






'''simple docstring'''






import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class a_    (    UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							LEDTokenizer
   _snake_case        =							LEDTokenizerFast
   _snake_case        =							True







   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      super().setUp()
      __snake_case				:      Optional[int]     							= [
          'l',
          'o',
          'w',
          'e',
          'r',
          's',
          't',
          'i',
          'd',
          'n',
          '\u0120',
          '\u0120l',
          '\u0120n',
          '\u0120lo',
          '\u0120low',
          'er',
          '\u0120lowest',
          '\u0120newer',
          '\u0120wider',
          '<unk>',
      ]
      __snake_case				:      Any     							= dict(zip(__a					,   range(len(__a))))
      __snake_case				:      str     							= ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
      __snake_case				:      int     							= {'unk_token': '<unk>'}

      __snake_case				:      Dict     							= os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES['vocab_file'])
      __snake_case				:      List[str]     							= os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES['merges_file'])
      with open(self.vocab_file					,   'w'					,   encoding='utf-8') as fp:
         fp.write(json.dumps(__a) + '\n')
      with open(self.merges_file					,   'w'					,   encoding='utf-8') as fp:
         fp.write('\n'.join(__a))







   def     SCREAMING_SNAKE_CASE__					(self					,   **__a)			->      Union[str, Any]:
      """simple docstring"""






      kwargs.update(self.special_tokens_map)
      return self.tokenizer_class.from_pretrained(self.tmpdirname					,   **__a)







   def     SCREAMING_SNAKE_CASE__					(self					,   **__a)			->      Optional[int]:
      """simple docstring"""






      kwargs.update(self.special_tokens_map)
      return self.rust_tokenizer_class.from_pretrained(self.tmpdirname					,   **__a)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      List[str]:
      """simple docstring"""






      return "lower newer", "lower newer"







   @cached_property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      return LEDTokenizer.from_pretrained('allenai/led-base-16384')







   @cached_property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      return LEDTokenizerFast.from_pretrained('allenai/led-base-16384')







   @require_torch
   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      __snake_case				:      List[Any]     							= ['A long paragraph for summarization.', 'Another paragraph for summarization.']
      __snake_case				:      int     							= [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         __snake_case				:      Any     							= tokenizer(__a					,   max_length=len(__a)					,   padding=__a					,   return_tensors='pt')
         self.assertIsInstance(__a					,   __a)

         self.assertEqual((2, 9)					,   batch.input_ids.shape)
         self.assertEqual((2, 9)					,   batch.attention_mask.shape)
         __snake_case				:      List[Any]     							= batch.input_ids.tolist()[0]
         self.assertListEqual(__a					,   __a)







   @require_torch
   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      str     							= ['A long paragraph for summarization.', 'Another paragraph for summarization.']
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         __snake_case				:      Tuple     							= tokenizer(__a					,   padding=__a					,   return_tensors='pt')
         self.assertIn('input_ids'					,   __a)
         self.assertIn('attention_mask'					,   __a)
         self.assertNotIn('labels'					,   __a)
         self.assertNotIn('decoder_attention_mask'					,   __a)







   @require_torch
   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      __snake_case				:      str     							= [
          'Summary of the text.',
          'Another summary.',
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         __snake_case				:      Any     							= tokenizer(text_target=__a					,   max_length=3_2					,   padding='max_length'					,   return_tensors='pt')
         self.assertEqual(3_2					,   targets['input_ids'].shape[1])







   @require_torch
   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         __snake_case				:      Optional[Any]     							= tokenizer(
             ['I am a small frog' * 1_0_2_4, 'I am a small frog']					,   padding=__a					,   truncation=__a					,   return_tensors='pt')
         self.assertIsInstance(__a					,   __a)
         self.assertEqual(batch.input_ids.shape					,   (2, 5_1_2_2))







   @require_torch
   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      int     							= ['A long paragraph for summarization.']
      __snake_case				:      List[str]     							= [
          'Summary of the text.',
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         __snake_case				:      str     							= tokenizer(__a					,   return_tensors='pt')
         __snake_case				:      Union[str, Any]     							= tokenizer(text_target=__a					,   return_tensors='pt')
         __snake_case				:      Any     							= inputs['input_ids']
         __snake_case				:      List[Any]     							= targets['input_ids']
         self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item())
         self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item())
         self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item())
         self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item())







