


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
__lowerCAmelCase									=				'''stas/tiny-wmt19-en-ru'''





class     __a     (       __UpperCamelCase			,			unittest.TestCase ):
 __lowercase					:				str  	=					FSMTTokenizer
 __lowercase					:				Optional[int]  	=					False
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[str]:




        '''simple docstring'''
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        lowercase__:  Optional[Any]         =  [
            '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>',
        ]
        lowercase__:  Optional[int]         =  dict(zip(lowerCAmelCase__							,					range(len(lowerCAmelCase__					)					)					)					)
        lowercase__:  List[Any]         =  ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

        lowercase__:  List[str]         =  ['en', 'ru']
        lowercase__:  Tuple         =  {
            'langs': self.langs,
            'src_vocab_size': 10,
            'tgt_vocab_size': 20,
        }

        lowercase__:  str         =  os.path.join(self.tmpdirname							,					VOCAB_FILES_NAMES['src_vocab_file']					)
        lowercase__:  Union[str, Any]         =  os.path.join(self.tmpdirname							,					VOCAB_FILES_NAMES['tgt_vocab_file']					)
        lowercase__:  List[Any]         =  os.path.join(self.tmpdirname							,					'tokenizer_config.json'					)
        lowercase__:  int         =  os.path.join(self.tmpdirname							,					VOCAB_FILES_NAMES['merges_file']					)
        with open(self.src_vocab_file							,					'w'					) as fp:
               fp.write(json.dumps(lowerCAmelCase__					)					)
        with open(self.tgt_vocab_file							,					'w'					) as fp:
               fp.write(json.dumps(lowerCAmelCase__					)					)
        with open(self.merges_file							,					'w'					) as fp:
               fp.write('\n'.join(lowerCAmelCase__					)					)
        with open(lowerCAmelCase__							,					'w'					) as fp:
               fp.write(json.dumps(lowerCAmelCase__					)					)
 @cached_property
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Dict:




        '''simple docstring'''
        return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'					)
 @cached_property
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Dict:




        '''simple docstring'''
        lowercase__:  Union[str, Any]         =  FSMTTokenizer.from_pretrained(lowerCAmelCase__					)
        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 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[str]:




        '''simple docstring'''
        lowercase__:  Tuple         =  FSMTTokenizer(self.langs							,					self.src_vocab_file							,					self.tgt_vocab_file							,					self.merges_file					)

        lowercase__:  Any         =  'lower'
        lowercase__:  Tuple         =  ['low', 'er</w>']
        lowercase__:  Optional[int]         =  tokenizer.tokenize(lowerCAmelCase__					)
        self.assertListEqual(lowerCAmelCase__							,					lowerCAmelCase__					)

        lowercase__:  Union[str, Any]         =  tokens + ['<unk>']
        lowercase__:  Any         =  [14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__					)							,					lowerCAmelCase__					)
 @slow
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							List[Any]:




        '''simple docstring'''
        lowercase__:  str         =  self.tokenizer_ru_en

        lowercase__:  int         =  tokenizer.encode('sequence builders'							,					add_special_tokens=lowerCAmelCase__					)
        lowercase__:  Dict         =  tokenizer.encode('multi-sequence build'							,					add_special_tokens=lowerCAmelCase__					)

        lowercase__:  str         =  tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__					)
        lowercase__:  Dict         =  tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__							,					lowerCAmelCase__					)

        assert encoded_sentence == text + [2]
        assert encoded_pair == text + [2] + text_a + [2]
 @slow
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Optional[Any]:




        '''simple docstring'''
        lowercase__:  Tuple         =  self.tokenizer_en_ru
        lowercase__:  Any         =  self.tokenizer_ru_en

        lowercase__:  Optional[Any]         =  [
            [
                '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:
               lowercase__:  Optional[int]         =  tokenizer_enc.encode(lowerCAmelCase__							,					return_tensors=lowerCAmelCase__					)
               self.assertListEqual(lowerCAmelCase__							,					lowerCAmelCase__					)

               # and decode backward, using the reversed languages model
               lowercase__:  Tuple         =  tokenizer_dec.decode(lowerCAmelCase__							,					skip_special_tokens=lowerCAmelCase__					)
               self.assertEqual(lowerCAmelCase__							,					lowerCAmelCase__					)
 @slow
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							Tuple:




        '''simple docstring'''
        lowercase__:  str         =  FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'							,					do_lower_case=lowerCAmelCase__					)
        lowercase__:  Optional[Any]         =  tokenizer.tokenize('USA is United States of America'					)
        lowercase__:  str         =  ['us', 'a</w>', 'is</w>', 'un', 'i', 'ted</w>', 'st', 'ates</w>', 'of</w>', 'am', 'er', 'ica</w>']
        self.assertListEqual(lowerCAmelCase__							,					lowerCAmelCase__					)
 @unittest.skip('FSMTConfig.__init__  requires non-optional args'					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        pass
 @unittest.skip('FSMTConfig.__init__  requires non-optional args'					)
 def 						SCREAMING_SNAKE_CASE__  (  self					)   ->							str:




        '''simple docstring'''
        pass






from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime





class     __a     (       __UpperCamelCase ):
 def __init__(  self							,					*lowerCAmelCase__							,					**lowerCAmelCase__					)   ->							Any:




        '''simple docstring'''
        super().__init__(*lowerCAmelCase__							,					**lowerCAmelCase__					)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Union[str, Any]:




        '''simple docstring'''
        lowercase__:  str         =  (
            next_prime(value % self.size_table					)
            if not is_prime(value % self.size_table					)
            else value % self.size_table
        )  # gt = bigger than
        return next_prime_gt - (data % next_prime_gt)
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)   ->							Union[str, Any]:




        '''simple docstring'''
        return (increment * self.__hash_function_a(lowerCAmelCase__							,					lowerCAmelCase__					)) % self.size_table
 def 						SCREAMING_SNAKE_CASE__  (  self							,					lowerCAmelCase__							,					lowerCAmelCase__=None					)   ->							str:




        '''simple docstring'''
        lowercase__:  Dict         =  1
        lowercase__:  str         =  self.hash_function(lowerCAmelCase__					)

        while self.values[new_key] is not None and self.values[new_key] != key:
               lowercase__:  List[Any]         =  (
                   self.__hash_double_function(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__					)
                   if self.balanced_factor() >= self.lim_charge
                   else None
               )
               if new_key is None:
                      break
               else:
                      i += 1

        return new_key


