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
  SCREAMING_SNAKE_CASE       = "stas/tiny-wmt19-en-ru"
  
  
  
  
  class 			UpperCAmelCase_						(		A_,     unittest.TestCase							):
      lowercase__		      =   FSMTTokenizer
      lowercase__		      =   False
  
  
  
  
      def 							__magic_name__							(					self		: Tuple   )   ->							Optional[int]:
  
  
  
  
       '''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(snake_case_			,				range(len(snake_case_   )   )   )   )
       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(snake_case_   )   )
       with open(self.tgt_vocab_file			,				"w"   ) as fp:
        fp.write(json.dumps(snake_case_   )   )
       with open(self.merges_file			,				"w"   ) as fp:
        fp.write("\n".join(snake_case_   )   )
       with open(snake_case_			,				"w"   ) as fp:
        fp.write(json.dumps(snake_case_   )   )
  
  
  
  
      @cached_property
      def 							__magic_name__							(					self		: List[str]   )   ->							Dict:
  
  
  
  
       '''simple docstring'''
  
       return FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"   )
  
  
  
  
      @cached_property
      def 							__magic_name__							(					self		: int   )   ->							Optional[int]:
  
  
  
  
       '''simple docstring'''
  
       return FSMTTokenizer.from_pretrained("facebook/wmt19-en-ru"   )
  
  
  
  
      def 							__magic_name__							(					self		: str   )   ->							int:
  
  
  
  
       '''simple docstring'''
  
       A__ =       FSMTTokenizer.from_pretrained(snake_case_   )
       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 							__magic_name__							(					self		: Dict   )   ->							List[Any]:
  
  
  
  
       '''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(snake_case_   )
       self.assertListEqual(snake_case_			,				snake_case_   )
  
       A__ =       tokens + ["<unk>"]
       A__ =       [14, 15, 20]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case_   )			,				snake_case_   )
  
  
  
  
      @slow
      def 							__magic_name__							(					self		: List[str]   )   ->							Optional[Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       self.tokenizer_ru_en
  
       A__ =       tokenizer.encode("sequence builders"			,				add_special_tokens=snake_case_   )
       A__ =       tokenizer.encode("multi-sequence build"			,				add_special_tokens=snake_case_   )
  
       A__ =       tokenizer.build_inputs_with_special_tokens(snake_case_   )
       A__ =       tokenizer.build_inputs_with_special_tokens(snake_case_			,				snake_case_   )
  
       assert encoded_sentence == text + [2]
       assert encoded_pair == text + [2] + text_a + [2]
  
  
  
  
      @slow
      def 							__magic_name__							(					self		: List[str]   )   ->							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(snake_case_			,				return_tensors=snake_case_   )
        self.assertListEqual(snake_case_			,				snake_case_   )
  
        # and decode backward, using the reversed languages model
        A__ =       tokenizer_dec.decode(snake_case_			,				skip_special_tokens=snake_case_   )
        self.assertEqual(snake_case_			,				snake_case_   )
  
  
  
  
      @slow
      def 							__magic_name__							(					self		: Dict   )   ->							Union[str, Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"			,				do_lower_case=snake_case_   )
       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(snake_case_			,				snake_case_   )
  
  
  
  
      @unittest.skip("FSMTConfig.__init__  requires non-optional args"   )
      def 							__magic_name__							(					self		: Any   )   ->							Any:
  
  
  
  
       '''simple docstring'''
  
       pass
  
  
  
  
      @unittest.skip("FSMTConfig.__init__  requires non-optional args"   )
      def 							__magic_name__							(					self		: int   )   ->							Union[str, Any]:
  
  
  
  
       '''simple docstring'''
  
       pass
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  SCREAMING_SNAKE_CASE       = {
      "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
  }
  
  try:
        if not is_vision_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE       = ["DetaImageProcessor"]
  
  try:
        if not is_torch_available():
              raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
        pass
  else:
        SCREAMING_SNAKE_CASE       = [
            "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
            "DetaForObjectDetection",
            "DetaModel",
            "DetaPreTrainedModel",
        ]
  
  
  if TYPE_CHECKING:
        from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig
  
        try:
              if not is_vision_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .image_processing_deta import DetaImageProcessor
  
        try:
              if not is_torch_available():
                    raise OptionalDependencyNotAvailable()
        except OptionalDependencyNotAvailable:
              pass
        else:
              from .modeling_deta import (
                  DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DetaForObjectDetection,
                  DetaModel,
                  DetaPreTrainedModel,
              )
  
  else:
        import sys
  
        SCREAMING_SNAKE_CASE       = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  
  
  
  