   @require_torch
   def     SCREAMING_SNAKE_CASE__					(self)			->      str:
      """simple docstring"""






      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
         __snake_case				:      List[Any]     							= ['Summary of the text.', 'Another summary.']
         __snake_case				:      List[str]     							= [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

         __snake_case				:      List[str]     							= tokenizer(__a					,   padding=__a)
         __snake_case				:      Optional[Any]     							= [[0] * len(__a) for x in encoded_output['input_ids']]
         __snake_case				:      Dict     							= tokenizer.pad(__a)
         self.assertSequenceEqual(outputs['global_attention_mask']					,   __a)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
            __snake_case				:      Optional[int]     							= self.rust_tokenizer_class.from_pretrained(__a					,   **__a)
            __snake_case				:      Union[str, Any]     							= self.tokenizer_class.from_pretrained(__a					,   **__a)
            __snake_case				:      List[str]     							= 'A, <mask> AllenNLP sentence.'
            __snake_case				:      int     							= tokenizer_r.encode_plus(__a					,   add_special_tokens=__a					,   return_token_type_ids=__a)
            __snake_case				:      Optional[int]     							= tokenizer_p.encode_plus(__a					,   add_special_tokens=__a					,   return_token_type_ids=__a)
            self.assertEqual(sum(tokens_r['token_type_ids'])					,   sum(tokens_p['token_type_ids']))
            self.assertEqual(
                sum(tokens_r['attention_mask']) / len(tokens_r['attention_mask'])					,   sum(tokens_p['attention_mask']) / len(tokens_p['attention_mask'])					,   )

            __snake_case				:      Optional[int]     							= tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
            __snake_case				:      Dict     							= tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'])
            self.assertSequenceEqual(tokens_p['input_ids']					,   [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2])
            self.assertSequenceEqual(tokens_r['input_ids']					,   [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2])

            self.assertSequenceEqual(
                __a					,   ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])
            self.assertSequenceEqual(
                __a					,   ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'])







'''simple docstring'''






from __future__ import annotations

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




@dataclass
class a_    :
   _snake_case        =							42
   _snake_case        =							None
   _snake_case        =							None




def        _SCREAMING_SNAKE_CASE  (				)    -> Node | None:







   """simple docstring"""
   __snake_case				:      str     							= Node(1  )
   __snake_case				:      Tuple     							= Node(2  )
   __snake_case				:      Optional[int]     							= Node(3  )
   __snake_case				:      List[str]     							= Node(4  )
   __snake_case				:      List[str]     							= Node(5  )
   return tree




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> list[int]:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> list[int]:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> list[int]:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> int:







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




def        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> Sequence[Node | None]:







   """simple docstring"""
   __snake_case				:      list[Any]     							= []

   if root is None:
      return output

   __snake_case				:      Optional[int]     							= deque([root]  )

   while process_queue:
      __snake_case				:      List[str]     							= 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        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  ,		A  :       int  )    -> Sequence[Node | None]:







   """simple docstring"""
   __snake_case				:      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        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  ,		A  :       int  )    -> Sequence[Node | None]:







   """simple docstring"""
   __snake_case				:      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        _SCREAMING_SNAKE_CASE  (				A  :       Node | None  )    -> Sequence[Node | None] | list[Any]:







   """simple docstring"""
   if root is None:
      return []

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

   __snake_case				:      List[Any]     							= 0
   __snake_case				:      int     							= height(A  )

   for h in range(1  ,		height_tree + 1  ):
      if not flag:
         output.append(get_nodes_from_left_to_right(A  ,		A  )  )
         __snake_case				:      int     							= 1
      else:
         output.append(get_nodes_from_right_to_left(A  ,		A  )  )
         __snake_case				:      Tuple     							= 0

   return output




def        _SCREAMING_SNAKE_CASE  (				)    -> None:  # Main function for testing.







   """simple docstring"""
   __snake_case				:      Optional[int]     							= 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()