





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			:       List[Any]		):





												'''simple docstring'''





												super().setUp()

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

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


						def  __A ( self			:       Any					,	**UpperCamelCase__			:       str		):





												'''simple docstring'''





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


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							'''lower newer'''
												SCREAMING_SNAKE_CASE							:					int           =							'''lower newer'''
												return input_text, output_text


						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self.get_tokenizer()
												SCREAMING_SNAKE_CASE							:					Dict           =							'''lower newer'''
												SCREAMING_SNAKE_CASE							:					str           =							['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
												SCREAMING_SNAKE_CASE							:					List[str]           =							tokenizer.tokenize(UpperCamelCase__		)
												self.assertListEqual(UpperCamelCase__					,	UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							tokens + [tokenizer.unk_token]
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__		)					,	UpperCamelCase__		)


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[Any]           =							self.get_tokenizer()
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							tokenizer('''Hello'''					,	'''World'''		)
												SCREAMING_SNAKE_CASE							:					Dict           =							[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
												self.assertListEqual(tokd['''token_type_ids''']					,	UpperCamelCase__		)


						@slow
						def  __A ( self			:       int		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							tokenizer.encode('''sequence builders'''					,	add_special_tokens=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					int           =							tokenizer.encode('''multi-sequence build'''					,	add_special_tokens=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Dict           =							tokenizer.encode(
												    '''sequence builders'''					,	add_special_tokens=UpperCamelCase__					,	add_prefix_space=UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Dict           =							tokenizer.encode(
												    '''sequence builders'''					,	'''multi-sequence build'''					,	add_special_tokens=UpperCamelCase__					,	add_prefix_space=UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					str           =							tokenizer.build_inputs_with_special_tokens(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							tokenizer.build_inputs_with_special_tokens(UpperCamelCase__					,	UpperCamelCase__		)

												assert encoded_sentence == encoded_text_from_decode
												assert encoded_pair == encoded_pair_from_decode



						@slow
						def  __A ( self			:       Optional[Any]		):





												'''simple docstring'''





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

												for tokenizer_class in tokenizer_classes:
																		SCREAMING_SNAKE_CASE							:					List[Any]           =							tokenizer_class.from_pretrained('''microsoft/deberta-base'''		)

																		SCREAMING_SNAKE_CASE							:					Union[str, 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.''',
																		]

																		SCREAMING_SNAKE_CASE							:					str           =							tokenizer(UpperCamelCase__					,	padding=UpperCamelCase__		)
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							[tokenizer.decode(UpperCamelCase__					,	skip_special_tokens=UpperCamelCase__		) for seq in encoding['''input_ids''']]

																		# fmt: off
																		SCREAMING_SNAKE_CASE							:					List[str]           =							{
																		    '''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

																		SCREAMING_SNAKE_CASE							:					str           =							[
																		    '''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 typing import Any





class       lowercase__		(     UpperCamelCase_):
						pass






class       lowercase__		:


						def __init__( self			:       Union[str, Any]					,	UpperCamelCase__			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							data
												SCREAMING_SNAKE_CASE							:					Node | None           =							None


						def __iter__( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self
												SCREAMING_SNAKE_CASE							:					Tuple           =							[]
												while node:
																		if node in visited:
																								raise ContainsLoopError
																		visited.append(UpperCamelCase__		)
																		yield node.data
																		SCREAMING_SNAKE_CASE							:					Dict           =							node.next_node



						@property
						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												try:
																		list(self		)
																		return False
												except ContainsLoopError:
																		return True


if __name__ == "__main__":
					__UpperCamelCase	: List[Any]    		=  Node(1)
					__UpperCamelCase	: str    		=  Node(2)
					__UpperCamelCase	: Dict    		=  Node(3)
					__UpperCamelCase	: List[Any]    		=  Node(4)
					print(root_node.has_loop)  # False
					__UpperCamelCase	: int    		=  root_node.next_node
					print(root_node.has_loop)  # True

					__UpperCamelCase	: Union[str, Any]    		=  Node(5)
					__UpperCamelCase	: Union[str, Any]    		=  Node(6)
					__UpperCamelCase	: List[Any]    		=  Node(5)
					__UpperCamelCase	: List[str]    		=  Node(6)
					print(root_node.has_loop)  # False

					__UpperCamelCase	: List[Any]    		=  Node(1)
					print(root_node.has_loop)  # False





