


from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
UpperCamelCase :				Optional[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 							UpperCamelCase_  (				__snake_case			:						str     )->      List[str]:




      '''simple docstring'''
      return [int(__A     ) for c in F'''{ruleset:08}'''[:8]]
def 							UpperCamelCase_  (				__snake_case			:						Tuple	,			__snake_case			:						int	,			__snake_case			:						Optional[int]     )->      Optional[int]:




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




      '''simple docstring'''
      __magic_name__ 		=			Image.new('RGB'	,			(len(cells[0]     ), len(__A     ))     )
      __magic_name__ 		=			img.load()
      # Generates image
      for w in range(img.width     ):
            for h in range(img.height     ):
                  __magic_name__ 		=			2_55 - int(2_55 * cells[h][w]     )
                  __magic_name__ 		=			(color, color, color)
      return img


if __name__ == "__main__":
     UpperCamelCase :				Union[str, Any]						    =       bin(int(input('''Rule:\n''').strip()))[2:]
     UpperCamelCase :				List[str]						    =       format_ruleset(int(rule_num))
     for time in range(16):
          CELLS.append(new_generation(CELLS, rule, time))
     UpperCamelCase :				Optional[int]						    =       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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase :				str						    =       {
    '''configuration_bart''': ['''BART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BartConfig''', '''BartOnnxConfig'''],
    '''tokenization_bart''': ['''BartTokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				Optional[int]						    =       ['''BartTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				Any						    =       [
					    '''BART_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''BartForCausalLM''',
					    '''BartForConditionalGeneration''',
					    '''BartForQuestionAnswering''',
					    '''BartForSequenceClassification''',
					    '''BartModel''',
					    '''BartPretrainedModel''',
					    '''PretrainedBartModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				Tuple						    =       [
					    '''TFBartForConditionalGeneration''',
					    '''TFBartForSequenceClassification''',
					    '''TFBartModel''',
					    '''TFBartPretrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase :				List[Any]						    =       [
					    '''FlaxBartDecoderPreTrainedModel''',
					    '''FlaxBartForCausalLM''',
					    '''FlaxBartForConditionalGeneration''',
					    '''FlaxBartForQuestionAnswering''',
					    '''FlaxBartForSequenceClassification''',
					    '''FlaxBartModel''',
					    '''FlaxBartPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
					from .tokenization_bart import BartTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bart_fast import BartTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bart import (
										    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BartForCausalLM,
										    BartForConditionalGeneration,
										    BartForQuestionAnswering,
										    BartForSequenceClassification,
										    BartModel,
										    BartPretrainedModel,
										    PretrainedBartModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_bart import (
										    TFBartForConditionalGeneration,
										    TFBartForSequenceClassification,
										    TFBartModel,
										    TFBartPretrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_bart import (
										    FlaxBartDecoderPreTrainedModel,
										    FlaxBartForCausalLM,
										    FlaxBartForConditionalGeneration,
										    FlaxBartForQuestionAnswering,
										    FlaxBartForSequenceClassification,
										    FlaxBartModel,
										    FlaxBartPreTrainedModel,
										)

else:
					import sys

					UpperCamelCase :				Optional[int]						    =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

