


'''simple docstring'''







from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
_SCREAMING_SNAKE_CASE       :   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							( UpperCamelCase__		):






				"""simple docstring"""



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



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



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



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					str						       =       Image.new("RGB"     ,   (len(cells[0]		), len(UpperCamelCase__		))		)
				__magic_name__				:					Dict						       =       img.load()
				# Generates image
				for w in range(img.width		):
								for h in range(img.height		):
												__magic_name__				:					List[Any]						       =       255 - int(255 * cells[h][w]		)
												__magic_name__				:					Optional[Any]						       =       (color, color, color)
				return img


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



'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE       :   Any        = {
    "configuration_deberta_v2": ["DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP", "DebertaV2Config", "DebertaV2OnnxConfig"],
    "tokenization_deberta_v2": ["DebertaV2Tokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE       :   List[Any]        = ["DebertaV2TokenizerFast"]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE       :   int        = [
		    "TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFDebertaV2ForMaskedLM",
		    "TFDebertaV2ForQuestionAnswering",
		    "TFDebertaV2ForSequenceClassification",
		    "TFDebertaV2ForTokenClassification",
		    "TFDebertaV2Model",
		    "TFDebertaV2PreTrainedModel",
		]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE       :   Union[str, Any]        = [
		    "DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "DebertaV2ForMaskedLM",
		    "DebertaV2ForMultipleChoice",
		    "DebertaV2ForQuestionAnswering",
		    "DebertaV2ForSequenceClassification",
		    "DebertaV2ForTokenClassification",
		    "DebertaV2Model",
		    "DebertaV2PreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_deberta_va import (
		    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    DebertaVaConfig,
		    DebertaVaOnnxConfig,
		)
		from .tokenization_deberta_va import DebertaVaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deberta_va import (
				    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDebertaVaForMaskedLM,
				    TFDebertaVaForQuestionAnswering,
				    TFDebertaVaForSequenceClassification,
				    TFDebertaVaForTokenClassification,
				    TFDebertaVaModel,
				    TFDebertaVaPreTrainedModel,
				)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deberta_va import (
				    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DebertaVaForMaskedLM,
				    DebertaVaForMultipleChoice,
				    DebertaVaForQuestionAnswering,
				    DebertaVaForSequenceClassification,
				    DebertaVaForTokenClassification,
				    DebertaVaModel,
				    DebertaVaPreTrainedModel,
				)

else:
		import sys

		_SCREAMING_SNAKE_CASE       :   Dict        = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)