






from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


a 			=							logging.get_logger(__name__)

a 			=							{
    'layoutlmv2-base-uncased': 'https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json',
    'layoutlmv2-large-uncased': 'https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
 import detectrona
class 							__snake_case(       __UpperCamelCase   ):
 lowercase			:							Optional[int]		   =		'''layoutlmv2'''



 def __init__(						self : Tuple			,		UpperCAmelCase__ : Any=30_522			,		UpperCAmelCase__ : str=768			,		UpperCAmelCase__ : Union[str, Any]=12			,		UpperCAmelCase__ : str=12			,		UpperCAmelCase__ : List[str]=3_072			,		UpperCAmelCase__ : int="gelu"			,		UpperCAmelCase__ : Any=0.1			,		UpperCAmelCase__ : List[Any]=0.1			,		UpperCAmelCase__ : int=512			,		UpperCAmelCase__ : Dict=2			,		UpperCAmelCase__ : Optional[Any]=0.02			,		UpperCAmelCase__ : Dict=1e-12			,		UpperCAmelCase__ : Any=0			,		UpperCAmelCase__ : Tuple=1_024			,		UpperCAmelCase__ : str=128			,		UpperCAmelCase__ : List[str]=32			,		UpperCAmelCase__ : int=True			,		UpperCAmelCase__ : List[str]=256			,		UpperCAmelCase__ : Union[str, Any]=64			,		UpperCAmelCase__ : Union[str, Any]=True			,		UpperCAmelCase__ : Union[str, Any]=[7, 7, 256]			,		UpperCAmelCase__ : Tuple=128			,		UpperCAmelCase__ : Any=128			,		UpperCAmelCase__ : str=True			,		UpperCAmelCase__ : str=True			,		UpperCAmelCase__ : Tuple=False			,		UpperCAmelCase__ : int=None			,		**UpperCAmelCase__ : Optional[int]			,		):

    """simple docstring"""






    super().__init__(
        vocab_size=UpperCAmelCase__			,		hidden_size=UpperCAmelCase__			,		num_hidden_layers=UpperCAmelCase__			,		num_attention_heads=UpperCAmelCase__			,		intermediate_size=UpperCAmelCase__			,		hidden_act=UpperCAmelCase__			,		hidden_dropout_prob=UpperCAmelCase__			,		attention_probs_dropout_prob=UpperCAmelCase__			,		max_position_embeddings=UpperCAmelCase__			,		type_vocab_size=UpperCAmelCase__			,		initializer_range=UpperCAmelCase__			,		layer_norm_eps=UpperCAmelCase__			,		pad_token_id=UpperCAmelCase__			,		**UpperCAmelCase__			,		)
    lowercase__    :						Dict       =			max_ad_position_embeddings
    lowercase__    :						str       =			max_rel_pos
    lowercase__    :						Dict       =			rel_pos_bins
    lowercase__    :						List[str]       =			fast_qkv
    lowercase__    :						int       =			max_rel_ad_pos
    lowercase__    :						Any       =			rel_ad_pos_bins
    lowercase__    :						Any       =			convert_sync_batchnorm
    lowercase__    :						Optional[Any]       =			image_feature_pool_shape
    lowercase__    :						Any       =			coordinate_size
    lowercase__    :						Dict       =			shape_size
    lowercase__    :						Optional[Any]       =			has_relative_attention_bias
    lowercase__    :						Union[str, Any]       =			has_spatial_attention_bias
    lowercase__    :						Optional[int]       =			has_visual_segment_embedding
    lowercase__    :						Any       =			(
        detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
    )



 @classmethod
 def     UpperCAmelCase     (						self : List[Any]						):

    """simple docstring"""






    return {
        "MODEL.MASK_ON": True,
        "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
        "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
        "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
        "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [128], [256], [512]],
        "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
        "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2_000,
        "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_000,
        "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_000,
        "MODEL.POST_NMS_TOPK_TEST": 1_000,
        "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
        "MODEL.ROI_HEADS.NUM_CLASSES": 5,
        "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
        "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
        "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
        "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
        "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
        "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
        "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
        "MODEL.RESNETS.DEPTH": 101,
        "MODEL.RESNETS.SIZES": [[32], [64], [128], [256], [512]],
        "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
        "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
        "MODEL.RESNETS.NUM_GROUPS": 32,
        "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
        "MODEL.RESNETS.STRIDE_IN_1X1": False,
    }







 def     UpperCAmelCase     (						self : List[Any]						):

    """simple docstring"""






    lowercase__    :						Union[str, Any]       =			detectrona.config.get_cfg()
    for k, v in self.detectrona_config_args.items():
       lowercase__    :						Tuple       =			k.split("""."""						)
       lowercase__    :						List[Any]       =			detectrona_config
       for attribute in attributes[:-1]:
          lowercase__    :						List[str]       =			getattr(UpperCAmelCase__			,		UpperCAmelCase__						)
       setattr(UpperCAmelCase__			,		attributes[-1]			,		UpperCAmelCase__						)

    return detectrona_config










from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
a 			=							[[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    a__    (	A       ):
   return [int(A       ) for c in F'''{ruleset:08}'''[:8]]
def    a__    (	A		,					A		,					A       ):
   lowercase__    :						List[Any]       =			len(cells[0]       )  # 31
   lowercase__    :						int       =			[]
   for i in range(A       ):
      # Get the neighbors of each cell
      # Handle neighbours outside bounds by using 0 as their value
      lowercase__    :						Optional[Any]       =			0 if i == 0 else cells[time][i - 1]
      lowercase__    :						Dict       =			0 if i == population - 1 else cells[time][i + 1]
      # Define a new cell and add it to the new generation
      lowercase__    :						str       =			7 - int(F'''{left_neighbor}{cells[time][i]}{right_neighbor}'''		,					2       )
      next_generation.append(rule[situation]       )
   return next_generation
def    a__    (	A       ):
   lowercase__    :						Tuple       =			Image.new("""RGB"""		,					(len(cells[0]       ), len(A       ))       )
   lowercase__    :						Optional[Any]       =			img.load()
   # Generates image
   for w in range(img.width       ):
      for h in range(img.height       ):
         lowercase__    :						Union[str, Any]       =			255 - int(255 * cells[h][w]       )
         lowercase__    :						int       =			(color, color, color)
   return img


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


