






"""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
_UpperCamelCase							:       Dict       					=		'stas/tiny-wmt19-en-ru'






class 		snake_case    (    UpperCAmelCase  ,					unittest.TestCase			):
    __magic_name__       	=	FSMTTokenizer
    __magic_name__       	=	False





    def 							lowerCamelCase__ (				self      :   Tuple     ):


      '''simple docstring'''




      super().setUp()

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

      a	:     List[str]							=  ['en', 'ru']
      a	:     Union[str, Any]							=  {
          'langs': self.langs,
          'src_vocab_size': 1_0,
          'tgt_vocab_size': 2_0,
      }

      a	:     Optional[Any]							=  os.path.join(self.tmpdirname							,						VOCAB_FILES_NAMES['src_vocab_file']     )
      a	:     Optional[Any]							=  os.path.join(self.tmpdirname							,						VOCAB_FILES_NAMES['tgt_vocab_file']     )
      a	:     int							=  os.path.join(self.tmpdirname							,						'tokenizer_config.json'     )
      a	:     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 							lowerCamelCase__ (				self      :   Optional[int]     ):


      '''simple docstring'''




      return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'     )





    @cached_property
    def 							lowerCamelCase__ (				self      :   Any     ):


      '''simple docstring'''




      return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'     )





    def 							lowerCamelCase__ (				self      :   Optional[Any]     ):


      '''simple docstring'''




      a	:     Dict							=  FSMTTokenizer.from_pretrained(A     )
      self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]							,						['en', 'ru']     )
      self.assertEqual(tokenizer.src_vocab_size							,						2_1     )
      self.assertEqual(tokenizer.tgt_vocab_size							,						2_1     )





    def 							lowerCamelCase__ (				self      :   Any     ):


      '''simple docstring'''




      a	:     int							=  FSMTTokenizer(self.langs							,						self.src_vocab_file							,						self.tgt_vocab_file							,						self.merges_file     )

      a	:     Union[str, Any]							=  'lower'
      a	:     int							=  ['low', 'er</w>']
      a	:     Tuple							=  tokenizer.tokenize(A     )
      self.assertListEqual(A							,						A     )

      a	:     Union[str, Any]							=  tokens + ['<unk>']
      a	:     int							=  [1_4, 1_5, 2_0]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A     )							,						A     )





    @slow
    def 							lowerCamelCase__ (				self      :   Union[str, Any]     ):


      '''simple docstring'''




      a	:     Optional[int]							=  self.tokenizer_ru_en

      a	:     str							=  tokenizer.encode('sequence builders'							,						add_special_tokens=A     )
      a	:     Union[str, Any]							=  tokenizer.encode('multi-sequence build'							,						add_special_tokens=A     )

      a	:     Dict							=  tokenizer.build_inputs_with_special_tokens(A     )
      a	:     int							=  tokenizer.build_inputs_with_special_tokens(A							,						A     )

      assert encoded_sentence == text + [2]
      assert encoded_pair == text + [2] + text_a + [2]





    @slow
    def 							lowerCamelCase__ (				self      :   Tuple     ):


      '''simple docstring'''




      a	:     Optional[Any]							=  self.tokenizer_en_ru
      a	:     Optional[int]							=  self.tokenizer_ru_en

      a	:     Tuple							=  [
          [
              'Here\'s a little song I wrote. Don\'t worry, be happy.',
              [2_4_7_0, 3_9, 1_1, 2_3_4_9, 7_2_2_2, 7_0, 5_9_7_9, 7, 8_4_5_0, 1_0_5_0, 1_3_1_6_0, 5, 2_6, 6_4_4_5, 7, 2],
          ],
          ['This is it. No more. I\'m done!', [1_3_2, 2_1, 3_7, 7, 1_4_3_4, 8_6, 7, 7_0, 6_4_7_6, 1_3_0_5, 4_2_7, 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	:     List[str]							=  tokenizer_enc.encode(A							,						return_tensors=A     )
        self.assertListEqual(A							,						A     )

        # and decode backward, using the reversed languages model
        a	:     Optional[Any]							=  tokenizer_dec.decode(A							,						skip_special_tokens=A     )
        self.assertEqual(A							,						A     )





    @slow
    def 							lowerCamelCase__ (				self      :   List[str]     ):


      '''simple docstring'''




      a	:     List[Any]							=  FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'							,						do_lower_case=A     )
      a	:     Optional[Any]							=  tokenizer.tokenize('USA is United States of America'     )
      a	:     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 							lowerCamelCase__ (				self      :   int     ):


      '''simple docstring'''




      pass





    @unittest.skip('FSMTConfig.__init__  requires non-optional args'     )
    def 							lowerCamelCase__ (				self      :   int     ):


      '''simple docstring'''




      pass












"""simple docstring"""


from cva import destroyAllWindows, imread, imshow, waitKey




def      snake_case    (A_ :Optional[int]      ,     A_ :int ):







  '''simple docstring'''

  a,	a	:     Tuple							=  img.shape[0], img.shape[1]

  def to_grayscale(A_ :Tuple      ,     A_ :List[str]      ,     A_ :Union[str, Any] ):
    return 0.2126 * red + 0.587 * green + 0.114 * blue

  def normalize(A_ :Union[str, Any] ):
    return min(A_      ,     2_5_5 )

  for i in range(A_ ):
    for j in range(A_ ):
      a	:     Union[str, Any]							=  int(to_grayscale(*img[i][j] ) )
      a	:     Any							=  [
          normalize(A_ ),
          normalize(greyscale + factor ),
          normalize(greyscale + 2 * factor ),
      ]

  return img


if __name__ == "__main__":
   # read original image
   _UpperCamelCase							:       List[str]       					=		{
       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()




