"""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        snake_case (				__UpperCAmelCase   ,					unittest.TestCase       ):




					"""simple docstring"""

					snake_case__       					=				DebertaTokenizer
					snake_case__       					=				True
					snake_case__       					=				DebertaTokenizerFast







					def 	__lowerCAmelCase		(       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(lowerCamelCase__					,range(len(lowerCamelCase__    )    )    )    )
									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(lowerCamelCase__    ) + '\n'    )
									with open(self.merges_file					,'w'					,encoding='utf-8'    ) as fp:
													fp.write('\n'.join(lowerCamelCase__    )    )







					def 	__lowerCAmelCase		(       self     :			Tuple					,**lowerCamelCase__     :			Optional[int]    ):
									kwargs.update(self.special_tokens_map    )
									return self.tokenizer_class.from_pretrained(self.tmpdirname					,**lowerCamelCase__    )







					def 	__lowerCAmelCase		(       self     :			int					,lowerCamelCase__     :			Any    ):
									UpperCAmelCase__    =							'lower newer'
									UpperCAmelCase__    =							'lower newer'
									return input_text, output_text







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									UpperCAmelCase__    =							self.get_tokenizer()
									UpperCAmelCase__    =							'lower newer'
									UpperCAmelCase__    =							['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
									UpperCAmelCase__    =							tokenizer.tokenize(lowerCamelCase__    )
									self.assertListEqual(lowerCamelCase__					,lowerCamelCase__    )

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







					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									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']					,lowerCamelCase__    )







					@slow
					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									UpperCAmelCase__    =							self.tokenizer_class.from_pretrained('microsoft/deberta-base'    )

									UpperCAmelCase__    =							tokenizer.encode('sequence builders'					,add_special_tokens=lowerCamelCase__    )
									UpperCAmelCase__    =							tokenizer.encode('multi-sequence build'					,add_special_tokens=lowerCamelCase__    )

									UpperCAmelCase__    =							tokenizer.encode(
									    'sequence builders'					,add_special_tokens=lowerCamelCase__					,add_prefix_space=lowerCamelCase__    )
									UpperCAmelCase__    =							tokenizer.encode(
									    'sequence builders'					,'multi-sequence build'					,add_special_tokens=lowerCamelCase__					,add_prefix_space=lowerCamelCase__    )

									UpperCAmelCase__    =							tokenizer.build_inputs_with_special_tokens(lowerCamelCase__    )
									UpperCAmelCase__    =							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     :			List[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(lowerCamelCase__					,padding=lowerCamelCase__    )
													UpperCAmelCase__    =							[tokenizer.decode(lowerCamelCase__					,skip_special_tokens=lowerCamelCase__    ) for seq in encoding['input_ids']]

													# fmt: off
													UpperCAmelCase__    =							{
													    '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

													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					,lowerCamelCase__    )

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




"""simple docstring"""






lowerCAmelCase__						:						Tuple		  =			range(2, 20 + 1)
lowerCAmelCase__						:						Optional[Any]		  =			[10**k for k in range(ks[-1] + 1)]
lowerCAmelCase__						:						dict[int, dict[int, list[list[int]]]]		  =			{}



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							sum(a_i[j] for j in range(lowerCamelCase      ,    len(lowerCamelCase ) ) )
				UpperCAmelCase__    =							sum(a_i[j] * base[j] for j in range(min(len(lowerCamelCase )      ,    lowerCamelCase ) ) )

				UpperCAmelCase__   ,			UpperCAmelCase__    =							0, 0
				UpperCAmelCase__    =							n - i

