




import functools
import operator

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


UpperCAmelCase__    :							int       		=     logging.get_logger(__name__)

UpperCAmelCase__    :							Optional[int]       		=     {
    """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   UpperCAmelCase    (      _a ):
   __SCREAMING_SNAKE_CASE		:				Dict      					=       """hubert"""






   def __init__(						self :							Dict    ,       UpperCamelCase_ :							List[str]=32    ,       UpperCamelCase_ :							Optional[int]=7_68    ,       UpperCamelCase_ :							Dict=12    ,       UpperCamelCase_ :							Optional[int]=12    ,       UpperCamelCase_ :							List[Any]=30_72    ,       UpperCamelCase_ :							Tuple="gelu"    ,       UpperCamelCase_ :							Any=0.1    ,       UpperCamelCase_ :							Any=0.1    ,       UpperCamelCase_ :							List[Any]=0.1    ,       UpperCamelCase_ :							Optional[int]=True    ,       UpperCamelCase_ :							Dict=0.0    ,       UpperCamelCase_ :							int=0.1    ,       UpperCamelCase_ :							int=0.1    ,       UpperCamelCase_ :							Union[str, Any]=0.02    ,       UpperCamelCase_ :							List[Any]=1E-5    ,       UpperCamelCase_ :							Union[str, Any]="group"    ,       UpperCamelCase_ :							List[str]="gelu"    ,       UpperCamelCase_ :							Dict=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)    ,       UpperCamelCase_ :							Dict=(5, 2, 2, 2, 2, 2, 2)    ,       UpperCamelCase_ :							List[str]=(10, 3, 3, 3, 3, 2, 2)    ,       UpperCamelCase_ :							Optional[int]=False    ,       UpperCamelCase_ :							str=1_28    ,       UpperCamelCase_ :							List[str]=16    ,       UpperCamelCase_ :							List[str]=False    ,       UpperCamelCase_ :							str=True    ,       UpperCamelCase_ :							Tuple=0.05    ,       UpperCamelCase_ :							Any=10    ,       UpperCamelCase_ :							Tuple=2    ,       UpperCamelCase_ :							str=0.0    ,       UpperCamelCase_ :							Optional[Any]=10    ,       UpperCamelCase_ :							List[str]=0    ,       UpperCamelCase_ :							Union[str, Any]="sum"    ,       UpperCamelCase_ :							Optional[Any]=False    ,       UpperCamelCase_ :							Tuple=False    ,       UpperCamelCase_ :							Union[str, Any]=2_56    ,       UpperCamelCase_ :							Union[str, Any]=0    ,       UpperCamelCase_ :							Optional[int]=1    ,       UpperCamelCase_ :							Optional[int]=2    ,       **UpperCamelCase_ :							str    ,       )->      Any:
     super().__init__(**snake_case_    ,       pad_token_id=snake_case_    ,       bos_token_id=snake_case_    ,       eos_token_id=snake_case_  )
     _lowercase:			List[str]      =     hidden_size
     _lowercase:			int      =     feat_extract_norm
     _lowercase:			List[str]      =     feat_extract_activation
     _lowercase:			Optional[int]      =     list(snake_case_  )
     _lowercase:			Tuple      =     list(snake_case_  )
     _lowercase:			Optional[int]      =     list(snake_case_  )
     _lowercase:			Dict      =     conv_bias
     _lowercase:			List[str]      =     num_conv_pos_embeddings
     _lowercase:			str      =     num_conv_pos_embedding_groups
     _lowercase:			Optional[int]      =     len(self.conv_dim  )
     _lowercase:			int      =     num_hidden_layers
     _lowercase:			Dict      =     intermediate_size
     _lowercase:			Union[str, Any]      =     hidden_act
     _lowercase:			Union[str, Any]      =     num_attention_heads
     _lowercase:			Any      =     hidden_dropout
     _lowercase:			Union[str, Any]      =     attention_dropout
     _lowercase:			List[str]      =     activation_dropout
     _lowercase:			Optional[int]      =     feat_proj_layer_norm
     _lowercase:			Union[str, Any]      =     feat_proj_dropout
     _lowercase:			Optional[int]      =     final_dropout
     _lowercase:			Optional[Any]      =     layerdrop
     _lowercase:			List[str]      =     layer_norm_eps
     _lowercase:			List[Any]      =     initializer_range
     _lowercase:			Union[str, Any]      =     vocab_size
     _lowercase:			List[Any]      =     do_stable_layer_norm
     _lowercase:			Any      =     use_weighted_layer_sum
     _lowercase:			int      =     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
     _lowercase:			List[Any]      =     apply_spec_augment
     _lowercase:			Optional[Any]      =     mask_time_prob
     _lowercase:			Optional[Any]      =     mask_time_length
     _lowercase:			Optional[int]      =     mask_time_min_masks
     _lowercase:			Any      =     mask_feature_prob
     _lowercase:			List[Any]      =     mask_feature_length
     _lowercase:			List[Any]      =     mask_feature_min_masks

     # ctc loss
     _lowercase:			Optional[Any]      =     ctc_loss_reduction
     _lowercase:			Optional[int]      =     ctc_zero_infinity






   @property
   def 			_a    (						self :							Dict  )->      Dict:
     return functools.reduce(operator.mul    ,       self.conv_stride    ,       1  )








"""simple docstring"""





from __future__ import annotations

from PIL import Image

# Define the first generation of cells
# fmt: off
A__       :			str=						[[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       )       ->						list[int]:
    """simple docstring"""

    return [int(SCREAMING_SNAKE_CASE       ) for c in F'{ruleset:08}'[:8]]







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						list[int]:
    """simple docstring"""

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







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						Image.Image:
    """simple docstring"""

    UpperCamelCase__								=     Image.new('RGB'			,						(len(cells[0]       ), len(SCREAMING_SNAKE_CASE       ))       )
    UpperCamelCase__								=     img.load()
    # Generates image
    for w in range(img.width       ):
        for h in range(img.height       ):
            UpperCamelCase__								=     2_55 - int(2_55 * cells[h][w]       )
            UpperCamelCase__								=     (color, color, color)
    return img


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




