





import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


A__:  Tuple       					=					logging.get_logger(__name__)

A__:  int       					=					{
    '''shi-labs/oneformer_ade20k_swin_tiny''': (
        '''https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'''
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}




class    _a  (  lowercase__):

      """simple docstring"""


      UpperCamelCase__       						=   '''oneformer'''
      UpperCamelCase__       						=   {'''hidden_size''': '''hidden_dim'''}






      def __init__(						self:	Tuple					,  __lowerCamelCase:	Optional[Dict] = None					,  __lowerCamelCase:	int = 255					,  __lowerCamelCase:	int = 150					,  __lowerCamelCase:	int = 0.1					,  __lowerCamelCase:	float = 2.0					,  __lowerCamelCase:	float = 5.0					,  __lowerCamelCase:	float = 5.0					,  __lowerCamelCase:	float = 0.5					,  __lowerCamelCase:	float = 0.07					,  __lowerCamelCase:	int = 1_2544					,  __lowerCamelCase:	float = 3.0					,  __lowerCamelCase:	float = 0.75					,  __lowerCamelCase:	float = 0.02					,  __lowerCamelCase:	float = 1.0					,  __lowerCamelCase:	float = 1e-05					,  __lowerCamelCase:	bool = False					,  __lowerCamelCase:	bool = True					,  __lowerCamelCase:	bool = True					,  __lowerCamelCase:	Optional[list] = [4, 8, 16, 32]					,  __lowerCamelCase:	int = 77					,  __lowerCamelCase:	int = 256					,  __lowerCamelCase:	int = 77					,  __lowerCamelCase:	int = 6					,  __lowerCamelCase:	int = 4_9408					,  __lowerCamelCase:	int = 2					,  __lowerCamelCase:	int = 16					,  __lowerCamelCase:	int = 256					,  __lowerCamelCase:	int = 256					,  __lowerCamelCase:	int = 256					,  __lowerCamelCase:	int = 1024					,  __lowerCamelCase:	str = "GN"					,  __lowerCamelCase:	int = 6					,  __lowerCamelCase:	int = 10					,  __lowerCamelCase:	bool = True					,  __lowerCamelCase:	int = 8					,  __lowerCamelCase:	float = 0.1					,  __lowerCamelCase:	int = 2048					,  __lowerCamelCase:	bool = False					,  __lowerCamelCase:	bool = False					,  __lowerCamelCase:	int = 2					,  __lowerCamelCase:	int = 4					,  **__lowerCamelCase:	Union[str, Any]					,  ):
          '''simple docstring'''


          if backbone_config is None:
              logger.info("`backbone_config` is unset. Initializing the config with the default `Swin` backbone."			)
              UpperCamelCase__:					int												=      CONFIG_MAPPING["swin"](
                  image_size=224					,  in_channels=3					,  patch_size=4					,  embed_dim=96					,  depths=[2, 2, 6, 2]					,  num_heads=[3, 6, 12, 24]					,  window_size=7					,  drop_path_rate=0.3					,  use_absolute_embeddings=lowerCAmelCase__					,  out_features=["stage1", "stage2", "stage3", "stage4"]					,  )
          elif isinstance(lowerCAmelCase__					,  lowerCAmelCase__			):
              UpperCamelCase__:					Optional[int]												=      backbone_config.get("model_type"			)
              UpperCamelCase__:					Dict												=      CONFIG_MAPPING[backbone_model_type]
              UpperCamelCase__:					List[str]												=      config_class.from_dict(lowerCAmelCase__			)

          UpperCamelCase__:					Optional[Any]												=      backbone_config

          UpperCamelCase__:					Optional[Any]												=      ignore_value
          UpperCamelCase__:					List[Any]												=      num_queries
          UpperCamelCase__:					List[Any]												=      no_object_weight
          UpperCamelCase__:					Optional[int]												=      class_weight
          UpperCamelCase__:					Optional[int]												=      mask_weight
          UpperCamelCase__:					Tuple												=      dice_weight
          UpperCamelCase__:					Any												=      contrastive_weight
          UpperCamelCase__:					Union[str, Any]												=      contrastive_temperature
          UpperCamelCase__:					Any												=      train_num_points
          UpperCamelCase__:					str												=      oversample_ratio
          UpperCamelCase__:					int												=      importance_sample_ratio
          UpperCamelCase__:					str												=      init_std
          UpperCamelCase__:					Optional[int]												=      init_xavier_std
          UpperCamelCase__:					List[str]												=      layer_norm_eps
          UpperCamelCase__:					Optional[Any]												=      is_training
          UpperCamelCase__:					List[str]												=      use_auxiliary_loss
          UpperCamelCase__:					Tuple												=      output_auxiliary_logits
          UpperCamelCase__:					Union[str, Any]												=      strides
          UpperCamelCase__:					List[str]												=      task_seq_len
          UpperCamelCase__:					str												=      text_encoder_width
          UpperCamelCase__:					Optional[int]												=      text_encoder_context_length
          UpperCamelCase__:					Tuple												=      text_encoder_num_layers
          UpperCamelCase__:					Dict												=      text_encoder_vocab_size
          UpperCamelCase__:					List[Any]												=      text_encoder_proj_layers
          UpperCamelCase__:					Union[str, Any]												=      text_encoder_n_ctx
          UpperCamelCase__:					Any												=      conv_dim
          UpperCamelCase__:					int												=      mask_dim
          UpperCamelCase__:					int												=      hidden_dim
          UpperCamelCase__:					Optional[Any]												=      encoder_feedforward_dim
          UpperCamelCase__:					int												=      norm
          UpperCamelCase__:					str												=      encoder_layers
          UpperCamelCase__:					Dict												=      decoder_layers
          UpperCamelCase__:					str												=      use_task_norm
          UpperCamelCase__:					Tuple												=      num_attention_heads
          UpperCamelCase__:					Union[str, Any]												=      dropout
          UpperCamelCase__:					Any												=      dim_feedforward
          UpperCamelCase__:					int												=      pre_norm
          UpperCamelCase__:					Optional[Any]												=      enforce_input_proj
          UpperCamelCase__:					Optional[Any]												=      query_dec_layers
          UpperCamelCase__:					Optional[Any]												=      common_stride
          UpperCamelCase__:					Optional[int]												=      decoder_layers

          super().__init__(**lowerCAmelCase__			)






      def 	UpperCAmelCase_   (						self:	Dict			):
          '''simple docstring'''


          UpperCamelCase__:					Dict												=      copy.deepcopy(self.__dict__			)
          UpperCamelCase__:					int												=      self.backbone_config.to_dict()
          UpperCamelCase__:					str												=      self.__class__.model_type
          return output








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()

