


'''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 						a__					(					a_      ,  unittest.TestCase   ):





			'''simple docstring'''

			A						:				Dict					      =			DebertaTokenizer
			A						:				Tuple					      =			True
			A						:				Optional[int]					      =			DebertaTokenizerFast
			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		Union[str, Any]:
						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(lowerCAmelCase_			,    range(len(lowerCAmelCase_	)	)	)	)
						__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(lowerCAmelCase_	) + '\n'	)
						with open(self.merges_file			,    'w'			,    encoding='utf-8'	) as fp:
									fp.write('\n'.join(lowerCAmelCase_	)	)
			def 					lowerCAmelCase     (  self							:			str			,    **lowerCAmelCase_							:			str	)      ->		Optional[int]:
						kwargs.update(self.special_tokens_map	)
						return self.tokenizer_class.from_pretrained(self.tmpdirname			,    **lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			Any	)      ->		Dict:
						__A=	'lower newer'
						__A=	'lower newer'
						return input_text, output_text
			def 					lowerCAmelCase     (  self							:			str	)      ->		Union[str, Any]:
						__A=	self.get_tokenizer()
						__A=	'lower newer'
						__A=	['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
						__A=	tokenizer.tokenize(lowerCAmelCase_	)
						self.assertListEqual(lowerCAmelCase_			,    lowerCAmelCase_	)

						__A=	tokens + [tokenizer.unk_token]
						__A=	[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_	)			,    lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		List[Any]:
						__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']			,    lowerCAmelCase_	)
			@slow
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Optional[Any]:
						__A=	self.tokenizer_class.from_pretrained('microsoft/deberta-base'	)

						__A=	tokenizer.encode('sequence builders'			,    add_special_tokens=lowerCAmelCase_	)
						__A=	tokenizer.encode('multi-sequence build'			,    add_special_tokens=lowerCAmelCase_	)

						__A=	tokenizer.encode(
						    'sequence builders'			,    add_special_tokens=lowerCAmelCase_			,    add_prefix_space=lowerCAmelCase_	)
						__A=	tokenizer.encode(
						    'sequence builders'			,    'multi-sequence build'			,    add_special_tokens=lowerCAmelCase_			,    add_prefix_space=lowerCAmelCase_	)

						__A=	tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_	)
						__A=	tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_			,    lowerCAmelCase_	)

						assert encoded_sentence == encoded_text_from_decode
						assert encoded_pair == encoded_pair_from_decode

			@slow
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		List[str]:
						__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(lowerCAmelCase_			,    padding=lowerCAmelCase_	)
									__A=	[tokenizer.decode(lowerCAmelCase_			,    skip_special_tokens=lowerCAmelCase_	) for seq in encoding['input_ids']]

									# fmt: off
									__A=	{
									    'input_ids': [
									        [1, 2_118, 11_126, 565, 35, 83, 25_191, 163, 18_854, 13, 12_156, 12, 16_101, 25_376, 13_807, 9, 22_205, 27_893, 1_635, 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, 2_118, 11_126, 565, 24_536, 80, 43_797, 4_878, 7_373, 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, 3_724, 1_538, 33_183, 11_303, 43_797, 1_938, 4, 870, 24_165, 29_105, 5, 739, 32_644, 33_183, 11_303, 36_173, 88, 80, 650, 7_821, 45_940, 6, 52, 2_559, 5, 1_836, 9, 5, 7_397, 13_171, 31, 5, 1_836, 9, 32_644, 33_183, 11_303, 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			,    lowerCAmelCase_	)

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







'''simple docstring'''





import math






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			if not isinstance(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE ):
						__A=	f"""Input value of [number={number}] must be an integer"""
						raise TypeError(_SCREAMING_SNAKE_CASE )

			if number < 1:
						__A=	f"""Input value of [number={number}] must be > 0"""
						raise ValueError(_SCREAMING_SNAKE_CASE )
			elif number == 1:
						return 3
			elif number == 2:
						return 5
			else:
						__A=	int(math.log(number // 3,2 ) ) + 2

						__A=	[3, 5]
						__A=	2
						__A=	3
						for block in range(1,_SCREAMING_SNAKE_CASE ):
									for _ in range(_SCREAMING_SNAKE_CASE ):
												proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1] )
												proth_index += 1
									increment *= 2

			return proth_list[number - 1]


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					for number in range(1_1):
										UpperCAmelCase__  			=				0
										try:
															UpperCAmelCase__  			=				proth(number)
										except ValueError:
															print(F"""ValueError: there is no {number}th Proth number""")
															continue

										print(F"""The {number}th Proth number: {value}""")



