






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        =			logging.get_logger(__name__)


A        =			{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

A        =			{
    """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        =			{"""facebook/blenderbot-3B""": 128}


class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				int				    =						VOCAB_FILES_NAMES
		UpperCAmelCase__      :				List[Any]				    =						PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase__      :				Any				    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase__      :				Any				    =						["input_ids", "attention_mask"]
		UpperCAmelCase__      :				Tuple				    =						BlenderbotTokenizer


		def __init__(						self ,    UpperCamelCase__=None ,    UpperCamelCase__=None ,    UpperCamelCase__=None ,    UpperCamelCase__="replace" ,    UpperCamelCase__="<s>" ,    UpperCamelCase__="</s>" ,    UpperCamelCase__="</s>" ,    UpperCamelCase__="<s>" ,    UpperCamelCase__="<unk>" ,    UpperCamelCase__="<pad>" ,    UpperCamelCase__="<mask>" ,    UpperCamelCase__=False ,    UpperCamelCase__=True ,    **UpperCamelCase__ ,    ):
						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__ ,    )

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

						SCREAMING_SNAKE_CASE_   :	Any													=   add_prefix_space

						SCREAMING_SNAKE_CASE_   :	Dict													=   'post_processor'
						SCREAMING_SNAKE_CASE_   :	int													=   getattr(self.backend_tokenizer ,    UpperCamelCase__ ,    UpperCamelCase__       )
						if tokenizer_component_instance:
										SCREAMING_SNAKE_CASE_   :	Any													=   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_   :	List[Any]													=   tuple(state['sep']       )
										if "cls" in state:
														SCREAMING_SNAKE_CASE_   :	List[Any]													=   tuple(state['cls']       )

										SCREAMING_SNAKE_CASE_   :	List[str]													=   False

										if state.get('add_prefix_space' ,    UpperCamelCase__       ) != add_prefix_space:
														SCREAMING_SNAKE_CASE_   :	Any													=   add_prefix_space
														SCREAMING_SNAKE_CASE_   :	Optional[int]													=   True

										if state.get('trim_offsets' ,    UpperCamelCase__       ) != trim_offsets:
														SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   trim_offsets
														SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   True

										if changes_to_apply:
														SCREAMING_SNAKE_CASE_   :	Dict													=   getattr(UpperCamelCase__ ,    state.pop('type'       )       )
														SCREAMING_SNAKE_CASE_   :	Tuple													=   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   __snake_case			(						self       ):




						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   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	str													=   AddedToken(UpperCamelCase__ ,    lstrip=UpperCamelCase__ ,    rstrip=UpperCamelCase__       ) if isinstance(UpperCamelCase__ ,    UpperCamelCase__       ) else value
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   value


		def   __snake_case			(						self ,    *UpperCamelCase__ ,    **UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   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   __snake_case			(						self ,    *UpperCamelCase__ ,    **UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   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   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self._tokenizer.model.save(UpperCamelCase__ ,    name=UpperCamelCase__       )
						return tuple(UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   [self.sep_token_id]
						SCREAMING_SNAKE_CASE_   :	List[str]													=   [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   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						return token_ids_a + [self.eos_token_id]






		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   []
						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__       )

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   '  '.join(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   self.encode(UpperCamelCase__       )
						if len(UpperCamelCase__       ) > self.model_max_length:
										SCREAMING_SNAKE_CASE_   :	Dict													=   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







def 					_lowerCamelCase(			):





				'''simple docstring'''







				return 1



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return 0 if x < 0 else two_pence(x - 2      ) + one_pence()



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return 0 if x < 0 else five_pence(x - 5      ) + two_pence(lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return 0 if x < 0 else ten_pence(x - 10      ) + five_pence(lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return 0 if x < 0 else twenty_pence(x - 20      ) + ten_pence(lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return 0 if x < 0 else fifty_pence(x - 50      ) + twenty_pence(lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return 0 if x < 0 else one_pound(x - 100      ) + fifty_pence(lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return 0 if x < 0 else two_pound(x - 200      ) + one_pound(lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int = 200      ):





				'''simple docstring'''







				return two_pound(lowerCAmelCase__      )


if __name__ == "__main__":
				print(solution(int(input().strip())))