

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__	:   Tuple								=     "stas/tiny-wmt19-en-ru"



class    __snake_case	(     lowerCAmelCase__  ,			unittest.TestCase							):
  __lowerCAmelCase  :						str    			=	FSMTTokenizer
  __lowerCAmelCase  :						str    			=	False

  def     lowerCAmelCase__  (       self):
      super().setUp()

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

      SCREAMING_SNAKE_CASE_						=    ['en', 'ru']
      SCREAMING_SNAKE_CASE_						=    {
          'langs': self.langs,
          'src_vocab_size': 10,
          'tgt_vocab_size': 20,
      }

      SCREAMING_SNAKE_CASE_						=    os.path.join(self.tmpdirname    ,		VOCAB_FILES_NAMES['src_vocab_file'])
      SCREAMING_SNAKE_CASE_						=    os.path.join(self.tmpdirname    ,		VOCAB_FILES_NAMES['tgt_vocab_file'])
      SCREAMING_SNAKE_CASE_						=    os.path.join(self.tmpdirname    ,		'tokenizer_config.json')
      SCREAMING_SNAKE_CASE_						=    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):
      return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en')

  @cached_property
  def     lowerCAmelCase__  (       self):
      return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru')

  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    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     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    FSMTTokenizer(self.langs    ,		self.src_vocab_file    ,		self.tgt_vocab_file    ,		self.merges_file)

      SCREAMING_SNAKE_CASE_						=    'lower'
      SCREAMING_SNAKE_CASE_						=    ['low', 'er</w>']
      SCREAMING_SNAKE_CASE_						=    tokenizer.tokenize(_A)
      self.assertListEqual(_A    ,		_A)

      SCREAMING_SNAKE_CASE_						=    tokens + ['<unk>']
      SCREAMING_SNAKE_CASE_						=    [14, 15, 20]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(_A)    ,		_A)

  @slow
  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    self.tokenizer_ru_en

      SCREAMING_SNAKE_CASE_						=    tokenizer.encode('sequence builders'    ,		add_special_tokens=_A)
      SCREAMING_SNAKE_CASE_						=    tokenizer.encode('multi-sequence build'    ,		add_special_tokens=_A)

      SCREAMING_SNAKE_CASE_						=    tokenizer.build_inputs_with_special_tokens(_A)
      SCREAMING_SNAKE_CASE_						=    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):
      SCREAMING_SNAKE_CASE_						=    self.tokenizer_en_ru
      SCREAMING_SNAKE_CASE_						=    self.tokenizer_ru_en

      SCREAMING_SNAKE_CASE_						=    [
          [
              '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:
          SCREAMING_SNAKE_CASE_						=    tokenizer_enc.encode(_A    ,		return_tensors=_A)
          self.assertListEqual(_A    ,		_A)

          # and decode backward, using the reversed languages model
          SCREAMING_SNAKE_CASE_						=    tokenizer_dec.decode(_A    ,		skip_special_tokens=_A)
          self.assertEqual(_A    ,		_A)

  @slow
  def     lowerCAmelCase__  (       self):
      SCREAMING_SNAKE_CASE_						=    FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'    ,		do_lower_case=_A)
      SCREAMING_SNAKE_CASE_						=    tokenizer.tokenize('USA is United States of America')
      SCREAMING_SNAKE_CASE_						=    ['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):
      pass


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





import random


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		List[Any]							):





    """simple docstring"""


    def is_sorted(_SCREAMING_SNAKE_CASE       :		List[Any]							):
        for i in range(len(_SCREAMING_SNAKE_CASE							) - 1							):
            if collection[i] > collection[i + 1]:
                return False
        return True

    while not is_sorted(_SCREAMING_SNAKE_CASE							):
        random.shuffle(_SCREAMING_SNAKE_CASE							)
    return collection


if __name__ == "__main__":
       UpperCamelCase__	:   Tuple								=     input("Enter numbers separated by a comma:\n").strip()
       UpperCamelCase__	:   List[str]								=     [int(item) for item in user_input.split(",")]
       print(bogo_sort(unsorted))


