





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__:  int       					=					logging.get_logger(__name__)

A__:  int       					=					{
    '''MBZUAI/swiftformer-xs''': '''https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json''',
}




class    _a  (  UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__       						=   "swiftformer"






      def __init__(						self:	Any					,  __lowerCamelCase:	Dict=3					,  __lowerCamelCase:	Union[str, Any]=[3, 3, 6, 4]					,  __lowerCamelCase:	Union[str, Any]=[48, 56, 112, 220]					,  __lowerCamelCase:	List[Any]=4					,  __lowerCamelCase:	List[str]=[True, True, True, True]					,  __lowerCamelCase:	Tuple="gelu"					,  __lowerCamelCase:	Any=3					,  __lowerCamelCase:	List[Any]=2					,  __lowerCamelCase:	Dict=1					,  __lowerCamelCase:	Optional[int]=0.0					,  __lowerCamelCase:	Tuple=True					,  __lowerCamelCase:	Dict=1e-5					,  __lowerCamelCase:	str=1e-5					,  **__lowerCamelCase:	List[str]					,  ):
          '''simple docstring'''


          super().__init__(**__A			)
          UpperCamelCase__:					Any												=      num_channels
          UpperCamelCase__:					Any												=      depths
          UpperCamelCase__:					List[str]												=      embed_dims
          UpperCamelCase__:					int												=      mlp_ratio
          UpperCamelCase__:					Any												=      downsamples
          UpperCamelCase__:					Union[str, Any]												=      hidden_act
          UpperCamelCase__:					Dict												=      down_patch_size
          UpperCamelCase__:					str												=      down_stride
          UpperCamelCase__:					Dict												=      down_pad
          UpperCamelCase__:					List[str]												=      drop_path_rate
          UpperCamelCase__:					Tuple												=      use_layer_scale
          UpperCamelCase__:					Optional[int]												=      layer_scale_init_value
          UpperCamelCase__:					Dict												=      batch_norm_eps




class    _a  (  UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__       						=   version.parse("""1.11""")






      @property
      def 	UpperCAmelCase_   (						self:	Optional[int]			):
          '''simple docstring'''


          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]			)






      @property
      def 	UpperCAmelCase_   (						self:	List[Any]			):
          '''simple docstring'''


          return 1e-4








from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
A__:  Tuple       					=					[[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_				(    A_):
    return [int(A_) for c in F"{ruleset:08}"[:8]]



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



def 							lowerCAmelCase_				(    A_):
    UpperCamelCase__:					int												=      Image.new("RGB"  ,(len(cells[0]), len(A_)))
    UpperCamelCase__:					Dict												=      img.load()
    # Generates image
    for w in range(img.width):
        for h in range(img.height):
            UpperCamelCase__:					List[str]												=      2_55 - int(2_55 * cells[h][w])
            UpperCamelCase__:					Union[str, Any]												=      (color, color, color)
    return img


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

