

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())




import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Union[str, Any]:





    '''simple docstring'''






    return EnvironmentCommand()


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    @staticmethod
    def 						snake_case__     (      lowercase_ :       ArgumentParser		)->    Dict:





        '''simple docstring'''







        A__          =   parser.add_parser('env'		)
        download_parser.set_defaults(func=lowercase_		)


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





        '''simple docstring'''







        A__          =   huggingface_hub.__version__

        A__          =   'not installed'
        A__          =   'NA'
        if is_torch_available():
            import torch

            A__          =   torch.__version__
            A__          =   torch.cuda.is_available()

        A__          =   'not installed'
        if is_transformers_available():
            import transformers

            A__          =   transformers.__version__

        A__          =   'not installed'
        if is_accelerate_available():
            import accelerate

            A__          =   accelerate.__version__

        A__          =   'not installed'
        if is_xformers_available():
            import xformers

            A__          =   xformers.__version__

        A__          =   {
            '`diffusers` version': version,
            'Platform': platform.platform(),
            'Python version': platform.python_version(),
            'PyTorch version (GPU?)': F'{pt_version} ({pt_cuda_available})',
            'Huggingface_hub version': hub_version,
            'Transformers version': transformers_version,
            'Accelerate version': accelerate_version,
            'xFormers version': xformers_version,
            'Using GPU in script?': '<fill in>',
            'Using distributed or parallel set-up in script?': '<fill in>',
        }

        print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'		)
        print(self.format_dict(lowercase_		)		)

        return info


    @staticmethod
    def 						snake_case__     (      lowercase_ :       int		)->    Optional[Any]:





        '''simple docstring'''







        return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]		) + "\n"

