code: 
  
  """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						=     """stas/tiny-wmt19-en-ru"""
  
  class 			lowerCamelCase  (					lowerCAmelCase__				,				unittest.TestCase      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     FSMTTokenizer
    SCREAMING_SNAKE_CASE							=     False
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          super().setUp()
  
          # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
          UpperCAmelCase__ :       Union[str, 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>""",
          ]
          UpperCAmelCase__ :       str						=						dict(zip(_lowerCamelCase       ,					range(len(_lowerCamelCase  )  )  )  )
          UpperCAmelCase__ :       Tuple						=						["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]
  
          UpperCAmelCase__ :       Tuple						=						["""en""", """ru"""]
          UpperCAmelCase__ :       Tuple						=						{
              """langs""": self.langs,
              """src_vocab_size""": 10,
              """tgt_vocab_size""": 20,
          }
  
          UpperCAmelCase__ :       Dict						=						os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES["""src_vocab_file"""]  )
          UpperCAmelCase__ :       str						=						os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES["""tgt_vocab_file"""]  )
          UpperCAmelCase__ :       int						=						os.path.join(self.tmpdirname       ,					"""tokenizer_config.json"""  )
          UpperCAmelCase__ :       Optional[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    _a			(self  ):
  
  
          """simple docstring"""
  
          return FSMTTokenizer.from_pretrained("""facebook/wmt19-ru-en"""  )
  
  
  
    @cached_property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          return FSMTTokenizer.from_pretrained("""facebook/wmt19-en-ru"""  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[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    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Union[str, Any]						=						FSMTTokenizer(self.langs       ,					self.src_vocab_file       ,					self.tgt_vocab_file       ,					self.merges_file  )
  
          UpperCAmelCase__ :       List[Any]						=						"""lower"""
          UpperCAmelCase__ :       List[str]						=						["""low""", """er</w>"""]
          UpperCAmelCase__ :       Any						=						tokenizer.tokenize(_lowerCamelCase  )
          self.assertListEqual(_lowerCamelCase       ,					_lowerCamelCase  )
  
          UpperCAmelCase__ :       Dict						=						tokens + ["""<unk>"""]
          UpperCAmelCase__ :       Optional[Any]						=						[14, 15, 20]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase  )       ,					_lowerCamelCase  )
  
  
  
    @slow
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						self.tokenizer_ru_en
  
          UpperCAmelCase__ :       Tuple						=						tokenizer.encode("""sequence builders"""       ,					add_special_tokens=_lowerCamelCase  )
          UpperCAmelCase__ :       Any						=						tokenizer.encode("""multi-sequence build"""       ,					add_special_tokens=_lowerCamelCase  )
  
          UpperCAmelCase__ :       List[Any]						=						tokenizer.build_inputs_with_special_tokens(_lowerCamelCase  )
          UpperCAmelCase__ :       int						=						tokenizer.build_inputs_with_special_tokens(_lowerCamelCase       ,					_lowerCamelCase  )
  
          assert encoded_sentence == text + [2]
          assert encoded_pair == text + [2] + text_a + [2]
  
  
  
    @slow
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[Any]						=						self.tokenizer_en_ru
          UpperCAmelCase__ :       Tuple						=						self.tokenizer_ru_en
  
          UpperCAmelCase__ :       Union[str, Any]						=						[
              [
                  """Here's a little song I wrote. Don't worry, be happy.""",
                  [2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 13160, 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:
                UpperCAmelCase__ :       Any						=						tokenizer_enc.encode(_lowerCamelCase       ,					return_tensors=_lowerCamelCase  )
                self.assertListEqual(_lowerCamelCase       ,					_lowerCamelCase  )
  
                # and decode backward, using the reversed languages model
                UpperCAmelCase__ :       List[Any]						=						tokenizer_dec.decode(_lowerCamelCase       ,					skip_special_tokens=_lowerCamelCase  )
                self.assertEqual(_lowerCamelCase       ,					_lowerCamelCase  )
  
  
  
    @slow
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Optional[int]						=						FSMTTokenizer.from_pretrained("""facebook/wmt19-ru-en"""       ,					do_lower_case=_lowerCamelCase  )
          UpperCAmelCase__ :       int						=						tokenizer.tokenize("""USA is United States of America"""  )
          UpperCAmelCase__ :       Optional[int]						=						["""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    _a			(self  ):
  
  
          """simple docstring"""
  
          pass
  
  
  
  
    @unittest.skip("""FSMTConfig.__init__  requires non-optional args"""  )
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          pass
  
  
style_context: 
  
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_keras_nlp_available,
      is_tensorflow_text_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _A						=     {
      """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
      """tokenization_gpt2""": ["""GPT2Tokenizer"""],
  }
  
  try:
      if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""GPT2TokenizerFast"""]
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GPT2DoubleHeadsModel""",
          """GPT2ForQuestionAnswering""",
          """GPT2ForSequenceClassification""",
          """GPT2ForTokenClassification""",
          """GPT2LMHeadModel""",
          """GPT2Model""",
          """GPT2PreTrainedModel""",
          """load_tf_weights_in_gpt2""",
      ]
  
  try:
      if not is_tf_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFGPT2DoubleHeadsModel""",
          """TFGPT2ForSequenceClassification""",
          """TFGPT2LMHeadModel""",
          """TFGPT2MainLayer""",
          """TFGPT2Model""",
          """TFGPT2PreTrainedModel""",
      ]
  
  try:
      if not is_keras_nlp_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""TFGPT2Tokenizer"""]
  
  try:
      if not is_flax_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]
  
  if TYPE_CHECKING:
      from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
      from .tokenization_gpta import GPTaTokenizer
  
      try:
          if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_fast import GPTaTokenizerFast
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_gpta import (
              GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTaDoubleHeadsModel,
              GPTaForQuestionAnswering,
              GPTaForSequenceClassification,
              GPTaForTokenClassification,
              GPTaLMHeadModel,
              GPTaModel,
              GPTaPreTrainedModel,
              load_tf_weights_in_gpta,
          )
  
      try:
          if not is_tf_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_tf_gpta import (
              TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGPTaDoubleHeadsModel,
              TFGPTaForSequenceClassification,
              TFGPTaLMHeadModel,
              TFGPTaMainLayer,
              TFGPTaModel,
              TFGPTaPreTrainedModel,
          )
  
      try:
          if not is_keras_nlp_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_tf import TFGPTaTokenizer
  
      try:
          if not is_flax_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel
  
  else:
      import sys
  
      _A						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  