


'''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 _lowerCamelCase	(						UpperCAmelCase_		,					unittest.TestCase							):



		'''simple docstring'''







		lowerCAmelCase     :      Any				   =    DebertaTokenizer
		lowerCAmelCase     :      List[str]				   =    True
		lowerCAmelCase     :      Optional[Any]				   =    DebertaTokenizerFast







		def        __lowerCAmelCase		( self		:						List[str]      )  ->			Tuple:
			super().setUp()

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

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







		def        __lowerCAmelCase		( self		:						int  ,					**_A		:						List[str]      )  ->			List[Any]:
			kwargs.update(self.special_tokens_map      )
			return self.tokenizer_class.from_pretrained(self.tmpdirname  ,					**__lowercase      )







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						int      )  ->			Any:
			__magic_name__	:   List[Any]					     =					'''lower newer'''
			__magic_name__	:   Union[str, Any]					     =					'''lower newer'''
			return input_text, output_text







		def        __lowerCAmelCase		( self		:						Any      )  ->			Dict:
			__magic_name__	:   int					     =					self.get_tokenizer()
			__magic_name__	:   Any					     =					'''lower newer'''
			__magic_name__	:   Optional[int]					     =					['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
			__magic_name__	:   Optional[int]					     =					tokenizer.tokenize(__lowercase      )
			self.assertListEqual(__lowercase  ,					__lowercase      )

			__magic_name__	:   str					     =					tokens + [tokenizer.unk_token]
			__magic_name__	:   Dict					     =					[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
			self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase      )  ,					__lowercase      )







		def        __lowerCAmelCase		( self		:						Any      )  ->			Tuple:
			__magic_name__	:   int					     =					self.get_tokenizer()
			__magic_name__	:   Union[str, Any]					     =					tokenizer('Hello'  ,					'World'      )
			__magic_name__	:   Optional[Any]					     =					[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
			self.assertListEqual(tokd['token_type_ids']  ,					__lowercase      )







		@slow
		def        __lowerCAmelCase		( self		:						int      )  ->			List[Any]:
			__magic_name__	:   int					     =					self.tokenizer_class.from_pretrained('microsoft/deberta-base'      )

			__magic_name__	:   Dict					     =					tokenizer.encode('sequence builders'  ,					add_special_tokens=__lowercase      )
			__magic_name__	:   List[Any]					     =					tokenizer.encode('multi-sequence build'  ,					add_special_tokens=__lowercase      )

			__magic_name__	:   Tuple					     =					tokenizer.encode(
			    'sequence builders'  ,					add_special_tokens=__lowercase  ,					add_prefix_space=__lowercase      )
			__magic_name__	:   Optional[Any]					     =					tokenizer.encode(
			    'sequence builders'  ,					'multi-sequence build'  ,					add_special_tokens=__lowercase  ,					add_prefix_space=__lowercase      )

			__magic_name__	:   Optional[int]					     =					tokenizer.build_inputs_with_special_tokens(__lowercase      )
			__magic_name__	:   Dict					     =					tokenizer.build_inputs_with_special_tokens(__lowercase  ,					__lowercase      )

			assert encoded_sentence == encoded_text_from_decode
			assert encoded_pair == encoded_pair_from_decode







		@slow
		def        __lowerCAmelCase		( self		:						Optional[int]      )  ->			List[Any]:
			__magic_name__	:   List[str]					     =					[self.tokenizer_class]
			if self.test_rust_tokenizer:
				tokenizer_classes.append(self.rust_tokenizer_class      )

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

				__magic_name__	:   Optional[int]					     =					[
				    '''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.''',
				]

				__magic_name__	:   int					     =					tokenizer(__lowercase  ,					padding=__lowercase      )
				__magic_name__	:   List[Any]					     =					[tokenizer.decode(__lowercase  ,					skip_special_tokens=__lowercase      ) for seq in encoding['''input_ids''']]

				# fmt: off
				__magic_name__	:   Tuple					     =					{
				    '''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

				__magic_name__	:   Optional[Any]					     =					[
				    '''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  ,					__lowercase      )

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



'''simple docstring'''

def lowerCamelCase      (      lowerCAmelCase    :     int		):





	"""simple docstring"""







	if not isinstance(lowerCAmelCase		,		lowerCAmelCase		):
		__magic_name__	:   int					     =					f'Input value of [number={number}] must be an integer'
		raise TypeError(lowerCAmelCase		)
	if number < 0:
		return False
	__magic_name__	:   Tuple					     =					number * number
	while number > 0:
		if number % 10 != number_square % 10:
			return False
		number //= 10
		number_square //= 10
	return True


if __name__ == "__main__":
							import doctest

							doctest.testmod()