



import json
from typing import 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_mvp import MvpTokenizer


UpperCAmelCase__     = logging.get_logger(__name__)


UpperCAmelCase__     = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all MVP models at https://huggingface.co/models?filter=mvp
UpperCAmelCase__     = {
    "vocab_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
    },
    "added_tokens.json": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
    },
    "merges_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json",
    },
}

UpperCAmelCase__     = {
    "RUCAIBox/mvp": 1024,
}







class     __lowerCAmelCase					(    A		):
			UpperCamelCase           =  VOCAB_FILES_NAMES
			UpperCamelCase           =  PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase           =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase           =  ['''input_ids''', '''attention_mask''']
			UpperCamelCase           =  MvpTokenizer



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



						"""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				,	)

						_UpperCAmelCase	 =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
						if pre_tok_state.get('add_prefix_space'				,	A) != add_prefix_space:
									_UpperCAmelCase	 =     getattr(A				,	pre_tok_state.pop('type'))
									_UpperCAmelCase	 =     add_prefix_space
									_UpperCAmelCase	 =     pre_tok_class(**A)

						_UpperCAmelCase	 =     add_prefix_space

						# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
						_UpperCAmelCase	 =     'post_processor'
						_UpperCAmelCase	 =     getattr(self.backend_tokenizer				,	A				,	A)
						if tokenizer_component_instance:
									_UpperCAmelCase	 =     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:
												_UpperCAmelCase	 =     tuple(state['sep'])
									if "cls" in state:
												_UpperCAmelCase	 =     tuple(state['cls'])

									_UpperCAmelCase	 =     False

									if state.get('add_prefix_space'				,	A) != add_prefix_space:
												_UpperCAmelCase	 =     add_prefix_space
												_UpperCAmelCase	 =     True

									if state.get('trim_offsets'				,	A) != trim_offsets:
												_UpperCAmelCase	 =     trim_offsets
												_UpperCAmelCase	 =     True

									if changes_to_apply:
												_UpperCAmelCase	 =     getattr(A				,	state.pop('type'))
												_UpperCAmelCase	 =     component_class(**A)
												setattr(self.backend_tokenizer				,	A				,	A)



			@property
			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					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     :						Dict				,	A     :						Tuple)     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     AddedToken(A				,	lstrip=A				,	rstrip=A) if isinstance(A				,	A) else value
						_UpperCAmelCase	 =     value



			def 					_lowerCamelCase (		self     :						List[Any]				,	*A     :						Dict				,	**A     :						Optional[int])     ->					BatchEncoding:



						"""simple docstring"""






						_UpperCAmelCase	 =     kwargs.get('is_split_into_words'				,	A)

						if is_split_into_words and not self.add_prefix_space:
									raise ValueError(
									    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     :						Union[str, Any]				,	*A     :						Any				,	**A     :						Dict)     ->					BatchEncoding:



						"""simple docstring"""






						_UpperCAmelCase	 =     kwargs.get('is_split_into_words'				,	A)

						if is_split_into_words and not self.add_prefix_space:
									raise ValueError(
									    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     :						Any				,	A     :						str				,	A     :						Optional[str] = None)     ->					Tuple[str]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self._tokenizer.model.save(A				,	name=A)
						return tuple(A)



			def 					_lowerCamelCase (		self     :						Any				,	A     :						str				,	A     :						Optional[int]=None)     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     [self.bos_token_id] + token_ids_a + [self.eos_token_id]
						if token_ids_a is None:
									return output

						return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



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



						"""simple docstring"""






						_UpperCAmelCase	 =     [self.sep_token_id]
						_UpperCAmelCase	 =     [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]





import os

# Precomputes a list of the 100 first triangular numbers
UpperCAmelCase__     = [int(0.5 * n * (n + 1)) for n in range(1, 101)]





def 							A			(  )       ->				List[str]:







			'''simple docstring'''
			_UpperCAmelCase	 =     os.path.dirname(os.path.realpath(_UpperCAmelCase					)					)
			_UpperCAmelCase	 =     os.path.join(_UpperCAmelCase							,     'words.txt'					)

			_UpperCAmelCase	 =     ''
			with open(_UpperCAmelCase					) as f:
						_UpperCAmelCase	 =     f.readline()

			_UpperCAmelCase	 =     [word.strip('"'					) for word in words.strip('\r\n'					).split(','					)]
			_UpperCAmelCase	 =     [
			    word
			    for word in [sum(ord(_UpperCAmelCase					) - 64 for x in word					) for word in words]
			    if word in TRIANGULAR_NUMBERS
			]
			return len(_UpperCAmelCase					)


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