"""simple docstring"""







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation


_a  :  Dict=				logging.get_logger(__name__)

_a  :  str=				{"tokenizer_file": "tokenizer.json"}

_a  :  str=				{
    "tokenizer_file": {
        "bigscience/tokenizer": "https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json",
        "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json",
        "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json",
        "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json",
        "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json",
        "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json",
        "bigscience/bloom": "https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json",
    },
}

class 	UpperCamelCase    (					lowercase		):
				UpperCAmelCase       :   Union[str, Any]					=	VOCAB_FILES_NAMES
				UpperCAmelCase       :   Any					=	PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase       :   Union[str, Any]					=	["""input_ids""", """attention_mask"""]
				UpperCAmelCase       :   Optional[int]					=	None



				def __init__(self       :   List[Any]			,						_A       :   Optional[Any]=None			,						_A       :   Any=None			,						_A       :   Dict=None			,						_A       :   Optional[Any]="<unk>"			,						_A       :   str="<s>"			,						_A       :   Dict="</s>"			,						_A       :   Union[str, Any]="<pad>"			,						_A       :   Any=False			,						_A       :   List[str]=False			,						**_A       :   str			,						)					->			List[Any]:
					super().__init__(
					    __a			,						__a			,						tokenizer_file=__a			,						unk_token=__a			,						bos_token=__a			,						eos_token=__a			,						pad_token=__a			,						add_prefix_space=__a			,						clean_up_tokenization_spaces=__a			,						**__a			,						)
					__snake_case    :							Optional[int]   	=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
					if pre_tok_state.get('add_prefix_space'			,						__a) != add_prefix_space:
						__snake_case    :							List[Any]   	=				getattr(__a			,						pre_tok_state.pop('type'))
						__snake_case    :							int   	=				add_prefix_space
						__snake_case    :							Union[str, Any]   	=				pre_tok_class(**__a)

					__snake_case    :							Tuple   	=				add_prefix_space



				def       _lowercase  (self       :   str			,						*_A       :   List[Any]			,						**_A       :   Tuple)					->			BatchEncoding:
					__snake_case    :							List[str]   	=				kwargs.get('is_split_into_words'			,						__a)
					if not (self.add_prefix_space or not is_split_into_words):
						raise Exception(
						    f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
						    ' pretokenized inputs.')

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



				def       _lowercase  (self       :   List[str]			,						*_A       :   Optional[int]			,						**_A       :   List[str])					->			BatchEncoding:
					__snake_case    :							Dict   	=				kwargs.get('is_split_into_words'			,						__a)

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

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



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   Optional[Any]			,						_A       :   str = None)					->			Tuple[str]:
					__snake_case    :							Union[str, Any]   	=				self._tokenizer.model.save(__a			,						name=__a)
					return tuple(__a)






				def       _lowercase  (self       :   int			,						_A       :   List[Any])					->			List[int]:
					__snake_case    :							Tuple   	=				[]
					for is_user, text in conversation.iter_texts():
						input_ids.extend(self.encode(__a			,						add_special_tokens=__a) + [self.eos_token_id])

					if len(__a) > self.model_max_length:
						__snake_case    :							int   	=				input_ids[-self.model_max_length :]
					return input_ids






"""simple docstring"""







from __future__ import annotations
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       int						)							->    list[int]:

	'''simple docstring'''





	__snake_case    :							Union[str, Any]   	=				[True] * limit
	__snake_case    :							Tuple   	=				False
	__snake_case    :							Union[str, Any]   	=				False
	__snake_case    :							List[str]   	=				True

	for i in range(3    ,							int(limit**0.5 + 1						)    ,							2						):
		__snake_case    :							List[str]   	=				i * 2
		while index < limit:
			__snake_case    :							List[str]   	=				False
			__snake_case    :							Union[str, Any]   	=				index + i

	__snake_case    :							str   	=				[2]

	for i in range(3    ,							UpperCAmelCase_    ,							2						):
		if is_prime[i]:
			primes.append(UpperCAmelCase_						)

	return primes
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       int = 1_00_00_00						)							->    int:

	'''simple docstring'''





	__snake_case    :							Tuple   	=				prime_sieve(UpperCAmelCase_						)
	__snake_case    :							int   	=				0
	__snake_case    :							str   	=				0

	for i in range(len(UpperCAmelCase_						)						):
		for j in range(i + length    ,							len(UpperCAmelCase_						)						):
			__snake_case    :							Union[str, Any]   	=				sum(primes[i:j]						)
			if sol >= ceiling:
				break

			if sol in primes:
				__snake_case    :							List[Any]   	=				j - i
				__snake_case    :							Optional[Any]   	=				sol

	return largest


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





