMES['''target_spm''']  )

       a		  =      MarianTokenizer.from_pretrained(self.tmpdirname  )
       tokenizer.save_pretrained(self.tmpdirname  )







      def       __lowerCAmelCase					(      self      :						Optional[int] ,					**__UpperCAmelCase      :						Dict  )    ->MarianTokenizer:




       """simple docstring"""


       return MarianTokenizer.from_pretrained(self.tmpdirname ,					**__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Tuple ,					__UpperCAmelCase      :						List[Any]  )    ->List[str]:




       """simple docstring"""


       return (
           "This is a test",
           "This is a test",
       )







      def       __lowerCAmelCase					(      self      :						Dict  )    ->Union[str, Any]:




       """simple docstring"""


       a		  =      '''</s>'''
       a		  =      0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(__UpperCAmelCase  ) ,					__UpperCAmelCase  )
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(__UpperCAmelCase  ) ,					__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Dict:




       """simple docstring"""


       a		  =      list(self.get_tokenizer().get_vocab().keys()  )

       self.assertEqual(vocab_keys[0] ,					'''</s>'''  )
       self.assertEqual(vocab_keys[1] ,					'''<unk>'''  )
       self.assertEqual(vocab_keys[-1] ,					'''<pad>'''  )
       self.assertEqual(len(__UpperCAmelCase  ) ,					9  )







      def       __lowerCAmelCase					(      self      :						Dict  )    ->List[str]:




       """simple docstring"""


       self.assertEqual(self.get_tokenizer().vocab_size ,					9  )







      def       __lowerCAmelCase					(      self      :						Union[str, Any]  )    ->Optional[Any]:




       """simple docstring"""


       a		  =      MarianTokenizer.from_pretrained(F"""{ORG_NAME}opus-mt-en-de"""  )
       a		  =      en_de_tokenizer(['''I am a small frog'''] ,					return_tensors=__UpperCAmelCase  )
       self.assertIsInstance(__UpperCAmelCase ,					__UpperCAmelCase  )
       a		  =      [38, 121, 14, 697, 38_848, 0]
       self.assertListEqual(__UpperCAmelCase ,					batch.input_ids[0]  )

       a		  =      tempfile.mkdtemp()
       en_de_tokenizer.save_pretrained(__UpperCAmelCase  )
       a		  =      [x.name for x in Path(__UpperCAmelCase  ).glob('''*'''  )]
       self.assertIn('''source.spm''' ,					__UpperCAmelCase  )
       MarianTokenizer.from_pretrained(__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Tuple  )    ->Optional[Any]:




       """simple docstring"""


       a		  =      self.get_tokenizer()

       a		  =      tok(
           ['''I am a small frog''' * 1_000, '''I am a small frog'''] ,					padding=__UpperCAmelCase ,					truncation=__UpperCAmelCase ,					return_tensors=__UpperCAmelCase  )
       self.assertIsInstance(__UpperCAmelCase ,					__UpperCAmelCase  )
       self.assertEqual(batch.input_ids.shape ,					(2, 512)  )







      def       __lowerCAmelCase					(      self      :						Any  )    ->Optional[Any]:




       """simple docstring"""


       a		  =      self.get_tokenizer()
       a		  =      tok(['''I am a tiny frog''', '''I am a small frog'''] ,					padding=__UpperCAmelCase ,					return_tensors=__UpperCAmelCase  )
       self.assertIsInstance(__UpperCAmelCase ,					__UpperCAmelCase  )
       self.assertEqual(batch_smaller.input_ids.shape ,					(2, 10)  )







      @slow
      def       __lowerCAmelCase					(      self      :						List[Any]  )    ->Tuple:




       """simple docstring"""


       a		  =      {'''input_ids''': [[43_495, 462, 20, 42_164, 1_369, 52, 464, 132, 1_703, 492, 13, 7_491, 38_999, 6, 8, 464, 132, 1_703, 492, 13, 4_669, 37_867, 13, 7_525, 27, 1_593, 988, 13, 33_972, 7_029, 6, 20, 8_251, 383, 2, 270, 5_866, 3_788, 2, 2_353, 8_251, 12_338, 2, 13_958, 387, 2, 3_629, 6_953, 188, 2_900, 2, 13_958, 8_011, 11_501, 23, 8_460, 4_073, 34_009, 20, 435, 11_439, 27, 8, 8_460, 4_073, 6_004, 20, 9_988, 375, 27, 33, 266, 1_945, 1_076, 1_350, 37_867, 3_288, 5, 577, 1_076, 4_374, 8, 5_082, 5, 26_453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 10_767, 6, 316, 304, 4_239, 3, 0], [148, 15_722, 19, 1_839, 12, 1_350, 13, 22_327, 5_082, 5_418, 47_567, 35_938, 59, 318, 19_552, 108, 2_183, 54, 14_976, 4_835, 32, 547, 1_114, 8, 315, 2_417, 5, 92, 19_088, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100], [36, 6_395, 12_570, 39_147, 11_597, 6, 266, 4, 45_405, 7_296, 3, 0, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100, 58_100]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=__UpperCAmelCase ,					model_name='''Helsinki-NLP/opus-mt-en-de''' ,					revision='''1a8c2263da11e68e50938f97e10cd57820bd504c''' ,					decode_kwargs={'''use_source_tokenizer''': True} ,					)







      def       __lowerCAmelCase					(      self      :						Optional[Any]  )    ->Dict:




       """simple docstring"""


       a		  =      MarianTokenizer.from_pretrained('''hf-internal-testing/test-marian-two-vocabs'''  )

       a		  =      '''Tämä on testi'''
       a		  =      '''This is a test'''

       a		  =      [76, 7, 2_047, 2]
       a		  =      [69, 12, 11, 940, 2]

       a		  =      tokenizer(__UpperCAmelCase  ).input_ids
       self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase  )

       a		  =      tokenizer(text_target=__UpperCAmelCase  ).input_ids
       self.assertListEqual(__UpperCAmelCase ,					__UpperCAmelCase  )

       a		  =      tokenizer.decode(__UpperCAmelCase ,					skip_special_tokens=__UpperCAmelCase  )
       self.assertEqual(__UpperCAmelCase ,					__UpperCAmelCase  )









import argparse
import json

from tqdm import tqdm







def  _SCREAMING_SNAKE_CASE				(   )      ->      List[Any]:
					__A	:						Tuple       =				argparse.ArgumentParser()

					# Required parameters
					parser.add_argument(
					    '--src_path'					,						type=a					,						default='biencoder-nq-dev.json'					,						help='Path to raw DPR training data'					,						)
					parser.add_argument(
					    '--evaluation_set'					,						type=a					,						help='where to store parsed evaluation_set file'					,						)
					parser.add_argument(
					    '--gold_data_path'					,						type=a					,						help='where to store parsed gold_data_path file'					,						)
					__A	:						Optional[int]       =				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	:						List[Any]       =				json.load(a  )
										for dpr_record in tqdm(a  ):
															__A	:						Dict       =				dpr_record['question']
															__A	:						Any       =				[context['title'] for context in dpr_record['positive_ctxs']]
															eval_file.write(question + '\n'  )
															gold_file.write('\t'.join(a  ) + '\n'  )


if __name__ == "__main__":
				main()




