






from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
A										=      [[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        _lowerCamelCase(      lowerCAmelCase__					:		int   ):





				'''simple docstring'''







				return [int(lowerCAmelCase__   ) for c in F'''{ruleset:08}'''[:8]]



def        _lowerCamelCase(      lowerCAmelCase__					:		list[list[int]]     ,     lowerCAmelCase__					:		list[int]     ,     lowerCAmelCase__					:		int   ):





				'''simple docstring'''







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



def        _lowerCamelCase(      lowerCAmelCase__					:		list[list[int]]   ):





				'''simple docstring'''







				SCREAMING_SNAKE_CASE_ :						int    				= Image.new('RGB'     ,     (len(cells[0]   ), len(lowerCAmelCase__   ))   )
				SCREAMING_SNAKE_CASE_ :						int    				= img.load()
				# Generates image
				for w in range(img.width   ):
								for h in range(img.height   ):
												SCREAMING_SNAKE_CASE_ :						Dict    				= 255 - int(255 * cells[h][w]   )
												SCREAMING_SNAKE_CASE_ :						Any    				= (color, color, color)
				return img


if __name__ == "__main__":
				A										=      bin(int(input('Rule:\n').strip()))[2:]
				A										=      format_ruleset(int(rule_num))
				for time in range(16):
								CELLS.append(new_generation(CELLS, rule, time))
				A										=      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, is_vision_available


A										=      {'configuration_detr': ['DETR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetrConfig', 'DetrOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A										=      ['DetrFeatureExtractor']
				A										=      ['DetrImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A										=      [
				    'DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'DetrForObjectDetection',
				    'DetrForSegmentation',
				    'DetrModel',
				    'DetrPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_detr import DetrFeatureExtractor
								from .image_processing_detr import DetrImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_detr import (
								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DetrForObjectDetection,
								    DetrForSegmentation,
								    DetrModel,
								    DetrPreTrainedModel,
								)

else:
				import sys

				A										=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)