



'''simple docstring'''

import json
from typing import TYPE_CHECKING, 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_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation

_A				:    int     						=logging.get_logger(__name__)


_A				:    Optional[Any]     						={
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

_A				:    Any     						={
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

_A				:    Union[str, Any]     						={'''facebook/blenderbot-3B''': 1_2_8}



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						 =						VOCAB_FILES_NAMES
					A_						 =						PRETRAINED_VOCAB_FILES_MAP
					A_						 =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A_						 =						["""input_ids""", """attention_mask"""]
					A_						 =						BlenderbotTokenizer




					def __init__(  self				:	str     ,  UpperCamelCase_				:	Dict=None     ,  UpperCamelCase_				:	str=None     ,  UpperCamelCase_				:	Union[str, Any]=None     ,  UpperCamelCase_				:	Any="replace"     ,  UpperCamelCase_				:	Optional[int]="<s>"     ,  UpperCamelCase_				:	List[Any]="</s>"     ,  UpperCamelCase_				:	Any="</s>"     ,  UpperCamelCase_				:	Dict="<s>"     ,  UpperCamelCase_				:	Optional[Any]="<unk>"     ,  UpperCamelCase_				:	int="<pad>"     ,  UpperCamelCase_				:	str="<mask>"     ,  UpperCamelCase_				:	Tuple=False     ,  UpperCamelCase_				:	str=True     ,  **UpperCamelCase_				:	str     ,  )  ->       Union[str, Any]:



						'''simple docstring'''





						super().__init__(
						    UpperCamelCase_     ,  UpperCamelCase_     ,  tokenizer_file=UpperCamelCase_     ,  errors=UpperCamelCase_     ,  bos_token=UpperCamelCase_     ,  eos_token=UpperCamelCase_     ,  sep_token=UpperCamelCase_     ,  cls_token=UpperCamelCase_     ,  unk_token=UpperCamelCase_     ,  pad_token=UpperCamelCase_     ,  mask_token=UpperCamelCase_     ,  add_prefix_space=UpperCamelCase_     ,  trim_offsets=UpperCamelCase_     ,  **UpperCamelCase_     ,  )

						_lowercase					:       Optional[Any]       =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
						if pre_tok_state.get('add_prefix_space'     ,  UpperCamelCase_					) != add_prefix_space:
							_lowercase					:       List[Any]       =		getattr(UpperCamelCase_     ,  pre_tok_state.pop('type'					)					)
							_lowercase					:       List[str]       =		add_prefix_space
							_lowercase					:       Any       =		pre_tok_class(**UpperCamelCase_					)

						_lowercase					:       Tuple       =		add_prefix_space

						_lowercase					:       Optional[Any]       =		'post_processor'
						_lowercase					:       Tuple       =		getattr(self.backend_tokenizer     ,  UpperCamelCase_     ,  UpperCamelCase_					)
						if tokenizer_component_instance:
							_lowercase					:       List[str]       =		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:
								_lowercase					:       Any       =		tuple(state['sep']					)
							if "cls" in state:
								_lowercase					:       int       =		tuple(state['cls']					)

							_lowercase					:       Optional[int]       =		False

							if state.get('add_prefix_space'     ,  UpperCamelCase_					) != add_prefix_space:
								_lowercase					:       List[Any]       =		add_prefix_space
								_lowercase					:       str       =		True

							if state.get('trim_offsets'     ,  UpperCamelCase_					) != trim_offsets:
								_lowercase					:       Optional[int]       =		trim_offsets
								_lowercase					:       Optional[int]       =		True

							if changes_to_apply:
								_lowercase					:       str       =		getattr(UpperCamelCase_     ,  state.pop('type'					)					)
								_lowercase					:       List[str]       =		component_class(**UpperCamelCase_					)
								setattr(self.backend_tokenizer     ,  UpperCamelCase_     ,  UpperCamelCase_					)




					@property
					# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       str:



						'''simple docstring'''





