
import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			copy.deepcopy(_lowerCamelCase							)
    snake_case__  =			sorted(_lowerCamelCase							)
    snake_case__  =			[bisect.bisect_right(_lowerCamelCase      ,						_lowerCamelCase							) for y in x]
    return quantized





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase=0							):
    snake_case__  =			np.arange(start=3      ,						stop=_lowerCamelCase      ,						step=4							).tolist() if k > 0 else [10]
    snake_case__  =			_quantize(_lowerCamelCase      ,						_lowerCamelCase							)
    # count number of elements per group
    snake_case__  =			np.unique(_lowerCamelCase      ,						return_counts=_lowerCamelCase							)[1]
    snake_case__  =			[0] + bins + [np.inf]
    logger.info("Using {} as bins for aspect lengths quantization".format(_lowerCamelCase							)							)
    logger.info("Count of instances per bin: {}".format(_lowerCamelCase							)							)
    return groups





class    _SCREAMING_SNAKE_CASE					( __lowerCAmelCase	):
      def __init__(     self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase	):




          if not isinstance(__SCREAMING_SNAKE_CASE						,		__SCREAMING_SNAKE_CASE	):
              raise ValueError(
                  "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(__SCREAMING_SNAKE_CASE	)	)
          snake_case__  =			sampler
          snake_case__  =			group_ids
          snake_case__  =			batch_size
      def __iter__(     self	):
          snake_case__  =			defaultdict(__SCREAMING_SNAKE_CASE	)
          snake_case__  =			defaultdict(__SCREAMING_SNAKE_CASE	)

          snake_case__  =			0
          for idx in self.sampler:
              snake_case__  =			self.group_ids[idx]
              buffer_per_group[group_id].append(__SCREAMING_SNAKE_CASE	)
              samples_per_group[group_id].append(__SCREAMING_SNAKE_CASE	)
              if len(buffer_per_group[group_id]	) == self.batch_size:
                  yield buffer_per_group[group_id]  # TODO
                  num_batches += 1
                  del buffer_per_group[group_id]
              assert len(buffer_per_group[group_id]	) < self.batch_size

          # now we have run out of elements that satisfy
          # the group criteria, let's return the remaining
          # elements so that the size of the sampler is
          # deterministic
          snake_case__  =			len(self	)
          snake_case__  =			expected_num_batches - num_batches
          if num_remaining > 0:
              # for the remaining batches, group the batches by similar lengths
              snake_case__  =			[]
              for group_id, idxs in sorted(buffer_per_group.items()						,		key=lambda lowerCamelCase	: x[0]	):
                  batch_idx.extend(__SCREAMING_SNAKE_CASE	)
                  if len(__SCREAMING_SNAKE_CASE	) >= self.batch_size:
                      yield batch_idx[: self.batch_size]
                      snake_case__  =			batch_idx[self.batch_size :]
                      num_remaining -= 1
              if len(__SCREAMING_SNAKE_CASE	) > 0:
                  yield batch_idx
                  num_remaining -= 1
          assert num_remaining == 0



      def __len__(     self	):
          return (len(self.sampler	) + self.batch_size - 1) // self.batch_size


from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
__magic_name__           =					[[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
          0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    return [int(__lowerCAmelCase							) for c in F"""{ruleset:08}"""[:8]]





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			len(cells[0]							)  # 31
    snake_case__  =			[]
    for i in range(__lowerCAmelCase							):
        # Get the neighbors of each cell
        # Handle neighbours outside bounds by using 0 as their value
        snake_case__  =			0 if i == 0 else cells[time][i - 1]
        snake_case__  =			0 if i == population - 1 else cells[time][i + 1]
        # Define a new cell and add it to the new generation
        snake_case__  =			7 - int(F"""{left_neighbor}{cells[time][i]}{right_neighbor}"""      ,						2							)
        next_generation.append(rule[situation]							)
    return next_generation





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    snake_case__  =			Image.new("RGB"      ,						(len(cells[0]							), len(__lowerCAmelCase							))							)
    snake_case__  =			img.load()
    # Generates image
    for w in range(img.width							):
        for h in range(img.height							):
            snake_case__  =			255 - int(255 * cells[h][w]							)
            snake_case__  =			(color, color, color)
    return img


if __name__ == "__main__":
     __magic_name__           =					bin(int(input('''Rule:\n''').strip()))[2:]
     __magic_name__           =					format_ruleset(int(rule_num))
     for time in range(16):
          CELLS.append(new_generation(CELLS, rule, time))
     __magic_name__           =					generate_image(CELLS)
     # Uncomment to save the image
     # img.save(f"rule_{rule_num}.png")
     img.show()



