
import functools
import operator

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


__magic_name__           =					logging.get_logger(__name__)

__magic_name__           =					{
    '''facebook/hubert-base-ls960''': '''https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json''',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}




class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						List[Any]      		=       'hubert'
      def __init__(     self						,		lowerCamelCase=32						,		lowerCamelCase=7_68						,		lowerCamelCase=12						,		lowerCamelCase=12						,		lowerCamelCase=30_72						,		lowerCamelCase="gelu"						,		lowerCamelCase=0.1						,		lowerCamelCase=0.1						,		lowerCamelCase=0.1						,		lowerCamelCase=True						,		lowerCamelCase=0.0						,		lowerCamelCase=0.1						,		lowerCamelCase=0.1						,		lowerCamelCase=0.0_2						,		lowerCamelCase=1e-5						,		lowerCamelCase="group"						,		lowerCamelCase="gelu"						,		lowerCamelCase=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)						,		lowerCamelCase=(5, 2, 2, 2, 2, 2, 2)						,		lowerCamelCase=(10, 3, 3, 3, 3, 2, 2)						,		lowerCamelCase=False						,		lowerCamelCase=1_28						,		lowerCamelCase=16						,		lowerCamelCase=False						,		lowerCamelCase=True						,		lowerCamelCase=0.0_5						,		lowerCamelCase=10						,		lowerCamelCase=2						,		lowerCamelCase=0.0						,		lowerCamelCase=10						,		lowerCamelCase=0						,		lowerCamelCase="sum"						,		lowerCamelCase=False						,		lowerCamelCase=False						,		lowerCamelCase=2_56						,		lowerCamelCase=0						,		lowerCamelCase=1						,		lowerCamelCase=2						,		**lowerCamelCase						,		):
          super().__init__(**lowerCamelCase						,		pad_token_id=lowerCamelCase						,		bos_token_id=lowerCamelCase						,		eos_token_id=lowerCamelCase	)
          snake_case__  =			hidden_size
          snake_case__  =			feat_extract_norm
          snake_case__  =			feat_extract_activation
          snake_case__  =			list(lowerCamelCase	)
          snake_case__  =			list(lowerCamelCase	)
          snake_case__  =			list(lowerCamelCase	)
          snake_case__  =			conv_bias
          snake_case__  =			num_conv_pos_embeddings
          snake_case__  =			num_conv_pos_embedding_groups
          snake_case__  =			len(self.conv_dim	)
          snake_case__  =			num_hidden_layers
          snake_case__  =			intermediate_size
          snake_case__  =			hidden_act
          snake_case__  =			num_attention_heads
          snake_case__  =			hidden_dropout
          snake_case__  =			attention_dropout
          snake_case__  =			activation_dropout
          snake_case__  =			feat_proj_layer_norm
          snake_case__  =			feat_proj_dropout
          snake_case__  =			final_dropout
          snake_case__  =			layerdrop
          snake_case__  =			layer_norm_eps
          snake_case__  =			initializer_range
          snake_case__  =			vocab_size
          snake_case__  =			do_stable_layer_norm
          snake_case__  =			use_weighted_layer_sum
          snake_case__  =			classifier_proj_size

          if (
              (len(self.conv_stride	) != self.num_feat_extract_layers)
              or (len(self.conv_kernel	) != self.num_feat_extract_layers)
              or (len(self.conv_dim	) != self.num_feat_extract_layers)
          ):
              raise ValueError(
                  "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
                  " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
                  F""" {len(self.conv_dim	)}`, `len(config.conv_stride) = {len(self.conv_stride	)}`,"""
                  F""" `len(config.conv_kernel) = {len(self.conv_kernel	)}`."""	)

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          snake_case__  =			apply_spec_augment
          snake_case__  =			mask_time_prob
          snake_case__  =			mask_time_length
          snake_case__  =			mask_time_min_masks
          snake_case__  =			mask_feature_prob
          snake_case__  =			mask_feature_length
          snake_case__  =			mask_feature_min_masks

          # ctc loss
          snake_case__  =			ctc_loss_reduction
          snake_case__  =			ctc_zero_infinity



      @property
      def A_     (     self	):
          return functools.reduce(operator.mul						,		self.conv_stride						,		1	)


from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
__magic_name__           =					[[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__      ( __lowerCAmelCase							):
    return [int(__lowerCAmelCase							) for c in F"""{ruleset:08}"""[:8]]





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





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    snake_case__  =			Image.new("RGB"      ,						(len(cells[0]							), len(__lowerCAmelCase							))							)
    snake_case__  =			img.load()
    # Generates image
    for w in range(img.width							):
        for h in range(img.height							):
            snake_case__  =			255 - int(255 * cells[h][w]							)
            snake_case__  =			(color, color, color)
    return img


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