						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 						__UpperCAmelCase     (  self				:	Optional[Any]     ,  UpperCamelCase_				:	Optional[int]					)  ->       Dict:



						'''simple docstring'''





						_lowercase					:       Dict       =		AddedToken(UpperCamelCase_     ,  lstrip=UpperCamelCase_     ,  rstrip=UpperCamelCase_					) if isinstance(UpperCamelCase_     ,  UpperCamelCase_					) else value
						_lowercase					:       List[Any]       =		value




					def 						__UpperCAmelCase     (  self				:	Optional[Any]     ,  *UpperCamelCase_				:	Any     ,  **UpperCamelCase_				:	Any					)  ->       BatchEncoding:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		kwargs.get('is_split_into_words'     ,  UpperCamelCase_					)
						assert self.add_prefix_space or not is_split_into_words, (
						    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
						    "to use it with pretokenized inputs."
						)

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




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]     ,  *UpperCamelCase_				:	Dict     ,  **UpperCamelCase_				:	Optional[Any]					)  ->       BatchEncoding:



						'''simple docstring'''





						_lowercase					:       str       =		kwargs.get('is_split_into_words'     ,  UpperCamelCase_					)

						assert self.add_prefix_space or not is_split_into_words, (
						    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
						    "to use it with pretokenized inputs."
						)

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




					def 						__UpperCAmelCase     (  self				:	Optional[Any]     ,  UpperCamelCase_				:	str     ,  UpperCamelCase_				:	Optional[str] = None					)  ->       Tuple[str]:



						'''simple docstring'''





						_lowercase					:       Union[str, Any]       =		self._tokenizer.model.save(UpperCamelCase_     ,  name=UpperCamelCase_					)
						return tuple(UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]     ,  UpperCamelCase_				:	List[int]     ,  UpperCamelCase_				:	Optional[List[int]] = None					)  ->       List[int]:



						'''simple docstring'''





						_lowercase					:       List[str]       =		[self.sep_token_id]
						_lowercase					:       Union[str, Any]       =		[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]




					def 						__UpperCAmelCase     (  self				:	int     ,  UpperCamelCase_				:	List[int]     ,  UpperCamelCase_				:	Optional[List[int]] = None					)  ->       Any:



						'''simple docstring'''





						return token_ids_a + [self.eos_token_id]




					def 						__UpperCAmelCase     (  self				:	Union[str, Any]     ,  UpperCamelCase_				:	"Conversation"					)  ->       List[int]:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		[]
						for is_user, text in conversation.iter_texts():
							if is_user:
								# We need to space prefix as it's being done within blenderbot
								inputs.append(' ' + text					)
							else:
								# Generated responses should contain them already.
								inputs.append(UpperCamelCase_					)

						_lowercase					:       Union[str, Any]       =		'  '.join(UpperCamelCase_					)
						_lowercase					:       Dict       =		self.encode(UpperCamelCase_					)
						if len(UpperCamelCase_					) > self.model_max_length:
							_lowercase					:       int       =		input_ids[-self.model_max_length :]
							logger.warning(F'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''					)
						return input_ids





'''simple docstring'''

def 						__UpperCamelCase				(							_lowercase					) ->			bool:
	return str(_lowercase					) == str(_lowercase					)[::-1]







def 						__UpperCamelCase				(							_lowercase					) ->			int:
	return int(_lowercase					) + int(str(_lowercase					)[::-1]					)







def 						__UpperCamelCase				(							_lowercase = 1_0000					) ->			int:
	_lowercase					:       List[str]       =		[]
	for num in range(1,				_lowercase					):
		_lowercase					:       Tuple       =		0
		_lowercase					:       Tuple       =		num
		while iterations < 50:
			_lowercase					:       Union[str, Any]       =		sum_reverse(_lowercase					)
			iterations += 1
			if is_palindrome(_lowercase					):
				break
		else:
			lychrel_nums.append(_lowercase					)
	return len(_lowercase					)


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