
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


UpperCAmelCase       =					logging.get_logger(__name__)

UpperCAmelCase       =					{'''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase       =					{
    '''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 			A_  (     __lowerCamelCase  ):




	'''simple docstring'''


	_UpperCamelCase	:	int									=			VOCAB_FILES_NAMES
	_UpperCamelCase	:	Dict									=			PRETRAINED_VOCAB_FILES_MAP
	_UpperCamelCase	:	str									=			["""input_ids""", """attention_mask"""]
	_UpperCamelCase	:	Tuple									=			None





	def __init__(			self						,       snake_case=None						,       snake_case=None						,       snake_case=None						,       snake_case="<unk>"						,       snake_case="<s>"						,       snake_case="</s>"						,       snake_case="<pad>"						,       snake_case=False						,       snake_case=False						,       **snake_case						,       ):
		super().__init__(
		    snake_case						,       snake_case						,       tokenizer_file=snake_case						,       unk_token=snake_case						,       bos_token=snake_case						,       eos_token=snake_case						,       pad_token=snake_case						,       add_prefix_space=snake_case						,       clean_up_tokenization_spaces=snake_case						,       **snake_case						,       )
		lowercase	    =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
		if pre_tok_state.get('add_prefix_space'						,       snake_case			) != add_prefix_space:
			lowercase	    =       getattr(snake_case						,       pre_tok_state.pop('type'			)			)
			lowercase	    =       add_prefix_space
			lowercase	    =       pre_tok_class(**snake_case			)

		lowercase	    =       add_prefix_space





	def       SCREAMING_SNAKE_CASE__		(			self						,       *snake_case						,       **snake_case			):
		lowercase	    =       kwargs.get('is_split_into_words'						,       snake_case			)
		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(*snake_case						,       **snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       *snake_case						,       **snake_case			):
		lowercase	    =       kwargs.get('is_split_into_words'						,       snake_case			)

		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(*snake_case						,       **snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case						,       snake_case = None			):
		lowercase	    =       self._tokenizer.model.save(snake_case						,       name=snake_case			)
		return tuple(snake_case			)





	def       SCREAMING_SNAKE_CASE__		(			self						,       snake_case			):
		lowercase	    =       []
		for is_user, text in conversation.iter_texts():
			input_ids.extend(self.encode(snake_case						,       add_special_tokens=snake_case			) + [self.eos_token_id]			)

		if len(snake_case			) > self.model_max_length:
			lowercase	    =       input_ids[-self.model_max_length :]
		return input_ids




"""simple docstring"""




def 						A							(  snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   1
			SCREAMING_SNAKE_CASE__										=   2
			while i * i <= n:
						SCREAMING_SNAKE_CASE__										=   0
						while n % i == 0:
									n //= i
									multiplicity += 1
						n_divisors *= multiplicity + 1
						i += 1
			if n > 1:
						n_divisors *= 2
			return n_divisors

def 						A							(  ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   1
			SCREAMING_SNAKE_CASE__										=   1

			while True:
						i += 1
						t_num += i

						if count_divisors(snake_case__     ) > 5_00:
									break

			return t_num


if __name__ == "__main__":
						print(solution())


