

"""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      _UpperCAmelCase   (							_lowerCAmelCase		,    unittest.TestCase       ):
	a__		: str					=					DebertaTokenizer
	a__		: Any					=					True
	a__		: Union[str, Any]					=					DebertaTokenizerFast



	def        a  (  self     :       Union[str, Any]						):
		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(_lowercase			,    range(len(_lowercase						)						)						)						)
		__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(_lowercase						) + '''\n'''						)
		with open(self.merges_file			,    '''w'''			,    encoding='''utf-8'''						) as fp:
			fp.write('''\n'''.join(_lowercase						)						)



	def        a  (  self     :       Tuple			,    **_lowercase     :       int						):
		kwargs.update(self.special_tokens_map						)
		return self.tokenizer_class.from_pretrained(self.tmpdirname			,    **_lowercase						)



	def        a  (  self     :       Union[str, Any]			,    _lowercase     :       List[str]						):
		__UpperCAmelCase      		= '''lower newer'''
		__UpperCAmelCase      		= '''lower newer'''
		return input_text, output_text



	def        a  (  self     :       int						):
		__UpperCAmelCase      		= self.get_tokenizer()
		__UpperCAmelCase      		= '''lower newer'''
		__UpperCAmelCase      		= ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
		__UpperCAmelCase      		= tokenizer.tokenize(_lowercase						)
		self.assertListEqual(_lowercase			,    _lowercase						)

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



	def        a  (  self     :       int						):
		__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''']			,    _lowercase						)



	@slow
	def        a  (  self     :       Any						):
		__UpperCAmelCase      		= self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''						)

		__UpperCAmelCase      		= tokenizer.encode('''sequence builders'''			,    add_special_tokens=_lowercase						)
		__UpperCAmelCase      		= tokenizer.encode('''multi-sequence build'''			,    add_special_tokens=_lowercase						)

		__UpperCAmelCase      		= tokenizer.encode(
		    '''sequence builders'''			,    add_special_tokens=_lowercase			,    add_prefix_space=_lowercase						)
		__UpperCAmelCase      		= tokenizer.encode(
		    '''sequence builders'''			,    '''multi-sequence build'''			,    add_special_tokens=_lowercase			,    add_prefix_space=_lowercase						)

		__UpperCAmelCase      		= tokenizer.build_inputs_with_special_tokens(_lowercase						)
		__UpperCAmelCase      		= tokenizer.build_inputs_with_special_tokens(_lowercase			,    _lowercase						)

		assert encoded_sentence == encoded_text_from_decode
		assert encoded_pair == encoded_pair_from_decode



	@slow
	def        a  (  self     :       Optional[Any]						):
		__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(_lowercase			,    padding=_lowercase						)
			__UpperCAmelCase      		= [tokenizer.decode(_lowercase			,    skip_special_tokens=_lowercase						) for seq in encoding['''input_ids''']]

			# fmt: off
			__UpperCAmelCase      		= {
			    '''input_ids''': [
			        [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 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, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 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, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 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			,    _lowercase						)

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





def        __lowerCAmelCase		(	__lowerCamelCase						:     int					, __lowerCamelCase						:     int						)	->    bool:
					return numa ^ numa < 0


if __name__ == "__main__":
				import doctest

				doctest.testmod()




