
from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
SCREAMING_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 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				int      )					->						list[int]:
				return [int(SCREAMING_SNAKE_CASE      ) for c in F"""{ruleset:08}"""[:8]]




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




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				list[list[int]]      )					->						Image.Image:
				__lowercase								= Image.new('RGB'   ,							(len(cells[0]      ), len(SCREAMING_SNAKE_CASE      ))      )
				__lowercase								= img.load()
				# Generates image
				for w in range(img.width      ):
								for h in range(img.height      ):
												__lowercase								= 255 - int(255 * cells[h][w]      )
												__lowercase								= (color, color, color)
				return img


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







from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE__							=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__							=    {
    """squeezebert/squeezebert-uncased""": (
        """https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"""
    ),
    """squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json""",
    """squeezebert/squeezebert-mnli-headless""": (
        """https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"""
    ),
}
class      A__       (						lowerCAmelCase__    ):
							lowerCAmelCase__		:						Tuple         =							SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
							lowerCAmelCase__		:						Optional[int]         =							"squeezebert"





							def __init__(			self  :       List[str]					,			_UpperCAmelCase  :       int=3_05_22					,			_UpperCAmelCase  :       Any=7_68					,			_UpperCAmelCase  :       Tuple=12					,			_UpperCAmelCase  :       Union[str, Any]=12					,			_UpperCAmelCase  :       Union[str, Any]=30_72					,			_UpperCAmelCase  :       Optional[Any]="gelu"					,			_UpperCAmelCase  :       Optional[int]=0.1					,			_UpperCAmelCase  :       Any=0.1					,			_UpperCAmelCase  :       Union[str, Any]=5_12					,			_UpperCAmelCase  :       int=2					,			_UpperCAmelCase  :       Dict=0.02					,			_UpperCAmelCase  :       str=1e-1_2					,			_UpperCAmelCase  :       Dict=0					,			_UpperCAmelCase  :       str=7_68					,			_UpperCAmelCase  :       Tuple=4					,			_UpperCAmelCase  :       Union[str, Any]=4					,			_UpperCAmelCase  :       Tuple=4					,			_UpperCAmelCase  :       Any=1					,			_UpperCAmelCase  :       List[Any]=4					,			_UpperCAmelCase  :       List[str]=4					,			**_UpperCAmelCase  :       Dict					,			)						->		Any:



											"""simple docstring"""


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

											__lowercase								= vocab_size
											__lowercase								= hidden_size
											__lowercase								= num_hidden_layers
											__lowercase								= num_attention_heads
											__lowercase								= hidden_act
											__lowercase								= intermediate_size
											__lowercase								= hidden_dropout_prob
											__lowercase								= attention_probs_dropout_prob
											__lowercase								= max_position_embeddings
											__lowercase								= type_vocab_size
											__lowercase								= initializer_range
											__lowercase								= layer_norm_eps
											__lowercase								= embedding_size
											__lowercase								= q_groups
											__lowercase								= k_groups
											__lowercase								= v_groups
											__lowercase								= post_attention_groups
											__lowercase								= intermediate_groups
											__lowercase								= output_groups






class      A__       (						lowerCAmelCase__    ):





							@property
							def       a__			(			self  :       Any    )						->		Mapping[str, Mapping[int, str]]:



											"""simple docstring"""


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





