
"""simple docstring"""


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

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_lowercase				 =					logging.get_logger(__name__)  # pylint: disable=invalid-name






def    _snake_case					(		snake_case__ :  Union[List, PIL.Image.Image, torch.Tensor]  ):
	warnings.warn(
	    'The preprocess method is deprecated and will be removed in a future version. Please'
	    ' use VaeImageProcessor.preprocess instead'						,   snake_case__						,   )
	if isinstance(snake_case__						,   torch.Tensor  ):
		return image
	elif isinstance(snake_case__						,   PIL.Image.Image  ):
		A     = [image]

	if isinstance(image[0]						,   PIL.Image.Image  ):
		A ,   A     = image[0].size
		A ,   A     = (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

		A     = [np.array(i.resize((w, h)						,   resample=PIL_INTERPOLATION['lanczos']  )  )[None, :] for i in image]
		A     = np.concatenate(snake_case__						,   axis=0  )
		A     = np.array(snake_case__  ).astype(np.floataa  ) / 255.0
		A     = image.transpose(0						,   3						,   1						,   2  )
		A     = 2.0 * image - 1.0
		A     = torch.from_numpy(snake_case__  )
	elif isinstance(image[0]						,   torch.Tensor  ):
		A     = torch.cat(snake_case__						,   dim=0  )
	return image






def    _snake_case					(		snake_case__ :  Union[List, PIL.Image.Image, torch.Tensor]  ):
	if isinstance(snake_case__						,   torch.Tensor  ):
		return mask
	elif isinstance(snake_case__						,   PIL.Image.Image  ):
		A     = [mask]

	if isinstance(mask[0]						,   PIL.Image.Image  ):
		A ,   A     = mask[0].size
		A ,   A     = (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
		A     = [np.array(m.convert('L'  ).resize((w, h)						,   resample=PIL_INTERPOLATION['nearest']  )  )[None, :] for m in mask]
		A     = np.concatenate(snake_case__						,   axis=0  )
		A     = mask.astype(np.floataa  ) / 255.0
		A     = 0
		A     = 1
		A     = torch.from_numpy(snake_case__  )
	elif isinstance(mask[0]						,   torch.Tensor  ):
		A     = torch.cat(snake_case__						,   dim=0  )
	return mask







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     UNetaDModel
						_lowerCamelCase:     RePaintScheduler


						def __init__(			self  :   List[str]	,A_  :   Any	,A_  :   str   )  -> Union[str, Any]:
							super().__init__()
							self.register_modules(unet=A_	,scheduler=A_   )




						@torch.no_grad()
						def __call__(			self  :   Tuple	,A_  :   Union[torch.Tensor, PIL.Image.Image]	,A_  :   Union[torch.Tensor, PIL.Image.Image]	,A_  :   int = 250	,A_  :   float = 0.0	,A_  :   int = 10	,A_  :   int = 10	,A_  :   Optional[Union[torch.Generator, List[torch.Generator]]] = None	,A_  :   Optional[str] = "pil"	,A_  :   bool = True	,)  -> Union[ImagePipelineOutput, Tuple]:
							A     = image

							A     = _preprocess_image(A_   )
							A     = original_image.to(device=self.device	,dtype=self.unet.dtype   )
							A     = _preprocess_mask(A_   )
							A     = mask_image.to(device=self.device	,dtype=self.unet.dtype   )

							A     = original_image.shape[0]

							# sample gaussian noise to begin the loop
							if isinstance(A_	,A_   ) and len(A_   ) != batch_size:
								raise ValueError(
								    F'You have passed a list of generators of length {len(A_   )}, but requested an effective batch'
								    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'   )

							A     = original_image.shape
							A     = randn_tensor(A_	,generator=A_	,device=self.device	,dtype=self.unet.dtype   )

							# set step values
							self.scheduler.set_timesteps(A_	,A_	,A_	,self.device   )
							A     = eta

							A     = self.scheduler.timesteps[0] + 1
							A     = generator[0] if isinstance(A_	,A_   ) else generator
							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps   )   ):
								if t < t_last:
									# predict the noise residual
									A     = self.unet(A_	,A_   ).sample
									# compute previous image: x_t -> x_t-1
									A     = self.scheduler.step(A_	,A_	,A_	,A_	,A_	,A_   ).prev_sample

								else:
									# compute the reverse: x_t-1 -> x_t
									A     = self.scheduler.undo_step(A_	,A_	,A_   )
								A     = t

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

							if not return_dict:
								return (image,)

							return ImagePipelineOutput(images=A_   )

"""simple docstring"""


import os

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






def    _snake_case					(		):
	A     = os.path.dirname(os.path.realpath(snake_case__  )  )
	A     = os.path.join(snake_case__						,   'words.txt'  )

	A     = ''
	with open(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(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())