

'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case_       =       {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case_       =       [
          'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'FlaubertForMultipleChoice',
          'FlaubertForQuestionAnswering',
          'FlaubertForQuestionAnsweringSimple',
          'FlaubertForSequenceClassification',
          'FlaubertForTokenClassification',
          'FlaubertModel',
          'FlaubertWithLMHeadModel',
          'FlaubertPreTrainedModel',
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case_       =       [
          'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFFlaubertForMultipleChoice',
          'TFFlaubertForQuestionAnsweringSimple',
          'TFFlaubertForSequenceClassification',
          'TFFlaubertForTokenClassification',
          'TFFlaubertModel',
          'TFFlaubertPreTrainedModel',
          'TFFlaubertWithLMHeadModel',
      ]


if TYPE_CHECKING:
      from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
      from .tokenization_flaubert import FlaubertTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flaubert import (
                FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaubertForMultipleChoice,
                FlaubertForQuestionAnswering,
                FlaubertForQuestionAnsweringSimple,
                FlaubertForSequenceClassification,
                FlaubertForTokenClassification,
                FlaubertModel,
                FlaubertPreTrainedModel,
                FlaubertWithLMHeadModel,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_flaubert import (
                TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFFlaubertForMultipleChoice,
                TFFlaubertForQuestionAnsweringSimple,
                TFFlaubertForSequenceClassification,
                TFFlaubertForTokenClassification,
                TFFlaubertModel,
                TFFlaubertPreTrainedModel,
                TFFlaubertWithLMHeadModel,
            )

else:
      import sys

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




'''simple docstring'''






from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
snake_case_       =       [[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			(      SCREAMING_SNAKE_CASE_     :	int    )							->   list[int]:
      """simple docstring"""

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


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	list[list[int]]  ,  SCREAMING_SNAKE_CASE_     :	list[int]  ,  SCREAMING_SNAKE_CASE_     :	int    )							->   list[int]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			List[str]     =       len(cells[0]    )  # 31
      SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       []
      for i in range(SCREAMING_SNAKE_CASE_    ):
            # 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_      :			Tuple     =       0 if i == population - 1 else cells[time][i + 1]
            # Define a new cell and add it to the new generation
            SCREAMING_SNAKE_CASE_      :			Dict     =       7 - int(F"{left_neighbor}{cells[time][i]}{right_neighbor}"  ,  2    )
            next_generation.append(rule[situation]    )
      return next_generation


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	list[list[int]]    )							->   Image.Image:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			List[Any]     =       Image.new("RGB"  ,  (len(cells[0]    ), len(SCREAMING_SNAKE_CASE_    ))    )
      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       img.load()
      # Generates image
      for w in range(img.width    ):
            for h in range(img.height    ):
                  SCREAMING_SNAKE_CASE_      :			List[str]     =       2_5_5 - int(2_5_5 * cells[h][w]    )
                  SCREAMING_SNAKE_CASE_      :			str     =       (color, color, color)
      return img


if __name__ == "__main__":
      snake_case_       =       bin(int(input('Rule:\n').strip()))[2:]
      snake_case_       =       format_ruleset(int(rule_num))
      for time in range(1_6):
            CELLS.append(new_generation(CELLS, rule, time))
      snake_case_       =       generate_image(CELLS)
      # Uncomment to save the image
      # img.save(f"rule_{rule_num}.png")
      img.show()

