

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  "▁"

lowercase_       		=  {"vocab_file": "sentencepiece.bpe.model"}

lowercase_       		=  {
    "vocab_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
        ),
    }
}

lowercase_       		=  {
    "facebook/mbart-large-50-one-to-many-mmt": 1024,
}

# fmt: off
lowercase_       		=  ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							VOCAB_FILES_NAMES
    lowerCamelCase			=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase			=							PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase			=							['input_ids', 'attention_mask']

    lowerCamelCase			=							[]
    lowerCamelCase			=							[]


    def __init__(      self :       List[Any],lowercase_ :       Optional[Any],lowercase_ :       List[Any]=None,lowercase_ :       List[str]=None,lowercase_ :       List[Any]="</s>",lowercase_ :       List[Any]="</s>",lowercase_ :       Dict="<s>",lowercase_ :       Union[str, Any]="<unk>",lowercase_ :       Dict="<pad>",lowercase_ :       str="<mask>",lowercase_ :       Optional[Dict[str, Any]] = None,**lowercase_ :       List[Any],)->    None:





        '''simple docstring'''







        A__          =   AddedToken(lowercase_,lstrip=lowercase_,rstrip=lowercase_		) if isinstance(lowercase_,lowercase_		) else mask_token

        A__          =   {} if sp_model_kwargs is None else sp_model_kwargs

        A__          =   kwargs.get('additional_special_tokens',[]		)
        kwargs["additional_special_tokens"] += [
            code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            src_lang=lowercase_,tgt_lang=lowercase_,eos_token=lowercase_,unk_token=lowercase_,sep_token=lowercase_,cls_token=lowercase_,pad_token=lowercase_,mask_token=lowercase_,sp_model_kwargs=self.sp_model_kwargs,**lowercase_,)

        A__          =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(str(lowercase_		)		)
        A__          =   vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # Mimic fairseq token-to-id alignment for the first 4 token
        A__          =   {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

        # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
        A__          =   1

        A__          =   len(self.sp_model		)
        A__          =   {
            code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowercase_		)
        }
        A__          =   {v: k for k, v in self.lang_code_to_id.items()}
        A__          =   len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset

        self.fairseq_tokens_to_ids.update(self.lang_code_to_id		)
        A__          =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}

        A__          =   src_lang if src_lang is not None else 'en_XX'
        A__          =   self.lang_code_to_id[self._src_lang]
        A__          =   tgt_lang
        self.set_src_lang_special_tokens(self._src_lang		)


    @property
    def 						snake_case__     (      self :       Tuple		)->    int:





        '''simple docstring'''







        return len(self.sp_model		) + len(self.lang_code_to_id		) + self.fairseq_offset + 1  # Plus 1 for the mask token


    @property
    def 						snake_case__     (      self :       List[Any]		)->    str:





        '''simple docstring'''







        return self._src_lang


    @src_lang.setter
    def 						snake_case__     (      self :       List[Any],lowercase_ :       str		)->    None:





        '''simple docstring'''







        A__          =   new_src_lang
        self.set_src_lang_special_tokens(self._src_lang		)


    def __getstate__(      self :       Any		)->    Dict:





        '''simple docstring'''







        A__          =   self.__dict__.copy()
        A__          =   None
        return state


    def __setstate__(      self :       Tuple,lowercase_ :       Dict		)->    None:





        '''simple docstring'''







        A__          =   d

        # for backward compatibility
        if not hasattr(self,'sp_model_kwargs'		):
            A__          =   {}

        A__          =   spm.SentencePieceProcessor(**self.sp_model_kwargs		)
        self.sp_model.Load(self.vocab_file		)


    def 						snake_case__     (      self :       List[str]		)->    Dict:





        '''simple docstring'''







        A__          =   {self.convert_ids_to_tokens(lowercase_		): i for i in range(self.vocab_size		)}
        vocab.update(self.added_tokens_encoder		)
        return vocab


    def 						snake_case__     (      self :       int,lowercase_ :       str		)->    List[str]:





        '''simple docstring'''







        return self.sp_model.encode(lowercase_,out_type=lowercase_		)


    def 						snake_case__     (      self :       Optional[Any],lowercase_ :       str		)->    int:





        '''simple docstring'''







        if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
        A__          =   self.sp_model.PieceToId(lowercase_		)

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


    def 						snake_case__     (      self :       Optional[int],lowercase_ :       int		)->    str:





