





from collections import OrderedDict
from typing import Mapping

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


A__:  Optional[int]       					=					logging.get_logger(__name__)

A__:  Optional[int]       					=					{
    '''google/mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'''
}




class    _a  (  UpperCamelCase__):

      """simple docstring"""


      UpperCamelCase__       						=   MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
      UpperCamelCase__       						=   """mobilebert"""






      def __init__(						self:	int					,  __lowerCamelCase:	Any=3_0522					,  __lowerCamelCase:	Union[str, Any]=512					,  __lowerCamelCase:	List[Any]=24					,  __lowerCamelCase:	str=4					,  __lowerCamelCase:	Optional[int]=512					,  __lowerCamelCase:	int="relu"					,  __lowerCamelCase:	int=0.0					,  __lowerCamelCase:	List[str]=0.1					,  __lowerCamelCase:	Tuple=512					,  __lowerCamelCase:	List[str]=2					,  __lowerCamelCase:	Dict=0.02					,  __lowerCamelCase:	Any=1e-12					,  __lowerCamelCase:	Optional[int]=0					,  __lowerCamelCase:	List[Any]=128					,  __lowerCamelCase:	Any=True					,  __lowerCamelCase:	int=True					,  __lowerCamelCase:	int=128					,  __lowerCamelCase:	Tuple=False					,  __lowerCamelCase:	List[str]=True					,  __lowerCamelCase:	str=4					,  __lowerCamelCase:	Optional[Any]="no_norm"					,  __lowerCamelCase:	List[str]=True					,  __lowerCamelCase:	List[str]=None					,  **__lowerCamelCase:	int					,  ):
          '''simple docstring'''


          super().__init__(pad_token_id=__lowerCamelCase					,  **__lowerCamelCase			)

          UpperCamelCase__:					Dict												=      vocab_size
          UpperCamelCase__:					Tuple												=      hidden_size
          UpperCamelCase__:					int												=      num_hidden_layers
          UpperCamelCase__:					List[str]												=      num_attention_heads
          UpperCamelCase__:					str												=      hidden_act
          UpperCamelCase__:					Tuple												=      intermediate_size
          UpperCamelCase__:					Dict												=      hidden_dropout_prob
          UpperCamelCase__:					Union[str, Any]												=      attention_probs_dropout_prob
          UpperCamelCase__:					Union[str, Any]												=      max_position_embeddings
          UpperCamelCase__:					int												=      type_vocab_size
          UpperCamelCase__:					Tuple												=      initializer_range
          UpperCamelCase__:					Dict												=      layer_norm_eps
          UpperCamelCase__:					List[Any]												=      embedding_size
          UpperCamelCase__:					Union[str, Any]												=      trigram_input
          UpperCamelCase__:					Optional[Any]												=      use_bottleneck
          UpperCamelCase__:					Optional[int]												=      intra_bottleneck_size
          UpperCamelCase__:					Any												=      use_bottleneck_attention
          UpperCamelCase__:					Union[str, Any]												=      key_query_shared_bottleneck
          UpperCamelCase__:					Optional[Any]												=      num_feedforward_networks
          UpperCamelCase__:					Optional[Any]												=      normalization_type
          UpperCamelCase__:					Union[str, Any]												=      classifier_activation

          if self.use_bottleneck:
              UpperCamelCase__:					Optional[int]												=      intra_bottleneck_size
          else:
              UpperCamelCase__:					Optional[int]												=      hidden_size

          UpperCamelCase__:					str												=      classifier_dropout




class    _a  (  UpperCamelCase__):

      """simple docstring"""


      @property
      def 	UpperCAmelCase_   (						self:	str			):
          '''simple docstring'''


          if self.task == "multiple-choice":
              UpperCamelCase__:					int												=      {0: "batch", 1: "choice", 2: "sequence"}
          else:
              UpperCamelCase__:					Union[str, Any]												=      {0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
                  ("token_type_ids", dynamic_axis),
              ]			)








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()

