code: 
  
  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
  snake_case_    						=				'''stas/tiny-wmt19-en-ru'''
  
  
  class    SCREAMING_SNAKE_CASE__      (__snake_case	, unittest.TestCase  ):
  				__lowerCamelCase		: Union[str, Any]														=  FSMTTokenizer
  				__lowerCamelCase		: Optional[Any]														=  False
  
  
  
  
  
  				def    snake_case_       (   self):
  									super().setUp()
  
  									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
  									lowercase__					:     List[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__					:     Any   = dict(zip(a		,     range(len(a))))
  									lowercase__					:     Optional[Any]   = ['l o 123', 'lo w 1456', 'e r</w> 1789', '']
  
  									lowercase__					:     Optional[int]   = ['en', 'ru']
  									lowercase__					:     Dict   = {
  									    'langs': self.langs,
  									    'src_vocab_size': 10,
  									    'tgt_vocab_size': 20,
  									}
  
  									lowercase__					:     Any   = os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['src_vocab_file'])
  									lowercase__					:     Tuple   = os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['tgt_vocab_file'])
  									lowercase__					:     Optional[int]   = os.path.join(self.tmpdirname		,     'tokenizer_config.json')
  									lowercase__					:     Any   = 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    snake_case_       (   self):
  									return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en')
  
  
  
  
  
  				@cached_property
  				def    snake_case_       (   self):
  									return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru')
  
  
  
  
  
  				def    snake_case_       (   self):
  									lowercase__					:     List[Any]   = 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    snake_case_       (   self):
  									lowercase__					:     Tuple   = FSMTTokenizer(self.langs		,     self.src_vocab_file		,     self.tgt_vocab_file		,     self.merges_file)
  
  									lowercase__					:     Union[str, Any]   = 'lower'
  									lowercase__					:     Union[str, Any]   = ['low', 'er</w>']
  									lowercase__					:     Dict   = tokenizer.tokenize(a)
  									self.assertListEqual(a		,     a)
  
  									lowercase__					:     List[str]   = tokens + ['<unk>']
  									lowercase__					:     int   = [14, 15, 20]
  									self.assertListEqual(tokenizer.convert_tokens_to_ids(a)		,     a)
  
  
  
  
  
  				@slow
  				def    snake_case_       (   self):
  									lowercase__					:     Union[str, Any]   = self.tokenizer_ru_en
  
  									lowercase__					:     Tuple   = tokenizer.encode('sequence builders'		,     add_special_tokens=a)
  									lowercase__					:     int   = tokenizer.encode('multi-sequence build'		,     add_special_tokens=a)
  
  									lowercase__					:     Union[str, Any]   = tokenizer.build_inputs_with_special_tokens(a)
  									lowercase__					:     Dict   = tokenizer.build_inputs_with_special_tokens(a		,     a)
  
  									assert encoded_sentence == text + [2]
  									assert encoded_pair == text + [2] + text_a + [2]
  
  
  
  
  
  				@slow
  				def    snake_case_       (   self):
  									lowercase__					:     Tuple   = self.tokenizer_en_ru
  									lowercase__					:     str   = self.tokenizer_ru_en
  
  									lowercase__					:     Any   = [
  									    [
  									        'Here\'s a little song I wrote. Don\'t worry, be happy.',
  									        [2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 1_3160, 5, 26, 6445, 7, 2],
  									    ],
  									    ['This is it. No more. I\'m done!', [132, 21, 37, 7, 1434, 86, 7, 70, 6476, 1305, 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__					:     List[str]   = tokenizer_enc.encode(a		,     return_tensors=a)
  														self.assertListEqual(a		,     a)
  
  														# and decode backward, using the reversed languages model
  														lowercase__					:     Union[str, Any]   = tokenizer_dec.decode(a		,     skip_special_tokens=a)
  														self.assertEqual(a		,     a)
  
  
  
  
  
  				@slow
  				def    snake_case_       (   self):
  									lowercase__					:     Tuple   = FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'		,     do_lower_case=a)
  									lowercase__					:     Dict   = tokenizer.tokenize('USA is United States of America')
  									lowercase__					:     int   = ['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    snake_case_       (   self):
  									pass
  
  
  
  
  
  				@unittest.skip('FSMTConfig.__init__  requires non-optional args')
  				def    snake_case_       (   self):
  									pass
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  snake_case_    						=				{'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''GPTJForCausalLM''',
             '''GPTJForQuestionAnswering''',
             '''GPTJForSequenceClassification''',
             '''GPTJModel''',
             '''GPTJPreTrainedModel''',
         ]
  
  try:
         if not is_tf_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''TFGPTJForCausalLM''',
             '''TFGPTJForQuestionAnswering''',
             '''TFGPTJForSequenceClassification''',
             '''TFGPTJModel''',
             '''TFGPTJPreTrainedModel''',
         ]
  
  try:
         if not is_flax_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''FlaxGPTJForCausalLM''',
             '''FlaxGPTJModel''',
             '''FlaxGPTJPreTrainedModel''',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_gptj import (
                    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
                    GPTJForCausalLM,
                    GPTJForQuestionAnswering,
                    GPTJForSequenceClassification,
                    GPTJModel,
                    GPTJPreTrainedModel,
                )
  
         try:
                if not is_tf_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_tf_gptj import (
                    TFGPTJForCausalLM,
                    TFGPTJForQuestionAnswering,
                    TFGPTJForSequenceClassification,
                    TFGPTJModel,
                    TFGPTJPreTrainedModel,
                )
  
         try:
                if not is_flax_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel
  
  else:
         import sys
  
         snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  