


"""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

__SCREAMING_SNAKE_CASE							:    Any		=    logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE							:    Any		=    {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

__SCREAMING_SNAKE_CASE							:    List[str]		=    {
    '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'
    },
}

__SCREAMING_SNAKE_CASE							:    List[Any]		=    {'facebook/blenderbot-3B': 128}





class      __A     (snake_case__):





							'''simple docstring'''

							__lowercase:		List[str]							      =    VOCAB_FILES_NAMES
							__lowercase:		Optional[int]							      =    PRETRAINED_VOCAB_FILES_MAP
							__lowercase:		int							      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowercase:		Any							      =    ["""input_ids""", """attention_mask"""]
							__lowercase:		Optional[Any]							      =    BlenderbotTokenizer



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



														"""simple docstring"""



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

														snake_case_           =		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
														if pre_tok_state.get("""add_prefix_space"""		,       __A       ) != add_prefix_space:
																					snake_case_           =		getattr(__A		,       pre_tok_state.pop("""type"""       )       )
																					snake_case_           =		add_prefix_space
																					snake_case_           =		pre_tok_class(**__A       )

														snake_case_           =		add_prefix_space

														snake_case_           =		"post_processor"
														snake_case_           =		getattr(self.backend_tokenizer		,       __A		,       __A       )
														if tokenizer_component_instance:
																					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:
																												snake_case_           =		tuple(state["""sep"""]       )
																					if "cls" in state:
																												snake_case_           =		tuple(state["""cls"""]       )

																					snake_case_           =		False

																					if state.get("""add_prefix_space"""		,       __A       ) != add_prefix_space:
																												snake_case_           =		add_prefix_space
																												snake_case_           =		True

																					if state.get("""trim_offsets"""		,       __A       ) != trim_offsets:
																												snake_case_           =		trim_offsets
																												snake_case_           =		True

																					if changes_to_apply:
																												snake_case_           =		getattr(__A		,       state.pop("""type"""       )       )
																												snake_case_           =		component_class(**__A       )
																												setattr(self.backend_tokenizer		,       __A		,       __A       )



							@property
							# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->List[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 		lowerCAmelCase       (				self							:      Optional[Any]		,       UpperCAmelCase_							:      List[Any]       )					->str:



														"""simple docstring"""



														snake_case_           =		AddedToken(__A		,       lstrip=__A		,       rstrip=__A       ) if isinstance(__A		,       __A       ) else value
														snake_case_           =		value



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       *UpperCAmelCase_							:      Optional[Any]		,       **UpperCAmelCase_							:      List[str]       )					->str:



														"""simple docstring"""



														snake_case_           =		kwargs.get("""is_split_into_words"""		,       __A       )
														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(*__A		,       **__A       )



							def 		lowerCAmelCase       (				self							:      str		,       *UpperCAmelCase_							:      Union[str, Any]		,       **UpperCAmelCase_							:      List[Any]       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_           =		kwargs.get("""is_split_into_words"""		,       __A       )

														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(*__A		,       **__A       )



							def 		lowerCAmelCase       (				self							:      str		,       UpperCAmelCase_							:      str		,       UpperCAmelCase_							:      Optional[str] = None       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		self._tokenizer.model.save(__A		,       name=__A       )
														return tuple(__A       )



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      List[int]		,       UpperCAmelCase_							:      Optional[List[int]] = None       )					->List[str]:



														"""simple docstring"""



														snake_case_           =		[self.sep_token_id]
														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]



							def 		lowerCAmelCase       (				self							:      Dict		,       UpperCAmelCase_							:      List[int]		,       UpperCAmelCase_							:      Optional[List[int]] = None       )					->int:



														"""simple docstring"""



														return token_ids_a + [self.eos_token_id]



							def 		lowerCAmelCase       (				self							:      List[str]		,       UpperCAmelCase_							:      "Conversation"       )					->Dict:



														"""simple docstring"""



														snake_case_           =		[]
														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(__A       )

														snake_case_           =		"  ".join(__A       )
														snake_case_           =		self.encode(__A       )
														if len(__A       ) > self.model_max_length:
																					snake_case_           =		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"""
from math import factorial







def 						_a				(      _SCREAMING_SNAKE_CASE = 20				)    ->				int:
							snake_case_           =		2 * n  # middle entry of odd rows starting at row 3 is the solution for n = 1,
							# 2, 3,...
							snake_case_           =		n // 2

							return int(factorial(_SCREAMING_SNAKE_CASE				) / (factorial(_SCREAMING_SNAKE_CASE				) * factorial(n - k				))				)


if __name__ == "__main__":
				import sys

				if len(sys.argv) == 1:
								print(solution(20))
				else:
								try:
												__SCREAMING_SNAKE_CASE							:    Optional[int]		=    int(sys.argv[1])
												print(solution(n))
								except ValueError:
												print('Invalid entry - please enter a number.')



