


from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any







@dataclass
class     _lowercase				:


       """simple docstring"""




       A__      		=    42
       A__      		=    None
       A__      		=    None



def    lowercase_	(	):

      """simple docstring"""




      lowerCamelCase__	:       int					=						Node(1		)
      lowerCamelCase__	:       str					=						Node(2		)
      lowerCamelCase__	:       int					=						Node(3		)
      lowerCamelCase__	:       Any					=						Node(4		)
      lowerCamelCase__	:       Dict					=						Node(5		)
      return tree

def    lowercase_	(	_A    :					Node | None		):

      """simple docstring"""




      return [root.data, *preorder(root.left		), *preorder(root.right		)] if root else []

def    lowercase_	(	_A    :					Node | None		):

      """simple docstring"""




      return postorder(root.left		) + postorder(root.right		) + [root.data] if root else []

def    lowercase_	(	_A    :					Node | None		):

      """simple docstring"""




      return [*inorder(root.left		), root.data, *inorder(root.right		)] if root else []

def    lowercase_	(	_A    :					Node | None		):

      """simple docstring"""




      return (max(height(root.left		)					,      height(root.right		)		) + 1) if root else 0

def    lowercase_	(	_A    :					Node | None		):

      """simple docstring"""




      lowerCamelCase__	:       list[Any]					=						[]

      if root is None:
            return output

      lowerCamelCase__	:       Optional[Any]					=						deque([root]		)

      while process_queue:
            lowerCamelCase__	:       List[Any]					=						process_queue.popleft()
            output.append(node.data		)

            if node.left:
                  process_queue.append(node.left		)
            if node.right:
                  process_queue.append(node.right		)
      return output

def    lowercase_	(	_A    :					Node | None					,      _A    :					int		):

      """simple docstring"""




      lowerCamelCase__	:       list[Any]					=						[]

      def populate_output(_A    :					Node | None					,      _A    :					int		) -> None:
            if not root:
                  return
            if level == 1:
                  output.append(root.data		)
            elif level > 1:
                  populate_output(root.left					,      level - 1		)
                  populate_output(root.right					,      level - 1		)

      populate_output(_A					,      _A		)
      return output

def    lowercase_	(	_A    :					Node | None					,      _A    :					int		):

      """simple docstring"""




      lowerCamelCase__	:       list[Any]					=						[]

      def populate_output(_A    :					Node | None					,      _A    :					int		) -> None:
            if root is None:
                  return
            if level == 1:
                  output.append(root.data		)
            elif level > 1:
                  populate_output(root.right					,      level - 1		)
                  populate_output(root.left					,      level - 1		)

      populate_output(_A					,      _A		)
      return output

def    lowercase_	(	_A    :					Node | None		):

      """simple docstring"""




      if root is None:
            return []

      lowerCamelCase__	:       list[Sequence[Node | None]]					=						[]

      lowerCamelCase__	:       Optional[Any]					=						0
      lowerCamelCase__	:       int					=						height(_A		)

      for h in range(1					,      height_tree + 1		):
            if not flag:
                  output.append(get_nodes_from_left_to_right(_A					,      _A		)		)
                  lowerCamelCase__	:       str					=						1
            else:
                  output.append(get_nodes_from_right_to_left(_A					,      _A		)		)
                  lowerCamelCase__	:       int					=						0

      return output

def    lowercase_	(	):  # Main function for testing.

      """simple docstring"""




      lowerCamelCase__	:       str					=						make_tree()
      print(F"In-order Traversal: {inorder(_A		)}"		)
      print(F"Pre-order Traversal: {preorder(_A		)}"		)
      print(F"Post-order Traversal: {postorder(_A		)}"					,      "\n"		)

      print(F"Height of Tree: {height(_A		)}"					,      "\n"		)

      print("Complete Level Order Traversal: "		)
      print(level_order(_A		)					,      "\n"		)

      print("Level-wise order Traversal: "		)

      for level in range(1					,      height(_A		) + 1		):
            print(F"Level {level}:"					,      get_nodes_from_left_to_right(_A					,      level=_A		)		)

      print("\nZigZag order Traversal: "		)
      print(zigzag(_A		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()






import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class     _lowercase				(							lowercase__							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    BarthezTokenizer
       A__      		=    BarthezTokenizerFast
       A__      		=    True
       A__      		=    True





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             super().setUp()

             lowerCamelCase__	:       List[str]					=						BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez" )
             tokenizer.save_pretrained(self.tmpdirname )
             tokenizer.save_pretrained(self.tmpdirname      ,   legacy_format=__lowerCamelCase )
             lowerCamelCase__	:       Tuple					=						tokenizer





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						"<pad>"
             lowerCamelCase__	:       Tuple					=						1

             self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase )      ,   __lowerCamelCase )
             self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase )      ,   __lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						list(self.get_tokenizer().get_vocab().keys() )

             self.assertEqual(vocab_keys[0]      ,   "<s>" )
             self.assertEqual(vocab_keys[1]      ,   "<pad>" )
             self.assertEqual(vocab_keys[-1]      ,   "<mask>" )
             self.assertEqual(len(__lowerCamelCase )      ,   101122 )





       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             self.assertEqual(self.get_tokenizer().vocab_size      ,   101122 )





       @require_torch
       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						["A long paragraph for summarization.", "Another paragraph for summarization."]
             lowerCamelCase__	:       str					=						[0, 57, 3018, 70307, 91, 2]

             lowerCamelCase__	:       Tuple					=						self.tokenizer(
                 __lowerCamelCase      ,   max_length=len(__lowerCamelCase )      ,   padding=__lowerCamelCase      ,   truncation=__lowerCamelCase      ,   return_tensors="pt" )
             self.assertIsInstance(__lowerCamelCase      ,   __lowerCamelCase )

             self.assertEqual((2, 6)      ,   batch.input_ids.shape )
             self.assertEqual((2, 6)      ,   batch.attention_mask.shape )
             lowerCamelCase__	:       Any					=						batch.input_ids.tolist()[0]
             self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             if not self.test_rust_tokenizer:
                   return

             lowerCamelCase__	:       Any					=						self.get_tokenizer()
             lowerCamelCase__	:       Tuple					=						self.get_rust_tokenizer()

             lowerCamelCase__	:       Union[str, Any]					=						"I was born in 92000, and this is falsé."

             lowerCamelCase__	:       Dict					=						tokenizer.tokenize(__lowerCamelCase )
             lowerCamelCase__	:       Optional[int]					=						rust_tokenizer.tokenize(__lowerCamelCase )
             self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						tokenizer.encode(__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase )
             lowerCamelCase__	:       List[Any]					=						rust_tokenizer.encode(__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase )
             self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase )

             lowerCamelCase__	:       List[str]					=						self.get_rust_tokenizer()
             lowerCamelCase__	:       Optional[Any]					=						tokenizer.encode(__lowerCamelCase )
             lowerCamelCase__	:       List[Any]					=						rust_tokenizer.encode(__lowerCamelCase )
             self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase )





       @slow
       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						{"input_ids": [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
             # fmt: on

             # moussaKam/mbarthez is a french model. So we also use french texts.
             lowerCamelCase__	:       List[str]					=						[
                 "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
                 "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
                 "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
                 "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
                 "telles que la traduction et la synthèse de texte.",
             ]

             self.tokenizer_integration_test_util(
                 expected_encoding=__lowerCamelCase      ,   model_name="moussaKam/mbarthez"      ,   revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"      ,   sequences=__lowerCamelCase      ,   )


