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


def 		UpperCAmelCase     (      a_						,  a_						,  a_						,  a_		)						->      Union[str, Any]:







				"""simple docstring"""



				__A       =					sum(a_i[j] for j in range(a_						,  len(a_		)		)		)
				__A       =					sum(a_i[j] * base[j] for j in range(min(len(a_		)						,  a_		)		)		)

				__A     ,				__A       =					0, 0
				__A       =					n - i

				__A       =					memo.get(a_		)

				if sub_memo is not None:
								__A       =					sub_memo.get(a_		)

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

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

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

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

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

				__A       =					sub_memo[c]

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

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


def 		UpperCAmelCase     (      a_						,  a_						,  a_						,  a_		)						->      Union[str, Any]:







				"""simple docstring"""



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

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

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

												ds_c += a_i[j]

								if addend > 0:
												break

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


def 		UpperCAmelCase     (      a_						,  a_						,  a_		)						->      Any:







				"""simple docstring"""



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

								if addend == 0:
												break

				while addend > 0:
								__A     ,				__A       =					divmod(a_						,  1_0		)
								digits.append(a_		)


def 		UpperCAmelCase     (      a_ = 1_0**1_5		)						->      int:







				"""simple docstring"""



				__A       =					[1]
				__A       =					1
				__A       =					0
				while True:
								__A     ,				__A       =					next_term(a_						,  2_0						,  i + dn						,  a_		)
								dn += terms_jumped
								if dn == n - i:
												break

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


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



'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


SCREAMING_SNAKE_CASE							:				int     	=			logging.get_logger(__name__)


SCREAMING_SNAKE_CASE							:				Dict     	=			{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE							:				str     	=			{
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

SCREAMING_SNAKE_CASE							:				Union[str, Any]     	=			{
    "facebook/bart-base": 1024,
    "facebook/bart-large": 1024,
    "facebook/bart-large-mnli": 1024,
    "facebook/bart-large-cnn": 1024,
    "facebook/bart-large-xsum": 1024,
    "yjernite/bart_eli5": 1024,
}






class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     VOCAB_FILES_NAMES
				_a    	=     PRETRAINED_VOCAB_FILES_MAP
				_a    	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_a    	=     ["""input_ids""", """attention_mask"""]
				_a    	=     BartTokenizer



				def __init__(    self,     _lowercase=None,     _lowercase=None,     _lowercase=None,     _lowercase="replace",     _lowercase="<s>",     _lowercase="</s>",     _lowercase="</s>",     _lowercase="<s>",     _lowercase="<unk>",     _lowercase="<pad>",     _lowercase="<mask>",     _lowercase=False,     _lowercase=True,     **_lowercase,     )     ->							Dict:
											super().__init__(
											    _lowercase,     _lowercase,     tokenizer_file=_lowercase,     errors=_lowercase,     bos_token=_lowercase,     eos_token=_lowercase,     sep_token=_lowercase,     cls_token=_lowercase,     unk_token=_lowercase,     pad_token=_lowercase,     mask_token=_lowercase,     add_prefix_space=_lowercase,     trim_offsets=_lowercase,     **_lowercase,     )

											SCREAMING_SNAKE_CASE_				=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
											if pre_tok_state.get('add_prefix_space',     _lowercase       ) != add_prefix_space:
																		SCREAMING_SNAKE_CASE_				=				getattr(_lowercase,     pre_tok_state.pop('type'       )       )
																		SCREAMING_SNAKE_CASE_				=				add_prefix_space
																		SCREAMING_SNAKE_CASE_				=				pre_tok_class(**_lowercase       )

											SCREAMING_SNAKE_CASE_				=				add_prefix_space

											# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
											SCREAMING_SNAKE_CASE_				=				'post_processor'
											SCREAMING_SNAKE_CASE_				=				getattr(self.backend_tokenizer,     _lowercase,     _lowercase       )
											if tokenizer_component_instance:
																		SCREAMING_SNAKE_CASE_				=				json.loads(tokenizer_component_instance.__getstate__()       )

																		# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																		if "sep" in state:
																									SCREAMING_SNAKE_CASE_				=				tuple(state['sep']       )
																		if "cls" in state:
																									SCREAMING_SNAKE_CASE_				=				tuple(state['cls']       )

																		SCREAMING_SNAKE_CASE_				=				False

																		if state.get('add_prefix_space',     _lowercase       ) != add_prefix_space:
																									SCREAMING_SNAKE_CASE_				=				add_prefix_space
																									SCREAMING_SNAKE_CASE_				=				True

																		if state.get('trim_offsets',     _lowercase       ) != trim_offsets:
																									SCREAMING_SNAKE_CASE_				=				trim_offsets
																									SCREAMING_SNAKE_CASE_				=				True

																		if changes_to_apply:
																									SCREAMING_SNAKE_CASE_				=				getattr(_lowercase,     state.pop('type'       )       )
																									SCREAMING_SNAKE_CASE_				=				component_class(**_lowercase       )
																									setattr(self.backend_tokenizer,     _lowercase,     _lowercase       )



				@property
				def   a__					(    self       )     ->							str:
											if self._mask_token is None:
																		if self.verbose:
																									logger.error('Using mask_token, but it is not set yet.'       )
																		return None
											return str(self._mask_token       )



				@mask_token.setter
				def   a__					(    self,     _lowercase       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				=				AddedToken(_lowercase,     lstrip=_lowercase,     rstrip=_lowercase       ) if isinstance(_lowercase,     _lowercase       ) else value
											SCREAMING_SNAKE_CASE_				=				value



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							BatchEncoding:
											SCREAMING_SNAKE_CASE_				=				kwargs.get('is_split_into_words',     _lowercase       )

											if is_split_into_words and not self.add_prefix_space:
																		raise ValueError(
																		    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
																		    'to use it with pretokenized inputs.'       )

											return super()._batch_encode_plus(*_lowercase,     **_lowercase       )



				def   a__					(    self,     *_lowercase,     **_lowercase       )     ->							BatchEncoding:
											SCREAMING_SNAKE_CASE_				=				kwargs.get('is_split_into_words',     _lowercase       )

											if is_split_into_words and not self.add_prefix_space:
																		raise ValueError(
																		    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
																		    'to use it with pretokenized inputs.'       )

											return super()._encode_plus(*_lowercase,     **_lowercase       )



				def   a__					(    self,     _lowercase,     _lowercase = None       )     ->							Tuple[str]:
											SCREAMING_SNAKE_CASE_				=				self._tokenizer.model.save(_lowercase,     name=_lowercase       )
											return tuple(_lowercase       )



				def   a__					(    self,     _lowercase,     _lowercase=None       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
											if token_ids_a is None:
																		return output

											return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





				def   a__					(    self,     _lowercase,     _lowercase = None       )     ->							List[int]:
											SCREAMING_SNAKE_CASE_				=				[self.sep_token_id]
											SCREAMING_SNAKE_CASE_				=				[self.cls_token_id]

											if token_ids_a is None:
																		return len(cls + token_ids_a + sep       ) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]





