'''simple docstring'''





import os

# Precomputes a list of the 100 first triangular numbers
SCREAMING_SNAKE_CASE_:					Tuple  =[int(0.5 * n * (n + 1)) for n in range(1, 1_01)]





def      lowerCAmelCase_    ( )   ->				Tuple:


     '''simple docstring'''




     UpperCAmelCase_	   =       os.path.dirname(os.path.realpath(snake_case_      )      )
     UpperCAmelCase_	   =       os.path.join(snake_case_ ,   "words.txt"      )

     UpperCAmelCase_	   =       ""
     with open(snake_case_      ) as f:
          UpperCAmelCase_	   =       f.readline()

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


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





'''simple docstring'''





import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   Union[str, Any]       ,      __a   :   VQModel       ,      __a   :   UNetaDModel       ,      __a   :   DDIMScheduler			):
         super().__init__()
         self.register_modules(vqvae=__a       ,      unet=__a       ,      scheduler=__a			)






    @torch.no_grad()
    def __call__(self   :   str       ,      __a   :   int = 1       ,      __a   :   Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,      __a   :   float = 0.0       ,      __a   :   int = 50       ,      __a   :   Optional[str] = "pil"       ,      __a   :   bool = True       ,      **__a   :   Optional[int]       ,      ):
         UpperCAmelCase_	   =       randn_tensor(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,      generator=__a       ,      )
         UpperCAmelCase_	   =       latents.to(self.device			)

         # scale the initial noise by the standard deviation required by the scheduler
         UpperCAmelCase_	   =       latents * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(__a			)

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         UpperCAmelCase_	   =       "eta" in set(inspect.signature(self.scheduler.step			).parameters.keys()			)

         UpperCAmelCase_	   =       {}
         if accepts_eta:
              UpperCAmelCase_	   =       eta

         for t in self.progress_bar(self.scheduler.timesteps			):
              UpperCAmelCase_	   =       self.scheduler.scale_model_input(__a       ,      __a			)
              # predict the noise residual
              UpperCAmelCase_	   =       self.unet(__a       ,      __a			).sample
              # compute the previous noisy sample x_t -> x_t-1
              UpperCAmelCase_	   =       self.scheduler.step(__a       ,      __a       ,      __a       ,      **__a			).prev_sample

         # decode the image latents with the VAE
         UpperCAmelCase_	   =       self.vqvae.decode(__a			).sample

         UpperCAmelCase_	   =       (image / 2 + 0.5).clamp(0       ,      1			)
         UpperCAmelCase_	   =       image.cpu().permute(0       ,      2       ,      3       ,      1			).numpy()
         if output_type == "pil":
              UpperCAmelCase_	   =       self.numpy_to_pil(__a			)

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=__a			)




