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 cva import destroyAllWindows, imread, imshow, waitKey
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					int    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__   ,				lowercase__					:     Optional[int]   = img.shape[0], img.shape[1]
  
       def to_grayscale(SCREAMING_SNAKE_CASE_	:					Union[str, Any]			,  SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]    ):
            return 0.2126 * red + 0.587 * green + 0.114 * blue
  
       def normalize(SCREAMING_SNAKE_CASE_	:					int    ):
            return min(SCREAMING_SNAKE_CASE_			,  255    )
  
       for i in range(SCREAMING_SNAKE_CASE_    ):
            for j in range(SCREAMING_SNAKE_CASE_    ):
                 lowercase__					:     str   = int(to_grayscale(*img[i][j]    )    )
                 lowercase__					:     Dict   = [
                     normalize(SCREAMING_SNAKE_CASE_    ),
                     normalize(greyscale + factor    ),
                     normalize(greyscale + 2 * factor    ),
                 ]
  
       return img
  
  
  if __name__ == "__main__":
         # read original image
         snake_case_    						=				{
             percentage: imread('''image_data/lena.jpg''', 1) for percentage in (10, 20, 30, 40)
         }
  
         for percentage, img in images.items():
                make_sepia(img, percentage)
  
         for percentage, img in images.items():
                imshow(F'''Original image with sepia (factor: {percentage})''', img)
  
         waitKey(0)
         destroyAllWindows()
  
  