
'''simple docstring'''
from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
lowerCAmelCase :  Union[str, Any]      =      [[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 						A_(			A		:						int):
							return [int(A) for c in f'''{ruleset:08}'''[:8]]







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







def 						A_(			A		:						list[list[int]]):
							UpperCamelCase					       =			Image.new('RGB'						,			(len(cells[0]), len(A)))
							UpperCamelCase					       =			img.load()
							# Generates image
							for w in range(img.width):
														for h in range(img.height):
																					UpperCamelCase					       =			255 - int(255 * cells[h][w])
																					UpperCamelCase					       =			(color, color, color)
							return img


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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_snake_case				= {"""configuration_dinat""": ["""DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DinatConfig"""]}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _snake_case				= [
      """DINAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """DinatForImageClassification""",
      """DinatModel""",
      """DinatPreTrainedModel""",
      """DinatBackbone""",
  ]

if TYPE_CHECKING:
  from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_dinat import (
        DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
        DinatBackbone,
        DinatForImageClassification,
        DinatModel,
        DinatPreTrainedModel,
    )

else:
  import sys

  _snake_case				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






