
'''simple docstring'''





import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
a__						:				Union[str, Any]           =    "stas/tiny-wmt19-en-ru"


class 	UpperCamelCase__					(      SCREAMING_SNAKE_CASE ,       unittest.TestCase):
 UpperCAmelCase__      :      Tuple  			=   FSMTTokenizer
 UpperCAmelCase__      :      List[Any]  			=   False






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







       '''simple docstring'''



       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       __A    		=     [
           'l',
           'o',
           'w',
           'e',
           'r',
           's',
           't',
           'i',
           'd',
           'n',
           'w</w>',
           'r</w>',
           't</w>',
           'lo',
           'low',
           'er</w>',
           'low</w>',
           'lowest</w>',
           'newer</w>',
           'wider</w>',
           '<unk>',
       ]
       __A    		=     dict(zip(_A		,			range(len(_A     )     )     )     )
       __A    		=     ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

       __A    		=     ['en', 'ru']
       __A    		=     {
           'langs': self.langs,
           'src_vocab_size': 10,
           'tgt_vocab_size': 20,
       }

       __A    		=     os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES['src_vocab_file']     )
       __A    		=     os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES['tgt_vocab_file']     )
       __A    		=     os.path.join(self.tmpdirname		,			'tokenizer_config.json'     )
       __A    		=     os.path.join(self.tmpdirname		,			VOCAB_FILES_NAMES['merges_file']     )
       with open(self.src_vocab_file		,			'w'     ) as fp:
             fp.write(json.dumps(_A     )     )
       with open(self.tgt_vocab_file		,			'w'     ) as fp:
             fp.write(json.dumps(_A     )     )
       with open(self.merges_file		,			'w'     ) as fp:
             fp.write('\n'.join(_A     )     )
       with open(_A		,			'w'     ) as fp:
             fp.write(json.dumps(_A     )     )






 @cached_property
 def 		lowercase_  (     self					:Tuple     )			->     str:







       '''simple docstring'''



       return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'     )






 @cached_property
 def 		lowercase_  (     self					:Optional[Any]     )			->     Any:







       '''simple docstring'''



       return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'     )






 def 		lowercase_  (     self					:Dict     )			->     Tuple:







       '''simple docstring'''



       __A    		=     FSMTTokenizer.from_pretrained(_A     )
       self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]		,			['en', 'ru']     )
       self.assertEqual(tokenizer.src_vocab_size		,			21     )
       self.assertEqual(tokenizer.tgt_vocab_size		,			21     )






 def 		lowercase_  (     self					:Optional[Any]     )			->     int:







       '''simple docstring'''



       __A    		=     FSMTTokenizer(self.langs		,			self.src_vocab_file		,			self.tgt_vocab_file		,			self.merges_file     )

       __A    		=     'lower'
       __A    		=     ['low', 'er</w>']
       __A    		=     tokenizer.tokenize(_A     )
       self.assertListEqual(_A		,			_A     )

       __A    		=     tokens + ['<unk>']
       __A    		=     [14, 15, 20]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(_A     )		,			_A     )






 @slow
 def 		lowercase_  (     self					:Dict     )			->     int:







       '''simple docstring'''



       __A    		=     self.tokenizer_ru_en

       __A    		=     tokenizer.encode('sequence builders'		,			add_special_tokens=_A     )
       __A    		=     tokenizer.encode('multi-sequence build'		,			add_special_tokens=_A     )

       __A    		=     tokenizer.build_inputs_with_special_tokens(_A     )
       __A    		=     tokenizer.build_inputs_with_special_tokens(_A		,			_A     )

       assert encoded_sentence == text + [2]
       assert encoded_pair == text + [2] + text_a + [2]






 @slow
 def 		lowercase_  (     self					:Any     )			->     Optional[int]:







       '''simple docstring'''



       __A    		=     self.tokenizer_en_ru
       __A    		=     self.tokenizer_ru_en

       __A    		=     [
           [
               'Here\'s a little song I wrote. Don\'t worry, be happy.',
               [2_470, 39, 11, 2_349, 7_222, 70, 5_979, 7, 8_450, 1_050, 13_160, 5, 26, 6_445, 7, 2],
           ],
           ['This is it. No more. I\'m done!', [132, 21, 37, 7, 1_434, 86, 7, 70, 6_476, 1_305, 427, 2]],
       ]

       # if data needs to be recreated or added, run:
       # import torch
       # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
       # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

       for src_text, tgt_input_ids in targets:
             __A    		=     tokenizer_enc.encode(_A		,			return_tensors=_A     )
             self.assertListEqual(_A		,			_A     )

             # and decode backward, using the reversed languages model
             __A    		=     tokenizer_dec.decode(_A		,			skip_special_tokens=_A     )
             self.assertEqual(_A		,			_A     )






 @slow
 def 		lowercase_  (     self					:Union[str, Any]     )			->     List[str]:







       '''simple docstring'''



       __A    		=     FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'		,			do_lower_case=_A     )
       __A    		=     tokenizer.tokenize('USA is United States of America'     )
       __A    		=     ['us', 'a</w>', 'is</w>', 'un', 'i', 'ted</w>', 'st', 'ates</w>', 'of</w>', 'am', 'er', 'ica</w>']
       self.assertListEqual(_A		,			_A     )






 @unittest.skip('FSMTConfig.__init__  requires non-optional args'     )
 def 		lowercase_  (     self					:Tuple     )			->     Optional[Any]:







       '''simple docstring'''



       pass






 @unittest.skip('FSMTConfig.__init__  requires non-optional args'     )
 def 		lowercase_  (     self					:Tuple     )			->     List[str]:







       '''simple docstring'''



       pass








'''simple docstring'''





import itertools
from pathlib import Path





def     snake_case							(						UpperCAmelCase		)->     int:







      """simple docstring"""
      __A    		=     [tuple(UpperCAmelCase		) for login in logins]

      __A    		=     {char for login in split_logins for char in login}

      for permutation in itertools.permutations(UpperCAmelCase		):
            __A    		=     True
            for login in logins:
                  if not (
                      permutation.index(login[0]		)
                      < permutation.index(login[1]		)
                      < permutation.index(login[2]		)
                  ):
                        __A    		=     False
                        break

            if satisfied:
                  return int(''.join(UpperCAmelCase		)		)

      raise Exception('Unable to find the secret passcode'		)





def     snake_case							(						UpperCAmelCase = "keylog.txt"		)->     int:







      """simple docstring"""
      __A    		=     Path(UpperCAmelCase		).parent.joinpath(UpperCAmelCase		).read_text().splitlines()

      return find_secret_passcode(UpperCAmelCase		)


if __name__ == "__main__":
 print(f'''{solution() = }''')






