

"""simple docstring"""

import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__=2_5_6      ) ->	Union[str, Any]:
 lowerCAmelCase__      :							int    =							min(img.size      )

 if s < target_image_size:
  raise ValueError(F"""min dim for image {s} < {target_image_size}"""      )

 lowerCAmelCase__      :							int    =							target_image_size / s
 lowerCAmelCase__      :							List[str]    =							(round(r * img.size[1]      ), round(r * img.size[0]      ))
 lowerCAmelCase__      :							Tuple    =							TF.resize(lowercase__				,				lowercase__				,				interpolation=PIL.Image.LANCZOS      )
 lowerCAmelCase__      :							int    =							TF.center_crop(lowercase__				,				output_size=2 * [target_image_size]      )
 lowerCAmelCase__      :							Any    =							torch.unsqueeze(T.ToTensor()(lowercase__      )				,				0      )
 return img



def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	Union[str, Any]:
 lowerCAmelCase__      :							str    =							2.0 * x - 1.0
 return x



def 			SCREAMING_SNAKE_CASE       (   lowercase__				,				lowercase__=True				,				lowercase__="RGB"      ) ->	Optional[int]:
 lowerCAmelCase__      :							List[str]    =							x.detach().cpu()
 if process:
  lowerCAmelCase__      :							str    =							post_process_tensor(lowercase__      )
 lowerCAmelCase__      :							Dict    =							x.numpy()
 if process:
  lowerCAmelCase__      :							Tuple    =							(2_5_5 * x).astype(np.uinta      )
 lowerCAmelCase__      :							Optional[Any]    =							Image.fromarray(lowercase__      )
 if not x.mode == mode:
  lowerCAmelCase__      :							Tuple    =							x.convert(lowercase__      )
 return x



def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	Any:
 lowerCAmelCase__      :							Optional[Any]    =							torch.clamp(lowercase__				,				-1.0				,				1.0      )
 lowerCAmelCase__      :							Optional[int]    =							(x + 1.0) / 2.0
 lowerCAmelCase__      :							Tuple    =							x.permute(1				,				2				,				0      )
 return x






def 			SCREAMING_SNAKE_CASE       (   lowercase__      ) ->	Dict:
 lowerCAmelCase__      :							List[Any]    =							post_process_tensor(x.squeeze()      )
 return x.permute(2				,				0				,				1      ).unsqueeze(0      )









"""simple docstring"""

from itertools import product



def 			SCREAMING_SNAKE_CASE       (   lowercase__ = 1_5      ) ->	int:
 lowerCAmelCase__      :							float    =							0.0
 lowerCAmelCase__      :							float
 lowerCAmelCase__      :							int
 lowerCAmelCase__      :							int
 lowerCAmelCase__      :							int
 lowerCAmelCase__      :							int
 lowerCAmelCase__      :							tuple[int, ...]

 for series in product(range(2      )				,				repeat=lowercase__      ):
  lowerCAmelCase__      :							Optional[int]    =							series.count(1      )
  lowerCAmelCase__      :							Dict    =							num_turns - num_blue
  if num_red >= num_blue:
   continue
  lowerCAmelCase__      :							Dict    =							1.0
  for ind, col in enumerate(lowercase__				,				2      ):
   if col == 0:
    prob *= (ind - 1) / ind
   else:
    prob *= 1 / ind

  total_prob += prob

 return int(1 / total_prob      )


if __name__ == "__main__":
    print(f"{solution() = }")






