

import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


lowercase_       		=  logging.get_logger(__name__)


class  A						:







    """simple docstring"""





    def __init__(      self :       Optional[int],lowercase_ :       List[Any],lowercase_ :       int		)->    Tuple:





        '''simple docstring'''







        A__          =   question_encoder
        A__          =   generator
        A__          =   self.question_encoder


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





        '''simple docstring'''







        if os.path.isfile(lowercase_		):
            raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'		)
        os.makedirs(lowercase_,exist_ok=lowercase_		)
        A__          =   os.path.join(lowercase_,'question_encoder_tokenizer'		)
        A__          =   os.path.join(lowercase_,'generator_tokenizer'		)
        self.question_encoder.save_pretrained(lowercase_		)
        self.generator.save_pretrained(lowercase_		)


    @classmethod
    def 						snake_case__     (      cls :       Any,lowercase_ :       Optional[Any],**lowercase_ :       Union[str, Any]		)->    Optional[Any]:





        '''simple docstring'''







        from ..auto.tokenization_auto import AutoTokenizer

        A__          =   kwargs.pop('config',lowercase_		)

        if config is None:
            A__          =   RagConfig.from_pretrained(lowercase_		)

        A__          =   AutoTokenizer.from_pretrained(
            lowercase_,config=config.question_encoder,subfolder='question_encoder_tokenizer'		)
        A__          =   AutoTokenizer.from_pretrained(
            lowercase_,config=config.generator,subfolder='generator_tokenizer'		)
        return cls(question_encoder=lowercase_,generator=lowercase_		)


    def __call__(      self :       Any,*lowercase_ :       Any,**lowercase_ :       Optional[Any]		)->    Dict:





        '''simple docstring'''







        return self.current_tokenizer(*lowercase_,**lowercase_		)


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





        '''simple docstring'''







        return self.generator.batch_decode(*lowercase_,**lowercase_		)


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





        '''simple docstring'''







        return self.generator.decode(*lowercase_,**lowercase_		)


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





        '''simple docstring'''







        A__          =   self.question_encoder


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





        '''simple docstring'''







        A__          =   self.generator


    def 						snake_case__     (      self :       int,lowercase_ :       List[str],lowercase_ :       Optional[List[str]] = None,lowercase_ :       Optional[int] = None,lowercase_ :       Optional[int] = None,lowercase_ :       str = "longest",lowercase_ :       str = None,lowercase_ :       bool = True,**lowercase_ :       Tuple,)->    BatchEncoding:





        '''simple docstring'''







        warnings.warn(
            '`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
            'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
            'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
            'details',lowercase_,)
        if max_length is None:
            A__          =   self.current_tokenizer.model_max_length
        A__          =   self(
            lowercase_,add_special_tokens=lowercase_,return_tensors=lowercase_,max_length=lowercase_,padding=lowercase_,truncation=lowercase_,**lowercase_,)
        if tgt_texts is None:
            return model_inputs
        # Process tgt_texts
        if max_target_length is None:
            A__          =   self.current_tokenizer.model_max_length
        A__          =   self(
            text_target=lowercase_,add_special_tokens=lowercase_,return_tensors=lowercase_,padding=lowercase_,max_length=lowercase_,truncation=lowercase_,**lowercase_,)
        A__          =   labels['input_ids']
        return model_inputs




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())