        '''simple docstring'''







        if index in self.fairseq_ids_to_tokens:
            return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset		)


    def 						snake_case__     (      self :       Optional[int],lowercase_ :       Tuple		)->    List[str]:





        '''simple docstring'''







        A__          =   []
        A__          =   ''
        A__          =   False
        for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
                if not prev_is_special:
                    out_string += " "
                out_string += self.sp_model.decode(lowercase_		) + token
                A__          =   True
                A__          =   []
            else:
                current_sub_tokens.append(lowercase_		)
                A__          =   False
        out_string += self.sp_model.decode(lowercase_		)
        return out_string.strip()


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





        '''simple docstring'''







        if not os.path.isdir(lowercase_		):
            logger.error(F'Vocabulary path ({save_directory}) should be a directory'		)
            return
        A__          =   os.path.join(
            lowercase_,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

        if os.path.abspath(self.vocab_file		) != os.path.abspath(lowercase_		) and os.path.isfile(self.vocab_file		):
            copyfile(self.vocab_file,lowercase_		)
        elif not os.path.isfile(self.vocab_file		):
            with open(lowercase_,'wb'		) as fi:
                A__          =   self.sp_model.serialized_model_proto()
                fi.write(lowercase_		)

        return (out_vocab_file,)


    def 						snake_case__     (      self :       int,lowercase_ :       List[int],lowercase_ :       Optional[List[int]] = None,lowercase_ :       bool = False		)->    List[int]:





        '''simple docstring'''







        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=lowercase_,token_ids_a=lowercase_,already_has_special_tokens=lowercase_		)

        A__          =   [1] * len(self.prefix_tokens		)
        A__          =   [1] * len(self.suffix_tokens		)
        if token_ids_a is None:
            return prefix_ones + ([0] * len(lowercase_		)) + suffix_ones
        return prefix_ones + ([0] * len(lowercase_		)) + ([0] * len(lowercase_		)) + suffix_ones


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





        '''simple docstring'''







        if token_ids_a is None:
            return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


    def 						snake_case__     (      self :       Optional[int],lowercase_ :       Dict,lowercase_ :       str,lowercase_ :       Optional[str],lowercase_ :       Optional[str],**lowercase_ :       Any		)->    Union[str, Any]:





        '''simple docstring'''







        if src_lang is None or tgt_lang is None:
            raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'		)
        A__          =   src_lang
        A__          =   self(lowercase_,add_special_tokens=lowercase_,return_tensors=lowercase_,**lowercase_		)
        A__          =   self.convert_tokens_to_ids(lowercase_		)
        A__          =   tgt_lang_id
        return inputs


    def 						snake_case__     (      self :       List[Any],lowercase_ :       List[str],lowercase_ :       str = "en_XX",lowercase_ :       Optional[List[str]] = None,lowercase_ :       str = "ro_RO",**lowercase_ :       Optional[int],)->    BatchEncoding:





        '''simple docstring'''







        A__          =   src_lang
        A__          =   tgt_lang
        return super().prepare_seqaseq_batch(lowercase_,lowercase_,**lowercase_		)


    def 						snake_case__     (      self :       Any		)->    List[str]:





        '''simple docstring'''







        return self.set_src_lang_special_tokens(self.src_lang		)


    def 						snake_case__     (      self :       int		)->    Tuple:





        '''simple docstring'''







        return self.set_tgt_lang_special_tokens(self.tgt_lang		)


    def 						snake_case__     (      self :       List[str],lowercase_ :       str		)->    None:





        '''simple docstring'''







        A__          =   self.lang_code_to_id[src_lang]
        A__          =   [self.cur_lang_code_id]
        A__          =   [self.eos_token_id]


    def 						snake_case__     (      self :       Union[str, Any],lowercase_ :       str		)->    None:





        '''simple docstring'''







        A__          =   self.lang_code_to_id[tgt_lang]
        A__          =   [self.cur_lang_code_id]
        A__          =   [self.eos_token_id]




import os

# Precomputes a list of the 100 first triangular numbers
lowercase_       		=  [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def 						_snake_case(    )							->      int:





    '''simple docstring'''






    A__          =   os.path.dirname(os.path.realpath(SCREAMING_SNAKE_CASE__      )      )
    A__          =   os.path.join(SCREAMING_SNAKE_CASE__    ,			'words.txt'      )

    A__          =   ''
    with open(SCREAMING_SNAKE_CASE__      ) as f:
        A__          =   f.readline()

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


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

