




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 		_lowercase		(       _UpperCAmelCase,   unittest.TestCase	):
   _UpperCAmelCase     	=			DebertaTokenizer
   _UpperCAmelCase     	=			True
   _UpperCAmelCase     	=			DebertaTokenizerFast

   def 		A							(      self			:       str							)       ->				Tuple:


    """simple docstring"""






    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    a						=		[
        "l",
        "o",
        "w",
        "e",
        "r",
        "s",
        "t",
        "i",
        "d",
        "n",
        "\u0120",
        "\u0120l",
        "\u0120n",
        "\u0120lo",
        "\u0120low",
        "er",
        "\u0120lowest",
        "\u0120newer",
        "\u0120wider",
        "[UNK]",
    ]
    a						=		dict(zip(A_							,   range(len(A_							)							)							)							)
    a						=		["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
    a						=		{"unk_token": "[UNK]"}

    a						=		os.path.join(self.tmpdirname							,   VOCAB_FILES_NAMES["vocab_file"]							)
    a						=		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 		A							(      self			:       Any							,   **__lowerCAmelCase			:       Union[str, Any]							)       ->				List[Any]:


    """simple docstring"""






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

   def 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       Tuple							)       ->				Dict:


    """simple docstring"""






    a						=		"lower newer"
    a						=		"lower newer"
    return input_text, output_text

   def 		A							(      self			:       Any							)       ->				List[str]:


    """simple docstring"""






    a						=		self.get_tokenizer()
    a						=		"lower newer"
    a						=		["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
    a						=		tokenizer.tokenize(A_							)
    self.assertListEqual(A_							,   A_							)

    a						=		tokens + [tokenizer.unk_token]
    a						=		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(A_							)							,   A_							)

   def 		A							(      self			:       str							)       ->				Union[str, Any]:


    """simple docstring"""






    a						=		self.get_tokenizer()
    a						=		tokenizer("Hello"							,   "World"							)
    a						=		[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
    self.assertListEqual(tokd["token_type_ids"]							,   A_							)

   @slow
   def 		A							(      self			:       Tuple							)       ->				List[Any]:


    """simple docstring"""






    a						=		self.tokenizer_class.from_pretrained("microsoft/deberta-base"							)

    a						=		tokenizer.encode("sequence builders"							,   add_special_tokens=A_							)
    a						=		tokenizer.encode("multi-sequence build"							,   add_special_tokens=A_							)

    a						=		tokenizer.encode(
        "sequence builders"							,   add_special_tokens=A_							,   add_prefix_space=A_							)
    a						=		tokenizer.encode(
        "sequence builders"							,   "multi-sequence build"							,   add_special_tokens=A_							,   add_prefix_space=A_							)

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

    assert encoded_sentence == encoded_text_from_decode
    assert encoded_pair == encoded_pair_from_decode




   @slow
   def 		A							(      self			:       List[str]							)       ->				Dict:


    """simple docstring"""






    a						=		[self.tokenizer_class]
    if self.test_rust_tokenizer:
     tokenizer_classes.append(self.rust_tokenizer_class							)

    for tokenizer_class in tokenizer_classes:
     a						=		tokenizer_class.from_pretrained("microsoft/deberta-base"							)

     a						=		[
         "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.",
     ]

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

     # fmt: off
     a						=		{
         "input_ids": [
             [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 1635, 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, 2118, 1_1126, 565, 2_4536, 80, 4_3797, 4878, 7373, 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, 3724, 1538, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 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

     a						=		[
         "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_							)






from __future__ import annotations

from collections.abc import Iterable, Iterator
from dataclasses import dataclass

A_		:     List[str]        =     (3, 9, -11, 0, 7, 5, 1, -1)
A_		:     Optional[int]        =     (4, 6, 2, 0, 8, 10, 3, -2)



@dataclass
class 		_lowercase		:
   _UpperCAmelCase     	=			42
   _UpperCAmelCase     	=			42



class 		_lowercase		:

   def __init__(      self			:       List[Any]							,   __lowerCAmelCase			:       Iterable[int]							)       ->				None:


    """simple docstring"""






    a						=		None
    for i in sorted(__lowerCAmelCase							,   reverse=__lowerCAmelCase							):
     a						=		Node(__lowerCAmelCase							,   self.head							)

   def __iter__(      self			:       Union[str, Any]							)       ->				Iterator[int]:


    """simple docstring"""






    a						=		self.head
    while node:
     yield node.data
     a						=		node.next_node

   def __len__(      self			:       Tuple							)       ->				int:


    """simple docstring"""






    return sum(1 for _ in self							)




   def __str__(      self			:       Union[str, Any]							)       ->				str:


    """simple docstring"""






    return " -> ".join([str(__lowerCAmelCase							) for node in self]							)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :SortedLinkedList ,					UpperCAmelCase__ :SortedLinkedList					):






 '''simple docstring'''

 return SortedLinkedList(list(UpperCAmelCase__					) + list(UpperCAmelCase__					)					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     A_		:     Optional[Any]        =     SortedLinkedList
     print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))

