"""simple docstring"""



import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class      _A	(						_UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					List[Any]							=					DebertaTokenizer
    UpperCamelCase_	:					Any							=					True
    UpperCamelCase_	:					int							=					DebertaTokenizerFast
    def  lowercase					(		self							:     str    )    ->       Tuple:
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __snake_case				=							[
             '''l''',
             '''o''',
             '''w''',
             '''e''',
             '''r''',
             '''s''',
             '''t''',
             '''i''',
             '''d''',
             '''n''',
             '''\u0120''',
             '''\u0120l''',
             '''\u0120n''',
             '''\u0120lo''',
             '''\u0120low''',
             '''er''',
             '''\u0120lowest''',
             '''\u0120newer''',
             '''\u0120wider''',
             '''[UNK]''',
         ]
         __snake_case				=							dict(zip(A_	,      range(len(A_    )    )    )    )
         __snake_case				=							['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
         __snake_case				=							{'''unk_token''': '''[UNK]'''}

         __snake_case				=							os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES['''vocab_file''']    )
         __snake_case				=							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  lowercase					(		self							:     Any	,      **A_							:     Union[str, Any]    )    ->       List[Any]:
         kwargs.update(self.special_tokens_map    )
         return self.tokenizer_class.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Tuple	,      A_							:     Tuple    )    ->       Dict:
         __snake_case				=							'''lower newer'''
         __snake_case				=							'''lower newer'''
         return input_text, output_text
    def  lowercase					(		self							:     Any    )    ->       List[str]:
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							'''lower newer'''
         __snake_case				=							['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
         __snake_case				=							tokenizer.tokenize(A_    )
         self.assertListEqual(A_	,      A_    )

         __snake_case				=							tokens + [tokenizer.unk_token]
         __snake_case				=							[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(A_    )	,      A_    )
    def  lowercase					(		self							:     str    )    ->       Union[str, Any]:
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							tokenizer('''Hello'''	,      '''World'''    )
         __snake_case				=							[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
         self.assertListEqual(tokd['''token_type_ids''']	,      A_    )
    @slow
    def  lowercase					(		self							:     Tuple    )    ->       List[Any]:
         __snake_case				=							self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''    )

         __snake_case				=							tokenizer.encode('''sequence builders'''	,      add_special_tokens=A_    )
         __snake_case				=							tokenizer.encode('''multi-sequence build'''	,      add_special_tokens=A_    )

         __snake_case				=							tokenizer.encode(
             '''sequence builders'''	,      add_special_tokens=A_	,      add_prefix_space=A_    )
         __snake_case				=							tokenizer.encode(
             '''sequence builders'''	,      '''multi-sequence build'''	,      add_special_tokens=A_	,      add_prefix_space=A_    )

         __snake_case				=							tokenizer.build_inputs_with_special_tokens(A_    )
         __snake_case				=							tokenizer.build_inputs_with_special_tokens(A_	,      A_    )

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode







    @slow
    def  lowercase					(		self							:     List[str]    )    ->       Dict:
         __snake_case				=							[self.tokenizer_class]
         if self.test_rust_tokenizer:
              tokenizer_classes.append(self.rust_tokenizer_class    )

         for tokenizer_class in tokenizer_classes:
              __snake_case				=							tokenizer_class.from_pretrained('''microsoft/deberta-base'''    )

              __snake_case				=							[
                  '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
                  '''ALBERT incorporates two parameter reduction techniques''',
                  '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
                  ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
                  ''' vocabulary embedding.''',
              ]

              __snake_case				=							tokenizer(A_	,      padding=A_    )
              __snake_case				=							[tokenizer.decode(A_	,      skip_special_tokens=A_    ) for seq in encoding['''input_ids''']]

              # fmt: off
              __snake_case				=							{
                  '''input_ids''': [
                      [1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 2, 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, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 2, 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, 0, 0, 0, 0, 0, 0, 0],
                      [1, 133, 78, 65, 16, 10, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 4, 2]
                  ],
                  '''token_type_ids''': [
                      [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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
                  ],
                  '''attention_mask''': [
                      [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],
                      [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, 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]
                  ]
              }
              # fmt: on

              __snake_case				=							[
                  '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
                  '''ALBERT incorporates two parameter reduction techniques''',
                  '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
                  ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
                  ''' vocabulary embedding.''',
              ]

              self.assertDictEqual(encoding.data	,      A_    )

              for expected, decoded in zip(A_	,      A_    ):
                   self.assertEqual(A_	,      A_    )
"""simple docstring"""



from __future__ import annotations

from collections.abc import Iterator
from typing import Any



class      _A	:




