




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 __snake_case			(			UpperCamelCase__ ,  unittest.TestCase		):
   UpperCAmelCase__  :    str          =  DebertaTokenizer
   UpperCAmelCase__  :    Any          =  True
   UpperCAmelCase__  :    Union[str, Any]          =  DebertaTokenizerFast







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        super().setUp()

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

        UpperCAmelCase_          =							os.path.join(self.tmpdirname					,						VOCAB_FILES_NAMES['''vocab_file'''])
        UpperCAmelCase_          =							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(UpperCamelCase_) + '''\n''')
        with open(self.merges_file					,						'''w'''					,						encoding='''utf-8''') as fp:
             fp.write('''\n'''.join(UpperCamelCase_))







   def lowerCamelCase						(      self		:						Union[str, Any]					,						**_snake_case		:						Union[str, Any]):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						List[str]					,						_snake_case		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							'''lower newer'''
        UpperCAmelCase_          =							'''lower newer'''
        return input_text, output_text







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							self.get_tokenizer()
        UpperCAmelCase_          =							'''lower newer'''
        UpperCAmelCase_          =							['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
        UpperCAmelCase_          =							tokenizer.tokenize(UpperCamelCase_)
        self.assertListEqual(UpperCamelCase_					,						UpperCamelCase_)

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







   def lowerCamelCase						(      self		:						List[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.get_tokenizer()
        UpperCAmelCase_          =							tokenizer('''Hello'''					,						'''World''')
        UpperCAmelCase_          =							[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
        self.assertListEqual(tokd['''token_type_ids''']					,						UpperCamelCase_)







   @slow
   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							self.tokenizer_class.from_pretrained('''microsoft/deberta-base''')

        UpperCAmelCase_          =							tokenizer.encode('''sequence builders'''					,						add_special_tokens=UpperCamelCase_)
        UpperCAmelCase_          =							tokenizer.encode('''multi-sequence build'''					,						add_special_tokens=UpperCamelCase_)

        UpperCAmelCase_          =							tokenizer.encode(
            '''sequence builders'''					,						add_special_tokens=UpperCamelCase_					,						add_prefix_space=UpperCamelCase_)
        UpperCAmelCase_          =							tokenizer.encode(
            '''sequence builders'''					,						'''multi-sequence build'''					,						add_special_tokens=UpperCamelCase_					,						add_prefix_space=UpperCamelCase_)

        UpperCAmelCase_          =							tokenizer.build_inputs_with_special_tokens(UpperCamelCase_)
        UpperCAmelCase_          =							tokenizer.build_inputs_with_special_tokens(UpperCamelCase_					,						UpperCamelCase_)

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode







   @slow
   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


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

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

             UpperCAmelCase_          =							[
                 '''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.''',
             ]

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

             # fmt: off
             UpperCAmelCase_          =							{
                 '''input_ids''': [
                     [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 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, 11126, 565, 24536, 80, 43797, 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, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 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

             UpperCAmelCase_          =							[
                 '''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					,						UpperCamelCase_)

             for expected, decoded in zip(UpperCamelCase_					,						UpperCamelCase_):
                  self.assertEqual(UpperCamelCase_					,						UpperCamelCase_)










from __future__ import annotations

from collections.abc import Iterator


class        _a							:

							def __init__(							self:	List[str]    ,						UpperCamelCase_:	int							)			->   None:





									"""simple docstring"""







									lowercase__            =      value
									lowercase__            =      None
									lowercase__            =      None


class        _a							:

							def __init__(							self:	Union[str, Any]    ,						UpperCamelCase_:	Node							)			->   None:





									"""simple docstring"""







									lowercase__            =      tree

							def       lowerCamelCase_ (							self:	Any    ,						UpperCamelCase_:	Node | None							)			->   int:





									"""simple docstring"""







									if node is None:
											return 0
									return node.value + (
									    self.depth_first_search(node.left							) + self.depth_first_search(node.right							)
									)


							def __iter__(							self:	List[str]							)			->   Iterator[int]:





									"""simple docstring"""







									yield self.depth_first_search(self.tree							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()


