






"""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_     :					Dict							=     "stas/tiny-wmt19-en-ru"



class 						a_ (      snake_case_    ,		unittest.TestCase					):







      '''simple docstring'''

      lowerCamelCase__			:							Union[str, Any]					=      FSMTTokenizer
      lowerCamelCase__			:							Dict					=      False




      def      a__				(self   ):

       '''simple docstring'''



       super().setUp()

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

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

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



       lowerCamelCase__	:     Optional[int]								=		FSMTTokenizer.from_pretrained(lowerCamelCase_   )
       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      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Optional[int]								=		FSMTTokenizer(self.langs,	self.src_vocab_file,	self.tgt_vocab_file,	self.merges_file   )

       lowerCamelCase__	:     List[str]								=		'lower'
       lowerCamelCase__	:     Optional[int]								=		['low', 'er</w>']
       lowerCamelCase__	:     Dict								=		tokenizer.tokenize(lowerCamelCase_   )
       self.assertListEqual(lowerCamelCase_,	lowerCamelCase_   )

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




      @slow
      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Tuple								=		self.tokenizer_ru_en

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

       lowerCamelCase__	:     Dict								=		tokenizer.build_inputs_with_special_tokens(lowerCamelCase_   )
       lowerCamelCase__	:     List[str]								=		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'''



       lowerCamelCase__	:     List[str]								=		self.tokenizer_en_ru
       lowerCamelCase__	:     Tuple								=		self.tokenizer_ru_en

       lowerCamelCase__	:     List[Any]								=		[
           [
               '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:
        lowerCamelCase__	:     Union[str, Any]								=		tokenizer_enc.encode(lowerCamelCase_,	return_tensors=lowerCamelCase_   )
        self.assertListEqual(lowerCamelCase_,	lowerCamelCase_   )

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




      @slow
      def      a__				(self   ):

       '''simple docstring'''



       lowerCamelCase__	:     Union[str, Any]								=		FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en',	do_lower_case=lowerCamelCase_   )
       lowerCamelCase__	:     int								=		tokenizer.tokenize('USA is United States of America'   )
       lowerCamelCase__	:     Dict								=		['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









"""simple docstring"""
class 						a_ :







      '''simple docstring'''

      def __init__(self,	lowerCamelCase_   ):

       '''simple docstring'''



       lowerCamelCase__	:     Dict								=		data
       lowerCamelCase__	:     BinaryTreeNode | None								=		None
       lowerCamelCase__	:     BinaryTreeNode | None								=		None






def 			lowerCamelCase_    (		_lowerCamelCase     ,  _lowerCamelCase							):
 if node is None:
  lowerCamelCase__	:     List[str]								=		BinaryTreeNode(_lowerCamelCase							)
  return node

 # binary search tree is not empty,
 # so we will insert it into the tree
 # if new_value is less than value of data in node,
 #  add it to left subtree and proceed recursively
 if new_value < node.data:
  lowerCamelCase__	:     str								=		insert(node.left_child     ,  _lowerCamelCase							)
 else:
  # if new_value is greater than value of data in node,
  #  add it to right subtree and proceed recursively
  lowerCamelCase__	:     str								=		insert(node.right_child     ,  _lowerCamelCase							)
 return node






def 			lowerCamelCase_    (		_lowerCamelCase							):  # if node is None,return
 if node:
  lowerCamelCase__	:     Any								=		inorder(node.left_child							)
  lowerCamelCase__	:     Tuple								=		[*inorder_array, node.data]
  lowerCamelCase__	:     List[Any]								=		inorder_array + inorder(node.right_child							)
 else:
  lowerCamelCase__	:     Any								=		[]
 return inorder_array






def 			lowerCamelCase_    (		):
 lowerCamelCase__	:     Tuple								=		insert(_lowerCamelCase     ,  15							)
 insert(_lowerCamelCase     ,  10							)
 insert(_lowerCamelCase     ,  25							)
 insert(_lowerCamelCase     ,  6							)
 insert(_lowerCamelCase     ,  14							)
 insert(_lowerCamelCase     ,  20							)
 insert(_lowerCamelCase     ,  60							)
 return root






def 			lowerCamelCase_    (		):
 # main function
 lowerCamelCase__	:     List[Any]								=		make_tree()
 print('Printing values of binary search tree in Inorder Traversal.'							)
 inorder(_lowerCamelCase							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()