				UpperCAmelCase__    =							memo.get(lowerCamelCase )

				if sub_memo is not None:
								UpperCAmelCase__    =							sub_memo.get(lowerCamelCase )

								if jumps is not None and len(lowerCamelCase ) > 0:
												# find and make the largest jump without going over
												UpperCAmelCase__    =							-1
												for _k in range(len(lowerCamelCase ) - 1      ,    -1      ,    -1 ):
																if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
																				UpperCAmelCase__    =							_k
																				break

												if max_jump >= 0:
																UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							jumps[max_jump]
																# since the difference between jumps is cached, add c
																UpperCAmelCase__    =							diff + c
																for j in range(min(lowerCamelCase      ,    len(lowerCamelCase ) ) ):
																				UpperCAmelCase__   ,			UpperCAmelCase__    =							divmod(lowerCamelCase      ,    1_0 )
																if new_c > 0:
																				add(lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase )

								else:
												UpperCAmelCase__    =							[]
				else:
								UpperCAmelCase__    =							{c: []}
								UpperCAmelCase__    =							sub_memo

				if dn >= max_dn or c + diff >= base[k]:
								return diff, dn

				if k > ks[0]:
								while True:
												# keep doing smaller jumps
												UpperCAmelCase__   ,			UpperCAmelCase__    =							next_term(lowerCamelCase      ,    k - 1      ,    i + dn      ,    lowerCamelCase )
												diff += _diff
												dn += terms_jumped

												if dn >= max_dn or c + diff >= base[k]:
																break
				else:
								# would be too small a jump, just compute sequential terms instead
								UpperCAmelCase__   ,			UpperCAmelCase__    =							compute(lowerCamelCase      ,    lowerCamelCase      ,    i + dn      ,    lowerCamelCase )
								diff += _diff
								dn += terms_jumped

				UpperCAmelCase__    =							sub_memo[c]

				# keep jumps sorted by # of terms skipped
				UpperCAmelCase__    =							0
				while j < len(lowerCamelCase ):
								if jumps[j][1] > dn:
												break
								j += 1

				# cache the jump for this value digitsum(b) and c
				sub_memo[c].insert(lowerCamelCase      ,    (diff, dn, k) )
				return (diff, dn)



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				if i >= n:
								return 0, i
				if k > len(lowerCamelCase ):
								a_i.extend([0 for _ in range(k - len(lowerCamelCase ) )] )

				# note: a_i -> b * 10^k + c
				# ds_b -> digitsum(b)
				# ds_c -> digitsum(c)
				UpperCAmelCase__    =							i
				UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							0, 0, 0
				for j in range(len(lowerCamelCase ) ):
								if j >= k:
												ds_b += a_i[j]
								else:
												ds_c += a_i[j]

				while i < n:
								i += 1
								UpperCAmelCase__    =							ds_c + ds_b
								diff += addend
								UpperCAmelCase__    =							0
								for j in range(lowerCamelCase ):
												UpperCAmelCase__    =							a_i[j] + addend
												UpperCAmelCase__   ,			UpperCAmelCase__    =							divmod(lowerCamelCase      ,    1_0 )

												ds_c += a_i[j]

								if addend > 0:
												break

				if addend > 0:
								add(lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase )
				return diff, i - start_i



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				for j in range(lowerCamelCase      ,    len(lowerCamelCase ) ):
								UpperCAmelCase__    =							digits[j] + addend
								if s >= 1_0:
												UpperCAmelCase__   ,			UpperCAmelCase__    =							divmod(lowerCamelCase      ,    1_0 )
												UpperCAmelCase__    =							addend // 1_0 + quotient
								else:
												UpperCAmelCase__    =							s
												UpperCAmelCase__    =							addend // 1_0

								if addend == 0:
												break

				while addend > 0:
								UpperCAmelCase__   ,			UpperCAmelCase__    =							divmod(lowerCamelCase      ,    1_0 )
								digits.append(lowerCamelCase )



def 						a_		(      lowerCamelCase = 1_0**1_5 ):
				UpperCAmelCase__    =							[1]
				UpperCAmelCase__    =							1
				UpperCAmelCase__    =							0
				while True:
								UpperCAmelCase__   ,			UpperCAmelCase__    =							next_term(lowerCamelCase      ,    2_0      ,    i + dn      ,    lowerCamelCase )
								dn += terms_jumped
								if dn == n - i:
												break

				UpperCAmelCase__    =							0
				for j in range(len(lowerCamelCase ) ):
								a_n += digits[j] * 1_0**j
				return a_n


if __name__ == "__main__":
						print(F"""{solution() = }""")