    """simple docstring"""




    def __init__(		self							:     Tuple	,      A_							:     Any    )    ->       Union[str, Any]:
         __snake_case				=							data
         __snake_case				=							None






class      _A	:




    """simple docstring"""




    def __init__(		self							:     Union[str, Any]    )    ->       List[Any]:
         __snake_case				=							None
         __snake_case				=							None
    def __iter__(		self							:     Optional[Any]    )    ->       Iterator[Any]:
         __snake_case				=							self.head
         while self.head:
              yield node.data
              __snake_case				=							node.next
              if node == self.head:
                   break
    def __len__(		self							:     List[Any]    )    ->       int:
         return sum(1 for _ in self    )
    def __repr__(		self							:     Optional[int]    )    ->       Union[str, Any]:
         return "->".join(str(A_    ) for item in iter(self    )    )
    def  lowercase					(		self							:     int	,      A_							:     Any    )    ->       None:
         self.insert_nth(len(self    )	,      A_    )
    def  lowercase					(		self							:     List[str]	,      A_							:     Any    )    ->       None:
         self.insert_nth(0	,      A_    )
    def  lowercase					(		self							:     List[Any]	,      A_							:     int	,      A_							:     Any    )    ->       None:
         if index < 0 or index > len(self    ):
              raise IndexError('''list index out of range.'''    )
         __snake_case				=							Node(A_    )
         if self.head is None:
              __snake_case				=							new_node  # first node points itself
              __snake_case				=							__snake_case				=							new_node
         elif index == 0:  # insert at head
              __snake_case				=							self.head
              __snake_case				=							__snake_case				=							new_node
         else:
              __snake_case				=							self.head
              for _ in range(index - 1    ):
                   __snake_case				=							temp.next
              __snake_case				=							temp.next
              __snake_case				=							new_node
              if index == len(self    ) - 1:  # insert at tail
                   __snake_case				=							new_node
    def  lowercase					(		self							:     int    )    ->       str:
         return self.delete_nth(0    )
    def  lowercase					(		self							:     List[Any]    )    ->       Any:
         return self.delete_nth(len(self    ) - 1    )
    def  lowercase					(		self							:     List[str]	,      A_							:     int = 0    )    ->       Any:
         if not 0 <= index < len(self    ):
              raise IndexError('''list index out of range.'''    )
         __snake_case				=							self.head
         if self.head == self.tail:  # just one node
              __snake_case				=							__snake_case				=							None
         elif index == 0:  # delete head node
              __snake_case				=							self.tail.next.next
              __snake_case				=							self.head.next
         else:
              __snake_case				=							self.head
              for _ in range(index - 1    ):
                   __snake_case				=							temp.next
              __snake_case				=							temp.next
              __snake_case				=							temp.next.next
              if index == len(self    ) - 1:  # delete at tail
                   __snake_case				=							temp
         return delete_node.data







    def  lowercase					(		self							:     Dict    )    ->       bool:
         return len(self    ) == 0






def    SCREAMING_SNAKE_CASE       (     ):
     __snake_case				=							CircularLinkedList()
     assert len(snake_case) == 0
     assert circular_linked_list.is_empty() is True
     assert str(snake_case) == ""

     try:
          circular_linked_list.delete_front()
          raise AssertionError  # This should not happen
     except IndexError:
          assert True  # This should happen

     try:
          circular_linked_list.delete_tail()
          raise AssertionError  # This should not happen
     except IndexError:
          assert True  # This should happen

     try:
          circular_linked_list.delete_nth(-1)
          raise AssertionError
     except IndexError:
          assert True

     try:
          circular_linked_list.delete_nth(0)
          raise AssertionError
     except IndexError:
          assert True

     assert circular_linked_list.is_empty() is True
     for i in range(5):
          assert len(snake_case) == i
          circular_linked_list.insert_nth(snake_case,      i + 1)
     assert str(snake_case) == "->".join(str(snake_case) for i in range(1,      6))

     circular_linked_list.insert_tail(6)
     assert str(snake_case) == "->".join(str(snake_case) for i in range(1,      7))
     circular_linked_list.insert_head(0)
     assert str(snake_case) == "->".join(str(snake_case) for i in range(0,      7))

     assert circular_linked_list.delete_front() == 0
     assert circular_linked_list.delete_tail() == 6
     assert str(snake_case) == "->".join(str(snake_case) for i in range(1,      6))
     assert circular_linked_list.delete_nth(2) == 3

     circular_linked_list.insert_nth(2,      3)
     assert str(snake_case) == "->".join(str(snake_case) for i in range(1,      6))

     assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
       import doctest

       doctest.testmod()