

'''simple docstring'''






from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_					  =	logging.get_logger(__name__)

snake_case_					  =	{
    'microsoft/prophetnet-large-uncased': (
        'https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'
    ),
}
class      SCREAMING_SNAKE_CASE__      (    _UpperCAmelCase				):
    _A         =							"prophetnet"
    _A         =							["past_key_values"]
    _A         =							{
        "num_attention_heads": "num_encoder_attention_heads",
    }




    def __init__( self    ,					lowercase__ = 0.1    ,					lowercase__ = "gelu"    ,					lowercase__ = 3_0522    ,					lowercase__ = 1024    ,					lowercase__ = 4096    ,					lowercase__ = 12    ,					lowercase__ = 16    ,					lowercase__ = 4096    ,					lowercase__ = 12    ,					lowercase__ = 16    ,					lowercase__ = 0.1    ,					lowercase__ = 0.1    ,					lowercase__ = 512    ,					lowercase__ = 0.02    ,					lowercase__ = True    ,					lowercase__ = True    ,					lowercase__ = 0    ,					lowercase__ = 2    ,					lowercase__ = 32    ,					lowercase__ = 128    ,					lowercase__ = False    ,					lowercase__ = 0.0    ,					lowercase__ = True    ,					lowercase__ = 0    ,					lowercase__ = 1    ,					lowercase__ = 2    ,					**lowercase__    ,					):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      str					=	vocab_size
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	hidden_size
          SCREAMING_SNAKE_CASE_				:      List[str]					=	encoder_ffn_dim
          SCREAMING_SNAKE_CASE_				:      str					=	num_encoder_layers
          SCREAMING_SNAKE_CASE_				:      Dict					=	num_encoder_attention_heads
          SCREAMING_SNAKE_CASE_				:      str					=	decoder_ffn_dim
          SCREAMING_SNAKE_CASE_				:      Tuple					=	num_decoder_layers
          SCREAMING_SNAKE_CASE_				:      int					=	num_decoder_attention_heads
          SCREAMING_SNAKE_CASE_				:      int					=	max_position_embeddings
          SCREAMING_SNAKE_CASE_				:      str					=	init_std  # Normal(0, this parameter)
          SCREAMING_SNAKE_CASE_				:      str					=	activation_function

          # parameters for prophetnet
          SCREAMING_SNAKE_CASE_				:      str					=	ngram
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	num_buckets
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	relative_max_distance
          SCREAMING_SNAKE_CASE_				:      Any					=	disable_ngram_loss
          SCREAMING_SNAKE_CASE_				:      Any					=	eps

          # 3 Types of Dropout
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	attention_dropout
          SCREAMING_SNAKE_CASE_				:      Any					=	activation_dropout
          SCREAMING_SNAKE_CASE_				:      str					=	dropout

          SCREAMING_SNAKE_CASE_				:      Dict					=	use_cache

          super().__init__(
              pad_token_id=lowercase__    ,					bos_token_id=lowercase__    ,					eos_token_id=lowercase__    ,					is_encoder_decoder=lowercase__    ,					add_cross_attention=lowercase__    ,					decoder_start_token_id=lowercase__    ,					**lowercase__    ,					)




    @property
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          return self.num_encoder_layers + self.num_decoder_layers







    @num_hidden_layers.setter
    def   __lowerCamelCase				( self    ,					lowercase__      ):






          """simple docstring"""






          raise NotImplementedError(
              "This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"
              " `num_decoder_layers`."      )




'''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_				:      Optional[Any]					=	len(cells[0]   )  # 31
      SCREAMING_SNAKE_CASE_				:      Dict					=	[]
      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_				:      Any					=	0 if i == 0 else cells[time][i - 1]
            SCREAMING_SNAKE_CASE_				:      Dict					=	0 if i == population - 1 else cells[time][i + 1]
            # Define a new cell and add it to the new generation
            SCREAMING_SNAKE_CASE_				:      List[Any]					=	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_				:      Tuple					=	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[Any]					=	2_5_5 - int(2_5_5 * cells[h][w]   )
                  SCREAMING_SNAKE_CASE_				:      Optional[int]					=	(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()

